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  • 101
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-08-12
    Description: The present book contains 14 articles that were accepted for publication in the Special Issue “Numerical Computation, Data Analysis and Software in Mathematics and Engineering” of the MDPI journal Mathematics. The topics of these articles include the aspects of the meshless method, numerical simulation, mathematical models, deep learning and data analysis. Meshless methods, such as the improved element-free Galerkin method, the dimension-splitting, interpolating, moving, least-squares method, the dimension-splitting, generalized, interpolating, element-free Galerkin method and the improved interpolating, complex variable, element-free Galerkin method, are presented. Some complicated problems, such as tge cold roll-forming process, ceramsite compound insulation block, crack propagation and heavy-haul railway tunnel with defects, are numerically analyzed. Mathematical models, such as the lattice hydrodynamic model, extended car-following model and smart helmet-based PLS-BPNN error compensation model, are proposed. The use of the deep learning approach to predict the mechanical properties of single-network hydrogel is presented, and data analysis for land leasing is discussed. This book will be interesting and useful for those working in the meshless method, numerical simulation, mathematical model, deep learning and data analysis fields.
    Keywords: cold-roll forming ; longitudinal strain ; cubic spline function ; cumulative chord ; elastic–plastic problem ; complex variable meshless method ; interpolating shape function ; singular weight function ; complete basis function ; mathematical model ; leased price ; total leased area ; data analysis ; residential land ; Beijing ; meshless method ; dimension splitting–interpolating moving least squares (DS-IMLS) method ; improved interpolating element-free Galerkin (IEFG) method ; potential problem ; traffic flow ; two-dimensional lattice hydrodynamic model ; driver’s predictive effect ; finite element method ; alkali-activated slag ceramsite compound insulation block ; ANSYS CFX ; thermal and mechanical performances ; indoor thermal environment ; dimension splitting method ; dimension splitting generalized interpolating element-free Galerkin method ; convection–diffusion–reaction problem ; deep learning ; hydrogel network ; mechanical property ; convolutional neural network ; self-avoiding walk ; personnel health monitoring ; construction site management ; smart helmet ; infrared temperature measurement ; temperature error compensation ; BP neural network ; COVID-19 ; peridynamics ; dual-horizon ; crack propagation ; variable horizon ; multi-grid ; car-following model ; visual angle model ; electronic throttle angle ; stability analysis ; heavy haul ; railway tunnel ; basement structure ; dynamic response characteristics ; defects ; the lattice hydrodynamic model ; control signal ; strong wind ; optimal estimation of flux difference integral ; improved element-free Galerkin method ; Helmholtz equation ; penalty method ; improved moving least-squares approximation ; n/a ; bic Book Industry Communication::K Economics, finance, business & management::KN Industry & industrial studies::KNT Media, information & communication industries::KNTX Information technology industries
    Language: English
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  • 102
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-05-06
    Description: This reprint presents advances in operation and maintenance in solar plants, wind farms and microgrids. This compendium of scientific articles will help clarify the current advances in this subject, so it is expected that it will please the reader.
    Keywords: wind turbine ; electric generator ; spectral analysis ; fault diagnosis ; photovoltaic power forecasting ; data-driven ; deep learning ; variational autoencoders ; RNN ; angle swinging ; grid frequency oscillations ; electromechanical system ; inertial masses ; microgrids ; coordination protection ; distributed generation ; photovoltaic resources ; DigSILENT ; photovoltaic module ; defect detection ; power plant ; efficiency ; thermal image ; photovoltaic aging ; dark I-V curves ; bidirectional power inverter ; online distributed measurement of dark I-V curves ; sustainability ; compressive strength ; Bolomey formula ; sustainable concrete ; glass powder ; solar cell ; solar panel ; parameter extraction ; analytical ; Lambert W-function ; spacecraft solar panels ; I-V curve ; modeling ; wind power ; non-conventional renewable energy ; forecasting ; energy bands ; combinatorial optimization ; deep learning (DL) ; unmanned aerial vehicle (UAV) ; photovoltaic (PV) systems ; image-processing ; image segmentation ; semantic segmentation ; faults diagnostic ; artificial intelligence ; unbalanced datasets ; synthetic data ; artificial neural network based MPPT ; hybrid boost converter ; renewable energies ; solar power system ; microgrid ; control system ; storage system ; primary control ; photovoltaic (PV) plants ; coverage path planning (CPP) ; corrosion monitoring ; FPGA ; offshore wind turbines ; ultrasound ; thickness loss ; SCADA ; visualisation ; software ; wind-turbine ; windfarm ; cross-platform ; HMI ; GUI ; corrosion ; monitoring ; photovoltaic systems ; expected energy models ; fleet-scale ; lasso regression ; performance modeling ; machine learning ; fault location in photovoltaic arrays ; failure modes simulation ; fault detection criterion ; adaptive protection ; distributed power generation ; power distribution ; power system protection ; bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general ; bic Book Industry Communication::P Mathematics & science::PH Physics
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  • 103
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-08-08
    Description: Rural development is an essential constituent of the global economy. However, within agriculture, a decrease in the quantity and quality of crop harvest and livestock productivity is observed due to a deterioration in soil fertility, environment, and irrational use of natural resources. At the same time, agricultural areas are under pressure from population growth, requiring more food production. As a result, it encourages people to move into intact primary areas in order to increase the area of crops, pastures, etc., which leads to the destruction of natural diversity. The solution to avoid disaster is increasing agricultural production efficiency to expand crop harvest and livestock productivity without deteriorating quality. It requires introducing innovative engineering technologies to agriculture. Fortunately, technology is developing rapidly nowadays, and new driving scientific forces are emerging. The Special Issue collected high-quality research and review articles from academics and industry-related researchers in the areas of Agricultural Engineering covering the following topics: harvesting and planting crops, livestock production, livestock and agrofood waste utilization, seed treatment and transportation, water treatment, agricultural robotic applications, solutions for digital and precision agriculture, hardware and software support for pest and weed control, machine learning, energy efficiency and conservation in agriculture.
    Keywords: microclimate ; ceiling fans ; electric thermal storage unit ; energy–saving ; heat supply system ; discrete element method ; soybean seed ; ellipsoidal shape ; parameter calibration ; rolling friction coefficient ; pulsed LED light ; continuous LED ; scanning LED light ; energy saving ; lettuce ; vertical farms ; growth ; cultivation ; pneumatic seeder ; pressure loss characteristics ; complex airway ; airway optimization ; harvesting ; force action ; potato ; working body ; harvesting machines ; device for assessing the suitability for harvesting ; transition metal ; charge compensator ; red emission ; artificial lighting ; photosynthetic pigments ; count red jujubes ; red jujube ; improved YOLOv5s ; ShuffleNet V2 Unit ; Stem ; BiFPN ; impact sprinkler ; non-circular nozzle ; water distribution ; aspect ratio ; 2D video disdrometer ; droplet kinetic energy distribution ; wild blueberry ; Vaccinium angustifolium ; Monilinia vaccinii-corymbosi ; deep learning ; coordinated attention ; synthetic data ; prediction accuracy ; multi-objective evolutionary algorithms ; double wishbone mechanisms ; multibody dynamics ; pareto solution set ; amaranth inflorescence wastes ; pyrolysis ; thermogravimetric analysis ; hydrocarbon rich bio-oil ; indoor climate ; air cooling ; water-evaporative systems ; sprayed panels ; heat recovery units ; technological process ; rod elevator ; lifting angle ; displacement ; experiment ; cleaning machine ; grain plants seeds ; Fusarium ; photoluminescence ; linear regression models ; green cold chain delivery ; fresh agricultural products ; customer value ; time-dependent road network ; biomass ; Amaranthus retroflexus ; bio-oil ; biochar ; WCH ; DCH ; feed length of the stalk ; conveying performance ; fuel consumption ; high-voltage electrical pulse ; processing ; plant tissue ; irreversible damage ; intracellular structure ; green energy supply ; agricultural electrification ; high-efficiency photovoltaic equipment ; complex energy supply systems ; n/a ; bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general ; bic Book Industry Communication::P Mathematics & science::PS Biology, life sciences ; bic Book Industry Communication::T Technology, engineering, agriculture
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  • 104
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-05-06
    Description: Recent advances in information technology have brought forth a paradigm shift in science, especially in the biology and medical fields. Statistical methodologies based on high-performance computing and big data analysis are now indispensable for the qualitative and quantitative understanding of experimental results. In fact, the last few decades have witnessed drastic improvements in high-throughput experiments in health science, for example, mass spectrometry, DNA microarray, next generation sequencing, etc. Those methods have been providing massive data involving four major branches of omics (genomics, transcriptomics, proteomics, and metabolomics). Information about amino acid sequences, protein structures, and molecular structures are fundamental data for the prediction of bioactivity of chemical compounds when screening drugs. On the other hand, cell imaging, clinical imaging, and personal healthcare devices are also providing important data concerning the human body and disease. In parallel, various methods of mathematical modelling such as machine learning have developed rapidly. All of these types of data can be utilized in computational approaches to understand disease mechanisms, diagnosis, prognosis, drug discovery, drug repositioning, disease biomarkers, driver mutations, copy number variations, disease pathways, and much more. In this Special Issue, we have published 8 excellent papers dedicated to a variety of computational problems in the biomedical field from the genomic level to the whole-person physiological level.
    Keywords: water temperature ; bathing ; ECG ; heart rate variability ; quantitative analysis ; t-test ; hypertrophic cardiomyopathy ; data mining ; automated curation ; molecular mechanisms ; atrial fibrillation ; sudden cardiac death ; heart failure ; left ventricular outflow tract obstruction ; cardiac fibrosis ; myocardial ischemia ; compound–protein interaction ; Jamu ; machine learning ; drug discovery ; herbal medicine ; data augmentation ; deep learning ; ECG quality assessment ; drug–target interactions ; protein–protein interactions ; chronic diseases ; drug repurposing ; maximum flow ; adenosine methylation ; m6A ; RNA modification ; neuronal development ; genetic variation ; copy number variants ; disease-related traits ; sequential order ; association test ; blood pressure ; cuffless measurement ; longitudinal experiment ; plethysmograph ; nonlinear regression ; n/a ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
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  • 105
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: In this Special Issue, we aim to represent the vibrant state of protein structure studies at the end of 2021. Recent decades have brought significant changes to the protein structure research field. Thanks to the genome projects and advances in structure determination methods, the number of solved protein structures has increased significantly. Protein structure research is experiencing a new renaissance, and in 2020 the number of deposited structures in the PDB database reached a new record. An assortment of many new frontiers are presented in this collection. A single Special Issue cannot give a comprehensive overview of a large field such as proteins science, but we aim to give a broad overview of current research.
    Keywords: configurational entropy ; force fields ; intrinsically disordered proteins ; protein folding ; NMR ; high hydrostatic pressure ; thermodynamic stability ; α-helical bundle ; Li-Fraumeni syndrome ; hereditary breast cancer ; germline TP53 missense variants ; quantitative prediction model ; protein conformation ; protein–protein interactions ; protein–protein binding ; protein–protein complex ; coarse-grained modeling ; multiscale modeling ; UFM1 ; UBA5 ; UFC1 ; protein-protein interactions ; complex structure ; oxidative stress ; Nrf2 ; Keap1 ; nuclear magnetic resonance spectroscopy ; hydrogen/deuterium exchange ; mass spectrometry ; circular dichroism ; intrinsically disordered ; bifidobacteria ; fucosidases ; glycosyl hydrolases ; conserved domains ; human milk ; analytical ultracentrifugation ; CO2 concentrating mechanism ; diffusion-ordered NMR spectroscopy ; electrospray ionization mass spectrometry ; homotetramer ; manganese ; metalloprotein ; photosynthesis ; small-angle X-ray scattering ; C1q ; calcium binding proteins ; genetic variation ; otoconia ; otolin-1 ; OTOL1 ; site-directed mutagenesis ; thermal shift assay ; B.1.1.7 ; B.1.617.2 ; COVID-19 ; E484Q ; T478K and L452R mutation ; N501Y mutation ; spike protein ; tetrabromobisphenol A ; tetrabromobisphenol S ; erythrocyte membrane ; retardants ; erythrocytes ; protein–ligand interactions ; protein dynamics ; FK506-binding protein ; FKBP12 ; FKBP51 ; oxidative folding ; glutathionylation ; nitrosylation ; cysteine reactivity ; ribosomal exit tunnel ; transient complex ; glutathione ; phosphorylation ; transmembrane proteins ; saturation mutagenesis ; deep sequencing ; residue packing ; deep learning ; convolutional neural network ; bidirectional long-short term memory ; protein ; prediction ; contact ; distance ; alphafold ; ProSPr ; CASP ; dataset ; retrainable ; mutual synergetic folding ; solvent accessibility of peptide bonds ; inter-subunit interaction ; solvent-accessible surface area ; Shannon information entropy ; amino acid composition ; glucose ; GlcNAc ; galactose ; GalNAc ; mannose ; xylose ; fucose ; Neu5Ac ; glucuronate ; iduronate ; tetrahydropyran ; entropy ; free energy ; free energy landscape ; energy-dependent protein folding ; co-translational protein folding ; molecular chaperones ; physical model of protein folding ; n/a ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TC Biochemical engineering::TCB Biotechnology
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  • 106
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-06-21
    Description: Extremely popular for statistical inference, Bayesian methods are also becoming popular in machine learning and artificial intelligence problems. Bayesian estimators are often implemented by Monte Carlo methods, such as the Metropolis–Hastings algorithm of the Gibbs sampler. These algorithms target the exact posterior distribution. However, many of the modern models in statistics are simply too complex to use such methodologies. In machine learning, the volume of the data used in practice makes Monte Carlo methods too slow to be useful. On the other hand, these applications often do not require an exact knowledge of the posterior. This has motivated the development of a new generation of algorithms that are fast enough to handle huge datasets but that often target an approximation of the posterior. This book gathers 18 research papers written by Approximate Bayesian Inference specialists and provides an overview of the recent advances in these algorithms. This includes optimization-based methods (such as variational approximations) and simulation-based methods (such as ABC or Monte Carlo algorithms). The theoretical aspects of Approximate Bayesian Inference are covered, specifically the PAC–Bayes bounds and regret analysis. Applications for challenging computational problems in astrophysics, finance, medical data analysis, and computer vision area also presented.
    Keywords: bifurcation ; dynamical systems ; Edward–Sokal coupling ; mean-field ; Kullback–Leibler divergence ; variational inference ; Bayesian statistics ; machine learning ; variational approximations ; PAC-Bayes ; expectation-propagation ; Markov chain Monte Carlo ; Langevin Monte Carlo ; sequential Monte Carlo ; Laplace approximations ; approximate Bayesian computation ; Gibbs posterior ; MCMC ; stochastic gradients ; neural networks ; Approximate Bayesian Computation ; differential evolution ; Markov kernels ; discrete state space ; ergodicity ; Markov chain ; probably approximately correct ; variational Bayes ; Bayesian inference ; Markov Chain Monte Carlo ; Sequential Monte Carlo ; Riemann Manifold Hamiltonian Monte Carlo ; integrated nested laplace approximation ; fixed-form variational Bayes ; stochastic volatility ; network modeling ; network variability ; Stiefel manifold ; MCMC-SAEM ; data imputation ; Bethe free energy ; factor graphs ; message passing ; variational free energy ; variational message passing ; approximate Bayesian computation (ABC) ; differential privacy (DP) ; sparse vector technique (SVT) ; Gaussian ; particle flow ; variable flow ; Langevin dynamics ; Hamilton Monte Carlo ; non-reversible dynamics ; control variates ; thinning ; meta-learning ; hyperparameters ; priors ; online learning ; online optimization ; gradient descent ; statistical learning theory ; PAC–Bayes theory ; deep learning ; generalisation bounds ; Bayesian sampling ; Monte Carlo integration ; PAC-Bayes theory ; no free lunch theorems ; sequential learning ; principal curves ; data streams ; regret bounds ; greedy algorithm ; sleeping experts ; entropy ; robustness ; statistical mechanics ; complex systems ; bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general ; bic Book Industry Communication::P Mathematics & science
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  • 107
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    Springer Nature | Springer
    Publication Date: 2024-04-14
    Description: This open access book constitutes revised selected papers from the 4th International Workshop on Brain-Inspired Computing, BrainComp 2019, held in Cetraro, Italy, in July 2019. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures.
    Keywords: artificial intelligence ; communication systems ; computer hardware ; computer networks ; computer programming ; computer systems ; computer vision ; deep learning ; distributed computer systems ; image analysis ; image processing ; machine learning ; network protocols ; neural networks ; signal processing ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYZ Human–computer interaction::UYZG User interface design and usability ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQV Computer vision ; thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications
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  • 108
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-09-11
    Description: "Health and Public Health Applications for Decision Support Using Machine Learning" is a reprint that explores the intersection of machine learning and health sciences. It presents a collection of research and innovations showcasing how data-driven algorithms can transform patient care, disease diagnosis, and public health management. The reprint covers a wide range of topics, including natural language processing for biomedical relation extraction, ensemble learning for blood glucose level forecasting in diabetes management, machine learning for predicting walking stability and fall risk among the elderly, deep learning for pneumonia-infected lung volume quantification, and more.The reprint also discusses applications in precision medicine, early detection of renal damage, cardiac health monitoring, stress classification for mental health assessment, and early diagnosis of intracranial internal carotid artery stenosis. It emphasizes the role of machine learning in managing health crises, such as COVID-19 detection using ECG, voice, and X-ray systems, and reviews AI models in diagnosing adult-onset dementia disorders.Overall, this reprint aims to inspire researchers and healthcare professionals by showcasing the transformative potential of machine learning in healthcare. It hopes to encourage further research and collaboration to advance healthcare and technological innovations for a healthier future.
    Keywords: adult-onset dementia ; Alzheimer’s disease ; magnetic resonance imaging ; artificial intelligence ; machine learning ; neural networks ; atherosclerosis ; Doppler ultrasound ; internal carotid artery ; hemodynamic modeling ; stroke ; stress ; emotion ; action units ; speech ; audio visual ; RNN-LSTM ; petri-plates ; colonies ; machine-learning models ; discrimination ; Measurement uncertainty ; Monte Carlo method ; ECG ; Cardiac health ; COVID-19 ; signal processing ; image processing ; computerized diagnostic systems ; subclinical renal damage ; risk assessment tool ; group-based trajectory modeling ; screening strategy ; CVD classification ; data selection ; convolutional neural network ; pretrained model ; deep learning ; transfer learning ; infected lung segmentation ; quantification of lung disease severity ; comparison between manual and automated image segmentation ; deep neural network ; COVID-19 detection ; COVID-19 severity assessment ; gait ; neuromuscular control ; movement synergy ; overground walking ; principal component analysis (PCA) ; largest Lyapunov exponent (LyE) ; time-series forecasting ; blood glucose ; diabetes ; ensemble learning ; artificial neural network ; DDI (drug–drug interaction) ; CPR (chemical–protein relation) ; transformer ; self-attention ; GAT (graph-attention network) ; relation extraction ; ChemProt ; T5 (text-to-text transfer transformer) ; n/a
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  • 109
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-03-27
    Description: Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images.
    Keywords: depthwise separable convolution (DSC) ; all convolutional network (ACN) ; batch normalization (BN) ; ensemble convolutional neural network (ECNN) ; electrocardiogram (ECG) ; MIT-BIH database ; cephalometric landmark ; X-ray ; deep learning ; ResNet ; registration ; electronic human-machine interface ; blindness ; gesture recognition ; inertial sensors ; IMU ; dynamic contrast-enhanced MRI ; kidney perfusion ; glomerular filtration rate ; pharmacokinetic modeling ; multi-layer perceptron ; parameter estimation ; instance segmentation ; computer vision ; retinal blood vessel image ; computer-aided diagnosis ; U-shaped neural network ; residual learning ; semantic gap ; intracranial hemorrhage ; computed tomography ; random forest ; sleep disorder ; obstructive sleep disorder ; overnight polysomnogram ; EEG ; EMG ; ECG ; HRV signals ; Electronic Medical Record (EMR) ; disease prediction ; Amyotrophic Lateral Sclerosis (ALS) ; weighted Jaccard index (WJI) ; lung cancer ; CT images ; CNN ; pulmonary fibrosis ; radiotherapy ; n/a ; thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
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  • 110
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-05
    Description: This volume is focused on a wide range of topics, including adaptive optic components and tools, wavefront sensing, different control algorithms, astronomy, and propagation through turbulent and turbid media.
    Keywords: adaptive optics ; influence function ; wavefront correction ; adaptive retroreflector ; tunable lens ; adaptive lens ; polymer optics ; divergence control ; fluidic lens ; tunable optics ; wavefront aberrations ; adaptive method ; Zernike functions ; wavefront sensor ; multichannel diffractive optical element ; coherent beam combining ; neural network ; laser beam array ; deep learning ; atmospheric turbulence ; optical vortices ; Zernike polynomials ; laser beam focusing ; scattering medium ; spatial light modulator ; Shack-Hartmann sensor ; deformable mirror ; laser ablation ; parallel-gap resistance microwelding ; wavefront control ; tip-tilt mirror ; FPGA ; angular stabilization system ; bimorph mirror ; fast adaptive optical system ; turbulence ; spectral analysis ; lattice geometry ; Walsh functions ; thema EDItEUR::P Mathematics and Science::PN Chemistry
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  • 111
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection.
    Keywords: TA1-2040 ; T1-995 ; opinion mining ; affect computing ; health insurance ; Twitter ; hybrid vectorization ; violence against women ; word association ; collaborative schemes of sentiment analysis and sentiment systems ; random forest ; cyber-aggression ; deep learning ; online review ; emotion analysis ; lexicon construction ; provider networks ; text mining ; sentiment lexicon ; social media ; sentiment-aware word embedding ; psychographic segmentation ; medical web forum ; gender classification ; racism ; sentiment analysis ; sentiment classification ; sentiment word analysis ; social networks ; convolutional neural network ; review data mining ; machine learning ; emotion classification ; big data-driven marketing ; text feature representation ; recommender system ; user preference prediction ; violence based on sexual orientation ; semantic networks ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
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  • 112
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-03-27
    Description: Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones.
    Keywords: wildfire ; satellite vegetation indices ; live fuel moisture ; empirical model function ; Southern California ; chaparral ecosystem ; forest fire ; forest recovery ; satellite remote sensing ; vegetation index ; burn index ; gross primary production ; South Korea ; land subsidence ; PS-InSAR ; uneven settlement ; building construction ; Beijing urban area ; floodplain delineation ; inaccessible region ; machine learning ; flash flood ; risk ; LSSVM ; China ; Himawari-8 ; threshold-based algorithm ; remote sensing ; dryness monitoring ; soil moisture ; NIR–Red spectral space ; Landsat-8 ; MODIS ; Xinjiang province of China ; SDE ; PE ; groundwater level ; compressible sediment layer ; tropical cyclone formation ; WindSat ; disaster monitoring ; wireless sensor network ; debris flow ; anomaly detection ; deep learning ; accelerometer sensor ; total precipitable water ; Himawari-8 AHI ; random forest ; deep neural network ; XGBoost ; n/a ; thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
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  • 113
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-06-21
    Description: This book is a reprint of the Special Issue entitled "The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?". Artificial intelligence is extending into the world of both digital radiology and digital pathology, and involves many scholars in the areas of biomedicine, technology, and bioethics. There is a particular need for scholars to focus on both the innovations in this field and the problems hampering integration into a robust and effective process in stable health care models in the health domain. Many professionals involved in these fields of digital health were encouraged to contribute with their experiences. This book contains contributions from various experts across different fields. Aspects of the integration in the health domain have been faced. Particular space was dedicated to overviewing the challenges, opportunities, and problems in both radiology and pathology. Clinal deepens are available in cardiology, the hystopathology of breast cancer, and colonoscopy. Dedicated studies were based on surveys which investigated students and insiders, opinions, attitudes, and self-perception on the integration of artificial intelligence in this field.
    Keywords: n/a ; eHealth ; medical devices ; mHealth ; digital radiology ; picture archive and communication system ; artificial intelligence ; electronic surveys ; chest CT ; chest radiography ; AI ; radiology ; awareness ; radiographers ; radiologists ; e-health ; m-health ; digital-pathology ; cytology ; histology ; diagnostic pathology ; breast cancer ; bibliometric analysis ; healthcare ; medical imaging ; VOSviewer ; digital-radiology ; artificial-intelligence ; acceptance ; consensus ; information technology ; cardiology ; imaging ; cervical cancer screening ; colposcopy ; deep learning ; machine learning ; medical students ; perceptions ; digitization in medicine ; bic Book Industry Communication::M Medicine::MB Medicine: general issues::MBG Medical equipment & techniques
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  • 114
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-08-12
    Description: This book is a collection of the accepted papers presented at the Workshop on Artificial Intelligence with Biased or Scarce Data (AIBSD) in conjunction with the 36th AAAI Conference on Artificial Intelligence 2022. During AIBSD 2022, the attendees addressed the existing issues of data bias and scarcity in Artificial Intelligence and discussed potential solutions in real-world scenarios. A set of papers presented at AIBSD 2022 is selected for further publication and included in this book.
    Keywords: out-of-distribution generalization ; forecasting ; temporal bias ; permutation equivariance ; optimization ; face-recognition models ; facial attributes ; social bias ; fairness ; natural language processing ; gender bias ; bias detection ; contextualized embeddings ; deep learning ; contrastive learning ; supervised contrastive learning ; transfer learning ; robustness ; noisy labels ; coresets ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
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  • 115
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-03-31
    Description: This reprint discussed the role of retinal and optic nerve imaging in addition to its application in ophthalmic diseases and clinical medicine. It includes some high-quality articles containing original research results as well as review articles of exceptional merit.
    Keywords: rhegmatogenous retinal detachment ; optical coherence tomography angiography ; vitrectomy ; foveal avascular zone ; macular vessel density ; Boswellia serrata ; curcumin ; diabetic macular edema ; celiac disease ; OCT ; optical coherence tomography ; retinal layers ; RNFL ; quality of life ; canaloplasty ; trabeculectomy ; medical therapy ; central serous chorioretinopathy ; pachychoroid ; en face optical coherence tomography ; choroid ; choroidal vascularity index ; oculomics ; artificial intelligence ; machine learning ; deep learning ; retinal imaging ; color fundus photograph ; systemic diseases ; cardiovascular diseases ; neurodegenerative diseases ; COVID ; SARS-CoV-2 ; retinal vein occlusion ; RVO ; vaccination ; branch retinal vein occlusion ; optic disc drusen ; visible optic disc drusen ; deep convolutional neural network ; DCNN ; inceptionv3 ; dyslexia ; reading ; retina ; macula ; fovea ; parafovea ; perifovea ; thickness ; segmentation ; Best disease ; choroideremia ; inherited retinal diseases ; retinitis pigmentosa ; Stargardt disease ; thema EDItEUR::M Medicine and Nursing ; thema EDItEUR::M Medicine and Nursing::MK Medical specialties, branches of medicine::MKG Pharmacology
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  • 116
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible.
    Keywords: TP248.13-248.65 ; T1-995 ; cancer treatment ; extreme learning ; independent prognostic power ; AID/APOBEC ; HP ; gene inactivation biomarkers ; biomarker discovery ; chemotherapy ; artificial intelligence ; epigenetics ; comorbidity score ; denoising autoencoders ; protein ; single-biomarkers ; gene signature extraction ; high-throughput analysis ; concatenated deep feature ; feature selection ; differential gene expression analysis ; colorectal cancer ; ovarian cancer ; multiple-biomarkers ; gefitinib ; cancer biomarkers ; classification ; cancer biomarker ; mutation ; hierarchical clustering analysis ; HNSCC ; cell-free DNA ; network analysis ; drug resistance ; hTERT ; variable selection ; KRAS mutation ; single-cell sequencing ; network target ; skin cutaneous melanoma ; telomeres ; Neoantigen Prediction ; datasets ; clinical/environmental factors ; StAR ; PD-L1 ; miRNA ; circulating tumor DNA (ctDNA) ; false discovery rate ; predictive model ; Computational Immunology ; brain metastases ; observed survival interval ; next generation sequencing ; brain ; machine learning ; cancer prognosis ; copy number aberration ; mutable motif ; steroidogenic enzymes ; tumor ; mortality ; tumor microenvironment ; somatic mutation ; transcriptional signatures ; omics profiles ; mitochondrial metabolism ; Bufadienolide-like chemicals ; cancer-related pathways ; intratumor heterogeneity ; estrogen ; locoregionally advanced ; RNA ; feature extraction and interpretation ; treatment de-escalation ; activation induced deaminase ; knockoffs ; R package ; copy number variation ; gene loss biomarkers ; cancer CRISPR ; overall survival ; histopathological imaging ; self-organizing map ; Network Analysis ; oral cancer ; biostatistics ; firehose ; Bioinformatics tool ; alternative splicing ; biomarkers ; diseases genes ; histopathological imaging features ; imaging ; TCGA ; decision support systems ; The Cancer Genome Atlas ; molecular subtypes ; molecular mechanism ; omics ; curative surgery ; network pharmacology ; methylation ; bioinformatics ; neurological disorders ; precision medicine ; cancer modeling ; miRNAs ; breast cancer detection ; functional analysis ; biomarker signature ; anti-cancer ; hormone sensitive cancers ; deep learning ; DNA sequence profile ; pancreatic cancer ; telomerase ; Monte Carlo ; mixture of normal distributions ; survival analysis ; tumor infiltrating lymphocytes ; curation ; pathophysiology ; GEO DataSets ; head and neck cancer ; gene expression analysis ; erlotinib ; meta-analysis ; traditional Chinese medicine ; breast cancer ; TCGA mining ; breast cancer prognosis ; microarray ; DNA ; interaction ; health strengthening herb ; cancer ; genomic instability ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TC Biochemical engineering::TCB Biotechnology
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-02-01
    Description: As faster and more efficient numerical algorithms become available, the understanding of the physics and the mathematical foundation behind these new methods will play an increasingly important role. This Special Issue provides a platform for researchers from both academia and industry to present their novel computational methods that have engineering and physics applications.
    Keywords: radial basis functions ; finite difference methods ; traveling waves ; non-uniform grids ; chaotic oscillator ; one-step method ; multi-step method ; computer arithmetic ; FPGA ; high strain rate impact ; modeling and simulation ; smoothed particle hydrodynamics ; finite element analysis ; hybrid nanofluid ; heat transfer ; non-isothermal ; shrinking surface ; MHD ; radiation ; multilayer perceptrons ; quaternion neural networks ; metaheuristic optimization ; genetic algorithms ; micropolar fluid ; constricted channel ; MHD pulsatile flow ; strouhal number ; flow pulsation parameter ; multiple integral finite volume method ; finite difference method ; Rosenau-KdV ; conservation ; solvability ; convergence ; transmission electron microscopy (TEM) ; convolutional neural networks (CNN) ; anomaly detection ; principal component analysis (PCA) ; machine learning ; deep learning ; neural networks ; Gallium-Arsenide (GaAs) ; radiation-based flowmeter ; two-phase flow ; feature extraction ; artificial intelligence ; time domain ; Boltzmann equation ; collision integral ; convolutional neural network ; annular regime ; scale layer-independent ; petroleum pipeline ; volume fraction ; dual energy technique ; prescribed heat flux ; similarity solutions ; dual solutions ; stability analysis ; RBF-FD ; node sampling ; lebesgue constant ; complex regions ; finite-difference methods ; data assimilation ; model order reduction ; finite elements analysis ; high dimensional data ; welding ; bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general ; bic Book Industry Communication::P Mathematics & science
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  • 118
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-07-06
    Description: Electrification, automation of vehicle control, digitalization and new mobility are the mega-trends in automotive engineering, and they are strongly connected. While many demonstrations for highly automated vehicles have been made worldwide, many challenges remain in bringing automated vehicles to the market for private and commercial use. The main challenges are as follows: reliable machine perception; accepted standards for vehicle-type approval and homologation; verification and validation of the functional safety, especially at SAE level 3+ systems; legal and ethical implications; acceptance of vehicle automation by occupants and society; interaction between automated and human-controlled vehicles in mixed traffic; human–machine interaction and usability; manipulation, misuse and cyber-security; the system costs of hard- and software and development efforts. This Special Issue was prepared in the years 2021 and 2022 and includes 15 papers with original research related to recent advances in the aforementioned challenges. The topics of this Special Issue cover: Machine perception for SAE L3+ driving automation; Trajectory planning and decision-making in complex traffic situations; X-by-Wire system components; Verification and validation of SAE L3+ systems; Misuse, manipulation and cybersecurity; Human–machine interactions, driver monitoring and driver-intention recognition; Road infrastructure measures for the introduction of SAE L3+ systems; Solutions for interactions between human- and machine-controlled vehicles in mixed traffic.
    Keywords: automated driving ; scenario-based testing ; software framework ; traffic signs ; ADAS ; traffic sign recognition system ; cooperative perception ; ITS ; digital twin ; sensor fusion ; edge cloud ; autonomous drifting ; model predictive control (MPC) ; successive linearization ; adaptive control ; vehicle motion control ; varying road surfaces ; vehicle dynamics ; Mask R-CNN ; transfer learning ; inverse gamma correction ; illumination ; instance segmentation ; pedestrian custom dataset ; deep learning ; wheel loaders ; throttle prediction ; state prediction ; automation ; safety validation ; automated driving systems ; decomposition ; modular safety approval ; modular testing ; fault tree analysis ; adaptive cruise control ; informed machine learning ; physics-guided reinforcement learning ; safety ; autonomous vehicles ; autonomous conflict management ; UTM ; UAV ; UGV ; U-Space ; framework development ; lane detection ; simulation and modelling ; multi-layer perceptron ; convolutional neural network ; driver drowsiness ; ECG signal ; heart rate variability ; wavelet scalogram ; automated driving (AD) ; driving simulator ; expression of trust ; acceptance ; simulator case study ; NASA TLX ; advanced driver assistant systems (ADAS) ; system usability scale ; driving school ; virtual validation ; ground truth ; reference measurement ; calibration method ; simulation ; traffic evaluation ; simulation and modeling ; connected and automated vehicle ; driver assistance system ; virtual test and validation ; radar sensor ; physical perception model ; virtual sensor model ; n/a ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-07-06
    Description: In this Special Issue on symmetry, we mainly discuss the application of symmetry in various structural health monitoring. For example, considering the health monitoring of a known structure, by obtaining the static or dynamic response of the structure, using different signal processing methods, including some advanced filtering methods, to remove the influence of environmental noise, and extract structural feature parameters to determine the safety of the structure. These damage diagnosis methods can also be effectively applied to various types of infrastructure and mechanical equipment. For this reason, the vibration control of various structures and the knowledge of random structure dynamics should be considered, which will promote the rapid development of the structural health monitoring. Among them, signal extraction and evaluation methods are also worthy of study. The improvement of signal acquisition instruments and acquisition methods improves the accuracy of data. A good evaluation method will help to correctly understand the performance with different types of infrastructure and mechanical equipment.
    Keywords: real-time hybrid simulation ; H∞ control ; time delay ; mixed sensitivity ; structural health monitoring ; deep learning ; data anomaly detection ; convolutional neural network ; time–frequency extraction ; micro inertial measurement unit (MIMU) ; variational mode decomposition (VMD) ; Hilbert–Huang transform (HHT) ; frequency-domain integration approach (FDIA) ; torsion angle calculation ; offshore oil platform ; self-anchored suspension bridge ; cable clamp ; slippage ; force analysis ; high formwork ; ARMA ; BPNN ; stress trend prediction ; crack detection ; improved YOLOv4 ; concrete surface ; substructure shake table testing ; integration algorithm ; finite element method ; damper ; digital twin ; prestressed steel structure ; construction process ; safety assessment ; intelligent construction ; structural health monitoring (SHM) ; vibration ; frequency domain ; time domain ; time-frequency domain ; technical codes ; multiple square loops (MSL)-string ; seismic excitation ; dynamic response ; seismic pulse ; near and far field ; three-dimensional laser scanning ; surface flatness of initial support of tunnel ; curved surface fitting ; flatness calculation datum ; curvedcontinuous girder bridge ; collision response ; seismic mitigation ; pounding mitigation and unseating prevention ; heavy-duty vehicle ; road ; coupling model ; terrestrial laser scanning ; RGB ; genetic algorithm ; artificial neutral network ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
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  • 120
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-01
    Description: This book provides a collection of comprehensive research articles on data analytics and applications of wearable devices in healthcare. This Special Issue presents 28 research studies from 137 authors representing 37 institutions from 19 countries. To facilitate the understanding of the research articles, we have organized the book to show various aspects covered in this field, such as eHealth, technology-integrated research, prediction models, rehabilitation studies, prototype systems, community health studies, ergonomics design systems, technology acceptance model evaluation studies, telemonitoring systems, warning systems, application of sensors in sports studies, clinical systems, feasibility studies, geographical location based systems, tracking systems, observational studies, risk assessment studies, human activity recognition systems, impact measurement systems, and a systematic review. We would like to take this opportunity to invite high quality research articles for our next Special Issue entitled “Digital Health and Smart Sensors for Better Management of Cancer and Chronic Diseases” as a part of Sensors journal.
    Keywords: eHealth ; wearable ; monitoring ; services ; integration ; IoT ; Telemedicine ; wearable sensors ; multivariate analysis ; longitudinal study ; functional decline ; exercise intervention ; accidental falls ; fall detection ; real-world ; signal analysis ; performance measures ; non-wearable sensors ; accelerometers ; cameras ; machine learning ; smart textiles ; healthcare ; talking detection ; activity recognition and monitoring ; patient health and state monitoring ; wearable sensing ; orientation-invariant sensing ; motion sensors ; accelerometer ; gyroscope ; magnetometer ; pattern classification ; artificial intelligence ; supervised machine learning ; predictive analytics ; hemodialysis ; non-contact sensor ; heart rate ; respiration rate ; heart rate variability ; time-domain features ; frequency-domain features ; principal component analysis ; behaviour analysis ; classifier efficiency ; personal risk detection ; one-class classification ; actigraphy ; encoding ; data compression ; denoising ; edge computing ; signal processing ; wearables ; activity monitoring ; citizen science ; cluster analysis ; physical activity ; sedentary behavior ; walking ; energy expenditure ; wearable device ; impedance pneumography ; neural network ; mechanocardiogram (MCG) ; smart clothes ; heart failure (HF) ; left ventricular ejection fraction (LVEF) ; technology acceptance model (TAM) ; physical activity classification ; free-living ; GENEactiv accelerometer ; Gaussian mixture model ; hidden Markov model ; wavelets ; skill assessment ; deep learning ; LSTM ; state space model ; probabilistic inference ; latent features ; human activity recognition ; MIMU ; genetic algorithm ; feature selection ; classifier optimization ; bispectrum ; entropy ; feature extraction ; heat stroke ; filtering algorithm ; physiological parameters ; exercise experiment ; biomedical signal processing ; wearable biomedical sensors ; wireless sensor network ; respiratory monitoring ; optoelectronic plethysmography ; biofeedback ; biomedical technology ; exercise therapy ; orthopedics ; mobile health ; qualitative ; human factors ; inertial measurement unit ; disease prevention ; occupational healthcare ; P-Ergonomics ; precision ergonomics ; musculoskeletal disorders ; wellbeing at work ; electrocardiogram ; conductive gels ; noncontact electrode ; myocardial ischemia ; pacemaker ; ventricular premature contraction ; upper extremity ; motion ; action research arm test ; activities of daily living ; IoT wearable monitor ; health ; posture analysis ; spinal posture ; wearable sensor ; embedded system ; recurrent neural networks ; physical workload ; wearable systems for healthcare ; machine learning for real-time applications ; actigraph ; body worn sensors ; clothing sensors ; cross correlation analysis ; healthcare movement sensing ; wearable devices ; calibration ; inertial measurement units ; human movement ; physical activity type ; real-life ; GPS ; GIS ; n/a ; thema EDItEUR::N History and Archaeology::NH History ; thema EDItEUR::J Society and Social Sciences::JB Society and culture: general::JBF Social and ethical issues
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-11-17
    Description: The reprint “Land Administration 2.0” is an extension of the previous reprint “Remote Sensing for Land Administration”, another Special Issue in Remote Sensing. This reprint unpacks the responsible use and integration of emerging remote sensing techniques into the domain of land administration, including land registration, cadastre, land use planning, land valuation, land taxation, and land development. The title was chosen as “Land Administration 2.0” in reference to both this Special Issue being the second volume on the topic “Land Administration” and the next-generation requirements of land administration including demands for 3D, indoor, underground, real-time, high-accuracy, lower-cost, and interoperable land data and information.
    Keywords: UAV ; cadastral mapping ; data quality ; geometric accuracy ; impact assessment ; ground control points ; feature extraction ; flight plan ; image segmentation ; deep-learning ; building outlines ; cadastre modernization ; FCN ; high-resolution aerial orthoimages ; LiDAR data ; remote sensing ; land tenure ; land administration ; geospatially informed analysis ; knowledge co-production ; land ; visible boundary ; deep learning ; neural network ; classification ; building footprint extraction ; cadastre ; change detection ; VHR aerial images ; property valuation ; property taxation ; photogrammetry ; aerial imagery ; HRSI ; lidar ; artificial intelligence ; cadastral survey ; detail survey ; handheld LiDAR scanner ; calibration ; LiDAR system ; segmentation ; edge detection ; agricultural land boundary ; LiDAR ; automated feature extraction ; cadaster ; land registration ; land use planning ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-04-05
    Description: Speech is the most spontaneous and natural means of communication, as well as the preferred modality for interacting with mobile or fixed electronic devices, but speech in-terfaces have drawbacks, such as a lack of user privacy; non-inclusivity for certain users; poor robustness in noisy conditions; and the difficulty of creating complex man–machine interfaces. The Special Issue “Future Speech Interfaces with Sensors and Machine Intelligence” assembles eleven contributions that cover multimodal and silent speech interfaces; lip reading applications; novel sensors for speech interfaces; and enhanced speech inclusivity tools for future speech interfaces. The articles make important improvements beyond the state of the art, advancing the state of the art to new frontiers in some cases. Short summaries of all articles, grouped by topic, are presented, followed by a global commentary and evaluation.
    Keywords: neural machine translation (NMT) ; transformer ; Arabic dialects ; modern standard Arabic ; subword units ; multi-head attention ; shared vocabulary ; self-attention ; 3D densely connected CNN ; 3D multi-layer feature fusion CNN ; convolutional neural network ; deep learning ; lipreading ; speech recognition ; visual speech recognition ; silent speech ; continuous-wave radar ; European Portuguese ; machine learning ; multimodal speech ; lip reading ; ultrasound tongue imaging ; pose estimation ; speech kinematics ; keypoints ; landmarks ; audio-visual speech recognition ; lip-reading ; application programming interface ; multi-modal interaction ; deep neural networks ; multi-view VSR ; attention mechanism ; spatial attention module ; local self-attention ; connectionist temporal classification ; text-to-lip ; speech synthesis ; text-to-speech ; speech-to-lip ; zero-shot adaptation ; generative models ; artificial intelligence ; objective measures ; hybrid models ; end-to-end recognition ; reliability measures ; decision fusion net ; articulation-to-speech synthesis ; silent speech interface ; speaker adaption ; voice conversion ; audiovisual speech recognition ; multimodal interaction ; edutainment ; virtual aquarium ; speech processing ; ultrasound imaging ; silent speech interfaces ; speech sensors ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-05
    Description: This book is a printed edition of the Special Issue Molecular Modeling in Drug Design that was published in Molecules
    Keywords: QD1-999 ; Q1-390 ; metadynamics ; natural compounds ; virtual screening ; probe energies ; molecular dynamics simulation ; human ecto-5?-nucleotidase ; neural networks ; quantitative structure-activity relationship (QSAR) ; artificial intelligence ; allosterism ; in silico screening ; drug discovery ; amyloid fibrils ; mechanical stability ; adenosine receptors ; adenosine receptor ; ligand binding ; promiscuous mechanism ; AutoGrid ; dynamic light scattering ; resultant dipole moment ; density-based clustering ; Alzheimer’s disease ; drug design ; biophenols ; enzymatic assays ; all-atom molecular dynamics simulation ; fragment screening ; adenosine ; docking ; molecular docking ; cosolvent molecular dynamics ; turbidimetry ; squalene synthase (SQS) ; molecular recognition ; protein-peptide interactions ; extracellular loops ; FimH ; binding affinity ; rational drug design ; de novo design ; hyperlipidemia ; AR ligands ; aggregation ; property prediction ; PPI inhibition ; deep learning ; proteins ; quantitative structure-property prediction (QSPR) ; protein protein interactions ; boron cluster ; target-focused pharmacophore modeling ; ligand–protofiber interactions ; structure-based drug design ; scoring function ; grid maps ; solvent effect ; adhesion ; molecular dynamics ; Traditional Chinese Medicine ; steered molecular dynamics ; interaction energy ; EphA2-ephrin A1 ; molecular modeling ; method development ; thema EDItEUR::P Mathematics and Science::PN Chemistry
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: This reprint sought high-quality contributions that highlight novel research results and emerging applications, addressing recent breakthroughs in UAS autonomous navigation and related fields, such as flight mechanics and control, structural design, sensor design, etc. The topics of interest included the following: two-dimensional and three-dimensional mapping, target detection, and obstacle avoidance; the active perception of targets in cluttered environments (foliage, forests, etc.); vision-based and optical flow techniques; sensors and sensor fusion techniques; design models for guidance and controlled flight; state estimation, data analysis and filtering techniques (KF, EKF, particle filtering, fuzzy logic, etc.); path planning and path management; optimal control and strategies (neural networks, fuzzy logic, reinforcement learning, evolutionary and genetic algorithms, AI, etc.); navigation in GPS-denied environments; autolanding and safe landing area definition (SLAD); environmental effects on UAVs (wind, etc.); autonomous UAV or MAV swarms, and distributed architectures; BVLOS autonomous navigation.
    Keywords: quadcopter ; unnamed aerial vehicle ; dynamic model ; PID controller ; fuzzy logic ; genetic algorithm ; intelligent control ; unmanned aircraft systems (UASs) ; IMU ; pressure sensors ; angle-of-attack estimation ; autonomous flight ; flight mechanics ; flight maneuvers ; hybrid data and model driven ; key-frame ; motion primitives ; iterative learning ; sliding mode control ; unmanned arial vehicles ; trajectory tracking ; air data system ; flow angle ; angle-of-attack ; angle-of-sideslip ; flight dynamics ; flight testing ; synthetic sensor ; analytical redundancy ; model-free ; physics-based ; flying ad-hoc network (FANET) ; millimeter-wave (mmWave) ; neighbor discovery ; unmanned aerial vehicle (UAV) ; free-space optical communication ; spatial mode diversity ; unmanned aerial vehicle ; emergency recovery communications ; floods ; beach litter ; object detection ; drone surveys ; unmanned aerial vehicles (UAVs) ; deep learning ; yolov5 ; geolocation ; litter monitoring ; beach cleaning ; digital elevation models ; unmanned aircraft systems ; ZED 2 stereo camera ; indoor localization ; real-time application ; sensor integrity ; collision avoidance ; sensor fusion ; unmanned vehicles ; n/a ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology ; thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilities
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-06-21
    Description: The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems.
    Keywords: high-level synthesis ; HLS ; SDSoC ; support vector machines ; SVM ; code refactoring ; Zynq ; ZedBoard ; extreme edge ; embedded edge computing ; internet of things deployment ; hardware design ; IoT security ; Contiki-NG ; trustability ; embedded systems ; collaborative filtering ; recommender systems ; parallelism ; reconfigurable hardware ; neuroevolution ; block-based neural network ; dynamic and partial reconfiguration ; scalability ; reinforcement learning ; embedded system ; artificial intelligence ; hardware acceleration ; neuromorphic processor ; power consumption ; harsh environment ; fog computing ; edge computing ; cloud computing ; IoT gateway ; LoRa ; WiFi ; low power consumption ; low latency ; flexible ; smart port ; quantisation ; evolutionary algorithm ; neural network ; FPGA ; Movidius VPU ; 2D graphics accelerator ; line-drawing ; Bresenham’s algorithm ; alpha-blending ; anti-aliasing ; field-programmable gate array ; deep learning ; performance estimation ; Gaussian process ; bic Book Industry Communication::K Economics, finance, business & management::KN Industry & industrial studies::KNT Media, information & communication industries::KNTX Information technology industries
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: Computational intelligence is a general term for a class of algorithms designed by nature's wisdom and human intelligence. Computer scientists have proposed many computational intelligence algorithms with heuristic features. These algorithms either mimic the evolutionary processes of the biological world, mimic the physiological structure and bodily functions of the organism,
    Keywords: TA1-2040 ; T1-995 ; individual updating strategy ; integrated design ; global optimum ; flexible job shop scheduling problem ; whale optimization algorithm ; EHO ; bat algorithm with multiple strategy coupling (mixBA) ; multi-objective DV-Hop localization algorithm ; optimization ; rock types ; variable neighborhood search ; biology ; average iteration times ; CEC2013 benchmarks ; slicing tree structure ; firefly algorithm (FA) ; benchmark ; single loop ; evolutionary computation ; memetic algorithm ; normal cloud model ; 0-1 knapsack problems ; elite strategy ; diversity maintenance ; material handling path ; artificial bee colony algorithm (ABC) ; urban design ; entropy ; evolutionary algorithms (EAs) ; monarch butterfly optimization ; numerical simulation ; architecture ; set-union knapsack problem ; Wilcoxon test ; convolutional neural network ; global position updating operator ; particle swarm optimization ; computation ; minimum load coloring ; topology structure ; adaptive multi-swarm ; minimum total dominating set ; mutation operation ; shape grammar ; greedy optimization algorithm ; ?-Hilbert space ; genetic algorithm ; large scale optimization ; large-scale optimization ; NSGA-II-DV-Hop ; constrained optimization problems (COPs) ; first-arrival picking ; transfer function ; SPEA 2 ; stochastic ranking (SR) ; wireless sensor networks (WSNs) ; acceleration search ; convergence point ; fuzzy c-means ; evolutionary algorithm ; success rates ; Artificial bee colony ; particle swarm optimizer ; random weight ; range detection ; adaptive weight ; large-scale ; automatic identification ; cloud model ; swarm intelligence ; evolutionary multi-objective optimization ; DV-Hop algorithm ; bat algorithm (BA) ; Friedman test ; quantum uncertainty property ; facility layout design ; local search ; deep learning ; Y conditional cloud generator ; benchmark functions ; discrete algorithm ; dispatching rule ; DE algorithm ; nonlinear convergence factor ; energy-efficient job shop scheduling ; t-test ; evolution ; dimension learning ; global optimization ; confidence term ; elephant herding optimization ; moth search algorithm ; evolutionary ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-02-24
    Description: This Special Issue of Metabolites, entitled “Islet Biology and Metabolism” is dedicated to islet research and the impact of islet function in metabolic health and disease. Specific topics include, but are not limited to, islet hormone processing and secretion, islet transplant research, novel proteins of the insulin secretory pathway, as well as islet drug targets for the management of T1D and T2D.
    Keywords: insulin secretory granule ; beta-cells ; granule protein purification ; free fatty acid receptor (FFA) 2 ; FFA3 ; gut microbiome ; incretin ; insulin secretion ; short-chain fatty acids ; type 1 diabetes ; cytosolic calcium concentration ; glucose ; metabolic amplification ; mitochondria ; nutrient secretagogues ; diabetes ; hypoglycemic unaware ; ischaemia ; islet cell transplantation ; organ donation ; pancreas ; insulin ; beta cell ; human ; islet ; polarisation ; machine learning ; deep learning ; cell segmentation ; automation ; pancreatic islets ; leptin ; exocytosis ; tissue-engineering ; in vivo imaging ; metabolism ; β-cell ; viral transduction ; transplantation ; amplifying pathway ; hyperglycemia ; adenylyl cyclase ; incretins ; glucokinase ; forskolin ; cAMP ; exenatide ; islets ; bioenergetics ; glucose-stimulated insulin secretion ; respiration ; advanced glycation end products ; alagebrium chloride ; cross-link breaker ; immunopeptidome ; MIN6N8 cell line ; NOD mouse ; autoimmune diabetes ; pancreatic beta-cells ; intrauterine growth restriction ; maternal obesity ; developmental programming ; islet amyloid polypeptide (IAPP) ; granin ; secretory pathway ; trans-Golgi network (TGN) ; granule ; pancreatic β-cell ; amino acid ; aminoaciduria ; diabetes incidence ; insulitis ; neutral amino acid transporter ; non-obese diabetic mouse ; pancreatic islet ; Slc6a19 deficiency ; n/a ; bic Book Industry Communication::M Medicine
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  • 128
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: Sensors are the eyes or/and ears of an intelligent system, such as UAV, AGV and robots. With the development of material, signal processing, and multidisciplinary interactions, more and more smart sensors are proposed and fabricated under increasing demands for homes, the industry, and military fields. Networks of sensors will be able to enhance the ability to obtain huge amounts of information (big data) and improve precision, which also mirrors the developmental tendency of modern sensors. Moreover, artificial intelligence is a novel impetus for sensors and networks, which gets sensors to learn and think and feed more efficient results back. This book includes new research results from academia and industry, on the subject of “Smart Sensors and Networks”, especially sensing technologies utilizing Artificial Intelligence. The topics include: smart sensors biosensors sensor network sensor data fusion artificial intelligence deep learning mechatronics devices for sensors applications of sensors for robotics and mechatronics devices
    Keywords: microelectromechanical systems ; inertial measurement unit ; long short term memory recurrent neural networks ; artificial intelligence ; deep learning ; CNN ; LSTM ; CO2 welding ; molten pool ; online monitoring ; mechanical sensor ; self-adaptiveness ; ankle-foot exoskeleton ; walking assistance ; visual tracking ; correlation filter ; color histogram ; adaptive hedge algorithm ; scenario generation ; autonomous vehicle ; smart sensor and device ; wireless sensor networks ; task assignment ; distributed ; reliable ; energy-efficient ; audification ; sensor ; visualization ; speech to text ; text to speech ; HF-OTH radar ; AIS ; radar tracking ; data fusion ; fuzzy functional dependencies ; maritime surveillance ; surgical robot end-effector ; clamping force estimation ; joint torque disturbance observer ; PSO-BPNN ; cable tension measurement ; queue length ; roadside sensor ; vehicle detection ; adverse weather ; roadside LiDAR ; data processing ; air pollution ; atmospheric data ; IoT ; machine learning ; RNN ; Sensors ; smart cities ; traffic flow ; traffic forecasting ; wireless sensor network ; fruit condition monitoring ; artificial neural network ; ethylene gas ; banana ripening ; unidimensional ACGAN ; signal recognition ; data augmentation ; link establishment behaviors ; DenseNet ; short-wave radio station ; landing gear ; adaptive landing ; vehicle classification ; FBG ; smart sensors ; outlier detection ; local outlier factor ; data streams ; air quality monitoring ; n/a ; evacuation path ; multi-story multi-exit building ; temperature sensors ; multi-time-slots planning ; optimization ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-03-21
    Description: This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field.
    Keywords: electrocardiographic imaging (ECGI) ; heart failure (HF) ; cardiac resynchronization therapy (CRT) ; ultrasound ; strain ; speckle tracking echocardiography ; in silico ; electrophysiology ; electrocardiogram ; ECG ; cardiac disease ; arrhythmia ; ischemia ; standardization ; computerized ECG ; personalized medicine ; telemedicine ; digital ECG data interchange protocol ; eHealth ; ECG equipment ; computerized electrocardiograph ; ECG analysis algorithms ; computerized ECG interpretation ; interatrial block ; partial interatrial block ; advanced interatrial block ; atypical patterns ; electrocardiogram (ECG) ; automated ECG analysis ; CSE study ; age ; sex ; race ; historical aspects ; electronic cohort ; mortality ; big data ; telehealth ; alarm fatigue ; annotation of ECG data ; arrhythmia alarms ; intensive care unit ; patient monitoring ; ambulatory ECG ; machine learning ; deep learning ; pattern recognition ; noise reduction ; Holter ECG ; ECG interpretation ; artificial intelligence ; body surface mapping ; electrocardiographic imaging ; image processing ; clinical applications ; n/a ; bic Book Industry Communication::M Medicine
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-09
    Description: This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs.
    Keywords: model-based design ; FPGA ; HDL code generation ; wearable sensors ; embedded devices ; face recognition ; face verification ; biometric sensors ; deep learning ; distillation ; convolutional neural networks ; spatial transformer network ; video coding ; discrete cosine transform ; directional transform ; VLSI ; alternative representations to float numbers ; posit arithmetic ; Deep Neural Networks (DNNs) ; neural network activation functions ; surface electromyography ; event-driven ; functional electrical stimulation ; embedded system ; resampling ; interpolating polynomial ; polyphase filter ; digital circuit design ; ASIC ; bitmap indexing ; processing in memory ; memory wall ; big data ; internet of things ; intelligent sensors ; autonomous driving ; cyber security ; HW accelerator ; on-chip random number generator (RNG) ; SHA2 ; ASIC standard-cell ; machine learning ; edge computing ; edge analytics ; ANN ; k-NN ; SVM ; decision trees ; ARM ; X-Cube-AI ; STM32 Nucleo ; rad-hard ; PLL (phase-locked loop) ; SEE (single event effects) ; Spacefibre ; TID (total ionization dose) ; charge pump ; phase/frequency detector ; frequency divider ; ring oscillator ; LC-tank oscillator ; SpaceFibre ; rad-hard circuits ; radiation effects ; high-speed data transfer ; support attitude ; inertial measurement unit ; coal mining ; unscented Kalman filter ; quaternion ; gradient descent ; research data collection and sharing ; connected and automated driving ; deployment and field testing ; vehicular sensors ; impact assessment ; knowledge management ; collaborative project methodology ; n/a ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-12-21
    Description: The biennial Congress of the Italian Society of Oral Pathology and Medicine (SIPMO) is an International meeting dedicated to the growing diagnostic challenges in the oral pathology and medicine field. The III International and XV National edition will be a chance to discuss clinical conditions which are unusual, rare, or difficult to define. Many consolidated national and international research groups will be involved in the debate and discussion through special guest lecturers, academic dissertations, single clinical case presentations, posters, and degree thesis discussions. The SIPMO Congress took place from the 17th to the 19th of October 2019 in Bari (Italy), and the enclosed copy of Proceedings is a non-exhaustive collection of abstracts from the SIPMO 2019 contributions.
    Keywords: R5-920 ; RB1-214 ; modeling ; underwater vehicle ; gesture-based language ; text classification ; navigation and control ; motion constraints ; autonomy ; dynamics ; marine robotics ; unmanned surface vehicle ; field trials ; actuator constraints ; robust control ; fault detection and isolation ; remotely operated vehicle ; underwater manipulator ; intelligent control ; object obstacle avoidance ; submersible vehicles ; overcome strong sea current ; underwater robot ; maneuverability identification ; ROV ; Lyapunov stability ; VGI ; ocean research ; two-ray ; path loss ; obstacle avoidance ; parallel control ; approximated optimal control ; sliding mode control ; automation systems ; fault-tolerant control ; numerical calculation ; backstepping control ; deep learning ; unmanned underwater vehicle (UUV) ; underwater human–robot interaction ; aerial underwater vehicle ; thruster fault ; airmax ; position control ; cross-medium ; free space ; second path planning ; flow sensing ; underwater vehicle-manipulator system ; marine systems ; low-level control ; dynamic modelling ; kinematics ; vehicle dynamics ; WLAN ; viscous hydrodynamics ; fault accommodation ; RSSI ; nonlinear systems ; guidance ; simulation model ; artificial lateral system ; autonomous underwater vehicle ; typhoon disaster ; force control ; bic Book Industry Communication::M Medicine
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  • 132
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-07-14
    Description: The fast development of the industrial internet is boosting the evolution of the manufacturing industry to a new stage of socialization, servitization, universal interaction and connection, and platformization. Under this background, social manufacturing emerged as a new kind of manufacturing paradigm, established based on the self-driven, self-organizing and self-adaptive cyber-physical–social interaction among extensive numbers of socialized manufacturing resource providers. Up until now, social manufacturing has drawn attention from both academic and industrial fields due to its promising research and application values. However, social manufacturing is still at its infant stage as the fast development of the industrial internet, artificial intelligence, collective intelligence, cloud/edge/fog computing, and a new generation of information and communication technologies, etc., are changing the interaction, configuration and operation mechanisms of social manufacturing every day. In this regard, this Special Issue is established to explore precisely how the newly emerged social manufacturing paradigm influences the trends of mass customization and the configuration/operation patterns during order delivery, and exactly how the advanced information technologies can boost the development and application of social manufacturing
    Keywords: social manufacturing ; additive manufacturing ; production planning ; distributed manufacturing systems ; heuristic algorithms ; graph matching ; Graph Convolutional Network (GCN) ; attention mechanism ; fully connected neural network ; cloud manufacturing ; Industrial Internet of Things ; data acquisition ; cloud–edge collaboration ; resource virtualization ; gateway ; decentralized Industrial Internet of Things ; blockchain middleware ; data security ; Industry 5.0 ; resilient manufacturing ; data acquisition network ; real-time energy consumption ; characteristic analysis ; intelligent workshops ; social value chain system ; value-adding ; key supporting technologies ; digital-driven technologies ; industry 4.0 ; turnkey project ; manufacturing system configuration ; manufacturing system operation ; key enabling tools ; industrial internet ; equipment asset management ; digitalization ; social digitalization platform ; system engineering ; fuzzy DEMATEL-TOPSIS ; industrial manufacturing ; deep learning ; data augmentation ; defect generation ; defect detection ; remote monitoring ; maintenance ; Industrial Internet ; dimensional error prediction ; grinding process ; bagging–GA–ELM ; robust analysis ; conductive particles ; LCD module ; ACF bonding ; automated optical inspection ; visual-alignment ; fieldbus control system ; synchronization control ; distributed clock ; ethernet fieldbus ; CANopen ; multimodal data ; product customization ; 3D content generation ; blockchain ; n/a ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: This book contains 19 peer-reviewed papers on the subject of BIM in the construction industry. These articles cover recent advances in the development of BIM technologies and applications in the field of architecture, engineering, and construction (AEC) industry.
    Keywords: green construction ; code checking ; mvdXML ; semantic technology ; SMEs ; BIM ; construction management system ; steel frame construction ; safety ; path planning ; A-Star Searching ; evacuation ; MEP ; logic chain ; Industry 4.0 ; construction industry ; building information modeling ; cyber-planning-physical system ; open BIM ; mobile BIM ; mobile application ; technology acceptance model (TAM) ; building information modeling (BIM) ; building project ; hindrance ; factor analysis ; structural equation modeling (SEM) ; managerial strategies ; Singapore ; Building Information Modeling ; process improvement ; construction management ; information and communication technologies ; Augmented Reality ; building design ; building performance simulation ; energy conservation ; fire safety inspection ; real-time location system ; smartphone ; crowdsourcing ; clash detection ; supervised machine learning ; openBIM ; information interoperability ; standards ; software ; fire disaster ; facility management ; lean construction ; production planning and control ; data-driven construction ; concrete formwork ; concrete maturity ; interoperability ; real-time monitoring ; fire safety rule ; visual language ; portable firefighting equipment ; Building Information Modeling (BIM) ; Industry Foundation Classes (IFC) ; partial model extraction ; query language ; selection set ; 3D Reconstruction ; 2D structural drawing ; object detection ; deep learning ; YOLO ; log data mining ; modeling performance ; collaborative environment ; behavioral patterns ; n/a ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-04-05
    Description: This reprint, "Flexible Micromanipulators and Micromanipulation," is a collection of 12 full research articles that provide insights into the latest development and applications in the field of micromanipulation which has important applications in areas, such as metal additive manufacturing, medical devices, and robotics. The topics of the reprint cover the design and fabrication of magnetic actuators, electromagnetic levitation systems, compliant manipulators and robots, micromanipulation of microparticles and microcapsules to characterize mechanical properties of microscale objects, and inchworm robot for inspection, assembly, and maintenance.
    Keywords: electromagnetism ; magnetic manipulator ; magnetic actuator ; deep learning ; ANN ; ANN/SA ; magnetic levitation ; additive manufacturing ; eddy current levitation ; direct energy deposition ; tripteron ; triaxial ; parallel-kinematic ; compliant manipulator ; compact structure ; micromanipulation ; microparticles ; motion control ; vibrations ; dry friction ; control ; oscillating platform ; automatic data analysis ; mechanical strength ; algorithms ; multibody systems ; CSFH ; event-driven scheme ; non-smooth contact ; LN-model ; Moreau time-stepping scheme ; capillary force ; water ; vision feedback ; non-contact ; flexible parallel mechanism ; 3-PSS ; optimal design ; kinematics ; dynamics ; compliant mechanisms ; flexible elements ; element transfer matrix ; transfer matrix approach ; micro-grippers ; micro-manipulators ; flexure ; CFSH ; compliant mechanism ; the tangent stiffness matrix ; plant-based ; non-synthetic ; microcapsules ; intrinsic mechanical properties ; apparent elastic modulus ; mathematical modelling ; finite element analysis ; XYθ position control ; holonomic inchworm robot ; optical encoder ; closed-loop control ; calibration ; crosstalk error ; n/a ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-12-20
    Description: With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.
    Keywords: QA75.5-76.95 ; T58.5-58.64 ; metadata ; image classification ; sensitivity analysis ; ROI detection ; residual learning ; image alignment ; adaptive convolutional kernels ; Hough transform ; class imbalance ; land surface temperature ; inundation mapping ; multiscale representation ; object-based ; convolutional neural networks ; scene classification ; morphological profiles ; hyperedge weight estimation ; hyperparameter sparse representation ; semantic segmentation ; vehicle classification ; flood ; Landsat imagery ; target detection ; multi-sensor ; building damage detection ; optimized kernel minimum noise fraction (OKMNF) ; sea-land segmentation ; nonlinear classification ; land use ; SAR imagery ; anti-noise transfer network ; sub-pixel change detection ; Radon transform ; segmentation ; remote sensing image retrieval ; TensorFlow ; convolutional neural network ; particle swarm optimization ; optical sensors ; machine learning ; mixed pixel ; optical remotely sensed images ; object-based image analysis ; very high resolution images ; single stream optimization ; ship detection ; ice concentration ; online learning ; manifold ranking ; dictionary learning ; urban surface water extraction ; saliency detection ; spatial attraction model (SAM) ; quality assessment ; Fuzzy-GA decision making system ; land cover change ; multi-view canonical correlation analysis ensemble ; land cover ; semantic labeling ; sparse representation ; dimensionality expansion ; speckle filters ; hyperspectral imagery ; fully convolutional network ; infrared image ; Siamese neural network ; Random Forests (RF) ; feature matching ; color matching ; geostationary satellite remote sensing image ; change feature analysis ; road detection ; deep learning ; aerial images ; image segmentation ; aerial image ; multi-sensor image matching ; HJ-1A/B CCD ; endmember extraction ; high resolution ; multi-scale clustering ; heterogeneous domain adaptation ; hard classification ; regional land cover ; hypergraph learning ; automatic cluster number determination ; dilated convolution ; MSER ; semi-supervised learning ; gate ; Synthetic Aperture Radar (SAR) ; downscaling ; conditional random fields ; urban heat island ; hyperspectral image ; remote sensing image correction ; skip connection ; ISPRS ; spatial distribution ; geo-referencing ; Support Vector Machine (SVM) ; very high resolution (VHR) satellite image ; classification ; ensemble learning ; synthetic aperture radar ; conservation ; convolutional neural network (CNN) ; THEOS ; visible light and infrared integrated camera ; vehicle localization ; structured sparsity ; texture analysis ; DSFATN ; CNN ; image registration ; UAV ; unsupervised classification ; SVMs ; SAR image ; fuzzy neural network ; dimensionality reduction ; GeoEye-1 ; feature extraction ; sub-pixel ; energy distribution optimizing ; saliency analysis ; deep convolutional neural networks ; sparse and low-rank graph ; hyperspectral remote sensing ; tensor low-rank approximation ; optimal transport ; SELF ; spatiotemporal context learning ; Modest AdaBoost ; topic modelling ; multi-seasonal ; Segment-Tree Filtering ; locality information ; GF-4 PMS ; image fusion ; wavelet transform ; hashing ; machine learning techniques ; satellite images ; climate change ; road segmentation ; remote sensing ; tensor sparse decomposition ; Convolutional Neural Network (CNN) ; multi-task learning ; deep salient feature ; speckle ; canonical correlation weighted voting ; fully convolutional network (FCN) ; despeckling ; multispectral imagery ; ratio images ; linear spectral unmixing ; hyperspectral image classification ; multispectral images ; high resolution image ; multi-objective ; convolution neural network ; transfer learning ; 1-dimensional (1-D) ; threshold stability ; Landsat ; kernel method ; phase congruency ; subpixel mapping (SPM) ; tensor ; MODIS ; GSHHG database ; compressive sensing ; bic Book Industry Communication::U Computing & information technology::UY Computer science
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-08-12
    Description: Photonics has had a decisive influence on recent scientific and technological achievements. It includes aspects of photon generation and photon–matter interaction. Although it finds many applications in the whole optical range of the wavelengths, most solutions operate in the visible and infrared range. Since the invention of the laser, a source of highly coherent optical radiation, optical measurements have become the perfect tool for highly precise and accurate measurements. Such measurements have the additional advantages of requiring no contact and a fast rate suitable for in-process metrology. However, their extreme precision is ultimately limited by, e.g., the noise of both lasers and photodetectors. The Special Issue of the Applied Science is devoted to the cutting-edge uses of optical sources, detectors, and optoelectronics systems in numerous fields of science and technology (e.g., industry, environment, healthcare, telecommunication, security, and space). The aim is to provide detail on state-of-the-art photonic technology for precision metrology and identify future developmental directions. This issue focuses on metrology principles and measurement instrumentation in optical technology to solve challenging engineering problems.
    Keywords: infrared thermometer ; mid-wave infrared ; indium arsenide antimony photodiode ; uncooled thermometer ; fibreoptic coupling ; chopper stabilised op-amp ; zero-drift pre-amplifier ; ammonia detection ; NH3 ; MOX sensors ; polymer sensors ; laser absorption spectroscopy ; CRDS ; CEAS ; MUPASS ; PAS ; HOT IR detectors ; HgCdTe ; P-i-N ; BLIP condition ; 2D material photodetectors ; colloidal quantum dot photodetectors ; low-light photodetectors ; fluorescence microscopy ; time-resolved fluorescence microscopy ; hybrid photodetector (HPD) ; single-molecule fluorescence detection ; fourier ptychography ; image classification ; deep learning ; neural network ; electro-optic modulator ; frequency modulation ; displacement measuring interferometer ; quantum cascade laser ; laser controller ; infrared modulator ; laser spectroscopy ; free space optics ; photonic metrology ; accuracy ; precision ; resolution ; FTIR ; absorption spectroscopy ; gas sensors ; optoelectronic sensors ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-09-11
    Description: With the development of computer technology and communication technology, various industries have collected a large amount of data in different forms, so-called big data. How to obtain valuable knowledge from these data is a very challenging task. Machine learning is such a direct and effective method for big data analytics. In recent years, a variety of advanced machine learning technologies have emerged, and they continue to play important roles in the era of big data. Considering advanced machine learning and big data together, we have selected a series of relevant works in this Special Issue to showcase the latest research advancements in this field. Specifically, a total of thirty-three articles are included in this Special Issue, which can be roughly categorized into six groups: time series analysis, evolutionary computation, pattern recognition, computer vision, image encryption, and others.
    Keywords: energy storage ; model predictive control ; peak shaving and frequency regulation ; output optimization ; global optimization ; meta-heuristic ; support vector machine swarm intelligence ; hyperspectral image classification ; CNN ; ELM ; PSO ; deep feature ; butterfly optimization algorithm ; random replacement ; crisscross search ; overseas Chinese associations ; support vector machine ; short-term traffic-flow forecasting ; bagging model ; stacking model ; ridge regression ; error coefficient ; least squares method ; support vector machines ; principal component analysis ; quick access recorder ; mean absolute error ; high-plateau flight ; event extraction ; event type ; event trigger words ; stock announcement news ; stock return ; traffic flow forecasting ; long short-term memory network ; graph convolutional network ; target detection ; infrared ; deep learning ; YOLOv5 algorithm ; design science research ; performance analysis ; machine learning ; classification algorithms ; clustering algorithms ; pilot abnormal behavior ; behavior detection ; YOLOv4 algorithm ; CBAM ; flight safety ; fault diagnosis ; variational mode decomposition ; composite multi-scale dispersion entropy ; particle swarm optimization ; deep belief network ; CBCFI ; combined prediction model ; ARMA ; GM ; GA ; BP ; hierarchical clustering ; Jaccard distance ; membership grade ; community clustering ; lightweight neural networks ; attentional mechanisms ; Hemerocallis citrina Baroni ; maturity detection ; cloud ; digital archives ; confidentiality management ; information system ; emotion-cause pair extraction ; heterogeneous graph ; graph attention network ; hierarchical model ; spatial-temporal systems ; neural networks ; information systems ; forecasting ; time series ; coupled map lattice ; polymorphic mapping ; color image ; hash function ; pixel level ; differential evolution ; capacitated vehicle routing planning ; saving mileage ; gravity search ; object detection ; computer vision ; border patrol ; COVID-19 ; warning system ; PROPHET ; health ; quantum dynamics ; neural architecture search ; image classification ; swarm intelligence ; whale optimization algorithm ; extreme learning machine ; talent stability prediction ; adversarial attacks ; document classification ; NLP ; convolutional neural networks ; disease classification ; generative adversarial network ; tomato leaf ; multi-strategy ; dual-update strategy ; mean-semivariance model ; portfolio optimization ; DNA computing ; DNA sequences design ; improved matrix particle swarm optimization algorithm (IMPSO) ; opposition-based learning ; signal-to-noise ratio distance ; time series classification ; complementary ensemble empirical mode decomposition (CEEMD) ; MultiRocket ; feature selection ; hybrid model ; multi-behavior recommendation ; sequential recommendation ; graph neural network ; embedding propagation ; 1D quadratic chaotic system ; image encryption ; splicing model ; DNA coding ; BaaS system ; blockchain consensus algorithm ; KNN ; service level agreement ; transaction priority ; data stream mining ; forex ; online learning ; adaptive learning ; incremental learning ; sliding window ; concept drift ; financial time series forecasting ; n/a ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-05-06
    Description: Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens.
    Keywords: star image ; image denoising ; reinforcement learning ; maximum likelihood estimation ; mixed Poisson–Gaussian likelihood ; machine learning-based classification ; non-uniform foundation ; stochastic analysis ; vehicle–pavement–foundation interaction ; forest growing stem volume ; coniferous plantations ; variable selection ; texture feature ; random forest ; red-edge band ; on-shelf availability ; semi-supervised learning ; deep learning ; image classification ; machine learning ; explainable artificial intelligence ; wildfire ; risk assessment ; Naïve bayes ; transmission-line corridors ; image encryption ; compressive sensing ; plaintext related ; chaotic system ; convolutional neural network ; color prior model ; object detection ; piston error detection ; segmented telescope ; BP artificial neural network ; modulation transfer function ; computer vision ; intelligent vehicles ; extrinsic camera calibration ; structure from motion ; convex optimization ; temperature estimation ; BLDC ; electric machine protection ; touchscreen ; capacitive ; display ; SNR ; stylus ; laser cutting ; quality monitoring ; artificial neural network ; burr formation ; cut interruption ; fiber laser ; semi-supervised ; fuzzy ; noisy ; real-world ; plankton ; marine ; activity recognition ; wearable sensors ; imbalanced activities ; sampling methods ; path planning ; Q-learning ; neural network ; YOLO algorithm ; robot arm ; target reaching ; obstacle avoidance ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: Modern power systems are affected by many sources of uncertainty, driven by the spread of renewable generation, by the development of liberalized energy market systems and by the intrinsic random behavior of the final energy customers. Forecasting is, therefore, a crucial task in planning and managing modern power systems at any level: from transmission to distribution networks, and in also the new context of smart grids. Recent trends suggest the suitability of ensemble approaches in order to increase the versatility and robustness of forecasting systems. Stacking, boosting, and bagging techniques have recently started to attract the interest of power system practitioners. This book addresses the development of new, advanced, ensemble forecasting methods applied to power systems, collecting recent contributions to the development of accurate forecasts of energy-related variables by some of the most qualified experts in energy forecasting. Typical areas of research (renewable energy forecasting, load forecasting, energy price forecasting) are investigated, with relevant applications to the use of forecasts in energy management systems.
    Keywords: TA1-2040 ; T1-995 ; forecast combination ; solar energy ; electricity price forecasting ; calibration window ; heuristic algorithm ; deep learning ; electric load forecasting ; smart grids ; hierarchical load forecasting ; predictive distribution ; solar PV ; solar farm ; microgrid ; energy management ; lower and upper bound estimation ; solar power prediction ; interval prediction ; kernel density estimation ; average probability forecast ; probabilistic forecasting ; forecasting ; distributed energy resources ; photovoltaic power ; conditional predictive ability ; clearness index ; Fourier series ; combining forecasts ; weather station combination ; distributed generation ; clear sky index ; extreme learning machine ; ensemble methods ; pinball score ; autoregression ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-09
    Description: Size reduction processes represent a significant part of the capital as well as the operating cost in ore processing. Advancing the understanding of and improving such processes is worthwhile since any measurable enhancement may lead to benefits, which may materialize as reductions in energy consumption or wear or improved performance in downstream processes. This book contains contributions dealing with various aspects of comminution, including those intended to improve our current level of understanding and quantification of particle breakage and ore characterization techniques that are relevant to size reduction, as well as studies involving modeling and simulation techniques. The affiliations of the authors of the articles published in this book span 14 countries around the globe, which attests to the highly international nature of research in this field. The themes of the manuscripts also vary widely, from several that are more focused on experimental studies to those that deal, in greater detail, with the development and application of modeling and simulation techniques in comminution. Size reduction technologies more directly addressed in the manuscripts include jaw crushing, vertical shaft impact crushing, SAG milling, stirred milling, planetary milling, and vertical roller milling. Ores involved directly in the investigations include those of copper, lead–zinc, gold, and iron as well as coal, talc, and quartz.
    Keywords: nanoscale talc ; wet milling ; high-energy ball milling ; ball size ; aggregation ; quantitative microstructural analysis ; X-ray computed tomography ; selective comminution ; texture ; structure ; mineral processing ; crushing ; grinding ; grinding behaviors ; energy consumption characterization ; sulfur content ; heterogeneous breakage ; split energy ; mining operation ; ore milling ; ore grinding ; rock ; liberation ; bed breakage ; iron ore ; comminution ; saturation ; piston-and-die ; compaction ; compression ; breakage ; single particle breakage ; energy input ; drop-weight tester ; breakage modelling ; grinding prediction ; jaw crusher ; Discrete Element Method ; Particle Replacement Model ; simulation ; modeling ; primary crushing ; particle breakage ; semi-autogenous grinding mill ; operational hardness ; energy consumption ; mining ; deep learning ; long short-term memory ; quartz ; shear stress ; tribochemistry ; fracturing ; mixed sulfides ; sphalerite ; galena ; VSI ; DEM ; sand ; modelling ; Vertimill ; Tower Mill ; liner wear ; fine grinding ; discrete element method ; n/a ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-05
    Keywords: QD1-999 ; Q1-390 ; QD450-801 ; information theoretic analysis ; multiplexing system ; HSI for biology ; point target detection ; digital elevation model ; neural networks ; oxygen saturation ; black polymers ; PZT ; blood detection ; multivariate analysis ; integral imaging ; hemispherical conical reflectance factor (HCRF) ; sprouting ; fluorescence ; multitemporal hyperspectral images ; plant phenotyping ; hyperspectral data mining and compression ; Raman ; medical imaging by HSI ; compressive detection ; stereo imaging ; image processing ; wound healing ; quality control ; lossless compression ; infrared hyperspectral imaging ; spectral tracking ; time series ; remote sensing ; diabetic foot ulcer ; classification ; Raman spectroscopy ; imaging ; fingerprints ; fusion ; wavelength selection ; Cramer–Rao lower bound ; three-dimensional imaging ; chemical imaging ; CS-MUSI ; total variation ; coastal dynamics ; forward observation model ; hyperspectral imaging ; fluorescence hyperspectral imaging ; age determination ; potatoes ; painting samples ; predictive coding ; hyperspectral ; video ; bi-directional reflectance distribution function (BRDF) ; optimal binary filters ; watercolours ; deep learning ; spectroscopy ; moving vehicle imaging ; sorting ; maximum likelihood ; multivariate data analysis ; interval partial least squares ; disease detection ; Raman hyperspectral imaging ; primordial leaf count ; machine learning ; spatial light modulators (SLM) ; Virginia Coast Reserve Long Term Ecological Research (VCR LTER) ; digital micromirror device (DMD) ; hyperspectral microscopy ; alternating direction method of multipliers ; statistical methods for HSI ; multiband image fusion ; digital light processor (DLP) ; linear mixture model ; retouching pigments ; liquid crystal ; principal component analysis ; Chemometrics ; compressive sensing ; PLSR ; Hyperspectral imaging ; thema EDItEUR::P Mathematics and Science::PN Chemistry
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  • 142
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-06-23
    Description: This topic has been successfully completed. A total of 180 scientific articles were sent, of which 66 have been accepted for publication. These 66 articles are published in this reprint.
    Keywords: artificial intelligence ; expert systems ; machine learning ; deep learning ; modeling ; prediction ; probabilistic models ; prediction models ; sustainable mobility ; smart cities ; smart grids ; sustainable energy systems ; renewable energy sources ; nonrenewable energy sources ; electrical storage ; green hydrogen ; sustainable greenhouses ; wind ; microgrids ; tidal ; solar ; biomass ; power network ; solar thermal ; solar photovoltaic ; hydraulics ; ANN ; neural networks, fuzzy logic, genetic algorithms ; hybrid models based on artificial intelligence ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-07-14
    Description: Computational modeling and simulation are essential to solid and structural mechanics. They have not only covered entire engineering fields (civil, aerospace, mechanical, etc.) but also various scales (from nano to macro) and physics (mono- and multiphysics). Recently, they have been found to be able to offer theoretical backgrounds of digital transformation. Society at large is increasingly enthusiastic about data-driven modeling and simulation, and the possibilities they offer. The aim of this Special Issue reprint was to provide a forum for researchers to discuss recent advanced computational modeling and simulation techniques of solids and structures, and applications to solve challenging engineering problems.
    Keywords: carbon nanotube ; strain sensor ; piezoresistive mechanism ; sensor pattern design ; learning tools ; teaching methodology ; educational software ; mechanism science ; problem-based learning ; bolted joints ; parameter identification ; thin-layer element ; pre-tightening torque ; contact interface ; FEM ; surrogate modeling ; mesh-free ; machine learning ; deep learning ; SIMP method ; impact condition ; non-matching interface ; condensed mortar method ; periodic structures ; polar coordinates ; wave propagation ; forced response of plates and shells ; finite element analysis ; unbounded structures ; deployment dynamics ; truss-link mechanism ; synthetic aperture radar ; friction compensation ; GP regression ; multi-objective optimization ; hot metal forming ; Inconel 625 ; multibody dynamics ; automotive driveshafts ; forced bending vibrations ; asymptotic method ; principal parametric resonance ; dynamic stability frontiers ; n/a ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: This book focuses on the intelligent processing of images and optical information acquired by various imaging methods. Intelligent image and optical information processing have paved the way for the recent epoch of new intelligence and information era. Certainly, information acquired by various imaging techniques is of tremendous value; thus, an intelligent analysis of them is necessary to make the best use of it. A broad range of research fields is included in this book. Many studies focus on object classification and detection. Registration, segmentation, and fusion are performed between a series of images. Many valuable and up-to-most recent technologies are provided to solve the real problems in selected papers.
    Keywords: change detection ; NSCT ; variogram function ; structure similarity ; Dongting Lake ; ego-motion estimation ; hand-eye calibration ; IMU ; lidar odometry ; sensor fusion ; texture classification ; Gabor filter ; parameter optimization ; feature selection ; hybrid ant lion optimizer ; wireless multimedia sensor networks ; wildlife monitoring image ; extraction ; Hermite ; adaptive mean-shift ; biomedical imaging ; bone fracture ; calcaneus ; CT image ; segmentation ; zebrafish egg ; microscopy image processing ; convolutional neural network ; digital image correlation ; high-temperature measurement ; heat waves ; thermal disturbance ; background-oriented schlieren ; fermentation monitoring ; quality inspection ; process automation ; deep learning ; superellipsoid model fitting ; optical sensor ; multi-sensor ; face registration ; inner-distance ; Student’s-t Mixtures Model ; image fusion ; continuous casting slabs ; surface defect classification ; discrete non-separable shearlet transform ; gray-level co-occurrence matrix ; kernel spectral regression ; block compressed sensing ; error resilience ; reconstruction ; image completion ; tensor decomposition models ; image interpolation ; image up-scaling ; numerical optimization ; ADAM ; machine learning ; stochastic gradient methods ; healthy and infected lemons ; Hyperspectral image ; Penicillium digitatum pathogen ; lemon skin ; dominant spectral wavelength ; spectral intensity ratio ; zebrafish larva ; microscopy image analysis ; deep neural network ; clustering evaluation ; clustering algorithm ; cluster validity index ; boundary point ; interior point ; radiographic image ; image processing ; feature extraction ; classifier ; defect detection ; generative models ; GAN (Generative adversarial networks) ; facial image ; generation ; database augmentation ; synthesis ; autofocus ; night vision goggles ; sparse and low-rank matrix decomposition ; n/a ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
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  • 145
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-09
    Description: In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot.
    Keywords: dynamic objects identification and localization ; laser cluster ; radial velocity similarity ; Pearson correlation coefficient ; particle filter ; trilateral indoor positioning ; RSSI filter ; RSSI classification ; stability ; accuracy ; inertial navigation system ; artificial neural network ; motion tracking ; sensor fusion ; indoor navigation system ; indoor positioning ; indoor navigation ; radiating cable ; leaky feeder ; augmented reality ; Bluetooth ; indoor positioning system ; smart hospital ; indoor ; positioning ; visually impaired ; deep learning ; multi-layered perceptron ; inertial sensor ; smartphone ; multi-variational message passing (M-VMP) ; factor graph (FG) ; second-order Taylor expansion ; cooperative localization ; joint estimation of position and clock ; RTLS ; indoor positioning system (IPS) ; position data ; industry 4.0 ; traceability ; product tracking ; fingerprinting localization ; Bluetooth low energy ; Wi-Fi ; performance metrics ; positioning algorithms ; location source optimization ; fuzzy comprehensive evaluation ; DCPCRLB ; UAV ; unmanned aerial vehicles ; NWPS ; indoor positioning systems ; GPS denied ; GNSS denied ; autonomous vehicles ; visible light positioning ; mobile robot ; calibration ; appearance-based localization ; computer vision ; Gaussian processes ; manifold learning ; robot vision systems ; image manifold ; descriptor manifold ; indoor fingerprinting localization ; Gaussian filter ; Kalman filter ; received signal strength indicator ; channel state information ; indoor localization ; visual-inertial SLAM ; constrained optimization ; path loss model ; particle swarm optimization ; beacon ; absolute position system ; cooperative algorithm ; intercepting vehicles ; robot framework ; UWB sensors ; Internet of Things (IoT) ; wireless sensor network (WSN) ; switched-beam antenna ; electronically steerable parasitic array radiator (ESPAR) antenna ; received signal strength (RSS) ; fingerprinting ; down-conversion ; GPS ; navigation ; RF repeaters ; up-conversion ; n/a ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues ; thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilities
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  • 146
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-09
    Description: The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application.
    Keywords: falls ; slips ; trips ; postural perturbations ; wearables ; stretch-sensors ; ankle kinematics ; rowing ; technology ; inertial sensor ; accelerometer ; performance ; signal processing ; sEMG ; knee ; random forest ; principal component analysis ; back propagation ; estimation model ; knee angle ; deep learning ; neural networks ; gait-phase classification ; electrogoniometer ; EMG sensors ; walking ; gait-event detection ; automotive radar ; machine learning ; walking analysis ; seated posture ; cognitive engagement ; stress level ; load cells ; embedded systems ; sensorized seat ; flexion-relaxation phenomenon ; surface electromyography ; wearable device ; WBSN ; automatic detection of the FRP ; Internet of Things (IoT) ; human activity recognition (HAR) ; motion analysis ; wearable sensors ; cerebral palsy ; hemiplegia ; motor disorders ; gait variability ; coefficient of variation ; surface EMG ; statistical gait analysis ; activation patterns ; co-activation ; Parkinson’s disease ; activity recognition ; rate invariance ; Lie group ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
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  • 147
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-02-20
    Description: Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer.
    Keywords: tumor mutational burden ; DNA damage repair genes ; immunotherapy ; biomarker ; biomedical informatics ; breast cancer ; estrogen receptor alpha ; persistent organic pollutants ; drug-drug interaction networks ; molecular docking ; NGS ; ctDNA ; VAF ; liquid biopsy ; filtering ; variant calling ; DEGs ; diagnosis ; ovarian cancer ; PUS7 ; RMGs ; CPA4 ; bladder urothelial carcinoma ; immune cells ; T cell exhaustion ; checkpoint ; architectural distortion ; image processing ; depth-wise convolutional neural network ; mammography ; bladder cancer ; Annexin family ; survival analysis ; prognostic signature ; therapeutic target ; R Shiny application ; RNA-seq ; proteomics ; multi-omics analysis ; T-cell acute lymphoblastic leukemia ; CCLE ; sitagliptin ; thyroid cancer (THCA) ; papillary thyroid cancer (PTCa) ; thyroidectomy ; metastasis ; drug resistance ; n/a ; biomarker identification ; transcriptomics ; machine learning ; prediction ; variable selection ; major histocompatibility complex ; bidirectional long short-term memory neural network ; deep learning ; cancer ; incidence ; mortality ; modeling ; forecasting ; Google Trends ; Romania ; ARIMA ; TBATS ; NNAR ; bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general ; bic Book Industry Communication::P Mathematics & science::PS Biology, life sciences
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  • 148
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-03-30
    Description: The celebrated information bottleneck (IB) principle of Tishby et al. has recently enjoyed renewed attention due to its application in the area of deep learning. This collection investigates the IB principle in this new context. The individual chapters in this collection: • provide novel insights into the functional properties of the IB; • discuss the IB principle (and its derivates) as an objective for training multi-layer machine learning structures such as neural networks and decision trees; and • offer a new perspective on neural network learning via the lens of the IB framework. Our collection thus contributes to a better understanding of the IB principle specifically for deep learning and, more generally, of information–theoretic cost functions in machine learning. This paves the way toward explainable artificial intelligence.
    Keywords: information theory ; variational inference ; machine learning ; learnability ; information bottleneck ; representation learning ; conspicuous subset ; stochastic neural networks ; mutual information ; neural networks ; information ; bottleneck ; compression ; classification ; optimization ; classifier ; decision tree ; ensemble ; deep neural networks ; regularization methods ; information bottleneck principle ; deep networks ; semi-supervised classification ; latent space representation ; hand crafted priors ; learnable priors ; regularization ; deep learning ; thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries
    Language: English
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  • 149
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-03-28
    Description: This reprint, named “Landslides in Forests around the World: Causes and Mitigation”, covers various topics, such as the impact mechanism of tree roots on landslide stability, landslide deformation monitoring, landslide disaster prevention and control engineering technology, the automatic identification of regional landslides, susceptibility and hazard assessment, and a rainfall-induced mass landslide warning.
    Keywords: shallow landslide ; probability of occurrence ; typhoon ; data-driven model ; Ningguo City ; landslide susceptibility ; antecedent effective precipitation ; daily precipitation ; hybrid landslide warning model ; multibaseline InSAR techniques ; Guang’an Village Landslide ; InSAR ; time-series analysis ; post-event deformation mapping ; root-soil composite ; root reinforcement coefficient ; shear strength parameters ; root distribution angle ; root diameter ; landslide susceptibility mapping ; random forest model ; qualitative analysis ; quantitative evaluation ; Yunyang County ; granular flow ; ring-shear test ; fluctuation characteristics ; landslide ; susceptibility ; tree species ; age group ; woodland type ; forest origin ; YOLOv3 ; deep learning ; automatic landslide identification ; remote sensing image ; flexible rockfall barrier ; energy capacity ; landslide debris ; field investigation ; coupled numerical simulation ; land-use suitability ; Hechuan District ; landslide susceptibility assessment ; machine learning ; Three Gorges Reservoir area ; early-warning model ; Random Forest ; model study ; nucleation process ; waveform similarity ; repeating earthquakes ; neighboring earthquakes ; reinforcement of roots ; slope stability ; growth and decay ; Japanese cedar trees ; shallow landslides ; n/a ; thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general ; thema EDItEUR::K Economics, Finance, Business and Management::KC Economics::KCV Economics of specific sectors::KCVG Environmental economics
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  • 150
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-06-23
    Description: Digital health, virtual assistance, and telemedicine are terms often used interchangeably to refer to remote medical assistance, monitoring and care. Several studies and insights have developed these issues, analyzing the advantages and disadvantages and successes and failures and offering reflections on the implications and issues of these technologies in the health domain. The results of these investigations are affecting the redesign of hospital and outpatient management based on digital innovation using eHealth and mHealth. During the COVID-19 pandemic, this approach made it possible to offer assistance and continue care at home, protecting patients, preserving health workers, limiting the spread of the virus, and reducing the need for hospitalization. This reprint contains contributions dealing with the development of DH during the COVID-19 pandemic. The contributions are from various experts in different fields regarding the application of digital health, which, in some cases is also integrated with artificial intelligence, including digital contact tracing, mHealth, virtual reality, mental health, physiology, and rehabilitation.
    Keywords: n/a ; digital contact tracing ; IMMUNI app ; COVID-19 ; students ; digital care visit ; online consultation ; medical staff ; healthcare personnel ; user experience ; magnetic resonance imaging (MRI) ; brain tumor ; machine learning ; digital health ; e-health ; pandemic ; physical activity ; performance evaluation ; eHealth ; self-care ; chronic diseases ; mental health ; mindfulness ; mobile health ; social isolation ; mental stress ; feature selection ; artificial intelligence ; human health ; lock down ; normative activation model ; COVID-19 prevention ; prevention intention ; IoT ; obesity ; classification ; regression ; real-time system ; COVID-19 pandemic ; contact tracing ; CNN ; chest X-ray images ; hybrid learning ; computer-aided diagnosis ; remote psychotherapy ; psychotherapy via telephone ; psychotherapy via videoconferencing ; tele-health ; e-mental-health ; psychotherapy ; qualitative psychotherapy research ; mixed-methods psychotherapy research ; exergaming ; breast neoplasms ; physical function ; telehealth ; (d)health literacy ; health literacy ; health intervention ; health strategy ; medical data ; medical imaging ; data classification ; image detection ; YOLOv4 ; logistic regression ; AI ; deep learning ; chatbot ; health ; health domain ; bic Book Industry Communication::M Medicine ; bic Book Industry Communication::M Medicine::MB Medicine: general issues::MBP Health systems & services::MBPK Mental health services
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  • 151
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-03-21
    Description: The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity.
    Keywords: ARIMA model ; time series analysis ; online optimization ; online model selection ; precipitation nowcasting ; deep learning ; autoencoders ; radar data ; generalization error ; recurrent neural networks ; machine learning ; model predictive control ; nonlinear systems ; neural networks ; low power ; quantization ; CNN architecture ; multi-objective optimization ; genetic algorithms ; evolutionary computation ; swarm intelligence ; Heating, Ventilation and Air Conditioning (HVAC) ; metaheuristics search ; bio-inspired algorithms ; smart building ; soft computing ; training ; evolution of weights ; artificial intelligence ; deep neural networks ; convolutional neural network ; deep compression ; DNN ; ReLU ; floating-point numbers ; hardware acceleration ; energy dissipation ; FLOW-3D ; hydraulic jumps ; bed roughness ; sensitivity analysis ; feature selection ; evolutionary algorithms ; nature inspired algorithms ; meta-heuristic optimization ; computational intelligence ; bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general ; bic Book Industry Communication::P Mathematics & science
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  • 152
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-11-17
    Description: This reprint is a collection of 31 original papers and four reviews, published from 2021 to 2022, focused on the application of a wide range of computational tools in medicinal chemistry projects: from molecular docking to artificial intelligence approaches. Applications of in silico tools are crucial in the early stages of drug design, such as planning more efficient and economic synthetic routes for chemical administration, screening of huge databases, as well as proposing hypotheses for probable mechanisms of action of drugs in macromolecular targets. Such endeavors are extremely complex and require the usage of modern and sophisticated approaches, such as artificial intelligence, data mining, computational molecular simulations through classical mechanics and quantum mechanics, molecular docking, chemoinformatics, applied mathematics, and biostatistics.
    Keywords: T-type calcium channel blocker ; homology modeling ; computer-aid drug design ; virtual drug screening ; L-type calcium channel ; mTOR kinase ; marine natural products ; ATP-competitive inhibitors ; structure-based pharmacophore modeling ; virtual screening ; molecular docking ; molecular dynamics simulations ; binding free energy ; in silico ADMET ; α-Glucosidase ; QSAR modeling ; ADMET profiling ; cervical cancer management ; computer-aided drug design ; E6 inhibitors ; in silico studies ; human papillomavirus ; manifold learning ; machine learning ; rdkit ; embeddings ; Tox21 ; principal component analysis ; autoencoder ; skin sensitization ; toxicity prediction ; in silico prediction ; random forest ; conformal prediction ; bioactivity descriptors ; SARS coronavirus ; SARS-CoV-2 main protease ; structure-based virtual screening ; molecular dynamic simulation ; hit identification ; Alzheimer’s disease ; multitarget ; natural-like compounds ; library of integrated network-based cellular signatures (LINCS) ; longevity ; gene regulating effects ; gene descriptors ; molecular fingerprints ; deep neural network ; drug repurposing ; Variola virus ; thymidylate kinase ; smallpox ; docking ; molecular dynamics ; molecular modeling ; permeability ; membrane disruption ; membrane proteins ; drugs ; antimicrobial peptides ; Ras ; RasGRF1 ; hydrogen-bond surrogate ; computational residue scanning ; MM-GBSA ; protein–protein interaction ; ERK signalling ; cocaine addiction ; intellectual disability (ID) ; autism spectrum disorder (ASD) ; gated recurrent unit ; recurrent neural network ; transfer learning ; caspase-6 ; inhibitor ; molecular design ; computational drug design ; deep learning ; multiscale ; polypharmacology ; Mycobacterium tuberculosis ; mycolic acid methyltransferases ; fragment-based ligand discovery ; binding energies ; molecular modelling ; heat shock protein ; HSP70 ; nucleotide-binding domain ; piperlongumine ; fluorescence spectroscopy ; circular dichroism ; molecular mechanics Poisson–Boltzmann surface area ; Parkinson’s disease ; catechol-O-methyltransferase ; inhibitors ; bioinformatics ; pharmacophore modeling ; cytotoxicity ; computational drug discovery ; chemical space ; parallelization ; high-performance computers and accelerators ; sulfonamides ; arylsulfonamide ; anticancer compounds ; telomerase inhibitors ; structure-based drug design ; computer drug design ; MolAr ; DNA intercalating agents ; SARS-CoV-2 ; main protease, Mpro ; docking benchmark ; non-steroidal anti-inflammatory drugs ; drug discovery ; lipoxygenase ; cyclooxygenase ; Hsp90 ; cancer ; QSAR ; pharmacophores ; in-silico drug design ; AlphaFold ; anti-CRISPR proteins ; prokaryotic defence mechanisms ; bacteriophages ; structural biology ; protein drug ; Merkel cell polyomavirus ; Merkel cell carcinomas ; drug design ; ADMET ; MD simulation ; antimicrobial peptide database ; antiviral peptides ; database filtering technology ; Ebola virus ; peptide design ; G-quadruplex DNA ; TERRA ; mass spectrometry ; biological assays ; mangrove natural products ; KRASG12C ; ligand-based pharmacophore modeling ; computational biology ; RVFV ; RdRp ; structural modeling ; GlyT1 ; schizophrenia ; DAT ; MD ; chagas ; leishmaniasis ; naphthoquinones ; antiprotozoal evaluation ; ADME ; COVID-19 ; NSP3 ; TCM ; MD simulations ; mutagenesis ; artificial intelligence ; biased signaling ; G protein-coupled receptor ; immunology ; flavonoids ; IDO1 ; free energy ; bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general ; bic Book Industry Communication::P Mathematics & science::PN Chemistry
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  • 153
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-05-06
    Description: Over the past century, the manufacturing industry has undergone a number of paradigm shifts: from the Ford assembly line (1900s) and its focus on efficiency to the Toyota production system (1960s) and its focus on effectiveness and JIDOKA; from flexible manufacturing (1980s) to reconfigurable manufacturing (1990s) (both following the trend of mass customization); and from agent-based manufacturing (2000s) to cloud manufacturing (2010s) (both deploying the value stream complexity into the material and information flow, respectively). The next natural evolutionary step is to provide value by creating industrial cyber-physical assets with human-like intelligence. This will only be possible by further integrating strategic smart sensor technology into the manufacturing cyber-physical value creating processes in which industrial equipment is monitored and controlled for analyzing compression, temperature, moisture, vibrations, and performance. For instance, in the new wave of the ‘Industrial Internet of Things’ (IIoT), smart sensors will enable the development of new applications by interconnecting software, machines, and humans throughout the manufacturing process, thus enabling suppliers and manufacturers to rapidly respond to changing standards. This reprint of “Sense and Respond” aims to cover recent developments in the field of industrial applications, especially smart sensor technologies that increase the productivity, quality, reliability, and safety of industrial cyber-physical value-creating processes.
    Keywords: EEG sensors ; manufacturing systems ; problem-solving ; deep learning ; TDOA ; sensor networks ; hyperboloids ; node distribution ; genetic algorithms ; asynchronous ; Cramér–Rao lower bound ; heteroscedasticity ; soft sensors ; industrial optical quality inspection ; artificial vision ; long-term monitoring benefits ; indoor air quality ; low cost ; occupational safety and health ; industry 4.0 ; IOTA tangle ; Industry 4.0 ; IIoT ; geometric deep learning ; lean management ; cramer rao lower bound ; localization ; LPS ; multi-objective optimization ; sensor failure ; wireless sensor networks ; conceptual framework ; sensors ; approaches ; tools ; data ; application ; project engineering ; LCA ; SDG 9 ; SDG 11 ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
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  • 154
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-01
    Description: The analysis of Big Data in biomedical as well as business and financial research has drawn much attention from researchers worldwide. This book provides a platform for the deep discussion of state-of-the-art statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical contributions are showcased.
    Keywords: high-dimensional ; nonlocal prior ; strong selection consistency ; estimation consistency ; generalized linear models ; high dimensional predictors ; model selection ; stepwise regression ; deep learning ; financial time series ; causal and dilated convolutional neural networks ; nuisance ; post-selection inference ; missingness mechanism ; regularization ; asymptotic theory ; unconventional likelihood ; high dimensional time-series ; segmentation ; mixture regression ; sparse PCA ; entropy-based robust EM ; information complexity criteria ; high dimension ; multicategory classification ; DWD ; sparse group lasso ; L2-consistency ; proximal algorithm ; abdominal aortic aneurysm ; emulation ; Medicare data ; ensembling ; high-dimensional data ; Lasso ; elastic net ; penalty methods ; prediction ; random subspaces ; ant colony system ; bayesian spatial mixture model ; inverse problem ; nonparamteric boostrap ; EEG/MEG data ; feature representation ; feature fusion ; trend analysis ; text mining ; thema EDItEUR::N History and Archaeology::NH History ; thema EDItEUR::J Society and Social Sciences::JB Society and culture: general::JBF Social and ethical issues
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  • 155
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-09
    Description: This book includes 23 published papers on Special issues of "Image and Video Processing and Recognition Based on Artificial Intelligence" in the journal Sensors. The purpose of this Special Issue was to invite high-quality and state-of-the-art academic papers on challenging issues in the field of AI-based image and video processing and recognition.
    Keywords: emotion recognition ; brain computer interface ; bag of deep features ; continuous wavelet transform ; face image analysis ; deep learning ; face parsing ; facial attributes classification ; building extraction ; convolutional neural networks ; mask R-CNN ; high-resolution remote sensing image ; autoencoders ; semi-supervised learning ; computer vision ; pathology ; epidermis ; skin ; image processing ; generative models ; generative adversarial net ; depth map ; super-resolution ; guidance ; residual network ; channel interaction ; pose estimation ; body orientation ; multi-person ; multi-task ; surface defect detection ; active learning ; generative adversarial network ; presentation attack detection ; artificial image generation ; presentation attack face images ; ultrasound image ; malignant thyroid nodule ; artificial intelligence ; weighted binary cross-entropy loss ; infrared circumferential scanning system ; target recognition ; deep convolutional neural networks ; data augmentation ; transfer learning ; bounding box regression ; loss function ; medical image fusion ; convolutional neural network ; image pyramid ; multi-scale decomposition ; armature ; surface inspection ; action recognition ; social robotics ; common spatial patterns ; vehicle recognition ; multi resolution network ; optimization ; semantic segmentation ; global context ; local context ; fully convolutional networks ; image-to-image conversion ; image de-raining ; label to photos ; edges to photos ; generative adversarial network (GAN) ; remote sensing ; helicopter footage ; crowd counting ; multitask learning ; normalized cross-correlation ; Marr wavelets ; entropy and response ; graph matching ; RANSAC ; GC–LSTM model ; typhoon ; satellite image ; prediction system ; monocular depth estimation ; feature distillation ; joint attention ; finger-vein recognition ; camera position ; finger position ; lighting ; unobserved database ; heterogeneous database ; domain adaptation ; cycle-consistent adversarial networks ; SDUMLA-HMT-DB ; HKPolyU-DB ; biometrics ; face recognition ; single-sample face recognition ; binarized statistical image features ; K-nearest neighbors ; sparse coding ; fast approximation ; homotopy iterative hard thresholding ; object recognition ; character recognition ; orthogonal polynomials ; orthogonal moments ; Krawtchouk polynomials ; Tchebichef polynomials ; support vector machine ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
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  • 156
    Publication Date: 2024-04-11
    Description: This book collects 14 articles from the Special Issue entitled “Deep Learning Applications with Practical Measured Results in Electronics Industries” of Electronics. Topics covered in this Issue include four main parts: (1) environmental information analyses and predictions, (2) unmanned aerial vehicle (UAV) and object tracking applications, (3) measurement and denoising techniques, and (4) recommendation systems and education systems. These authors used and improved deep learning techniques (e.g., ResNet (deep residual network), Faster-RCNN (faster regions with convolutional neural network), LSTM (long short term memory), ConvLSTM (convolutional LSTM), GAN (generative adversarial network), etc.) to analyze and denoise measured data in a variety of applications and services (e.g., wind speed prediction, air quality prediction, underground mine applications, neural audio caption, etc.). Several practical experiments were conducted, and the results indicate that the performance of the presented deep learning methods is improved compared with the performance of conventional machine learning methods.
    Keywords: TA1-2040 ; T1-995 ; faster region-based CNN ; visual tracking ; intelligent tire manufacturing ; eye-tracking device ; neural networks ; A* ; information measure ; oral evaluation ; GSA-BP ; tire quality assessment ; humidity sensor ; rigid body kinematics ; intelligent surveillance ; residual networks ; imaging confocal microscope ; update mechanism ; multiple linear regression ; geometric errors correction ; data partition ; Imaging Confocal Microscope ; image inpainting ; lateral stage errors ; dot grid target ; K-means clustering ; unsupervised learning ; recommender system ; underground mines ; digital shearography ; optimization techniques ; saliency information ; gated recurrent unit ; multivariate time series forecasting ; multivariate temporal convolutional network ; foreign object ; data fusion ; update occasion ; generative adversarial network ; CNN ; compressed sensing ; background model ; image compression ; supervised learning ; geometric errors ; UAV ; nonlinear optimization ; reinforcement learning ; convolutional network ; neuro-fuzzy systems ; deep learning ; image restoration ; neural audio caption ; hyperspectral image classification ; neighborhood noise reduction ; GA ; MCM uncertainty evaluation ; binary classification ; content reconstruction ; kinematic modelling ; long short-term memory ; transfer learning ; network layer contribution ; instance segmentation ; smart grid ; unmanned aerial vehicle ; forecasting ; trajectory planning ; discrete wavelet transform ; machine learning ; computational intelligence ; tire bubble defects ; offshore wind ; multiple constraints ; human computer interaction ; Least Squares method ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
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  • 157
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: The reprint focuses on artificial intelligence-based learning approaches and their applications in remote sensing fields. The explosive development of machine learning, deep learning approaches and its wide applications in signal processing have been witnessed in remote sensing. The new developments in remote sensing have led to a high resolution monitoring of ground on a global scale, giving a huge amount of ground observation data. Thus, artificial intelligence-based deep learning approaches and its applied signal processing are required for remote sensing. These approaches can be universal or specific tools of artificial intelligence, including well known neural networks, regression methods, decision trees, etc. It is worth compiling the various cutting-edge techniques and reporting on their promising applications.
    Keywords: pine wilt disease dataset ; GIS application visualization ; test-time augmentation ; object detection ; hard negative mining ; video synthetic aperture radar (SAR) ; moving target ; shadow detection ; deep learning ; false alarms ; missed detections ; synthetic aperture radar (SAR) ; on-board ; ship detection ; YOLOv5 ; lightweight detector ; remote sensing image ; spectral domain translation ; generative adversarial network ; paired translation ; synthetic aperture radar ; ship instance segmentation ; global context modeling ; boundary-aware box prediction ; land-use and land-cover ; built-up expansion ; probability modelling ; landscape fragmentation ; machine learning ; support vector machine ; frequency ratio ; fuzzy logic ; artificial intelligence ; remote sensing ; interferometric phase filtering ; sparse regularization (SR) ; deep learning (DL) ; neural convolutional network (CNN) ; semantic segmentation ; open data ; building extraction ; unet ; deeplab ; classifying-inversion method ; AIS ; atmospheric duct ; ship detection and classification ; rotated bounding box ; attention ; feature alignment ; weather nowcasting ; ResNeXt ; radar data ; spectral-spatial interaction network ; spectral-spatial attention ; pansharpening ; UAV visual navigation ; Siamese network ; multi-order feature ; MIoU ; imbalanced data classification ; data over-sampling ; graph convolutional network ; semi-supervised learning ; troposcatter ; tropospheric turbulence ; intercity co-channel interference ; concrete bridge ; visual inspection ; defect ; deep convolutional neural network ; transfer learning ; interpretation techniques ; weakly supervised semantic segmentation ; n/a ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology
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  • 158
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-03-28
    Description: The forest, as the main body of the terrestrial ecosystem, has a huge carbon sink function and plays an important role in coping with global climate change. This reprint on “Monitoring forest carbon sequestration with remote sensing” mainly focuses on new remote sensing theories, methods, and technologies for monitoring carbon sinks in forest ecosystems (including urban forest ecosystems).
    Keywords: forest height ; synthetic aperture radar (SAR) ; interferometry ; random volume over ground (RVoG) model ; three-stage inversion method ; bamboo forest ; BEPS model ; gross primary productivity ; net primary productivity ; spatiotemporal evolution ; climate change ; backscatter coefficients ; polarization decomposition ; collinearity ; ridge regression ; RF ; PCA ; aboveground carbon density ; LiDAR ; stratified estimation ; machine learning algorithm ; Northeast China ; canopy closure ; the GOST model ; fisheye camera photos ; transects ; LAI ; forest height inversion ; three-stage algorithm ; coherence optimization ; complex coherence amplitude inversion ; SRTM ; random forest ; stochastic gradient boosting ; random forest Kriging ; wavelet analysis ; carbon storage ; land use/cover change ; scenario simulation ; PLUS model ; InVEST model ; remote sensing inversion ; dynamic change ; driving factors ; Shaoguan City ; above-ground biomass (AGB) ; airborne LiDAR ; airborne hyperspectral ; wavelet transform ; feature fusion ; Landsat time-series ; VCT model ; classifying forest types ; forest aboveground biomass ; forest aboveground biomass (AGB) ; scale effect ; random forest (RF) ; scale correction ; phenology ; dynamic threshold method ; northeast China ; TIMESAT ; forest carbon stocks ; simulation ; LUCC ; multi-source data ; feature selection ; aboveground biomass ; habitat dataset ; Landsat 8-OLI images ; pine forest ; model comparison ; 3D green volume ; UAV-Lidar ; urban forest ; random forest model ; remote sensing ; MODIS ; FY-3C VIRR ; Yunnan Province ; mangrove forests ; Hainan Island ; deep learning ; influential mechanism ; Bayesian hierarchical modelling ; geostatistics ; Eucalyptus grandis ; Eucalyptus camaldulensis ; Pinus patula ; spatial random effects ; spatially varying coefficient ; rubber plantation ; time series ; shapelet ; Landsat ; Pinus densata ; terrain niche index ; dynamic model ; canopy volume ; diameter at breast height (DBH) ; aboveground biomass (AGB) ; stem volume (V) ; near-infrared reflectance of vegetation ; carbon budget ; L-band PolInSAR ; RVoG model ; forest density ; terrain slope ; coherence ; extinction coefficient ; signal penetration ; 3-PG model ; eucalyptus ; forest age ; forest structure ; sensitivity ; clumping index ; estimation ; impact analysis ; field measurement ; Sentinel-2 images ; artificial neural network ; random forests ; quantile regression neural network ; Pinus densata forests ; thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general ; thema EDItEUR::P Mathematics and Science ; thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics
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  • 159
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: The purpose of this Special Issue is to create an an academic platform whereby high-quality research papers are published on the applications of innovative AI algorithms to transportation planning and operation. The authors present their original research articles related to the applications of AI or machine-learning techniques to transportation planning and operation. The topics of the articles encompass traffic surveillance, traffic safety, vehicle emission reduction, congestion management, traffic speed forecasting, and ride sharing strategy.
    Keywords: autoencoder ; deep learning ; traffic volume ; vehicle counting ; CycleGAN ; bottleneck and gridlock identification ; gridlock prediction ; urban road network ; long short-term memory ; link embedding ; traffic speed prediction ; traffic flow centrality ; reachability analysis ; spatio-temporal data ; artificial neural network ; context-awareness ; dynamic pricing ; reinforcement learning ; ridesharing ; supply improvement ; taxi ; preventive automated driving system ; automated vehicle ; traffic accidents ; deep neural networks ; vehicle GPS data ; driving cycle ; micro-level vehicle emission estimation ; link emission factors ; MOVES ; black ice ; CNN ; prevention ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
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  • 160
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    Unknown
    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-03-28
    Description: Fast, non-destructive detection technology and equipment for food quality and safety is a powerful technical support tool to ensure the development of food industry informatization and intelligence, with the advantages of fast speed, convenient operation, and easy online inspection. During the past two decades, such technologies have found numerous successful applications for food and agricultural product detection and processing. Owing to improvements in the manufacturing of photoelectric sensor pieces and progress in artificial intelligence and software algorithms, fast non-destructive detection technologies are able to provide more accurate, reliable, and stable solutions for food quality and safety detection and processing. They are closely integrated with the Internet of Things and intelligent manufacturing, promoting a new wave of innovation in intelligent manufacturing in the food industry. The application of new sensing technology and equipment in the fast, non-destructive detection of food has always been at the forefront of scientific and technological research. This Special Issue aims to focus on the latest research progress of this application and jointly discuss the focus of development of this research direction.
    Keywords: maize ; moldy level ; catalase activity ; hyperspectral image ; data fusion ; feature selection ; fruit quality monitoring ; room-temperature ethylene sensor ; density functional theory ; adsorption energy ; band energy alignment ; apple ; NIR ; size correction ; extinction coefficient ; fruit diameter difference ; acceptability ; benchtop NMR ; mandarins ; NMR ; successive projective algorithm ; uninformative variable elimination ; support vector regression ; Korla fragrant pear ; stone cell content ; intelligent evaluation ; cultivation ; visible/near infrared spectrum ; fresh jujube ; model update ; variable fusion ; defective apples ; apple grading ; deep learning ; object detection ; semantic segmentation ; shrimp ; hot air drying ; quality change ; hyperspectral images ; low field magnetic resonance ; micro Raman ; microfluidic chip ; fungal spores ; crop disease ; numerical simulation ; degree of milling ; multi-scale information fusion ; residual network model ; Bayesian optimization algorithm ; hyperspectral imaging ; maize seeds ; defect detection ; convolutional neural network ; tomato ; leaf mildew ; terahertz time-domain spectroscopy ; near infrared hyperspectral technology ; multi-source information fusion ; YOLOv5 ; walnut kernels ; impurities detection ; small object detection ; liposomes ; high stability ; freshness ; bi-layer indicator ; light penetration depth ; spatial-frequency domain imaging ; depth-resolved ; bruise ; scattering ; near infrared spectroscopy ; vegetables ; anthocyanidins ; fast determination ; Curcumae Longae Rhizoma ; volatile oil ; 60Co ; GC–IMS ; SERS detection ; chromium contamination ; tea sample ; carbimazole hydrolysate ; Au@Ag nanoparticles ; PAEs ; Raman ; DFT ; HF ; theoretical study ; gas sensor ; spoilage monitoring ; early warning ; logistics control ; simulated annealing ; surface-enhanced Raman spectroscopy ; flexible substrate ; polycyclic aromatic hydrocarbons ; in situ detection ; common carp ; texture ; machine learning ; visualization ; n/a ; thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general ; thema EDItEUR::P Mathematics and Science::PS Biology, life sciences ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes
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  • 161
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-02-02
    Description: Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI.
    Keywords: lung ; conventional radiography ; diagnostic procedure ; chronic obstructive pulmonary disease ; COVID-19 ; computed tomography ; lungs ; variability ; segmentation ; hybrid deep learning ; artificial intelligence ; deep learning ; computer-based devices ; radiology ; thoracic diagnostic imaging ; chest X-ray ; CT ; observer tests ; performance ; lung CT images ; nodule detection ; VGG-SegNet ; pre-trained VGG19 ; cardiac amyloidosis ; AL/TTR amyloidosis ; hypertrophic cardiomyopathy ; left ventricular hypertrophy ; convolutional neural network ; Tuberculosis (TB) ; drug resistance ; chest X-rays ; generalization ; localization ; Electrical Impedance Tomography ; lung imaging ; cardiopulmonary monitoring ; aorta ; lung cancer ; pulmonary artery ; pulmonary hypertension ; modality-specific knowledge ; object detection ; RetinaNet ; ensemble learning ; pneumonia ; mean average precision ; source data set ; supervised classification ; coronary artery disease ; machine learning ; cardiopulmonary disease ; faster CNN ; medical imaging ; X-rays ; transfer learning ; explainability ; n/a ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
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  • 162
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-04-05
    Description: Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI.
    Keywords: lung ; conventional radiography ; diagnostic procedure ; chronic obstructive pulmonary disease ; COVID-19 ; computed tomography ; lungs ; variability ; segmentation ; hybrid deep learning ; artificial intelligence ; deep learning ; computer-based devices ; radiology ; thoracic diagnostic imaging ; chest X-ray ; CT ; observer tests ; performance ; lung CT images ; nodule detection ; VGG-SegNet ; pre-trained VGG19 ; cardiac amyloidosis ; AL/TTR amyloidosis ; hypertrophic cardiomyopathy ; left ventricular hypertrophy ; convolutional neural network ; Tuberculosis (TB) ; drug resistance ; chest X-rays ; generalization ; localization ; Electrical Impedance Tomography ; lung imaging ; cardiopulmonary monitoring ; aorta ; lung cancer ; pulmonary artery ; pulmonary hypertension ; modality-specific knowledge ; object detection ; RetinaNet ; ensemble learning ; pneumonia ; mean average precision ; source data set ; supervised classification ; coronary artery disease ; machine learning ; cardiopulmonary disease ; faster CNN ; medical imaging ; X-rays ; transfer learning ; explainability ; n/a ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
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  • 163
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    Unknown
    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-04-05
    Description: This reprint addresses the unique opportunities and challenges associated with human–computer interaction with intelligent systems. First, state-of-the-art reviews are presented about speech emotions, automatic spelling correction, and art usage in virtual reality. We encouraged authors to submit reports describing systems built for different languages and multilingual systems. The linguistic, emotional, prosodic, and dialogue aspects of speech communication are investigated. Special attention is given to sentiment and emotional analysis from text and speech. Speech audiometry, offline speech recognition, and text-independent speaker verification systems are elaborated. The rapidly growing domain of virtual reality applications is of interest both as an application domain in which new interfaces and interaction methods are needed and as a potential testbed for evaluating speech and other interface modalities.
    Keywords: pediatric speech audiometry ; hearing tests ; conditioned play audiometry ; human–computer interaction ; mechatronic devices ; Internet of Things ; cyber-physical systems ; system control ; augmented reality ; mixed reality ; Azure cloud ; sentiment analysis ; opinion classification ; lexicon-based approach ; hybrid approach ; lexicon generation ; lexicon labelling ; particle swarm optimization ; spelling correction ; natural language processing ; diacritization ; error model ; context model ; Natural Language Processing ; deep learning ; grammar error detection ; word embedding ; text-independent speaker verification system ; self-attentive pooling ; multi-layer aggregation ; feature recalibration ; deep length normalization ; speaker embedding ; shortcut connections ; convolutional neural networks ; ResNet ; human–robot interaction ; dictionary approach ; machine learning approach ; social robotics ; human-robot interaction ; mental workload ; heart rate variability ; machine learning ; sentiment level evaluation ; handicraft product ; 3D handicraft products ; smartphone applications ; user interaction ; user’s attracting attention ; question-answering forum ; healthcare informatics ; recommendation system ; user study ; speech emotion recognition ; attention mechanism ; recurrent neural network ; long short-term memory ; user experience ; usability evaluation methods ; domain usability ; domain-specific languages ; graphical user interfaces ; virtual reality ; art therapy ; rehabilitation ; neurorehabilitation ; neuroplasticity ; brain injury ; ASR ; speech-to-text ; edge AI ; Wav2Vec ; transformers ; PyTorch ; emotion recognition ; dimensional to categorical emotion representation mapping ; activation ; arousal and valence regression ; X-vectors ; SVM ; n/a ; bic Book Industry Communication::K Economics, finance, business & management::KN Industry & industrial studies::KNT Media, information & communication industries::KNTX Information technology industries ; bic Book Industry Communication::U Computing & information technology::UY Computer science
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  • 164
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: "Advances in UAV Detection, Classification and Tracking" is a comprehensive book that explores the latest techniques and advancements in unmanned aerial vehicle (UAV) detection, classification, and tracking. As UAV technology continues to evolve and become more accessible, there is a growing need for effective methods to detect, identify, and track these devices in various scenarios. This reprint provides a thorough overview of the state-of-the-art approaches for UAV detection, classification, and tracking, covering both theoretical and practical aspects.The reprint begins by introducing the basics of UAVs and their various applications, followed by a detailed overview of the challenges associated with UAV detection, classification, and tracking. The authors then present the latest techniques and algorithms used in the field, including machine-learning-based approaches, computer vision techniques, and sensor fusion techniques. The reprint also covers the challenges of real-world applications, such as dealing with occlusions, sensor noise, and environmental factors.With contributions from leading experts in the field, "Advances in UAV Detection, Classification and Tracking" is an essential resource for researchers, engineers, and practitioners working on UAV detection, classification, and tracking. It is also a valuable reference for graduate students and anyone interested in the latest advancements in this rapidly evolving field.
    Keywords: distributed electric propulsion ; coordinated thrust control ; fault-tolerant control ; flight simulation ; autonomous navigation ; gimbal design ; obstacle avoidance ; target tracking ; unmanned aerial vehicles ; law enforcement ; tiltrotor ; blade element theory ; flight mechanical model ; stability analysis ; social learning ; ant colony optimization ; multi-agent system ; visual tracking system ; embedded system ; drone ; omnidirectional mobile robot ; multi-target association ; topological sequences ; triangular networks ; global consistency ; similar transformation invariance ; micro-Doppler ; radar ; target ; classification ; unmanned aerial vehicle ; motion planning ; optimization techniques ; attention mechanism ; anti-occlusion ; location prediction ; convolutional neural network CNN ; YOLO deep learning ; UAV ; drone detection ; drone recognition ; automatic target recognition (ATR) ; classify while scan (CWS) ; drone detection radar ; detection response time (DRT) ; unmanned air traffic management (UTM) ; object detection ; deep learning ; adaptive cluster ; cognitive micro-Doppler radar ; Doppler resolution ; JEM signals ; radar dwell time ; advancement ; tracking and communication threats ; three-dimensional circumnavigation control ; elliptical multi-orbit ; UAV group ; small-object detection ; backbone design ; object positioning ; object classification ; UAV flight experiment ; astronaut detection ; astronaut accompanying ; intravehicular visual navigation ; semi-structured environment ; dynamic scenes ; n/a ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology ; thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilities
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  • 165
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    Unknown
    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-07-06
    Description: One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot.
    Keywords: text mining ; big data ; analytics ; review ; self-organization ; computational philosophy ; brain ; synaptic learning ; adaptation ; functional plasticity ; activity-dependent resonance states ; circular causality ; somatosensory representation ; prehensile synergies ; robotics ; COVID-19 ; social media ; hashtag networks ; emotional profiling ; cognitive science ; network science ; sentiment analysis ; computational social science ; Twitter ; VADER scoring ; correlation ; semantic network analysis ; intellectual disability ; adolescents ; EEG ; emotional states ; working memory ; depression ; anxiety ; graph theory ; classification ; machine learning ; neural networks ; phonotactic probability ; neighborhood density ; sub-lexical representations ; lexical representations ; phonemes ; biphones ; cognitive network ; smart assistants ; knowledge generation ; intelligent systems ; web components ; deep learning ; web-based interaction ; cognitive network science ; text analysis ; natural language processing ; artificial intelligence ; emotional recall ; cognitive data ; AI ; pharmacological text corpus ; automatic relation extraction ; gender stereotypes ; story tropes ; movie plots ; network analysis ; word co-occurrence network ; n/a ; bic Book Industry Communication::K Economics, finance, business & management::KN Industry & industrial studies::KNT Media, information & communication industries::KNTX Information technology industries
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  • 166
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    Unknown
    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-03-28
    Description: The book highlights recent research efforts in the monitoring of aquatic districts with remote sensing observations and proximal sensing technology integrated with laboratory measurements. Optical satellite imagery gathered at spatial resolutions down to few meters has been used for quantitative estimations of harmful algal bloom extent and Chl-a mapping, as well as winds and currents from SAR acquisitions. The knowledge and understanding gained from this book can be used for the sustainable management of bodies of water across our planet.
    Keywords: polymer optical fibers ; ammonia detection ; optical fiber coating ; aquaculture ; French Alps ; optical remote sensing ; multitemporal ; linear spectral unmixing ; NDVI ; drought ; Rana temporaria ; ecohydrology ; mountain temporary pools ; Lake Tana ; water hyacinth ; waterbody temperature ; turbidity ; lake level ; Planetscope ; remote sensing ; sensors ; ocean color ; sediment ; turbid water ; chlorophyll ; geostationary satellite ; aquaculture ponds ; extraction ; inland lake ; self-attention ; Ulva ; Sentinel-2 ; satellite ; algal bloom ; coral reefs ; Pacific lagoons ; HAB ; multi-source remote sensing ; MODIS ; Landsat ; sentinel ; Chaohu Lake ; ecological status class of lakes ; European Union Water Framework Directive (2000/60/EC) ; water quality parameters ; water level ; Sentinel-3 ; Cryosat-2 ; shallow lakes ; synergy ; altimetry data ; optical data ; CDOM absorbance ; spectroscopic indices ; DOC ; Arctic ; shelf seas ; estuarial and coastal areas ; drone applications ; surface water ; groundwater ; photogrammetry ; optical sensing ; thermal infrared ; deep learning ; convolutional neural network ; chlorophyll-a ; hydrodynamic model ; empirical models ; multiple regression ; Paldang Reservoir ; SAR ; Doppler Centroid Anomaly ; inland waters ; physical limnology ; hydrodynamics ; thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general ; thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography
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  • 167
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-02-01
    Description: The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal.
    Keywords: smart grid ; nontechnical losses ; electricity theft detection ; synthetic minority oversampling technique ; K-means cluster ; random forest ; smart grids ; smart energy system ; smart meter ; GDPR ; data privacy ; ethics ; multi-label learning ; Non-intrusive Load Monitoring ; appliance recognition ; fryze power theory ; V-I trajectory ; Convolutional Neural Network ; distance similarity matrix ; activation current ; electric vehicle ; synthetic data ; exponential distribution ; Poisson distribution ; Gaussian mixture models ; mathematical modeling ; machine learning ; simulation ; Non-Intrusive Load Monitoring (NILM) ; NILM datasets ; power signature ; electric load simulation ; data-driven approaches ; smart meters ; text convolutional neural networks (TextCNN) ; time-series classification ; data annotation ; non-intrusive load monitoring ; semi-automatic labeling ; appliance load signatures ; ambient influences ; device classification accuracy ; NILM ; signature ; load disaggregation ; transients ; pulse generator ; smart metering ; smart power grids ; power consumption data ; energy data processing ; user-centric applications of energy data ; convolutional neural network ; energy consumption ; energy data analytics ; energy disaggregation ; real-time ; smart meter data ; transient load signature ; attention mechanism ; deep neural network ; electrical energy ; load scheduling ; satisfaction ; Shapley Value ; solar photovoltaics ; review ; deep learning ; deep neural networks ; n/a ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues
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  • 168
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: Increasing energy efficiency; reducing energy demand, greenhouse gas emissions, and the use of waste; and integrating renewable and recycled heat from low-temperature sources are significant challenges today and are key parts of 4th Generation District Heating (4GDH) concept. On the other hand, currently about one billion people around the world are suffering from water scarcity, and another three billion are approaching this situation. Only 2.5% of all water on the planet is freshwater, of which around 70% is not available and only 0.4% constitutes the most valuable portion of freshwater. Adsorption cooling technology is one of the most effective ways of addressing both these issues. This technology cools and produces potable water from the renewable and wasted heat of the near ambient temperature, including from sewage water, solar heat, and underground resources. This Special Issue Reprint Book provides the detailed information concerning the above-mentioned issues.
    Keywords: adsorption chiller ; coefficient of performance ; desalination ; energy efficiency ; low-temperature heat ; silica gel ; specific cooling power ; waste heat recovery ; sorption processes ; deep learning ; neural networks ; Long Short-Term Memory (LSTM) ; additives ; sorption capacity ; sorption process time ; kinetics sorption ; adsorption ; exergy ; dead state ; adsorption cooling ; reheat cycle, mass recovery ; chiller ; adsorptive water harvesting from the atmosphere ; metal–organic frameworks ; MIL-160 ; water vapor adsorption ; specific water productivity ; specific energy consumption ; zeolite ; SAPO-34 ; mass recovery ; variable mode ; adsorption working pairs ; coated beds ; comparative analysis ; natural refrigerants ; preheating ; steam ; copper ; cycle time ; CFD ; metal organic silica ; nanocomposites ; sorption ; thermal diffusivity ; n/a ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TD Industrial chemistry and manufacturing technologies::TDC Industrial chemistry and chemical engineering
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  • 169
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-03-28
    Description: Renewable energy resources are used as distributed generation (DG) units and installed near to where the energy is converted and consumed. Further, the integration of renewable energy sources at home is very important. IoT helps smart grids to support various network functions throughout the generation, distribution, and consumption of energy by incorporating IoT devices (such as sensors, actuators, and smart meters), as well as by providing connectivity, automation, and tracking for such devices. For these applications, the use of low-power long-range wireless networks (LPWAN) is fundamental to facilitate all the necessary tasks in the smart grids in City 4.0 and Industry 4.0. The integration of renewable energies (photovoltaic solar, wind energy, biomass energy, hydroelectric energy, and other sources) in smart grids implies the monitoring of households, cities, industries, and electric vehicles at all times. In this sense, the development of monitoring and control applications using mobile devices is a fundamental tool in this type of system, which complements all the possibilities offered by the IoT. Smart energy meters are used to allow for communication between consumers and utility command centers to exchange messages about electrical consumption. Thus, it is essential to have access from any location and instant access to information using mobile devices or computers.
    Keywords: knowledge-based sensor ; Internet of Things ; high-concentration photovoltaic systems ; sun tracker ; buildings energy management ; deep learning ; energy consumption prediction ; LSTM ; autoencoder ; load forecasting ; smart sensors ; smart meter ; temporal data granularity ; electric load profile ; time slices ; time series ; advanced metering infrastructure ; monitoring ; data acquisition systems ; renewable energy ; multi-objective optimization ; reactive power (RP) planning ; hybrid algorithm ; virus colony search ; particle swarm optimization ; LoRaWAN ; smart irrigation systems ; smart energy ; IoT ; renewable energy sources ; photovoltaic energy ; I-V curve ; monitoring and data acquisition ; microgrid ; open-source ; communication protocols ; DC interrupting ; digitization ; remote control ; electric energy measurement ; miniature circuit breaker ; power meter ; internet of things ; load control ; energy meter ; smart socket ; intelligent campus ; smart building ; internet of things platform ; remote monitoring and control ; classification ; data anomalies ; data imputation ; energy consumption data ; ensemble classifiers ; machine learning ; smart home data ; smart meter data ; tracebase dataset ; home energy management system ; smart home ; cloud infrastructure ; distributed PV ; energy management system ; energy storage units ; charging piles ; smart grid ; redundancy ; Home Assistant ; low-carbon island ; Kinmen ; thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general ; thema EDItEUR::P Mathematics and Science::PH Physics
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  • 170
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-03-07
    Description: Polymer-processing techniques are of the utmost importance for producing polymeric parts. They must produce parts with the desired qualities, which are usually related to mechanical performance, dimensional conformity, and appearance. Aiming to maximize the overall efficiency of the polymer-processing techniques, advanced modeling codes along with experimental measurements are needed to simulate and optimize the processes. Thus, this reprint exploits the digital transformation of the plastics industry, both through the creation of more robust and accurate modeling tools and the development of cutting-edge experimental techniques. Furthermore, it addresses advanced topics, such as crystallization during the solidification processes, prediction of fiber orientation in the cases of short and long fiber composites, prediction of the foaming process (such as microcellular foaming), and flow instabilities by including viscoelastic constitutive equations.
    Keywords: polymer electrolyte membrane for fuel cell ; molecular dynamics simulations ; side chain ; penetration ; injection molding ; thermoplastic composites ; mold heating ; mold temperature control ; melt filling ; thin wall injection molding ; suspensions ; micro-polar fluids ; yield stress ; extrusion ; extrudate swell ; interface tracking ; least-squares volume-to-point interpolation ; consistent PISO ; finite volume method ; OpenFOAM ; poly(ether ether ketone) ; thermo–mechanical response ; constitutive modeling ; polymer processing ; elongational flow ; vane extruder ; eccentric rotor extruder ; numerical simulation ; warpage ; prediction ; crystallinity ; multi-layer structure ; simulation ; annocatacin B ; ND1 subunit ; mitochondrial respiratory complex I ; MRC-I ; MD ; Hirshfeld charges ; MM/PBSA ; poly(lactic acid) ; urea ; melt blending ; slow-release fertilizer ; leakage flow ; modeling and simulation ; sheet die design ; manufacturing process design ; coat-hanger die ; modeling ; rheology ; constant shear-rate die ; non-Newtonian fluids ; poly (3-hydroxybutyric-co-3-hydroxyvaleric acid) (PHBV) ; flax ; hemp ; short fibers ; properties ; lithium-ion ; high energy pouch cell ; state of charge ; electrolyte ; load position ; motor core ; iron sheet ; computer-aided engineering tools ; gluing ; machine learning ; multilayer perceptron ; neural network ; regression ; plasticizing ; polymers ; basic settings ; data-based ; model ; quality ; conformal cooling ; sustainability ; industrial design ; manufacturing ; degree of assembly ; a family mold system ; CAE-DOE optimization ; green channels ; temperature maps ; finite difference methods ; meshless interpolation ; numerical solution ; polymer flows ; viscoelastic flows ; plastic optical barrel ; roundness ; concentricity ; Taguchi method ; RGD peptide (1FUV) ; ab initio molecular dynamics ; total bond order ; partial charge ; dielectric function ; suspension ; rodlike particles ; micropolar fluids ; anisotropy ; hysteresis ; fiber reinforced polymer composites ; lead nanoparticles ; shielding ; attenuation coefficient ; empirical derivation ; PEG-PCL ; non-isothermal crystallization ; flash differential scanning calorimeter ; polymer blends ; microstructure ; multiscale simulation ; hybrid injection molding ; continuous fiber-reinforced thermoplastics ; finite element analysis (fem) ; FDM ; Taguchi ; multilateral ; CAE ; transfer learning ; LDPE ; triangular-loop shear ; trapezoidal-loop shear ; time-dependent viscoelastic property ; Rivlin–Sawyers equation ; fillers ; rubber compounds ; viscoelasticity ; thixotropy ; structure ; tailings flocculation ; seawater ; calcium and magnesium removal ; lime ; sodium carbonate ; FEM ; pipe die ; polymer melt ; Herschel–Bulkley fluids ; free-surfaces ; conformal cooling channel ; rapid tooling technology ; mold material ; cooling medium ; polymer solution ; Giesekus ; eXtended Pom-Pom ; visualization ; analytical solution ; deep learning ; stacked learning ; Oldroyd-B fluid ; Giesekus fluid ; sphere drag coefficient ; plastic pallet ; flatness ; sequential valve gate system ; molding flow analysis ; particle settling ; dilute polymeric solutions ; Oldroyd-B model ; microfluidic rheometry ; drag coefficient ; hydraulic fracturing ; polyethylene recycling ; artificial engineering ; polymer extrusion ; mold additive manufacturing ; polymer molds ; subtractive manufacturing ; mold characterization ; rapid tooling ; thermal homogenisation ; pre-distribution ; heat pipe ; blown film extrusion ; CFD ; n/a ; pyrolysis ; mixed polymers ; thermogravimetric analyzer (TGA) ; artificial neural networks (ANN) ; polypropylene ; glass fiber ; fiber reinforced ; fiber shortening ; compound ; SIGMA ; dynamic image analysis ; Monte Carlo ; nanoporous matter ; proton ; transport behavior ; GEANT4 code ; boundary layer ; Herschel–Bulkley fluid ; Carbopol ; cement ; bacterial cellulose ; plasma treatment ; magnetron sputtering ; silver nanoparticles ; antimicrobial activity ; X-ray photoelectron spectroscopy ; fully implicit coupled solver ; viscoelastic flow ; log-conformation tensor approach ; non-isothermal effects ; Phan-Thien-Tanner constitutive equation ; semi-analytical method ; solvent viscosity contribution ; pipe flow ; channel flow ; Au nanoparticles ; plasmonics ; polymer matrix ; nanocomposite ; thermal annealing ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
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  • 171
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-05-06
    Description: Since the 1990s, ultrasound (US) has played a major role in the assessment of thyroid nodules and their risk of malignancy. Over the last decade, the most eminent international societies have published US-based systems for the risk stratification of thyroid lesions, namely, Thyroid Imaging Reporting And Data Systems (TIRADSs). The introduction of TIRADSs into clinical practice has significantly increased the diagnostic power of US to a level approaching that of fine-needle aspiration cytology (FNAC). At present, we are probably approaching a new era in which US could be the primary tool to diagnose thyroid cancer. However, before using US in this new dominant role, we need further proof. This Special Issue, which includes reviews and original articles, aims to pave the way for the future in the field of thyroid US. Highly experienced thyroidologists focused on US are asked to contribute to achieve this goal.
    Keywords: n/a ; thyroid ; ultrasonography ; follicular neoplasm ; follicular lesion of unknown significance ; follicular thyroid cancer ; papillary thyroid carcinoma ; neoplasm metastasis ; biopsy ; fine-needle ; thyroglobulin ; US-guided minimally invasive techniques ; radiofrequency ablation ; RFA ; benign thyroid nodules ; thyroid cancer ; DTC recurrences ; PTMC ; long term ; follow-up ; regrowth ; classification system ; ultrasound classification system ; TIRAD ; nodule ; risk stratification ; TI-RADS ; fine-needle aspiration ; cancer ; ultrasound ; scintigraphy ; non-autonomously functioning ; thyroid imaging reporting and data systems (TIRADS) ; risk of malignancy (ROM) ; thyroid nodules ; paediatrics ; radiotherapy ; risk assessment ; DTC ; thyroid neoplasm ; medical imaging ; artificial intelligence ; machine learning ; deep learning ; radiomics ; prediction ; diagnosis ; Thyroid Imaging Reporting and Data Systems (TIRADS) ; pediatric thyroid nodules ; neck ultrasound ; contrast-enhanced ultrasound (CEUS) ; papillary thyroid cancer ; TIRADS ; thyroid nodule ; fine-needle aspiration biopsy ; elastosonography ; bic Book Industry Communication::M Medicine ; bic Book Industry Communication::M Medicine::MJ Clinical & internal medicine
    Language: English
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  • 172
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-05-06
    Description: This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains.
    Keywords: tourism big data ; text mining ; NLP ; deep learning ; clinical named entity recognition ; information extraction ; multitask model ; long short-term memory ; conditional random field ; relation extraction ; entity recognition ; long short-term memory network ; multi-turn chatbot ; dialogue context encoding ; WGAN-based response generation ; BERT word embedding ; text summary ; reinforce learning ; FAQ classification ; encoder-decoder neural network ; multi-level word embeddings ; BERT ; bidirectional RNN ; cloze test ; Korean dataset ; machine comprehension ; neural language model ; sentence completion ; primary healthcare ; chief complaint ; virtual medical assistant ; spoken natural language ; disease diagnosis ; medical specialist ; protein–protein interactions ; deep learning (DL) ; convolutional neural networks (CNN) ; bidirectional long short-term memory (bidirectional LSTM) ; dialogue management ; user simulation ; reward shaping ; conversation knowledge ; multi-agent reinforcement learning ; language modeling ; classification ; error probability ; error assessment ; logic error ; neural network ; LSTM ; attention mechanism ; programming education ; neural architecture search ; word ordering ; Korean syntax ; adversarial attack ; adversarial example ; sentiment classification ; dual pointer network ; context-to-entity attention ; text classification ; rule-based ; word embedding ; Doc2vec ; paraphrase identification ; encodings ; R-GCNs ; contextual features ; sentence retrieval ; TF−ISF ; BM25 ; partial match ; sequence similarity ; word to vector ; word embeddings ; antonymy detection ; polarity ; text normalization ; natural language processing ; deep neural networks ; causal encoder ; question classification ; multilingual ; convolutional neural networks ; Natural Language Processing (NLP) ; transfer learning ; open information extraction ; recurrent neural networks ; bilingual translation ; speech-to-text ; LaTeX decompilation ; word representation learning ; word2vec ; sememes ; structural information ; sentiment analysis ; zero-shot learning ; news analysis ; cross-lingual classification ; multilingual transformers ; knowledge base ; commonsense ; sememe prediction ; attention model ; ontologies ; fixing ontologies ; quick fix ; quality metrics ; online social networks ; rumor detection ; Cantonese ; XGA model ; delayed combination ; CNN dictionary ; named entity recognition ; deep learning NER ; bidirectional LSTM CRF ; CoNLL ; OntoNotes ; toxic comments ; neural networks ; n/a ; bic Book Industry Communication::K Economics, finance, business & management::KN Industry & industrial studies::KNT Media, information & communication industries::KNTX Information technology industries ; bic Book Industry Communication::U Computing & information technology::UY Computer science
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  • 173
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: This book presents recent research results related to various applications of computer vision methods in the widely understood contexts of automation and robotics. As the current progress of image analysis applications may be easily observed in various areas of everyday life, it becomes one of the most essential elements of development of Industry 4.0 solutions. Some of the examples, partially discussed in individual chapters, may be related to the visual navigation of mobile robots and drones, monitoring of industrial production lines, non-destructive evaluation and testing, monitoring of the IoT devices or the 3D printing process and the quality assessment of manufactured objects, video surveillance systems, and decision support in autonomous vehicles.
    Keywords: machine vision ; defect inspection ; image registration ; feature region ; contour point distribution ; edge gradient direction ; augmented reality ; calibration ; head mounted displays ; optical see-through display ; computer vision ; infrared pedestrian detection ; encoder-decoder ; attention ; convolutional neural network ; deep learning ; domain adaptation ; semantic segmentation ; generative adversarial networks ; convolutional neural networks ; aerial imagery ; image processing ; fertilizers ; distribution ; monitoring ; component association ; part recognition ; feature descriptor ; histogram ; feature subset selection ; industrial objects ; sorting ; k-NN algorithm ; transparent plastic granulate ; recycling ; air nozzles ; additive manufacturing ; 3D prints ; surface quality assessment ; image analysis ; combined metrics ; structural similarity ; video analysis ; visual inspection and diagnostics ; industrial and robotic vision systems ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
    Language: English
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  • 174
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: In recent years, we have been witnessing the exponential proliferation of the Internet of Things (IoT), networks of physical devices, vehicles, appliances and other items embedded with electronics, software, sensors, actuators and connectivity that enables these objects to connect and exchange data. Enabling the introduction of highly efficient IoT, wireless sensing and network technologies will reduce the need for traditional processes that are currently be carried out manually, thus freeing up the precious resources of dwindling working staff, to do more meaningful and human-centered work. This reprint aims to bring together innovative developments in areas related to IoT, wireless sensing and networking. The aspects covered include software-defined network (SDN)-based IoT networks, artificial intelligence (AI) for IoT, industrial IoT, smart sensors, optimization of energy efficiency for IoT, and wireless sensor networks, IoT applications for agriculture, smart cities, healthcare, localization and environment monitoring.
    Keywords: lawful interception ; hybrid SDN ; intercept access point ; minimum vertex cover ; text detection ; natural scene ; feature fusion ; soil water content ; sensor networks ; distributed sensing ; IoT measurements ; Precision Agriculture ; moisture sensor ; wireless communication ; LoRa ; LoRaWAN™ ; indoor localisation ; fingerprinting ; landmark ; wearable device ; inertial measurement device ; motion mode detection ; body shadowing compensation ; nearest neighbour ; label-free biosensor ; machine learning ; support vector machine ; artificial neural network ; principal component analysis ; Green IoT ; IIoT ; edge computing ; AI ; edge AI ; sustainability ; digital transition ; digital circular economy ; Industry 5.0 ; direction-of-arrival estimation ; geometric algebra ; ESPRIT algorithm ; electromagnetic vector-sensor array ; hardware security ; electromagnetic sensing ; real time ; cybersecurity ; anomaly detection ; the Internet of Things ; deep learning ; blockchain ; 5G/6G ; URLLC ; tactile Internet ; industrial IoT ; network emulator ; robotic simulator ; virtual testbed ; wireless power transfer ; energy harvesting ; power conversion efficiency ; single diode rectifier ; voltage doubler ; harmonic balance method ; autonomous sensor node ; wireless sensor network ; multi-tone signal ; full-wave simulations of PCB ; millimeter wave ; new radio ; unmanned aerial vehicles ; LoS blockage ; closed-from approximation ; rooftop deployments ; coverage path planning ; unmanned aerial vehicle ; cell decomposition ; decomposition methods ; energy-aware approaches ; energy optimal path ; multi-robot systems ; multi-UAV ; smart cities ; Internet of Things (IoT) ; sensors ; 6G ; wireless communications ; resilience ; climate change ; connectivity ; data ; wireless systems ; mobile sensors ; D2D ; technological development ; Internet of things ; LoRaWAN ; reliability ; downlink ; safety ; IoT ; LPWAN ; proximal soil sensor device ; conventional communication methods ; ultralow power consumption ; long-distance transmission ; economic value ; inventoried sensor devices ; digital twin ; LSTM model ; n/a ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology ; thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilities
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  • 175
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: Polymer-processing techniques are of the utmost importance for producing polymeric parts. They must produce parts with the desired qualities, which are usually related to mechanical performance, dimensional conformity, and appearance. Aiming to maximize the overall efficiency of the polymer-processing techniques, advanced modeling codes along with experimental measurements are needed to simulate and optimize the processes. Thus, this reprint exploits the digital transformation of the plastics industry, both through the creation of more robust and accurate modeling tools and the development of cutting-edge experimental techniques. Furthermore, it addresses advanced topics, such as crystallization during the solidification processes, prediction of fiber orientation in the cases of short and long fiber composites, prediction of the foaming process (such as microcellular foaming), and flow instabilities by including viscoelastic constitutive equations.
    Keywords: polymer electrolyte membrane for fuel cell ; molecular dynamics simulations ; side chain ; penetration ; injection molding ; thermoplastic composites ; mold heating ; mold temperature control ; melt filling ; thin wall injection molding ; suspensions ; micro-polar fluids ; yield stress ; extrusion ; extrudate swell ; interface tracking ; least-squares volume-to-point interpolation ; consistent PISO ; finite volume method ; OpenFOAM ; poly(ether ether ketone) ; thermo–mechanical response ; constitutive modeling ; polymer processing ; elongational flow ; vane extruder ; eccentric rotor extruder ; numerical simulation ; warpage ; prediction ; crystallinity ; multi-layer structure ; simulation ; annocatacin B ; ND1 subunit ; mitochondrial respiratory complex I ; MRC-I ; MD ; Hirshfeld charges ; MM/PBSA ; poly(lactic acid) ; urea ; melt blending ; slow-release fertilizer ; leakage flow ; modeling and simulation ; sheet die design ; manufacturing process design ; coat-hanger die ; modeling ; rheology ; constant shear-rate die ; non-Newtonian fluids ; poly (3-hydroxybutyric-co-3-hydroxyvaleric acid) (PHBV) ; flax ; hemp ; short fibers ; properties ; lithium-ion ; high energy pouch cell ; state of charge ; electrolyte ; load position ; motor core ; iron sheet ; computer-aided engineering tools ; gluing ; machine learning ; multilayer perceptron ; neural network ; regression ; plasticizing ; polymers ; basic settings ; data-based ; model ; quality ; conformal cooling ; sustainability ; industrial design ; manufacturing ; degree of assembly ; a family mold system ; CAE-DOE optimization ; green channels ; temperature maps ; finite difference methods ; meshless interpolation ; numerical solution ; polymer flows ; viscoelastic flows ; plastic optical barrel ; roundness ; concentricity ; Taguchi method ; RGD peptide (1FUV) ; ab initio molecular dynamics ; total bond order ; partial charge ; dielectric function ; suspension ; rodlike particles ; micropolar fluids ; anisotropy ; hysteresis ; fiber reinforced polymer composites ; lead nanoparticles ; shielding ; attenuation coefficient ; empirical derivation ; PEG-PCL ; non-isothermal crystallization ; flash differential scanning calorimeter ; polymer blends ; microstructure ; multiscale simulation ; hybrid injection molding ; continuous fiber-reinforced thermoplastics ; finite element analysis (fem) ; FDM ; Taguchi ; multilateral ; CAE ; transfer learning ; LDPE ; triangular-loop shear ; trapezoidal-loop shear ; time-dependent viscoelastic property ; Rivlin–Sawyers equation ; fillers ; rubber compounds ; viscoelasticity ; thixotropy ; structure ; tailings flocculation ; seawater ; calcium and magnesium removal ; lime ; sodium carbonate ; FEM ; pipe die ; polymer melt ; Herschel–Bulkley fluids ; free-surfaces ; conformal cooling channel ; rapid tooling technology ; mold material ; cooling medium ; polymer solution ; Giesekus ; eXtended Pom-Pom ; visualization ; analytical solution ; deep learning ; stacked learning ; Oldroyd-B fluid ; Giesekus fluid ; sphere drag coefficient ; plastic pallet ; flatness ; sequential valve gate system ; molding flow analysis ; particle settling ; dilute polymeric solutions ; Oldroyd-B model ; microfluidic rheometry ; drag coefficient ; hydraulic fracturing ; polyethylene recycling ; artificial engineering ; polymer extrusion ; mold additive manufacturing ; polymer molds ; subtractive manufacturing ; mold characterization ; rapid tooling ; thermal homogenisation ; pre-distribution ; heat pipe ; blown film extrusion ; CFD ; n/a ; pyrolysis ; mixed polymers ; thermogravimetric analyzer (TGA) ; artificial neural networks (ANN) ; polypropylene ; glass fiber ; fiber reinforced ; fiber shortening ; compound ; SIGMA ; dynamic image analysis ; Monte Carlo ; nanoporous matter ; proton ; transport behavior ; GEANT4 code ; boundary layer ; Herschel–Bulkley fluid ; Carbopol ; cement ; bacterial cellulose ; plasma treatment ; magnetron sputtering ; silver nanoparticles ; antimicrobial activity ; X-ray photoelectron spectroscopy ; fully implicit coupled solver ; viscoelastic flow ; log-conformation tensor approach ; non-isothermal effects ; Phan-Thien-Tanner constitutive equation ; semi-analytical method ; solvent viscosity contribution ; pipe flow ; channel flow ; Au nanoparticles ; plasmonics ; polymer matrix ; nanocomposite ; thermal annealing ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
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  • 176
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: This book is based on Special Issue "Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS" from early 2020 to 2021. This book includes seven papers related to the application of artificial intelligence, machine learning and deep learning algorithms using remote sensing and GIS techniques in urban areas.
    Keywords: groundwater potential ; specific capacity ; machine learning ; boosted tree ; ensemble models ; prototype selection ; river pollution ; supervised classification ; WSN ; probabilistic method ; Monte Carlo simulation ; physical slope model ; Mt. Umyeon landslides ; Seoul ; synthetic aperture radar ; land subsidence ; GIS ; time-series ; Jakarta ; land subsidence susceptibility mapping ; time-series InSAR ; StaMPS processing ; seismic vulnerability map ; DPM method ; Sentinel-1 ; seismic literacy ; neural networks ; urban vegetation ; urban open spaces ; Monterrey Metropolitan Area ; sustainable development ; deep learning ; transfer learning ; artificial intelligence ; remote sensing ; earth observation ; DInSAR ; change detection ; space data science ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology
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  • 177
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: This reprint focuses on electroencephalography (EEG) signal processing in biomedical engineering applications. EEG signals are used widely in clinical and research settings to provide cognitive and emotional state information. In addition to capturing complex neural patterns at high speeds, EEG signals are a reliable and non-invasive way of measuring the electrical activity in the brain. By examining various novel analysis and signal processing methods, this collection of papers provides a better understanding of cognitive states and brain activity.
    Keywords: EEG ; transfer learning ; review ; decoding ; classification ; e-textile ; head phantom ; electroencephalography ; conductive material ; mental stress ; data analysis ; connectivity network ; machine Learning ; deep learning ; vigilance decrement ; sustained attention ; mental fatigue ; cross-participant ; cross-task ; task-generic ; electroencephalogram ; wavelet spectrum ; ridge ; segmentation ; phase connectivity ; epilepsy ; traumatic brain injury ; feature extraction ; functional connectivity network ; time-frequency features ; machine learning ; ALS ; classifier ; neural ; connectivity ; frequency-specific ; BCI ; acupuncture ; dimensionality ; neural subspace ; latent variables ; attractor ; adaptive threshold ; coherence ; functional connectivity ; multilayer network ; otsu ; phase locking value ; weighted phase lag index ; complex Pearson correlation coefficients ; transcranial magnetic stimulation ; cerebral cortex stimulation ; electromagnetic influence ; neurostimulation ; brain activity ; virtual reality ; neuropathic pain ; spinal cord injury ; fractal dimension ; ERP ; speech discrimination ; seizure detection ; features ; feature selection ; motion artifact ; electroencephalogram (EEG) ; functional near-infrared spectroscopy (fNIRS) ; wavelet packet decomposition (WPD) ; canonical correlation analysis (CCA) ; n/a ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
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  • 178
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-03-30
    Description: This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others.
    Keywords: sleep stage scoring ; neural network-based refinement ; residual attention ; T-end annotation ; signal quality index ; tSQI ; optimal shrinkage ; emotion ; EEG ; DEAP ; CNN ; surgery image ; disgust ; autonomic nervous system ; electrocardiogram ; galvanic skin response ; olfactory training ; psychophysics ; smell ; wearable sensors ; wine sensory analysis ; accuracy ; convolution neural network (CNN) ; classifiers ; electrocardiography ; k-fold validation ; myocardial infarction ; sensitivity ; sleep staging ; electroencephalography (EEG) ; brain functional connectivity ; frequency band fusion ; phase-locked value (PLV) ; wearable device ; emotional state ; mental workload ; stress ; heart rate ; eye blinks rate ; skin conductance level ; emotion recognition ; electroencephalogram (EEG) ; photoplethysmography (PPG) ; machine learning ; feature extraction ; feature selection ; deep learning ; non-stationarity ; individual differences ; inter-subject variability ; covariate shift ; cross-participant ; inter-participant ; drowsiness detection ; EEG features ; drowsiness classification ; fatigue detection ; residual network ; Mish ; spatial transformer networks ; non-local attention mechanism ; Alzheimer’s disease ; fall detection ; event-centered data segmentation ; accelerometer ; window duration ; n/a ; thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries
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  • 179
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: This reprint, entitled “Process Control and Smart Manufacturing for Industry 4.0”, contains the extended papers from the Series of annual IFSA conferences on Automation, Robotics and Communications for Industry 4.0/5.0 (ARCI) on the following topics: Process Automation, Process Control and Monitoring, Design Principles in Industry 4.0, Smart Manufacturing and Technologies, Smart Factories, Machine Learning and Artificial Intelligence in Manufacturing. The reprint contains 13 chapters written by 54 ARCI conference participants from seven countries: China, Croatia, Denmark, Germany, Italy, Poland and Romania. This reprint will inform readers of cutting-edge developments in the field and provide effective starting points and a road map for further research and development. All chapters follow the same structure: firstly, an introduction to the specific topic under study; secondly, a description of the field, including sensing or/and measuring applications. Each chapter ends with a curated list of references, including books, journals, conference proceedings and websites. “Process Control and Smart Manufacturing for Industry 4.0” is intended for researchers and scientists from academia and industry, as well as for postgraduate students.
    Keywords: steel alloys ; resistance spot welding ; RSW ; electrode wear ; electrode tip-dressing ; process monitoring ; mushrooming ; plateau forming ; quality control ; COVID-19 ; FDM ; 3D printing ; injection molding ; personal protection ; rapid prototyping ; protective face shields ; mechatronics line ; visual servoing system ; wheeled mobile robot ; industrial robotic manipulator ; Industry 4.0 ; NDT ; magnetic particle inspection ; optimization ; condition monitoring ; vibration ; acoustic emission ; drive train ; data fusion ; machine learning ; product morphology ; core data model ; phase rule filter ; phase private data model ; storage system ; forklift AGV ; deep learning ; semantic segmentation ; H-Swish ; community transformation ; community innovation governance ; ternary space ; coupling and coordination analysis ; lean manufacturing ; lean principles ; pull principle ; production control mechanisms ; production processes ; lean implementation ; batch process ; partial least squares ; multi-phase ; multi-mode ; master production scheduling ; make-to-order ; mathematical programming ; agent-based ; overtime ; earliness ; tardiness ; equipment selection decision ; business compass ; energy consumption ; processing time ; beetle antennae search algorithm ; sustainable blank dimension design ; energy-saving ; low-carbon ; grey wolf algorithm ; gas–solid ; cyclone ; separator ; gas dynamics ; erosion ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
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  • 180
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-03-27
    Description: The rapid growth of the world population has resulted in an exponential expansion of both urban and agricultural areas. Identifying and managing such earthly changes in an automatic way poses a worth-addressing challenge, in which remote sensing technology can have a fundamental role to answer—at least partially—such demands. The recent advent of cutting-edge processing facilities has fostered the adoption of deep learning architectures owing to their generalization capabilities. In this respect, it seems evident that the pace of deep learning in the remote sensing domain remains somewhat lagging behind that of its computer vision counterpart. This is due to the scarce availability of ground truth information in comparison with other computer vision domains. In this book, we aim at advancing the state of the art in linking deep learning methodologies with remote sensing image processing by collecting 20 contributions from different worldwide scientists and laboratories. The book presents a wide range of methodological advancements in the deep learning field that come with different applications in the remote sensing landscape such as wildfire and postdisaster damage detection, urban forest mapping, vine disease and pavement marking detection, desert road mapping, road and building outline extraction, vehicle and vessel detection, water identification, and text-to-image matching.
    Keywords: synthetic aperture radar ; despeckling ; multi-scale ; LSTM ; sub-pixel ; high-resolution remote sensing imagery ; road extraction ; machine learning ; DenseUNet ; scene classification ; lifting scheme ; convolution ; CNN ; image classification ; deep features ; hand-crafted features ; Sinkhorn loss ; remote sensing ; text image matching ; triplet networks ; EfficientNets ; LSTM network ; convolutional neural network ; water identification ; water index ; semantic segmentation ; high-resolution remote sensing image ; pixel-wise classification ; result correction ; conditional random field (CRF) ; satellite ; object detection ; neural networks ; single-shot ; deep learning ; global convolution network ; feature fusion ; depthwise atrous convolution ; high-resolution representations ; ISPRS vaihingen ; Landsat-8 ; faster region-based convolutional neural network (FRCNN) ; single-shot multibox detector (SSD) ; super-resolution ; remote sensing imagery ; edge enhancement ; satellites ; open-set domain adaptation ; adversarial learning ; min-max entropy ; pareto ranking ; SAR ; Sentinel–1 ; Open Street Map ; U–Net ; desert ; road ; infrastructure ; mapping ; monitoring ; deep convolutional networks ; outline extraction ; misalignments ; nearest feature selector ; hyperspectral image classification ; two stream residual network ; Batch Normalization ; plant disease detection ; precision agriculture ; UAV multispectral images ; orthophotos registration ; 3D information ; orthophotos segmentation ; wildfire detection ; convolutional neural networks ; densenet ; generative adversarial networks ; CycleGAN ; data augmentation ; pavement markings ; visibility ; framework ; urban forests ; OUDN algorithm ; object-based ; high spatial resolution remote sensing ; Generative Adversarial Networks ; post-disaster ; building damage assessment ; anomaly detection ; Unmanned Aerial Vehicles (UAV) ; xBD ; feature engineering ; orthophoto ; unsupervised segmentation ; thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-09
    Description: Many recent studies on medical image processing have involved the use of machine learning (ML) and deep learning (DL). This special issue, “Machine Learning/Deep Learning in Medical Image Processing”, has been launched to provide an opportunity for researchers in the area of medical image processing to highlight recent developments made in their fields with ML/DL. Seven excellent papers that cover a wide variety of medical/clinical aspects are selected in this special issue.
    Keywords: pancreas ; segmentation ; computed tomography ; deep learning ; data augmentation ; neoplasm metastasis ; ovarian neoplasms ; radiation exposure ; tomography ; x-ray computed ; prostate carcinoma ; microscopic ; convolutional neural network ; machine learning ; handcrafted ; oral carcinoma ; medical image segmentation ; colon cancer ; colon polyps ; OCT ; optical biopsy ; animal rat models ; CADx ; airway volume analysis ; artificial intelligence ; coronary artery disease ; SPECT MPI scans ; convolutional neural networks ; transfer learning ; classification models ; n/a ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
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  • 182
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-03-30
    Description: In the era of the Internet of Things, images have played important roles in human–computer interactions, and with the arrival of big data technology, people have higher requirements regarding image quality, especially for images collected in dark light. This can be addressed through the development of camera hardware quality, i.e., the resolution and exposure time of cameras, which may require high computational costs. As an alternative, image enhancement techniques can exact salient features to improve the quality of captured images according to the differences in diverse features, although they suffer from some challenges, i.e., a low contrast, artifacts, and overexposure, thus making it decidedly necessary to determine how to use advanced image enhancement techniques. The topic of advances in the image enhancement of electronics is presented in this reprint, which brings together the research accomplishments of researchers from academia and industry. The secondary goal of this reprint is to display the latest research results of advances in image enhancement.
    Keywords: dual networks ; enhanced CNN ; fine learning block ; image super-resolution ; attention mechanism ; convolutional neural networks ; deep learning ; generative adversarial networks ; multiple domains ; translate images ; restart strategy ; adaptive adjustment ; particle swarm optimization ; spline interpolation ; image denoising ; GAN ; optimization algorithm ; autoencoder ; ResNet ; object detection ; YOLOv5s ; image segmentation ; wavelet scattering ; loss function ; active contour ; medical image ; image stitching ; camera calibration ; layered projection ; binocular ranging ; stereo correction ; HOG ; feature fusion ; DHV recognition ; image enhancement ; cross stage partial network ; zero-reference ; Ghost module ; NDT registration ; map building ; RandLa-Net ; random sampling ; semantic segmentation ; capsule network ; power line scene recognition ; complex background ; Visual SLAM ; dynamic scene ; YOLOv5 ; K-means clustering ; probability update ; side-scan sonar ; segmentation ; CNN ; SE-block ; multi-channel ; blockchain technology ; electronic bidding ; system design ; A-star algorithm ; artificial potential field method ; least squares method ; path planning ; night image dehazing ; encoder–decoder architecture ; image fusion ; multi-scale network ; serial architecture ; U-net ; blind watermark removal ; low illumination ; Retinex theory ; histogram equalization ; wavelet transform ; color moments ; non-local mean filter ; thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries ; thema EDItEUR::U Computing and Information Technology::UY Computer science
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  • 183
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-03-27
    Description: Contributions in this collection discuss storm deposits dating from Neogene time between 23 and 1.8 million years ago, as well as the last 1.8 million years, including the Pleistocene and Holocene. As today, past hurricane events were responsible for the erosion of rocky shorelines due to the impact of storm waves, in addition to flood deposits due to heavy rainfall after big storms, resulting in landfall. The former typically resulted in coastal boulder deposits (CBDs) and the latter in coastal outwash deposits (CODs). Study locations covered by this treatment include three within the confines of Mexico’s Gulf of California and three in the northeast Atlantic Ocean, including the Canary Islands and Azores, as well as the coast of Norway. Rock types canvassed in these studies are dominated by igneous rocks that include surface flows such as andesite and basalt as well as surface exposures of plutonic rocks that originated deep below the surface such as granite and near-mantle rocks like low-grade chromite. These rock types reflect a range in rock density, which has an effect on the ability of storm waves to degrade rocky shores in the production of CBDs. The site-specific studies in this collection also share an application treating the shape of boulders resulting from shore erosion. The collection is introduced by a survey covering Neogene CODs registered in the geological literature and a concluding paper focused on the use of satellite images as a means for detecting previously unrecognized coastal storm deposits.
    Keywords: bibliography ; large clasts ; Miocene ; Pliocene ; rocky shore ; storm ; tsunami ; barrier boulder deposits ; hurricane storm surge ; hydrodynamic equation ; Gulf of California (Mexico) ; remote sensing ; bouldering tourism ; Iberian Peninsula ; Mediterranean ; Indonesia ; Central America ; coastal boulder deposits ; storm surge ; hydrodynamic equations ; Holocene ; Pleistocene ; MIS 5e (Marine Isotope Substage 5e) ; NE Atlantic Ocean ; storm waves ; western North America ; coastal storm deposits ; high-latitude settings ; upper pleistocene ; marine isotope substage 5e ; North Atlantic Ocean ; coastal erosion ; Marine Isotope Substage 5e ; Gulf of California ; n/a ; thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
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  • 184
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine.
    Keywords: electrocardiogram ; deep metric learning ; k-nearest neighbors classifier ; premature ventricular contraction ; dimensionality reduction ; classifications ; Laplacian eigenmaps ; locality preserving projections ; compressed sensing ; convolutional neural network ; EEG ; epileptic seizure detection ; RISC-V ; ultra-low-power ; sepsis ; atrial fibrillation ; prediction ; heart rate variability ; feature extraction ; random forest ; annotations ; myoelectric prosthesis ; sEMG ; grasp phases analysis ; grasp classification ; machine learning ; electronic nose ; liver dysfunction ; cirrhosis ; semiconductor metal oxide gas sensor ; vagus nerve ; intraneural ; decoding ; intrafascicular ; recording ; carbon nanotube ; artificial intelligence ; lens-free shadow imaging technique ; cell-line analysis ; cell signal enhancement ; deep learning ; ECG signal ; reconstruction dictionaries ; projection matrices ; signal classifications ; osteopenia ; sarcopenia ; XAI ; SHAP ; IMU ; gait analysis ; sensors ; convolutional neural networks ; Parkinson’s disease ; biomedical monitoring ; accelerometer ; pressure sensor ; disease management ; electromyography ; correlation ; high blood pressure ; hypertension ; photoplethysmography ; electrocardiography ; calibration ; classification models ; COVID-19 ; ECG trace image ; transfer learning ; Convolutional Neural Networks (CNN) ; feature selection ; sympathetic activity (SNA) ; skin sympathetic nerve activity (SKNA) ; electrodes ; electrocardiogram (ECG) ; cardiac time interval ; dynamic time warping ; fiducial point detection ; heart failure ; seismocardiography ; wearable electroencephalography ; motor imagery ; motor execution ; beta rebound ; brain–machine interface ; EEG classification ; n/a ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
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  • 185
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-11-30
    Description: In today's global food market, ensuring both consumer satisfaction and the highest standards of safety is paramount. Food quality analysis covers chemical composition, physical properties, taste evaluation, and even traceability. Traditional methods are often slow, expensive, and eco-unfriendly due to their destructive nature. Here's the exciting part! Advanced spectroscopy techniques offer solutions. Imagine using non-destructive methods like X-rays, hyperspectral imaging, NMR, and Raman—quick, cost-effective, and eco-friendly, using less solvent. Now, let's demystify chemometrics—it extracts hidden info from spectra or image data, creating models for both qualitative and quantitative food analysis. This reprint presents recent advances in spectroscopy and chemometrics, focusing on their role in food analysis, quality evaluation, safety, and practical industry use. It's all about ensuring safe, delicious, and trustworthy food. Whether you're a curious consumer, food enthusiast, or industry insider, this reprint unveils cutting-edge methods for maintaining top food standards. With advanced spectroscopy and chemometrics, we're on track to boost consumer confidence in the food we love.
    Keywords: hyperspectral imaging ; pesticide residue ; table grape ; deep learning ; non-destructive detection ; Salvia spp. ; GC/Q-ToF analysis ; chemometrics ; quality evaluation ; chemical fingerprints ; 1H-NMR ; carbohydrates ; fruits ; PCA ; LDA ; laser-induced breakdown spectroscopy ; brown rice flour adulteration ; time-resolved spectra ; machine learning ; red pepper powder ; multivariate analysis ; moisture adjustment ; Theobroma cacao L. ; dry matter ; fermentation index ; protein content ; meliponine honey ; physicochemical properties ; biomes ; antioxidant potential ; mineral profile ; mass spectrometry analysis ; spatial frequency domain imaging (SFDI) ; optical properties ; absorption ; reduced scattering ; long short-term memory (LSTM) ; FTIR ; mid-infrared ; caprine milk ; milk absorbance spectra ; variance components ; sources of variation ; Fritillaria thunbergii ; heavy metals ; variable selection ; food analysis ; food authenticity ; food chemicals ; spectroscopy techniques ; n/a ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues
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  • 186
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-03-27
    Description: This book focuses on fundamental and applied research on geo-information technology, notably optical and radar remote sensing and algorithm improvements, and their applications in environmental monitoring. This Special Issue presents ten high-quality research papers covering up-to-date research in land cover change and desertification analyses, geo-disaster risk and damage evaluation, mining area restoration assessments, the improvement and development of algorithms, and coastal environmental monitoring and object targeting. The purpose of this Special Issue is to promote exchanges, communications and share the research outcomes of scientists worldwide and to bridge the gap between scientific research and its applications for advancing and improving society.
    Keywords: earthquake ; damaged groups of buildings ; classification ; remote sensing images ; Convolution Neural Network (CNN) ; block vector data ; shoreline change ; landsat ; planet scope ; coastline ; morphological changes ; building extraction ; improved anchor-free instance segmentation ; high-resolution remote sensing images ; deep learning ; land use/land cover (LULC) ; GF-6 WFV ; object-oriented ; change detection ; double constraints ; REE mines ; mining and restoration assessment indicators (MRAIs) ; damage ; time trajectory ; effectiveness of management ; aeolian process ; desertification ; multi-sensor fusion ; interferometric SAR ; time-series analysis ; mussel farming ; high-resolution image ; transitional water management ; environmental pollution ; open source software ; synthetic aperture radar (SAR) ; target ; sea surface ; multiple scattering ; geo-hazard mapping ; Gaofen-1 satellite ; land cover ; environmental factors ; susceptibility ; post-classification differencing ; generalized difference vegetation index (GDVI) ; multiple linear regression ; logistic regression ; n/a ; thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
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  • 187
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing.
    Keywords: TJ1-1570 ; TA1-2040 ; T1-995 ; artificial neural network ; n/a ; model switching ; sensitivity analysis ; neural networks ; logit boost ; Qaidam Basin ; land subsidence ; land use/land cover (LULC) ; naïve Bayes ; multilayer perceptron ; convolutional neural networks ; single-class data descriptors ; logistic regression ; feature selection ; mapping ; particulate matter 10 (PM10) ; Bayes net ; gray-level co-occurrence matrix ; multi-scale ; Logistic Model Trees ; classification ; Panax notoginseng ; large scene ; coarse particle ; grayscale aerial image ; Gaofen-2 ; environmental variables ; variable selection ; spatial predictive models ; weights of evidence ; landslide prediction ; random forest ; boosted regression tree ; convolutional network ; Vietnam ; model validation ; colorization ; data mining techniques ; spatial predictions ; SCAI ; unmanned aerial vehicle ; high-resolution ; texture ; spatial sparse recovery ; landslide susceptibility map ; machine learning ; reproducible research ; constrained spatial smoothing ; support vector machine ; random forest regression ; model assessment ; information gain ; ALS point cloud ; bagging ensemble ; one-class classifiers ; leaf area index (LAI) ; landslide susceptibility ; landsat image ; ionospheric delay constraints ; spatial spline regression ; remote sensing image segmentation ; panchromatic ; Sentinel-2 ; remote sensing ; optical remote sensing ; materia medica resource ; GIS ; precise weighting ; change detection ; TRMM ; traffic CO ; crop ; training sample size ; convergence time ; object detection ; gully erosion ; deep learning ; classification-based learning ; transfer learning ; landslide ; traffic CO prediction ; hybrid model ; winter wheat spatial distribution ; logistic ; alternating direction method of multipliers ; hybrid structure convolutional neural networks ; geoherb ; predictive accuracy ; real-time precise point positioning ; spectral bands ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TD Industrial chemistry and manufacturing technologies::TDC Industrial chemistry and chemical engineering::TDCW Pharmaceutical chemistry and technology
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  • 188
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-02-01
    Description: Energy systems are transiting from conventional energy systems to modernized and smart energy systems. This Special Issue covers new advances in the emerging technologies for modern energy systems from both technical and management perspectives. In modern energy systems, an integrated and systematic view of different energy systems, from local energy systems and islands to national and multi-national energy hubs, is important. From the customer perspective, a modern energy system is required to have more intelligent appliances and smart customer services. In addition, customers require the provision of more useful information and control options. Another challenge for the energy systems of the future is the increased penetration of renewable energy sources. Hence, new operation and planning tools are required for hosting renewable energy sources as much as possible.
    Keywords: hybrid systems ; photovoltaic ; wind energy ; energy economics ; RES investments ; Zimbabwe ; Africa and energy security ; electricity price forecasting (EPF) ; wind power forecasting (WPF) ; spot market ; balancing market ; ARMAX ; NARX-ANN ; 100% renewable power system ; secondary voltage control ; tertiary voltage control ; grid code ; wind farms ; photovoltaic parks ; energy transition ; renewable energy sources ; island power systems ; hybrid power plants ; wind turbines ; battery energy storage systems ; marine microgrid ; tidal generation system ; black widow optimization ; supplementary control ; fractional integrator ; non-linear fractional integrator ; 100% renewable power generation ; nexus ; food ; energy ; water ; greenhouse gas emission ; microgrid ; ancillary services ; energy storage ; power management ; solar hot waters ; thermosyphon ; thermal performance ; Morocco ; economic outcomes ; CO2 environmental assessment ; solar system ; domestic hot water production ; solar water heaters ; individual and collective solar water heater systems ; dynamic simulation ; TRNbuild ; TRNSYSstudio ; energy management ; residential and commercial loads ; short-term load forecasting ; deep learning ; bidirectional long short-term memory (Bi-LSTM) ; n/a ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues
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  • 189
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: Maritime traffic data (e.g., radar data, AIS data, and CCTV data) provide designers, officers on watch, and traffic operators with extensive information about the states of ships at present and in history, representing a treasure trove for behavior analysis. Additionally, navigation rules and regulations (i.e., knowledge) offer valuable prior knowledge about ship manners at sea. Combining multisource heterogeneous big data and artificial intelligence techniques inspires innovative and important means for the development of MASS. This reprint collects twelve contributions published in “Data-/Knowledge-Driven Behavior Analysis of Maritime Autonomous Surface Ships” Special Issue during 2021–2022, aiming to provide new views on data-/knowledge-driven analytical tools for maritime autonomous surface ships, including data-driven behavior modeling, knowledge-driven behavior modeling, multisource heterogeneous traffic data fusion, risk analysis and management of MASS, etc.
    Keywords: unmanned surface vehicle ; velocity obstacle ; collision avoidance ; obstacles classification ; fuzzy rules ; mixed waterborne traffic ; ship behavior ; ship autonomy ; information perception ; intelligent decision-making ; execution ; COLREGs ; ship object ; formal expression ; complex waters ; ship traffic flow ; spatiotemporal dependence ; gate recurrent unit ; motion planning ; unmanned surface vehicle (USV) ; effects of wind and current ; regularization-trajectory cell ; inland waterway transportation ; AIS data ; trajectory classification ; clustering ; deep convolutional neural network ; ship intention identification ; AIS ; RANSAC ; Bayesian framework ; YOLO ; intersection ; maritime autonomous surface ships ; hybrid causal logic ; preliminary hazard analysis ; risk assessment ; hazard identification ; autonomous ship ; ship manoeuvrability ; deduction of the manoeuvring process ; ship exhaust behavior ; detection and tracking ; multi-sensor ; deep learning ; morphological operation ; collision alert system (CAS) ; available maneuvering margins (AMM) ; ship domain ; ship stability ; maritime safety ; semantic modeling ; cognitive space ; multi-scale analysis ; ontology ; n/a ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TR Transport technology and trades
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  • 190
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-07-06
    Description: The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications.
    Keywords: large margin nearest neighbor regression ; distance metrics ; prototypes ; evolutionary algorithm ; approximate differential optimization ; multiple point hill climbing ; adaptive sampling ; free radical polymerization ; autonomous driving ; object tracking ; trajectory prediction ; deep neural networks ; stochastic methods ; applied machine learning ; classification and regression ; data mining ; ensemble model ; engineering informatics ; gender-based violence in Mexico ; twitter messages ; class imbalance ; k-nearest neighbor ; instance-based learning ; graph neural network ; deep learning ; hyperparameters ; machine learning ; optimization ; inference ; metaheuristics ; animal-inspired ; exploration ; exploitation ; hot rolled strip steel ; surface defects ; defect classification ; knockout tournament ; dynamic programming algorithm ; computational complexity ; combinatorics ; intelligent transport systems ; traffic control ; spatial-temporal variable speed limit ; multi-agent systems ; reinforcement learning ; distributed W-learning ; urban motorways ; multi-agent framework ; .NET framework ; simulations ; agent-based systems ; agent algorithms ; software design ; multisensory fingerprint ; interoperability ; DeepFKTNet ; classification ; generative adversarial networks ; image classification ; transfer learning ; plastic bottle ; n/a ; bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general ; bic Book Industry Communication::P Mathematics & science
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  • 191
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-04-05
    Description: Nowadays, Forestry has become an important field for the Environmental and Earth sciences due to its importance in global systems. While forest recreation focuses on forests as the main destination for public recreational activities, landscape protection refers to the conservation and restoration of natural beauty. Urban and peri-urban forests have become a place for the recreational activities of people living in big cities. They provide benefits to the quality of life. Landscape protection is also important, not only for scientific and ecological reasons but also for social and cultural ones. This reprint presents different contributions by the authors dealing with recent scientific research on forestry, recreational activities, landscape management, ecosystem services, etc., in different areas of the world.
    Keywords: tree cover loss ; environment ; degradation ; Mediterranean Region ; forest recreation ; ecosystem services ; forest management ; visitors monitoring ; single-track bike trails ; NEP ; nature experience ; Asia ; outdoor recreation ; urban forest ; green space ; airborne particulate matter ; meteorological parameters ; height ; air pollution ; particulate matter ; WAI ; urban trees ; brown leaf area index ; protected area management ; text mining ; natural language processing ; sentiment analysis ; multidimensional scaling method ; web scraping ; customer satisfaction ; TripAdvisor reviews ; endemic ; landscape protection ; conservation ; Magnolia sulawesiana ; habitat characteristic ; spatial distribution ; forest landscape ; deepsentibank ; deep learning ; geotagged photos ; human health ; forest air ; monoterpenes ; path analysis ; Pinus pinaster ; mountain biking ; recreation ; trade-off ; use conflict ; commercial forestry ; plantation forest ; Pinus radiata ; New Zealand ; n/a ; bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general ; bic Book Industry Communication::K Economics, finance, business & management::KC Economics::KCN Environmental economics
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  • 192
    Publication Date: 2024-04-05
    Description: The idea of preparing an Energies Special Issue on “Structural Prognostics and Health Management in Power & Energy Systems” is to compile information on the recent advances in structural prognostics and health management (SPHM). Continued improvements on SPHM have been made possible through advanced signature analysis, performance degradation assessment, as well as accurate modeling of failure mechanisms by introducing advanced mathematical approaches/tools. Through combining deterministic and probabilistic modeling techniques, research on SPHM can provide assurance for new structures at a design stage and ensure construction integrity at a fabrication phase. Specifically, power and energy system failures occur under multiple sources of uncertainty/variability resulting from load variations in usage, material properties, geometry variations within tolerances, and other uncontrolled variations. Thus, advanced methods and applications for theoretical, numerical, and experimental contributions that address these issues on SPHM are desired and expected, which attempt to prevent overdesign and unnecessary inspection and provide tools to enable a balance between safety and economy to be achieved. This Special Issue has attracted submissions from China, USA, Portugal, and Italy. A total of 26 submissions were received and 11 articles finally published.
    Keywords: B1-5802 ; empirical mode decomposition ; underground powerhouse ; sensitivity analysis ; DNN ; fault detection ; neural networks ; structural health monitoring ; analysis mode decomposition ; dynamic analysis of the structure ; residual useful life ; renewable energy ; remaining useful life ; retrofitting activities ; wind turbine blade ; optimized deep belief networks ; strain prediction ; offshore wind turbines ; low frequency tail fluctuation ; oil and gas platforms ; supporting vector machine (SVM) ; wave–structure interaction (WSI) ; sifting stop criterion ; probabilistic analyses of stochastic processes and frequency ; mode mixing ; non-probabilistic reliability index ; data-driven ; prognostics ; turbine blisk ; wind turbines ; supervisory control and data acquisition system ; fuzzy safety criterion ; analysis-empirical mode decomposition ; rotation of hydraulic generator ; life cycle cost ; health monitoring ; reliability ; wavelet decomposition ; weighted regression ; similarity-based approach ; vibration transmission mechanism ; wind and wave analysis ; full-scale static test ; deep learning ; multioperation condition ; extremum surface response method ; lithium-ion battery ; vibration test ; lateral-river vibration ; operational modal analysis ; dynamic analysis ; regeneration phenomenon ; machine learning ; prognostic and Health Management ; offshore structures ; NAR neural network ; techno-economic assessments ; stochastic subspace identification ; vertical axis wind turbine ; dynamic fuzzy reliability analysis ; thema EDItEUR::Q Philosophy and Religion::QD Philosophy
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  • 193
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-08-12
    Description: Due to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging. The aim of this Special Issue "Hyperspectral Imaging and Applications" is to publish new ideas and technologies to facilitate the utility of hyperspectral imaging in data exploitation and to further explore its potential in different applications. This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories with the number of papers published in every category included in its open parenthesis. 1. Data Unmixing (2 papers)2. Spectral variability (2 papers)3. Target Detection (3 papers)4. Hyperspectral Image Classification (6 papers)5. Band Selection (2 papers)6. Data Fusion (2 papers)7. Applications (8 papers) Under every category each paper is briefly summarized by a short description so that readers can quickly grab its content to find what they are interested in.
    Keywords: biodiversity ; peatland ; vegetation type ; classification ; hyperspectral ; in situ measurements ; hyperspectral image (HSI) ; multiscale union regions adaptive sparse representation (MURASR) ; multiscale spatial information ; imaging spectroscopy ; airborne laser scanning ; minimum noise fraction ; class imbalance ; Africa ; agroforestry ; tree species ; hyperspectral unmixing ; endmember extraction ; band selection ; spectral variability ; prototype space ; ensemble learning ; rotation forest ; semi-supervised local discriminant analysis ; optical spectral region ; thermal infrared spectral region ; mineral mapping ; data integration ; HyMap ; AHS ; raw material ; remote sensing ; nonnegative matrix factorization ; data-guided constraints ; sparseness ; evenness ; hashing ensemble ; hierarchical feature ; hyperspectral classification ; band expansion process (BEP) ; constrained energy minimization (CEM) ; correlation band expansion process (CBEP) ; iterative CEM (ICEM) ; nonlinear band expansion (NBE) ; Otsu’s method ; sparse unmixing ; local abundance ; nuclear norm ; hyperspectral detection ; target detection ; sprout detection ; constrained energy minimization ; iterative algorithm ; adaptive window ; hyperspectral imagery ; recursive anomaly detection ; local summation RX detector (LS-RXD) ; sliding window ; band selection (BS) ; band subset selection (BSS) ; hyperspectral image classification ; linearly constrained minimum variance (LCMV) ; successive LCMV-BSS (SC LCMV-BSS) ; sequential LCMV-BSS (SQ LCMV-BSS) ; vicarious calibration ; reflectance-based method ; irradiance-based method ; Dunhuang site ; 90° yaw imaging ; terrestrial hyperspectral imaging ; vineyard ; water stress ; machine learning ; tree-based ensemble ; progressive sample processing (PSP) ; real-time processing ; image fusion ; hyperspectral image ; panchromatic image ; structure tensor ; image enhancement ; weighted fusion ; spectral mixture analysis ; fire severity ; AVIRIS ; deep belief networks ; deep learning ; texture feature enhancement ; band grouping ; hyperspectral compression ; lossy compression ; on-board compression ; orthogonal projections ; Gram–Schmidt orthogonalization ; parallel processing ; anomaly detection ; sparse coding ; KSVD ; hyperspectral images (HSIs) ; SVM ; composite kernel ; algebraic multigrid methods ; hyperspectral pansharpening ; panchromatic ; intrinsic image decomposition ; weighted least squares filter ; spectral-spatial classification ; label propagation ; superpixel ; semi-supervised learning ; rolling guidance filtering (RGF) ; graph ; deep pipelined background statistics ; high-level synthesis ; data fusion ; data unmixing ; hyperspectral imaging ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
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  • 194
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-01-31
    Description: Target object detection and identification are among the primary uses for a remote sensing system. This is crucial in several fields, including environmental and urban monitoring, hazard and disaster management, and defense and military. In recent years, these analyses have used the tremendous amount of data acquired by sensors mounted on satellite, airborne, and unmanned aerial vehicle (UAV) platforms. This book promotes papers exploiting different remote sensing data for target object detection and identification, such as synthetic aperture radar (SAR) imaging and multispectral and hyperspectral imaging. Several cutting-edge contributions, which provide examples of how to select of a technology or another depending on the specific application, will be detailed.
    Keywords: G1-922 ; Q1-390 ; satellite videos ; nonconvex tensor robust principle component analysis ; infrared ; phase unwrapping ; non-independent and identical distribution (non-i.i.d.) mixture of Gaussians ; dictionary construction ; Color Markov Chain ; convolutional neural networks ; ground-based detection ; hazard prevention ; ADMM ; observability ; pixel-tracking ; multi-scale pyramidal features ; thermal infrared target tracking ; visible ; component mixture model ; hyperspectral imagery ; flux density ; particle filter framework ; processor ; detecting distance ; rivers water-flow elevation estimation ; non-convex optimization ; convolutional neural networks (CNNs) ; infrared small-faint target detection ; target detection ; infrared imaging ; synthetic aperture radar (SAR) ; low-rank representation ; local prior analysis ; remote sensing images ; hardware architecture ; remote sensing image ; unsupervised saliency model ; variational Bayesian ; SAR ; hyperspectral ; anomaly detection ; infrared small target detection ; object detection ; partial sum of the tensor nuclear norm ; superpixel segmentation ; multi-model ; deep learning ; mask sparse representation ; oil tank detection ; tiny and dim target detection ; HSI reconstruction ; part-based ; semantic features ; region proposals ; unmanned aerial vehicle ; object matching ; hidden danger identification ; remote sensing imagery ; target identification ; Lp-norm constraint ; low rank sparse decomposition ; bottom-up and top-down ; contextual information ; multi-scale strategies ; sparse coding ; very-high-resolution (VHR) remote sensing imagery ; vehicle detection ; alternating direction method of multipliers ; adaptive weighting ; flood hazard ; tower failure ; earth entry vehicle
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  • 195
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-08-12
    Description: Fluid–structure interactions (FSIs) play a crucial role in the design, construction, service and maintenance of many engineering applications, e.g., aircraft, towers, pipes, offshore platforms and long-span bridges. The old Tacoma Narrows Bridge (1940) is probably one of the most infamous examples of serious accidents due to the action of FSIs. Aircraft wings and wind-turbine blades can be broken because of FSI-induced oscillations. To alleviate or eliminate these unfavorable effects, FSIs must be dealt with in ocean, coastal, offshore and marine engineering to design safe and sustainable engineering structures. In addition, the wind effects on plants and the resultant wind-induced motions are examples of FSIs in nature. To meet the objectives of progress and innovation in FSIs in various scenarios of engineering applications and control schemes, this book includes 15 research studies and collects the most recent and cutting-edge developments on these relevant issues. The topics cover different areas associated with FSIs, including wind loads, flow control, energy harvesting, buffeting and flutter, complex flow characteristics, train–bridge interactions and the application of neural networks in related fields. In summary, these complementary contributions in this publication provide a volume of recent knowledge in the growing field of FSIs.
    Keywords: aerodynamic forces ; pressure distribution ; turbulence intensity ; twin-box girder ; trailing-edge reattachment ; trailing edge ; trailing-edge-changeable streamlined section mode ; limit cycle flutter ; hard flutter ; flutter stability ; wind engineering ; wind tunnel test ; wind-train-bridge system ; flow visualization ; flapping fringe ; CFD simulation ; vortex attenuation ; aerodynamics enhancement ; unsteady aerodynamic force ; single box girder ; Strouhal number ; linear stability analysis ; high-speed train ; enclosed housing for sound emission alleviation ; pressure wave ; unsteady aerodynamic pressure ; load patterns ; wake control ; drag reduction ; MSBC ; square cylinder ; numerical simulation ; wind characteristics ; wind tunnel testing ; complex terrain ; model truncation ; transition section ; deep learning ; prediction ; aerostatic performance ; shape ; convolutional neural networks ; long-span bridge ; buffeting response ; sectional model ; aerodynamic admittance ; integrated transfer function ; flow control ; traveling wave wall ; circular cylinder ; CFD ; wind turbines ; aerodynamic characteristics ; vortex shedding ; time domain method ; frequency domain method ; background and resonance coupled components ; wind induced dynamic responses ; equivalent static wind load ; aerodynamic shape optimization ; surrogate model ; wind energy harvester ; galloping ; passive jet control ; tower wake characteristics ; cobra probe ; n/a ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
    Language: English
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-01-05
    Description: This book focuses on the characterization of the physical properties of the Earth’s ionosphere, contributing to unveiling the nature of several processes responsible for a plethora of space weather-related phenomena taking place in a wide range of spatial and temporal scales. This is made possible by the exploitation of a huge amount of high-quality data derived from both remote sensing and in situ facilities such as ionosondes, radars, satellites and Global Navigation Satellite Systems receivers.
    Keywords: electron temperature ; topside ionosphere ; ESA Swarm satellites ; International Reference Ionosphere model ; Langmuir Probes in-situ data ; Incoherent Scatter Radar data ; GNSS ; GBAS ; ionospheric gradient anomaly ; one class support vector machine ; earthquake ; pre-earthquake anomalies ; swarm satellites ; ionospheric plasma ; deep learning ; physical mechanisms ; τ ; geomagnetic equator ; magnetic storm ; ionosphere ; vertical pulse sounding ; ionosonde ; ionogram ; software-defined radio ; radar imaging ; aspect angle ; field-aligned plasma irregularities ; mid-latitude E region ; norm-constrained Capon ; VHF radar ; plasma turbulence ; ionospheric irregularities ; ionosphere F region ; high latitude ; pressure-gradient current ; diamagnetic current ; swarm measurements ; oblique ionogram ; automatic inversion ; electron density profile ; quasi-parabolic segments ; the sporadic E layer ; internal fine structure ; high-resolution ionosphere imaging ; frequency domain interferometry technique ; auroral ionosphere ; E × B plasma motion ; ionospheric scintillation ; GNSS-R ; CYGNSS ; earthquakes ; polar ionosphere ; VIPIR ; Dynasonde ; Jang Bogo Station (JBS) ; Antarctica ; solar eclipse ; precise point positioning ; total electron content ; rate of total electron content index ; Swarm satellite measurements ; electron density ; main ionospheric trough ; high latitude trough ; ring ionospheric trough ; low latitude trough ; auroral diffuse precipitation ; n/a ; bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general ; bic Book Industry Communication::R Earth sciences, geography, environment, planning::RG Geography
    Language: English
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  • 197
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-01-08
    Description: In the orthopedic surgical field, knee surgeries, including articular cartilage repair procedures, meniscus surgery, ligament reconstruction surgery, osteotomy surgery, and partial/total knee arthroplasty surgery, have made great advances over the last few decades. This Special Issue highlights and focuses on the surgical concepts and techniques, decision-making processes, perioperative management protocols, and clinical outcomes of the recent various advanced knee surgery procedures.
    Keywords: high tibial osteotomy ; TomoFix ; plate position ; anatomical conformity ; dual-energy CT ; Hounsfield unit ; bone mineral density ; volumetric phantomless BMD ; opportunistic CT ; orthopedic surgeon ; planning ; survey ; total knee arthroplasty ; unicompartmental knee arthroplasty ; anterior cruciate ligament ; reconstruction ; bone tunnel widening ; adjustable-loop device ; interference screw ; hamstring tendon ; autograft ; tibial component alignment ; radiographic references ; extramedullary system ; tranexamic acid ; clamping time ; transfusion ; estimated blood loss ; continuous cold flow therapy ; cryotherapy ; pain ; opioids consumption ; patient satisfaction ; bone marrow lesion ; knee ; meniscus ; root tear ; root repair ; femorotibial joint ; chondromalacia ; aging ; body mass index ; magnetic resonance imaging ; automated detection ; detection algorithm ; deep learning ; venous thromboembolism ; medial collateral ligament ; strain ; video extensometer ; medial opening-wedge high tibial osteotomy ; central sensitization ; patient-reported outcomes ; osteotomy site pain ; minimal clinically important difference ; human umbilical cord blood derived mesenchymal stem cells ; cartilage regeneration ; cartilage repair ; osteoarthritis treatment ; stem cell therapy ; Outerbridge ; degeneration ; spacer block ; intramedullary rod ; femorotibial congruence ; unicompartmental arthroplasty ; osteoarthritis ; cartilage ; stem cells ; umbilical cord blood ; femur fracture ; polyethylene insert ; osteoporosis ; multivariate logistic analysis ; atelocollagen ; microfracture ; ACIC ; bone marrow aspirate concentrate ; human umbilical cord blood-derived mesenchymal stem cells ; knee osteoarthritis ; loosening ; arthroplasty ; machine learning ; transfer learning ; review ; prosthesis ; meniscus root ; medial meniscus posterior root ; medial meniscus posterior root tear ; meniscus root repair ; transtibial pull-out repair ; bone tunnel enlargement ; anterior cruciate ligament reconstruction ; landmark ; lateral tibial spine ; anatomy ; bic Book Industry Communication::M Medicine
    Language: English
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  • 198
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-09
    Description: Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study.
    Keywords: online signature verification ; shape contexts ; function features ; SC-DTW ; symbolic representation ; two-stage method ; finger features ; multimodal recognition ; local coding ; Gabor filter ; LGS ; human identification ; biomarker ; ECG ; machine learning ; Physionet ; Lviv Biometric Dataset ; biometry ; identification ; bloodstream ; image recognition ; multi-biometrics ; bit planes ; block ; mutual information ; cross-device ; dorsal hand vein recognition ; person re-identification ; superpixel ; temporally aligned pooling ; walking cycle ; automatic recognition ; face ; voice ; body motion ; autism spectrum disorder (ASD) ; assessment ; intervention ; curve similarity ; curve similarity model ; curve similarity transformation ; similarity distance ; segmentation matching ; evolutionary computation ; finger vein recognition ; hand vein recognition ; contactless acquisition device ; public vascular pattern dataset ; biometric recognition performance evaluation ; face verification ; optical correlation ; Hausdorff distance ; image classification ; face detection ; depth map ensemble ; filtering ; geometric deep learning ; ear detection ; structured prediction ; semantic segmentation ; rotation equivariance ; Gaussian mixture model ; superpixels ; face recognition systems ; person identification ; biometric systems ; survey ; automatic signature verification ; touch-screen sensor ; data quality ; enrollment phase ; performance assessment ; augmented signature ; security enhancement ; mobile conditions ; biometric recognition ; visible light iris images ; image quality assessment ; image covariates ; quality filtering ; vascular biometric recognition ; wrist vein recognition ; contactless dataset ; pattern recognition ; infrared camera ; non-contact devices ; Scale-Invariant Feature Transform (SIFT®) ; Speeded Up Robust Features (SURF®) ; Oriented FAST and Rotated BRIEF (ORB) ; fingerprint ; presentation attack detection ; deep learning ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
    Language: English
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-09-11
    Description: This reprint contains a summary of what has been presented at the 19th “Calorimetry for High Energy Physics Experiment” (CALOR) conference, which was held at the University of Sussex (Brighton, UK) in May 2022. The state of the art of particle detection, as well as of simulation tools for particle interactions with material, is presented and discussed by world leaders in the Research & Development of particle detectors, as well as early career researchers, sparking very exciting conversations within the detector development community during the five days of the conference.
    Keywords: ATLAS ; Tile Calorimeter ; calorimeter calibration ; calorimeter performance ; liquid scintillator ; PMTs ; calibration ; energy nonlinearity ; energy resolution ; dual calorimetry ; fast-timing ; electromagnetic calorimetry ; radiation-hard detectors ; LAr ; upgrade ; calorimeter ; readout ; electronics ; electromagnetic calorimeter ; compact muon solenoid ; high luminosity large hadron collider ; liquid argon detectors ; liquid argon calibration ; signal processing ; particle identification ; resolution ; pre-shower ; scintillating glass ; hadronic calorimeter ; high granularity calorimetry ; silicon photomultiplier ; cosmic rays ; calorimeters ; space instrumentation ; large detector systems for particle and astroparticle physics ; energy reconstruction ; cluster splitting algorithm ; resistive plate chambers ; gaseous imaging and tracking detectors ; hadron calorimetry ; dual-readout calorimetry ; future colliders ; Phase-1 ; commissioning ; calorimetry ; particle detectors ; photomultipliers ; Higgs factory ; crystal ; SiPM ; high granularity ; Geant4 ; geant-val ; simulation ; hadronic interaction ; test-beam ; Cherenkov calorimeter ; high-granularity ; neural network ; GNN ; CNN ; electromagnetic showers ; Cherenkov light ; optical fibres ; liquid argon ; plasma light source ; scintillation light ; dark photon ; signal reconstruction ; machine learning ; graph neural network ; high energy physics ; calorimeter reconstruction ; secondary electron emission ; radiation hardness ; forward calorimetry ; dual-readout ; ADRIANO2 ; JUNO ; JUNO-TAO ; neutrino detectors ; reactor neutrinos ; MAPS ; silicon detectors ; linear collider ; astroparticles ; γ-ray astronomy ; scintillation ; light yield ; tile calorimeter ; large hadron collider ; FCC ; liquified noble gas ; irradiation ; crystals ; scintillators ; protons ; neutrons ; proton CT ; image reconstruction ; proton tracking ; deep learning ; SiPMs ; calorimetry for high energy physics ; dual readout detector R&D ; PbF2 ; mechanics ; LHC ; trigger ; FPGA ; SLR ; LYSO ; intensity frontier ; ceramics ; time of flight ; ultrafast calorimetry ; charge transfer luminescence ; fast timing ; front-end ; MPPC ; flavour violation ; hadron calorimeter ; silicon photomultipliers ; CMS ; phase-2 ; HGCAL ; particle flow ; TileCal ; Phase-II upgrade ; quality assurance testing ; burn-in ; transformer-coupled buck converters ; bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general ; bic Book Industry Communication::P Mathematics & science::PH Physics
    Language: English
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2023-02-20
    Description: Radiomics is one of the most successful branches of research in the field of image processing and analysis, as it provides valuable quantitative information for the personalized medicine. It has the potential to discover features of the disease that cannot be appreciated with the naked eye in both preclinical and clinical studies. In general, all quantitative approaches based on biomedical images, such as positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI), have a positive clinical impact in the detection of biological processes and diseases as well as in predicting response to treatment. This Special Issue, “Image Processing and Analysis for Preclinical and Clinical Applications”, addresses some gaps in this field to improve the quality of research in the clinical and preclinical environment. It consists of fourteen peer-reviewed papers covering a range of topics and applications related to biomedical image processing and analysis.
    Keywords: deep learning ; segmentation ; prostate ; MRI ; ENet ; UNet ; ERFNet ; radiomics ; gamma knife ; imaging quantification ; [11C]-methionine positron emission tomography ; cancer ; atrial fibrillation ; 4D-flow ; stasis ; pulmonary vein ablation ; convolutional neural network ; transfer learning ; maxillofacial fractures ; computed tomography images ; radiography ; xenotransplant ; cancer cells ; zebrafish image analysis ; in vivo assay ; convolutional neural network (CNN) ; magnetic resonance imaging (MRI) ; neoadjuvant chemoradiation therapy (nCRT) ; pathologic complete response (pCR) ; rectal cancer ; radiomics feature robustness ; PET/MRI co-registration ; image registration ; fundus image ; feature extraction ; glomerular filtration rate ; Gate’s method ; renal depth ; computed tomography ; computer-aided diagnosis ; medical-image analysis ; automated prostate-volume estimation ; abdominal ultrasound images ; image-patch voting ; soft tissue sarcoma ; volume estimation ; artificial intelligence ; Basal Cell Carcinoma ; skin lesion ; classification ; colon ; positron emission tomography-computed tomography ; nuclear medicine ; image pre-processing ; high-level synthesis ; X-ray pre-processing ; pipelined architecture ; n/a ; bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general ; bic Book Industry Communication::P Mathematics & science::PN Chemistry
    Language: English
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