ALBERT

Description: Sensors, Vol. 18, Pages 1814: Centralized Duplicate Removal Video Storage System with Privacy Preservation in IoT Sensors doi: 10.3390/s18061814 Authors: Hongyang Yan Xuan Li Yu Wang Chunfu Jia In recent years, the Internet of Things (IoT) has found wide application and attracted much attention. Since most of the end-terminals in IoT have limited capabilities for storage and computing, it has become a trend to outsource the data from local to cloud computing. To further reduce the communication bandwidth and storage space, data deduplication has been widely adopted to eliminate the redundant data. However, since data collected in IoT are sensitive and closely related to users’ personal information, the privacy protection of users’ information becomes a challenge. As the channels, like the wireless channels between the terminals and the cloud servers in IoT, are public and the cloud servers are not fully trusted, data have to be encrypted before being uploaded to the cloud. However, encryption makes the performance of deduplication by the cloud server difficult because the ciphertext will be different even if the underlying plaintext is identical. In this paper, we build a centralized privacy-preserving duplicate removal storage system, which supports both file-level and block-level deduplication. In order to avoid the leakage of statistical information of data, Intel Software Guard Extensions (SGX) technology is utilized to protect the deduplication process on the cloud server. The results of the experimental analysis demonstrate that the new scheme can significantly improve the deduplication efficiency and enhance the security. It is envisioned that the duplicated removal system with privacy preservation will be of great use in the centralized storage environment of IoT.

Description: Sensors, Vol. 18, Pages 1811: Pedestrian Dead Reckoning Based on Motion Mode Recognition Using a Smartphone Sensors doi: 10.3390/s18061811 Authors: Boyuan Wang Xuelin Liu Baoguo Yu Ruicai Jia Xingli Gan This paper presents a pedestrian dead reckoning (PDR) approach based on motion mode recognition using a smartphone. The motion mode consists of pedestrian movement state and phone pose. With the support vector machine (SVM) and the decision tree (DT), the arbitrary combinations of movement state and phone pose can be recognized successfully. In the traditional principal component analysis based (PCA-based) method, the obtained horizontal accelerations in one stride time interval cannot be guaranteed to be horizontal and the pedestrian’s direction vector will be influenced. To solve this problem, we propose a PCA-based method with global accelerations (PCA-GA) to infer pedestrian’s headings. Besides, based on the further analysis of phone poses, an ambiguity elimination method is also developed to calibrate the obtained headings. The results indicate that the recognition accuracy of the combinations of movement states and phone poses can be 92.4%. The 50% and 75% absolute estimation errors of pedestrian’s headings are 5.6° and 9.2°, respectively. This novel PCA-GA based method can achieve higher accuracy than traditional PCA-based method and heading offset method. The localization error can reduce to around 3.5 m in a trajectory of 164 m for different movement states and phone poses.

Description: Sensors, Vol. 18, Pages 1807: Considerations about the Determination of the Depolarization Calibration Profile of a Two-Telescope Lidar and Its Implications for Volume Depolarization Ratio Retrieval Sensors doi: 10.3390/s18061807 Authors: Adolfo Comerón Alejandro Rodríguez-Gómez Michaël Sicard Rubén Barragán Constantino Muñoz-Porcar Francesc Rocadenbosch María José Granados-Muñoz We propose a new method for calculating the volume depolarization ratio of light backscattered by the atmosphere and a lidar system that employs an auxiliary telescope to detect the depolarized component. It takes into account the possible error in the positioning of the polarizer used in the auxiliary telescope. The theory of operation is presented and then applied to a few cases for which the actual position of the polarizer is estimated, and the improvement of the volume depolarization ratio in the molecular region is quantified. In comparison to the method used before, i.e., without correction, the agreement between the volume depolarization ratio with correction and the theoretical value in the molecular region is improved by a factor of 2–2.5.

Description: Remote Sensing, Vol. 10, Pages 934: Concentric Circle Pooling in Deep Convolutional Networks for Remote Sensing Scene Classification Remote Sensing doi: 10.3390/rs10060934 Authors: Kunlun Qi Qingfeng Guan Chao Yang Feifei Peng Shengyu Shen Huayi Wu Convolutional neural networks (CNNs) have been increasingly used in remote sensing scene classification/recognition. The conventional CNNs are sensitive to the rotation of the image scene, which will inevitably result in the misclassification of remote sensing scene images that belong to the same category. In this work, we equip the networks with a new pooling strategy, “concentric circle pooling”, to alleviate the above problem. The new network structure, called CCP-net can generate a concentric circle-based spatial-rotation-invariant representation of an image, hence improving the classification accuracy. The square kernel is adopted to approximate the circle kernels in concentric circle pooling, which is much more efficient and suitable for CNNs to propagate gradients. We implement the training of the proposed network structure with standard back-propagation, thus CCP-net is an end-to-end trainable CNNs. With these advantages, CCP-net should in general improve CNN-based remote sensing scene classification methods. Experiments using two publicly available remote sensing scene datasets demonstrate that using CCP-net can achieve competitive classification results compared with the state-of-art methods.

Description: Algorithms, Vol. 11, Pages 80: Scheduling a Single Machine with Primary and Secondary Objectives Algorithms doi: 10.3390/a11060080 Authors: Nodari Vakhania We study a scheduling problem in which jobs with release times and due dates are to be processed on a single machine. With the primary objective to minimize the maximum job lateness, the problem is strongly NP-hard. We describe a general algorithmic scheme to minimize the maximum job lateness, with the secondary objective to minimize the maximum job completion time. The problem of finding the Pareto-optimal set of feasible solutions with these two objective criteria is strongly NP-hard. We give the dominance properties and conditions when the Pareto-optimal set can be formed in polynomial time. These properties, together with our general framework, provide the theoretical background, so that the basic framework can be expanded to (exponential-time) implicit enumeration algorithms and polynomial-time approximation algorithms (generating the Pareto sub-optimal frontier with a fair balance between the two objectives). Some available in the literature experimental results confirm the practical efficiency of the proposed framework.

Description: Sensors, Vol. 18, Pages 1926: A Smart Collaborative Routing Protocol for Reliable Data Diffusion in IoT Scenarios Sensors doi: 10.3390/s18061926 Authors: Zheng-Yang Ai Yu-Tong Zhou Fei Song It is knotty for current routing protocols to meet the needs of reliable data diffusion during the Internet of Things (IoT) deployments. Due to the random placement, limited resources and unattended features of existing sensor nodes, the wireless transmissions are easily exposed to unauthorized users, which becomes a vulnerable area for various malicious attacks, such as wormhole and Sybil attacks. However, the scheme based on geographic location is a suitable candidate to defend against them. This paper is inspired to propose a smart collaborative routing protocol, Geographic energy aware routing and Inspecting Node (GIN), for guaranteeing the reliability of data exchanging. The proposed protocol integrates the directed diffusion routing, Greedy Perimeter Stateless Routing (GPSR), and the inspecting node mechanism. We first discuss current wireless routing protocols from three diverse perspectives (improving transmission rate, shortening transmission range and reducing transmission consumption). Then, the details of GIN, including the model establishment and implementation processes, are presented by means of the theoretical analysis. Through leveraging the game theory, the inspecting node is elected to monitor the network behaviors. Thirdly, we evaluate the network performances, in terms of transmission delay, packet loss ratio, and throughput, between GIN and three traditional schemes (i.e., Flooding, GPSR, and GEAR). The simulation results illustrate that the proposed protocol is able to outperform the others.

Description: Algorithms, Vol. 11, Pages 85: ILC with Initial State Learning for Fractional Order Linear Distributed Parameter Systems Algorithms doi: 10.3390/a11060085 Authors: Yong-Hong Lan Zhe-Min Cui This paper presents a second order P-type iterative learning control (ILC) scheme with initial state learning for a class of fractional order linear distributed parameter systems. First, by analyzing the control and learning processes, a discrete system for P-type ILC is established, and the ILC design problem is then converted to a stability problem for such a discrete system. Next, a sufficient condition for the convergence of the control input and the tracking errors is obtained by introducing a new norm and using the generalized Gronwall inequality, which is less conservative than the existing one. Finally, the validity of the proposed method is verified by a numerical example.

Description: Forests, Vol. 9, Pages 355: Do Silviculture and Forest Management Affect the Genetic Diversity and Structure of Long-Impacted Forest Tree Populations? Forests doi: 10.3390/f9060355 Authors: Filippos A. (Phil) Aravanopoulos The consequences of silviculture and management on the genetic variation and structure of long-impacted populations of forest tree are reviewed assessed and discussed, using Mediterranean forests as a working paradigm. The review focuses on silviculture and management systems, regeneration schemes, the consequences of coppicing and coppice conversion to high forest, the effects of fragmentation and exploitation, and the genetic impact of forestry plantations. It emerges that averaging genetic diversity parameters, such as those typically reported in the assessment of forest population genetics, do not generally present significant differences between populations under certain silvicultural systems/forest management methods and “control” populations. Observed differences are usually rather subtler and regard the structure of the genetic variation and the lasting adaptive potential of natural forest tree populations. Therefore, forest management and silvicultural practices have a longer-term impact on the genetic diversity and structure and resilience of long-impacted populations of forest tree; their assessment should be based on parameters that are sensitive to population perturbations and bottlenecks. The nature and extent of genetic effects and impact of silviculture and forest management practices, call for a concerted effort regarding their thorough study using genetic, genomic, as well as monitoring approaches, in order to provide insight and potential solutions for future silviculture and management regimes.

Description: Forests, Vol. 9, Pages 354: Growth and Physicochemical Changes of Carpinus betulus L. Influenced by Salinity Treatments Forests doi: 10.3390/f9060354 Authors: Qi Zhou Zunling Zhu Man Shi Longxia Cheng Carpinus betulus L. is a deciduous tree widely distributed in Europe with strong adaptation, and it plays a key role in landscaping and timbering because of its variety of colors and shapes. Recently introduced to China for similar purposes, this species needs further study as to its physiological adaptability under various soil salinity conditions. In this study, the growth and physicochemical changes of C. betulus seedlings cultivated in soil under six different levels of salinity stress (NaCl: 0, 17, 34, 51, 68, and 85 mM) were studied for 14, 28 and 42 days. The plant growth and gas exchange parameters were not changed much by 17 and 34 mM NaCl, but they were significantly affected after treatments with 51 ~ 85 mM NaCl. The chlorophyll content was not significantly affected at 17 and 34 mM salinity, and the relative water content, malondialdehyde content and cell membrane stability of C. betulus did not change obviously under the 17 and 34 mM treatments, indicating that C. betulus is able to adapt to low-salinity conditions. The amount of osmotic adjustment substances and the antioxidant enzyme activity of C. betulus increased after 14 and 28 days and then decreased with increasing salinity gradients, but the proline content was increased during the entire time for different salinities. The Na content of different organs increased in response to salinity, and the K/Na, Ca/Na, and Mg/Na ratios were significantly affected by salinity. These results suggest that the ability of C. betulus to synthesize osmotic substances and enzymatic antioxidants may be impaired under severe saline conditions (68 ~ 85 mM NaCl) but that it can tolerate and accumulate salt at low salinity concentrations (17 ~ 34 mM NaCl). Such information is useful for land managers considering introducing this species to sites with various soil salinity conditions.

Description: Sensors, Vol. 18, Pages 1938: A Novel Friendly Jamming Scheme in Industrial Crowdsensing Networks against Eavesdropping Attack Sensors doi: 10.3390/s18061938 Authors: Xuran Li Qiu Wang Hong-Ning Dai Hao Wang Eavesdropping attack is one of the most serious threats in industrial crowdsensing networks. In this paper, we propose a novel anti-eavesdropping scheme by introducing friendly jammers to an industrial crowdsensing network. In particular, we establish a theoretical framework considering both the probability of eavesdropping attacks and the probability of successful transmission to evaluate the effectiveness of our scheme. Our framework takes into account various channel conditions such as path loss, Rayleigh fading, and the antenna type of friendly jammers. Our results show that using jammers in industrial crowdsensing networks can effectively reduce the eavesdropping risk while having no significant influence on legitimate communications.

Description: Sensors, Vol. 18, Pages 1909: Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework Sensors doi: 10.3390/s18061909 Authors: Changjian Deng Kun Lv Debo Shi Bo Yang Song Yu Zhiyi He Jia Yan In this paper, a novel feature selection and fusion framework is proposed to enhance the discrimination ability of gas sensor arrays for odor identification. Firstly, we put forward an efficient feature selection method based on the separability and the dissimilarity to determine the feature selection order for each type of feature when increasing the dimension of selected feature subsets. Secondly, the K-nearest neighbor (KNN) classifier is applied to determine the dimensions of the optimal feature subsets for different types of features. Finally, in the process of establishing features fusion, we come up with a classification dominance feature fusion strategy which conducts an effective basic feature. Experimental results on two datasets show that the recognition rates of Database I and Database II achieve 97.5% and 80.11%, respectively, when k = 1 for KNN classifier and the distance metric is correlation distance (COR), which demonstrates the superiority of the proposed feature selection and fusion framework in representing signal features. The novel feature selection method proposed in this paper can effectively select feature subsets that are conducive to the classification, while the feature fusion framework can fuse various features which describe the different characteristics of sensor signals, for enhancing the discrimination ability of gas sensors and, to a certain extent, suppressing drift effect.

Description: Sensors, Vol. 18, Pages 1908: Numerical and Experimental Evaluation of High-Frequency Unfocused Polymer Transducer Arrays Sensors doi: 10.3390/s18061908 Authors: Anowarul Habib Sanat Wagle Adit Decharat Frank Melandsø High-frequency unfocused polymer array transducers are developed using an adhesive-free layer-by-layer assembly method. The current paper focuses on experimental and numerical methods for measuring the acoustic performance of these types of array transducers. Two different types of numerical approaches were used to simulate the transducer performance, including a finite element method (FEM) study of the transducer response done in COMSOL 5.2a Multiphysics, and modeling of the excited ultrasonic pressure fields using the open source software k-Wave 1.2.1. The experimental characterization also involves two methods (narrow and broadband pulses), which are measurements of the acoustic reflections picked up by the transducer elements. Later on, measurements were undertaken of the ultrasonic pressure fields in a water-scanning tank using a hydrophone system. Ultrasonic pressure field measurements were visualized at various distances from the transducer surface and compared with the numerical findings.

Description: Sensors, Vol. 18, Pages 1911: Sensitive and Selective Detection of Tartrazine Based on TiO2-Electrochemically Reduced Graphene Oxide Composite-Modified Electrodes Sensors doi: 10.3390/s18061911 Authors: Quanguo He Jun Liu Xiaopeng Liu Guangli Li Peihong Deng Jing Liang Dongchu Chen TiO2-reduced graphene oxide composite-modified glassy carbon electrodes (TiO2–ErGO–GCE) for the sensitive detection of tartrazine were prepared by drop casting followed by electrochemical reduction. The as-prepared material was characterized by transmission electron microscopy (TEM) and X-ray diffraction (XRD). Cyclic voltammetry and second-order derivative linear scan voltammetry were performed to analyze the electrochemical sensing of tartrazine on different electrodes. The determination conditions (including pH, accumulation potential, and accumulation time) were optimized systematically. The results showed that the TiO2–ErGO composites increased the electrochemical active area of the electrode and enhanced the electrochemical responses to tartrazine significantly. Under the optimum detection conditions, the peak current was found to be linear for tartrazine concentrations in the range of 2.0 × 10−8–2.0 × 10−5 mol/L, with a lower detection limit of 8.0 × 10−9 mol/L (S/N = 3). Finally, the proposed TiO2–ErGO–GCEs were successfully applied for the detection of trace tartrazine in carbonated beverage samples.

Description: Sensors, Vol. 18, Pages 1933: Effects of Fire Suppression Agents and Weathering in the Analysis of Fire Debris by HS-MS eNose Sensors doi: 10.3390/s18061933 Authors: Barbara Falatová Marta Ferreiro-González Carlos Martín-Alberca Danica Kačíková Štefan Galla Miguel Palma Carmelo G. Barroso In arson attacks the detection of ignitable liquid residues (ILRs) at fire scenes provides key evidence since ignitable liquids, such as gasoline, are commonly used to initiate the fire. In most forensic laboratories gas chromatography-mass spectrometry is employed for the analysis of ILRs. When a fire occurs, suppression agents are used to extinguish the fire and, before the scene is investigated, the samples at the scene are subjected to a variety of processes such as weathering, which can significantly modify the chemical composition and thus lead to erroneous conclusions. In order to avoid this possibility, the application of chemometric tools that help the analyst to extract useful information from data is very advantageous. The study described here concerned the application of a headspace-mass spectrometry electronic nose (HS-MS eNose) combined with chemometric tools to determine the presence/absence of gasoline in weathered fire debris samples. The effect of applying two suppression agents (Cafoam Aquafoam AF-6 and Pyro-chem PK-80 Powder) and delays in the sampling time (from 0 to 48 h) were studied. It was found that, although the suppression systems affect the mass spectra, the HS-MS eNose in combination with suitable pattern recognition chemometric tools, such as linear discriminant analysis, is able to identify the presence of gasoline in any of the studied situations (100% correct classification).

Description: Remote Sensing, Vol. 10, Pages 961: Temporal and Spatial Characteristics of EVI and Its Response to Climatic Factors in Recent 16 years Based on Grey Relational Analysis in Inner Mongolia Autonomous Region, China Remote Sensing doi: 10.3390/rs10060961 Authors: Dong He Guihua Yi Tingbin Zhang Jiaqing Miao Jingji Li Xiaojuan Bie The Inner Mongolia Autonomous Region (IMAR) is a major source of rivers, catchment areas, and ecological barriers in the northeast of China, related to the nation’s ecological security and improvement of the ecological environment. Therefore, studying the response of vegetation to climate change has become an important part of current global change research. Since existing studies lack detailed descriptions of the response of vegetation to different climatic factors using the method of grey correlation analysis based on pixel, the temporal and spatial patterns and trends of enhanced vegetation index (EVI) are analyzed in the growing season in IMAR from 2000 to 2015 based on moderate resolution imaging spectroradiometer (MODIS) EVI data. Combined with the data of air temperature, relative humidity, and precipitation in the study area, the grey relational analysis (GRA) method is used to study the time lag of EVI to climate change, and the study area is finally zoned into different parts according to the driving climatic factors for EVI on the basis of lag analysis. The driving zones quantitatively show the characteristics of temporal and spatial differences in response to different climatic factors for EVI. The results show that: (1) The value of EVI generally features in spatial distribution, increasing from the west to the east and the south to the north. The rate of change is 0.22/10°E from the west to the east, 0.28/10°N from the south to the north; (2) During 2000–2015, the EVI in IMAR showed a slightly upward trend with a growth rate of 0.021/10a. Among them, the areas with slight and significant improvement accounted for 21.1% and 7.5% of the total area respectively, ones with slight and significant degradation being 24.6% and 4.3%; (3) The time lag analysis of climatic factors for EVI indicates that vegetation growth in the study area lags behind air temperature by 1–2 months, relative humidity by 1–2 months, and precipitation by one month respectively; (4) During the growing season, the EVI of precipitation driving zone (21.8%) in IMAR is much larger than that in the air temperature driving zone (8%) and the relative humidity driving zone (11.6%). The growth of vegetation in IMAR generally has the closest relationship with precipitation. The growth of vegetation does not depend on the change of a single climatic factor. Instead, it is the result of the combined action of multiple climatic factors and human activities.

Description: Remote Sensing, Vol. 10, Pages 959: The Temperature Vegetation Dryness Index (TVDI) Based on Bi-Parabolic NDVI-Ts Space and Gradient-Based Structural Similarity (GSSIM) for Long-Term Drought Assessment Across Shaanxi Province, China (2000–2016) Remote Sensing doi: 10.3390/rs10060959 Authors: Ying Liu Hui Yue Traditional NDVI-Ts space is triangular or trapezoidal, but Liu et al. (2015) discovered that the NDVI-Ts space was bi-parabolic when the study area was covered with low biomass vegetation. Moreover, the numerical value of the indicator was considered in most of the study when the drought conditions in the space domain were evaluated. In addition, quantitatively assessing the spatial-temporal changes of the drought was not enough. In this study, first, we used MODIS NDVI and Ts data to reexamine if the NDVI-Ts space with “time” and a single pixel domain is bi-parabolic in the Shaanxi province of China, which is vegetated with low biomass to high biomass. This is compared with the triangular NDVI-Ts space and one of the well-known drought indexes called the temperature-vegetation index (TVX). The results demonstrated that dry and wet edges exhibited a parabolic shape again in scatter plots of Ts and NDVI in the Shaanxi province, which was linear in the triangular NDVI-Ts space. The Temperature Vegetation Dryness Index (TVDIc) was obtained from bi-parabolic NDVI-Ts andTVDIt was obtained from the triangular NDVI-Ts space and TVX were compared with 10-cm depth relative soil moisture. By estimating the 10-cm depth soil moisture, TVDIc was better than TVDIt, which were all apparently better than TVX. Second, combined with MODIS data, the drought conditions of the study area were assessed by TVDIc between 2000 to 2016. Spatially, the drought in the Shaanxi Province between 2000 to 2016 were mainly distributed in the northwest, North Shaanxi, and the North and East Guanzhong plain. The drought area of the Shaanxi province accounted for 31.95% in 2000 and 27.65% in 2016, respectively. Third, we quantitatively evaluated the variation of the drought status by using Gradient-based Structural Similarity (GSSIM) methods. The area of the drought conditions significantly changed and moderately changed at 5.34% and 40.22%, respectively, between 2000 and 2016. Finally, the possible reasons for drought change were discussed. The change of precipitation, temperature, irrigation, destruction or betterment of vegetation, and the enlargement of opening mining, etc., can lead to the variations of drought.

Description: Remote Sensing, Vol. 10, Pages 963: A Seismic Capacity Evaluation Approach for Architectural Heritage Using Finite Element Analysis of Three-Dimensional Model: A Case Study of the Limestone Hall in the Ming Dynasty Remote Sensing doi: 10.3390/rs10060963 Authors: Siliang Chen Shaohua Wang Chen Li Qingwu Hu Hongjun Yang A lot of architectural heritage in China are urgently in need to carry out seismic assessment for further conservation. In this paper, a seismic capacity evaluation approach for architectural heritage using finite element analysis with precision three-dimensional data was proposed. The Limestone Hall of Shaanxi Province was taken as an example. First, low attitude unmanned aerial vehicle photogrammetry and a close-range photogrammetry camera were used to collect multiple view images to obtain the precision three-dimensional current model of the Limestone. Second, the dimensions of internal structures of Limestone Hall are obtained by means of structural analysis; re-establishing the ideal model of Limestone Hall based on the modeling software. Third, a finite element analysis was conducted to find out the natural frequency and seismic stress in various conditions with the 3D model using ANSYS software. Finally, the seismic capacity analysis results were comprehensively evaluated for the risk assessment and simulation. The results showed that for architectural heritage with a multilayer structure, utilizing photogrammetric surveying and mapping, 3D software modeling, finite element software simulation, and seismic evaluation for simulation was feasible where the precision of the modeling and parameters determine the accuracy of the simulation. The precise degree of the three-dimensional model, the accurate degree of parameter measurement and estimation, the setting of component attributes in the finite element model and the strategy of finite element analysis have an important effect on the result of seismic assessment. The main body structure of the Limestone Hall could resist an VII-degree earthquake at most, and the ridge of the second floor could not resist a V-degree earthquake due to unsupported conditions. The maximum deformation of the Limestone Hall during the earthquake occurred in the tabia layer below the second roof.

Description: Sensors, Vol. 18, Pages 1954: Fire Source Range Localization Based on the Dynamic Optimization Method for Large-Space Buildings Sensors doi: 10.3390/s18061954 Authors: Guoyong Wang Xiaoliang Feng Zhenzhong Zhang This paper is concerned to the fire localization problem for large-space buildings. Two kinds of circular fire source arrangement localization methods are proposed on the basis of the dynamic optimization technology. In the Range-Point-Range frame, a dynamic optimization localization is proposed to globally estimate the circle center of the circular arrangement to be determined based on all the point estimates of the fire source. In the Range-Range-Range frame, a dynamic optimization localization method is developed by solving a non-convex optimization problem. In this way, the circle center and the radius are obtained simultaneously. Additionally, the dynamic angle bisector method is evaluated. Finally, a simulation with three simulation scenes is provided to illustrate the effectiveness and availability of the proposed methods.

Description: Sensors, Vol. 18, Pages 1953: Polystyrene Oxygen Optodes Doped with Ir(III) and Pd(II) meso-Tetrakis(pentafluorophenyl)porphyrin Using an LED-Based High-Sensitivity Phosphorimeter Sensors doi: 10.3390/s18061953 Authors: Alexandre Filho Pedro Gewehr Joaquim Maia Douglas Jakubiak This paper presents a gaseous oxygen detection system based on time-resolved phosphorimetry (time-domain), which is used to investigate O2 optical transducers. The primary sensing elements were formed by incorporating iridium(III) and palladium(II) meso-tetrakis(pentafluorophenyl)porphyrin complexes (IrTFPP-CO-Cl and PdTFPP) in polystyrene (PS) solid matrices. Probe excitation was obtained using a violet light-emitting diode (LED) (low power), and the resulting phosphorescence was detected by a high-sensitivity compact photomultiplier tube. The detection system performance and the preparation of the transducers are presented along with their optical properties, phosphorescence lifetimes, calibration curves and photostability. The developed lifetime measuring system showed a good signal-to-noise ratio, and reliable results were obtained from the optodes, even when exposed to moderate levels of O2. The new IrTFPP-CO-Cl membranes exhibited room temperature phosphorescence and moderate sensitivity: <τ0>/<τ21%> ratio of ≈6. A typically high degree of dynamic phosphorescence quenching was observed for the traditional indicator PdTFPP: <τ0>/<τ21%> ratio of ≈36. Pulsed-source time-resolved phosphorimetry combined with a high-sensitivity photodetector can offer potential advantages such as: (i) major dynamic range, (ii) extended temporal resolution (Δτ/Δ[O2]) and (iii) high operational stability. IrTFPP-CO-Cl immobilized in polystyrene is a promising alternative for O2 detection, offering adequate photostability and potentially mid-range sensitivity over Pt(II) and Pd(II) metalloporphyrins.

Description: Sensors, Vol. 18, Pages 1945: Energy-Aware Computation Offloading of IoT Sensors in Cloudlet-Based Mobile Edge Computing Sensors doi: 10.3390/s18061945 Authors: Xiao Ma Chuang Lin Han Zhang Jianwei Liu Mobile edge computing is proposed as a promising computing paradigm to relieve the excessive burden of data centers and mobile networks, which is induced by the rapid growth of Internet of Things (IoT). This work introduces the cloud-assisted multi-cloudlet framework to provision scalable services in cloudlet-based mobile edge computing. Due to the constrained computation resources of cloudlets and limited communication resources of wireless access points (APs), IoT sensors with identical computation offloading decisions interact with each other. To optimize the processing delay and energy consumption of computation tasks, theoretic analysis of the computation offloading decision problem of IoT sensors is presented in this paper. In more detail, the computation offloading decision problem of IoT sensors is formulated as a computation offloading game and the condition of Nash equilibrium is derived by introducing the tool of a potential game. By exploiting the finite improvement property of the game, the Computation Offloading Decision (COD) algorithm is designed to provide decentralized computation offloading strategies for IoT sensors. Simulation results demonstrate that the COD algorithm can significantly reduce the system cost compared with the random-selection algorithm and the cloud-first algorithm. Furthermore, the COD algorithm can scale well with increasing IoT sensors.

Description: Sensors, Vol. 18, Pages 1958: Design of Inkjet-Printed RFID-Based Sensor on Paper: Single- and Dual-Tag Sensor Topologies Sensors doi: 10.3390/s18061958 Authors: Sangkil Kim Apostolos Georgiadis Manos M. Tentzeris The detailed design considerations for the printed RFID-based sensor system is presented in this paper. Starting from material selection and metallization method, this paper discusses types of RFID-based sensors (single- & dual-tag sensor topologies), design procedures, and performance evaluation methods for the wireless sensor system. The electrical properties of the paper substrates (cellulose-based and synthetic papers) and the silver nano-particle-based conductive film are thoroughly characterized for RF applications up to 8 GHz. The reported technology could potentially set the foundation for truly “green”, low-cost, scalable wireless topologies for autonomous Internet-of-Things (IoT), bio-monitoring, and “smart skin” applications.

Description: Algorithms, Vol. 11, Pages 86: Performance Optimal PI controller Tuning Based on Integrating Plus Time Delay Models Algorithms doi: 10.3390/a11060086 Authors: Christer Dalen David Di Ruscio A method for tuning PI controller parameters, a prescribed maximum time delay error or a relative time delay error is presented. The method is based on integrator plus time delay models. The integral time constant is linear in the relative time delay error, and the proportional constant is seen inversely proportional to the relative time delay error. The keystone in the method is the method product parameter, i.e., the product of the PI controller proportional constant, the integral time constant, and the integrator plus time delay model, velocity gain. The method product parameter is found to be constant for various PI controller tuning methods. Optimal suggestions are given for choosing the method product parameter, i.e., optimal such that the integrated absolute error or, more interestingly, the Pareto performance objective (i.e., integrated absolute error for combined step changes in output and input disturbances) is minimised. Variants of the presented tuning method are demonstrated for tuning PI controllers for motivated (possible) higher order process model examples, i.e., the presented method is combined with the model reduction step (process–reaction curve) in Ziegler–Nichols.

Description: Forests, Vol. 9, Pages 360: Effects of Oxidized Brown Coal Humic Acid Fertilizer on the Relative Height Growth Rate of Three Tree Species Forests doi: 10.3390/f9060360 Authors: Ganchudur Tsetsegmaa Khaulenbek Akhmadi Wonwoo Cho Sora Lee Romika Chandra Choi Eun Jeong Rogers Wainkwa Chia Hoduck Kang This study aimed to identify the effects of oxidized brown coal humic acid fertilizer on the relative growth rate of several tree species intended for reforestation. Field experiments were carried out during 2011–2014 at the Research and Experimental Center for Combating Desertification located at the Elsen Tasarkhai station in central Mongolia. The trees studied were Populus sibirica Tausch., Salix ledebouriana Trautv., and Acer tataricum L. The experiment was conducted with concentrations of 2000, 10,000, and 20,000 mg L−1 of humic acid fertilization treatment. Measurement of the relative height growth rate (RHGR) was undertaken for a period of four years. The results demonstrated significant differences between the humic fertilizer concentrations, which varied depending on the species. Compared to monthly RHGR over the study period, the treatment using fertilizers yielded significantly better tree growth. P. sibirica, when treated with 2000 mg L−1 and 10,000 mg L−1 humic acid fertilizers, had significant height growth rates. S. ledebouriana with 20,000 mg L−1 of humic acid fertilzers treatments showed the highest RHGR. In addition, when the humic acid treatments were compared to the control, results showed that oxidized brown coal humic acid fertilizers as an organic fertilizer can have a significant effect on the growth of A. tataricum. The results equally showed that the soil chemical properties EC, CO2, NO3, and K2O were significant among all the treatments compared to control. The effect on P2O5 significantly increased in all the treatments; however, there was no significant effect on pH and Mg among all treatments. Combining the results obtained with reforestation and sustainable land-management practices can help to improve soil organics in degraded sandy soil regions.

Description: Forests, Vol. 9, Pages 368: A Flexible Height–Diameter Model for Tree Height Imputation on Forest Inventory Sample Plots Using Repeated Measures from the Past Forests doi: 10.3390/f9060368 Authors: Christoph Gollob Tim Ritter Sonja Vospernik Clemens Wassermann Arne Nothdurft In this study, height–diameter relations were modeled using two different mixed model types for imputation of missing heights from longitudinal data. Model Type A had a hierarchical structure of sample plot-specific and measurement occasion-specific random effects. In Model Type B, a possible temporal variance was modeled by a sample plot-specific linear time trend. Furthermore, various calibration strategies of random effects were performed on past and current data, and a combination of both. The performance of the mixed models was compared on independent data using bias and root mean square error (RMSE). The results showed that Model Type A achieved the highest precision (lowest RMSE), if sample plot-specific random effects were predicted from old data and measurement occasion-specific ones were predicted from new data. In comparison, however, Model Type B had a higher RMSE, and lower bias. Model performance was almost unaffected from the usage of past or current data for the prediction of random effects. Results revealed that a certain calibration strategy should be simultaneously applied to all random effects from the same hierarchy level. Otherwise, predictions would become imprecise and a serious bias may result. In comparison with traditional uniform height curves, the novel mixed model approach performed slightly better.

Description: Forests, Vol. 9, Pages 364: Age-Effect on Intra-Annual δ13C-Variability within Scots Pine Tree-Rings from Central Siberia Forests doi: 10.3390/f9060364 Authors: Marina V. Fonti Eugene A. Vaganov Christian Wirth Alexander V. Shashkin Natalya V. Astrakhantseva Еrnst-Detlef Schulze Intra-annual tree-ring parameters are increasingly used in dendroecology thanks to their high temporal resolution. To better understand the nature of intra-ring proxy signals, we compared old and young trees according to the different ways in which they respond to climate. The study was carried out in central Siberia (Russia, 60°75′ N, 89°38′ E) in two even-aged Pinus sylvestris L. stands of different ages (20 and 220 years). Ring width, cell size, and intra-annual δ¹³С were measured for 4 to 27 tree rings, depending on age group (young vs. old) and tree-ring parameter. Wood formation was monitored to link tree-ring position to its time of formation. Results indicated more distinct intra-annual δ¹³С patterns at both the beginning and end of the ring of young trees compared to old ones. Older trees showed a stronger significant correlation between δ¹³С across the ring border, indicating a stronger carry-over effect of the previous year’s growing conditions on current year wood production. This suggests that tree age/size influences the magnitude of the transfer of mobile carbon reserves across the years.

Description: Remote Sensing, Vol. 10, Pages 975: A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) Model for Urban Earthquake Vulnerability Assessment Remote Sensing doi: 10.3390/rs10060975 Authors: Mohsen Alizadeh Ibrahim Ngah Mazlan Hashim Biswajeet Pradhan Amin Beiranvand Pour Vulnerability assessment is one of the prerequisites for risk analysis in disaster management. Vulnerability to earthquakes, especially in urban areas, has increased over the years due to the presence of complex urban structures and rapid development. Urban vulnerability is a result of human behavior which describes the extent of susceptibility or resilience of social, economic, and physical assets to natural disasters. The main aim of this paper is to develop a new hybrid framework using Analytic Network Process (ANP) and Artificial Neural Network (ANN) models for constructing a composite social, economic, environmental, and physical vulnerability index. This index was then applied to Tabriz City, which is a seismic-prone province in the northwestern part of Iran with recurring devastating earthquakes and consequent heavy casualties and damages. A Geographical Information Systems (GIS) analysis was used to identify and evaluate quantitative vulnerability indicators for generating an earthquake vulnerability map. The classified and standardized indicators were subsequently weighed and ranked using an ANP model to construct the training database. Then, standardized maps coupled with the training site maps were presented as input to a Multilayer Perceptron (MLP) neural network for producing an Earthquake Vulnerability Map (EVM). Finally, an EVM was produced for Tabriz City and the level of vulnerability in various zones was obtained. South and southeast regions of Tabriz City indicate low to moderate vulnerability, while some zones of the northeastern tract are under critical vulnerability conditions. Furthermore, the impact of the vulnerability of Tabriz City on population during an earthquake was included in this analysis for risk estimation. A comparison of the result produced by EVM and the Population Vulnerability (PV) of Tabriz City corroborated the validity of the results obtained by ANP-ANN. The findings of this paper are useful for decision-makers and government authorities to obtain a better knowledge of a city’s vulnerability dimensions, and to adopt preparedness strategies in the future for Tabriz City. The developed hybrid framework of ANP and ANN Models can easily be replicated and applied to other urban regions around the world for sustainability and environmental management.

Description: Forests, Vol. 9, Pages 373: Ungulate Browsing Limits Bird Diversity of the Central European Hardwood Floodplain Forests Forests doi: 10.3390/f9070373 Authors: Ivo Machar Petr Cermak Vilem Pechanec Temperate hardwood floodplain forests along lowland rivers are considered important forest biodiversity refugia in the European cultural landscape. The absence of apex predators combined with an artificial feeding of herbivore populations in winter seasons has caused an increase in browsing pressure on hardwood trees, nearly preventing their regeneration in some localities. There are still important knowledge gaps in understanding the relationships between deer abundance (and browsing pressure) and the abundance (and diversity) of forest bird species in unmanaged hardwood forests. We have studied the red deer and fallow deer browsing pressure in Central European unmanaged hardwood floodplain forests using a novel method based on monitoring browsing pressure along transects combined with bird census data in the Litovelské Pomoraví Protected Landscape Area (Czech Republic). The monitoring data suggested a very high browsing pressure on hardwood trees, causing a strong reduction of the shrub layer and young tree layer (30–210 cm above ground surface). The bird census data from the study area were collected using the territory mapping method. Our results revealed a bird diversity decline in all study plots and the bush nesters guild was found to be completely absent. As bird species from the bush nesters guild are generally common (usually dominant) in hardwood floodplain forest ecosystems with a rich shrub and young tree layer and low browsing pressure, we conclude that intense browsing by large herbivores represents a limiting factor to the bird diversity (especially bush nesters) of hardwood floodplain forests.

Description: Forests, Vol. 9, Pages 370: Mechanized Tree Planting in Sweden and Finland: Current State and Key Factors for Future Growth Forests doi: 10.3390/f9070370 Authors: Back Tomas Ersson Tiina Laine Timo Saksa In Fennoscandia, mechanized tree planting is time-efficient and produces high-quality regeneration. However, because of low cost-efficiency, the mechanization of Fennoscandian tree planting has been struggling. To determine key factors for its future growth, we compared the operational, planning, logistical, and organizational characteristics of mechanized planting in Sweden and Finland. Through interviews with planting machine contractors and client company foresters, we establish that mechanized tree planting in Sweden and Finland presently shares more similarities than differences. Some notable differences include typically longer planting seasons in Sweden, and a tendency towards two-shift operation and less frequent worksite pre-inspection by contractors in Finland. Because of similar challenges, mechanized planting in both countries can improve cost-efficiency through education of involved foresters, flexible information systems, efficient seedling logistics, and continued technical development of planting machines. By striving to have multiple client companies, contractors can reduce their operating radii and increase their machine utilization rates. Above all, our results provide international readers with unprecedented detailed and comprehensive figures and characteristics of Swedish and Finnish mechanized tree-planting activities. We conclude that cooperation between Sweden’s and Finland’s forest industries and research institutes could enhance the mechanization level of Fennoscandian tree planting.

Description: Forests, Vol. 9, Pages 371: Effects of Plot Positioning Errors on the Optimality of Harvest Prescriptions When Spatial Forest Planning Relies on ALS Data Forests doi: 10.3390/f9070371 Authors: Adrián Pascual Timo Pukkala Sergio de-Miguel Forest management planning is increasingly relying on airborne laser scanning (ALS) in forest inventory. The area-based method to interpret ALS data requires sample plots measured in the field. The aim of this study was to assess and trace the impacts of the positioning errors of field plots along the entire forest management planning process, from their effect on forest inventory results to the outcome of forest management planning. This research links plot positioning errors with the spatio-temporal allocation of forest treatments and calculates the inoptimality losses arising from plot positioning errors in ALS-based forest inventory. The study area was a forest management unit in Central Spain. Growing stock attributes were predicted for a grid of square-shaped cells. Alternative management schedules were simulated for the grid cells by using growth and yield models. Then, a spatial forest planning problem aiming at maximizing timber production with even-flow cuttings was formulated. Spatial objective variables were used to cluster management prescriptions into dynamic treatment units. We used simulated annealing to conduct spatial optimization. First, the true plot locations were used and then the whole process was repeated with normally distributed random errors with standard deviation equal to 2.5, 5 and 10 m, resulting in an average error of 1.47, 3.06 and 8.34 m, respectively. Increasing the level of positioning errors resulted in higher variability in the estimated growing stock attributes and in the achieved values of management goals. Sub-optimal prescriptions caused by the tested plot positioning errors caused up to 4.62% losses in timber production and up to 3.35% losses in utility. The losses increased with increasing plot positioning error.

Description: Future Internet, Vol. 10, Pages 56: Big Data Perspective and Challenges in Next Generation Networks Future Internet doi: 10.3390/fi10070056 Authors: Kashif Sultan Hazrat Ali Zhongshan Zhang With the development towards the next generation cellular networks, i.e., 5G, the focus has shifted towards meeting the higher data rate requirements, potential of micro cells and millimeter wave spectrum. The goals for next generation networks are very high data rates, low latency and handling of big data. The achievement of these goals definitely require newer architecture designs, upgraded technologies with possible backward support, better security algorithms and intelligent decision making capability. In this survey, we identify the opportunities which can be provided by 5G networks and discuss the underlying challenges towards implementation and realization of the goals of 5G. This survey also provides a discussion on the recent developments made towards standardization, the architectures which may be potential candidates for deployment and the energy concerns in 5G networks. Finally, the paper presents a big data perspective and the potential of machine learning for optimization and decision making in 5G networks.

Description: Remote Sensing, Vol. 10, Pages 976: Physical Retrieval of Land Surface Emissivity Spectra from Hyper-Spectral Infrared Observations and Validation with In Situ Measurements Remote Sensing doi: 10.3390/rs10060976 Authors: Guido Masiello Carmine Serio Sara Venafra Giuliano Liuzzi Laurent Poutier Frank-M. Göttsche A fully physical retrieval scheme for land surface emissivity spectra is presented, which applies to high spectral resolution infrared observations from satellite sensors. The surface emissivity spectrum is represented with a suitably truncated Principal Component Analysis (PCA) transform and PCA scores are simultaneously retrieved with surface temperature and atmospheric parameters. The retrieval methodology has been developed within the general framework of Optimal Estimation and, in this context, is the first physical scheme based on a PCA representation of the emissivity spectrum. The scheme has been applied to IASI (Infrared Atmospheric Sounder Interferometer) and the retrieved emissivities have been validated with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. It has been found that the retrieved emissivity spectra are independent of background information and in good agreement with in situ observations.

Description: Remote Sensing, Vol. 10, Pages 993: Landslide Identification and Monitoring along the Jinsha River Catchment (Wudongde Reservoir Area), China, Using the InSAR Method Remote Sensing doi: 10.3390/rs10070993 Authors: Chaoying Zhao Ya Kang Qin Zhang Zhong Lu Bin Li Landslide identification and monitoring are two significant research aspects for landslide analysis. In addition, landslide mode deduction is key for the prevention of landslide hazards. Surface deformation results with different scales can serve for different landslide analysis. L-band synthetic aperture radar (SAR) data calculated with Interferometric Point Target Analysis (IPTA) are first employed to detect potential landslides at the catchment-scale Wudongde reservoir area. Twenty-two active landslides are identified and mapped over more than 2500 square kilometers. Then, for one typical landslide, Jinpingzi landslide, its spatiotemporal deformation characteristics are analyzed with the small baseline subsets (SBAS) interferometric synthetic aperture radar (InSAR) technique. High-precision surface deformation results are obtained by comparing with in-situ georobot measurements. The spatial deformation pattern reveals the different stabilities among five different sections of Jinpingzi landslide. InSAR results for Section II of Jinpingzi landslide show that this active landslide is controlled by two boundaries and geological structure, and its different landslide deformation magnitudes at different sections on the surface companying with borehole deformation reveals the pull-type landslide mode. Correlation between time series landslide motion and monthly precipitation, soil moisture inverted from SAR intensity images and water level fluctuations suggests that heavy rainfall is the main trigger factor, and the maximum deformation of the landslide was highly consistent with the peak precipitation with a time lag of about 1 to 2 months, which gives us important guidelines to mitigate and prevent this kind of hazard.

Description: Sensors, Vol. 18, Pages 2013: Feature Selection Method Based on High-Resolution Remote Sensing Images and the Effect of Sensitive Features on Classification Accuracy Sensors doi: 10.3390/s18072013 Authors: Yi Zhou Rui Zhang Shixin Wang Futao Wang With the advent of high spatial resolution remote sensing imagery, numerous image features can be utilized. Applying a reasonable feature selection approach is critical to effectively reduce feature redundancy and improve the efficiency and accuracy of classification. This paper proposes a novel feature selection approach, in which ReliefF, genetic algorithm, and support vector machine (RFGASVM) are integrated to extract buildings. We adopt the ReliefF algorithm to preliminary filter high-dimensional features in the feature database. After eliminating the sorted features, the feature subset and the C and γ parameters of support vector machine (SVM) are encoded into the chromosome of the genetic algorithm. A fitness function is constructed considering the sample identification accuracy, the number of selected features, and the feature cost. The proposed method was applied to high-resolution images obtained from different sensors, GF-2, BJ-2, and unmanned aerial vehicles (UAV). The confusion matrix, precision, recall and F1-score were applied to assess the accuracy. The results showed that the proposed method achieved feature reduction, and the overall accuracy (OA) was more than 85%, with Kappa coefficient values of 0.80, 0.83 and 0.85, respectively. The precision of each image was more than 85%. The time efficiency of the proposed method was two-fold greater than SVM with all the features. The RFGASVM method has the advantages of large feature reduction and high extraction performance and can be applied in feature selection.

Description: Sensors, Vol. 18, Pages 2016: European In-Situ Snow Measurements: Practices and Purposes Sensors doi: 10.3390/s18072016 Authors: Roberta Pirazzini Leena Leppänen Ghislain Picard Juan Ignacio Lopez-Moreno Christoph Marty Giovanni Macelloni Anna Kontu Annakaisa von Lerber Cemal Melih Tanis Martin Schneebeli Patricia de Rosnay Ali Nadir Arslan In-situ snow measurements conducted by European institutions for operational, research, and energy business applications were surveyed in the framework of the European Cooperation in Science and Technology (COST) Action ES1404, called “A European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology, and numerical weather prediction”. Here we present the results of this survey, which was answered by 125 participants from 99 operational and research institutions, belonging to 38 European countries. The typologies of environments where the snow measurements are performed range from mountain to low elevated plains, including forests, bogs, tundra, urban areas, glaciers, lake ice, and sea ice. Of the respondents, 93% measure snow macrophysical parameters, such as snow presence, snow depth (HS), snow water equivalent (SWE), and snow density. These describe the bulk characteristics of the whole snowpack or of a snow layer, and they are the primary snow properties that are needed for most operational applications (such as hydrological monitoring, avalanche forecast, and weather forecast). In most cases, these measurements are done with manual methods, although for snow presence, HS, and SWE, automatized methods are also applied by some respondents. Parameters characterizing precipitating and suspended snow (such as the height of new snow, precipitation intensity, flux of drifting/blowing snow, and particle size distribution), some of which are crucial for the operational services, are measured by 74% of the respondents. Parameters characterizing the snow microstructural properties (such as the snow grain size and shape, and specific surface area), the snow electromagnetic properties (such as albedo, brightness temperature, and backscatter), and the snow composition (such as impurities and isotopes) are measured by 41%, 26%, and 13% of the respondents, respectively, mostly for research applications. The results of this survey are discussed from the perspective of the need of enhancing the efficiency and coverage of the in-situ observational network applying automatic and cheap measurement methods. Moreover, recommendations for the enhancement and harmonization of the observational network and measurement practices are provided.

Description: Sensors, Vol. 18, Pages 2011: Insect-Mimetic Imaging System Based on a Microlens Array Fabricated by a Patterned-Layer Integrating Soft Lithography Process Sensors doi: 10.3390/s18072011 Authors: Minwon Seo Jong-Mo Seo Dong-il “Dan” Cho Kyoin Koo In nature, arthropods have evolved to utilize a multiaperture vision system with a micro-optical structure which has advantages, such as compact size and wide-angle view, compared to that of a single-aperture vision system. In this paper, we present a multiaperture imaging system using a microlens array fabricated by a patterned-layer integrating soft lithography (PLISL) process which is based on a molding technique that can transfer three-dimensional structures and a gold screening layer simultaneously. The imaging system consists of a microlens array, a lens-adjusting jig, and a conventional (charge-coupled device) CCD image sensor. The microlens array has a light screening layer patterned among all the microlenses by the PLISL process to prevent light interference. The three-dimensionally printed jig adjusts the microlens array on the conventional CCD sensor for the focused image. The manufactured imaging system has a thin optic system and a large field-of-view of 100 degrees. The developed imaging system takes multiple images at once. To show its possible applications, multiple depth plane images were reconstructed based on the taken subimages with a single shot.

Description: Sensors, Vol. 18, Pages 2010: Recent Advances in Enhancement Strategies for Electrochemical ELISA-Based Immunoassays for Cancer Biomarker Detection Sensors doi: 10.3390/s18072010 Authors: Sunil K. Arya Pedro Estrela Electrochemical enzyme-linked immunosorbent assay (ELISA)-based immunoassays for cancer biomarker detection have recently attracted much interest owing to their higher sensitivity, amplification of signal, ease of handling, potential for automation and combination with miniaturized analytical systems, low cost and comparative simplicity for mass production. Their developments have considerably improved the sensitivity required for detection of low concentrations of cancer biomarkers present in bodily fluids in the early stages of the disease. Recently, various attempts have been made in their development and several methods and processes have been described for their development, amplification strategies and testing. The present review mainly focuses on the development of ELISA-based electrochemical immunosensors that may be utilized for cancer diagnosis, prognosis and therapy monitoring. Various fabrication methods and signal enhancement strategies utilized during the last few years for the development of ELISA-based electrochemical immunosensors are described.

Description: Sensors, Vol. 18, Pages 2006: Hydrogen Sulfide Gas Detection via Multivariate Optical Computing Sensors doi: 10.3390/s18072006 Authors: Bin Dai Christopher Michael Jones Megan Pearl Mickey Pelletier Mickey Myrick Hydrogen-sulfide gas is a toxic, colorless gas with a pungent odor that occurs naturally as a decomposition by-product. It is critical to monitor the concentration of hydrogen sulfide. Multivariate optical computing (MOC) is a method that can monitor analytes while minimizing responses to interferences. MOC is a technique by which an analogue calculation is performed entirely in the optical domain, which simplifies instrument design, prevents the drift of a calibration, and increases the strength and durability of spectroscopic instrumentation against physical perturbation when used for chemical detection and identification. This paper discusses the detection of hydrogen-sulfide gas in the ultraviolet (UV) spectral region in the presence of interfering gaseous species. A laboratory spectroscopic measurement system was set up to acquire the UV spectra of H2S and interference gas mixtures in high-pressure/high-temperature (HPHT) conditions. These spectra were used to guide the design and fabrication of a multivariate optical element (MOE), which has an expected measurement relative accuracy of 3.3% for H2S, with a concentration in the range of 0–150 nmol/mL. An MOC validation system with the MOE was used to test three samples of H2S and mercaptans mixtures under various pressures, and the relative accuracy of H2S measurement was determined to be 8.05%.

Description: Forests, Vol. 9, Pages 379: Structural Diversity in a Mixed Spruce-Fir-Beech Old-Growth Forest Remnant of the Western Carpathians Forests doi: 10.3390/f9070379 Authors: Zuzana Parobeková Ján Pittner Stanislav Kucbel Milan Saniga Michal Filípek Denisa Sedmáková Jaroslav Vencurik Peter Jaloviar Old-growth forests are a unique source of information for close-to-nature silviculture. In the National Nature Reserve Dobročský prales (Slovakia), a remnant of mixed old-growth forests of the Western Carpathians, we analyzed changes in tree species composition, stand structure, and creation and closure of canopy gaps. The results were based on data from forest inventories of an entire reserve conducted in 1978 and 2015, extended by detailed measurements in a research plot of 250 × 250 m. We observed the expansion of common beech (Fagus sylvatica L.) at the expense of conifers (Abies alba Mill., Picea abies L. Karst.) in all layers of the stand. Due to a lack of conifers in the category of saplings >130 cm and an abundance of coniferous deadwood, we hypothesize that this development will lead to the dominance of beech. All development stages revealed a reverse J-shaped diameter structure; however, they differed in the majority of basic stand characteristics (e.g., growing stock, basal area, tree density, deadwood volume). Most of the structural indices did not differ between development stages, confirming a relatively high degree of structural differentiation throughout the development cycle. The total gap area reached 18%, with the dominance of small gaps ≤100 m2. Nevertheless, only canopy gaps >100 m2 formed by the mortality of three or more trees were of higher importance for the extensive establishment of natural regeneration.

Description: Remote Sensing, Vol. 10, Pages 1000: Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing Remote Sensing doi: 10.3390/rs10071000 Authors: Xuedong Li Hongyan Zhang Guangbin Yang Yanling Ding Jianjun Zhao The increasing frequency of fires inhibits the estimation of carbon reserves in boreal forest ecosystems because fires release significant amounts of carbon into the atmosphere through combustion. However, less is known regarding the effects of vegetation succession processes on ecosystem C-flux that follow fires. This paper describes intra- and inter-annual vegetation restoration trajectories via MODIS time-series and Landsat data. The temporal and spatial characteristics of the natural succession were analyzed from 2000 to 2016. Finally, we regressed post-fire MODIS EVI, LST and LSWI values onto GPP and NPP values to identify the main limiting factors during post-fire carbon exchange. The results show immediate variations after the fire event, with EVI and LSWI decreasing by 0.21 and 0.31, respectively, and the LST increasing to 6.89 °C. After this initial variation, subsequent fire-induced variations were significantly smaller; instead, seasonality began governing the change characteristics. The greatest differences in EVI, LST and LSWI were observed in August and September compared to those in other months (0.29, 6.9 and 0.35, respectively), including July, which was the second month after the fire. We estimated the mean EVI recovery periods under different fire intensities (approximately 10, 12 and 16 years): the LST recovery time is one year earlier than that of the EVI. GPP and NPP decreased after the fire by 22–45 g C·m−2·month−1 (30–80%) and 0.13–0.35 kg C·m−2·year−1 (20–60%), respectively. Excluding the winter period, when no photosynthesis occurred, the correlation between the EVI and GPP was the strongest, and the correlation coefficient varied with the burn intensity. When changes in EVI, LST and LSWI after the fire in the boreal forest were more significant, the severity of the fire determined the magnitude of the changes, and the seasonality aggravated these changes. On the other hand, the seasonality is another important factor that affects vegetation restoration and land-surface energy fluxes in boreal forests. The strong correlations between EVI and GPP/NPP reveal that the C-flux can be simply and directly estimated on a per-pixel basis from EVI data, which can be used to accurately estimate land-surface energy fluxes during vegetation restoration and reduce uncertainties in the estimation of forests’ carbon reserves.

Description: Sensors, Vol. 18, Pages 2019: Rectification of Images Distorted by Microlens Array Errors in Plenoptic Cameras Sensors doi: 10.3390/s18072019 Authors: Suning Li Yanlong Zhu Chuanxin Zhang Yuan Yuan Heping Tan A plenoptic cameras is a sensor that records the 4D light-field distribution of target scenes. The surface errors of a microlens array (MLA) can cause the degradation and distortion of the raw image captured by a plenoptic camera, resulting in the confusion or loss of light-field information. To address this issue, we propose a method for the local rectification of distorted images using white light-field images. The method consists of microlens center calibration, geometric rectification, and grayscale rectification. The scope of its application to different sized errors and the rectification accuracy of three basic surface errors, including the overall accuracy and the local accuracy, are analyzed through simulation of imaging experiments. The rectified images have a significant improvement in quality, demonstrating the provision of precise light-field data for reconstruction of real objects.

Description: Remote Sensing, Vol. 10, Pages 1018: An Appraisal of the Potential of Landsat 8 in Estimating Chlorophyll-a, Ammonium Concentrations and Other Water Quality Indicators Remote Sensing doi: 10.3390/rs10071018 Authors: Vassiliki Markogianni Dionissios Kalivas George P. Petropoulos Elias Dimitriou In-situ monitoring of lake water quality in synergy with satellite remote sensing represents the latest scientific trend in many water quality monitoring programs worldwide. This study investigated the suitability of the Operational Land Imager (OLI) instrument onboard the Landsat 8 satellite platform in accurately estimating key water quality parameters such as chlorophyll-a and nutrient concentrations. As a case study the largest freshwater body of Greece (Trichonis Lake) was used. Two Landsat 8 images covering the study site were acquired on 30 October 2013 and 30 August 2014 respectively. Near concurrent in-situ observations from two water sampling campaigns were also acquired from 22 stations across the lake under study. In-situ measurements (nutrients and chlorophyll-a concentrations) were statistically correlated with various spectral band combinations derived from the Landsat imagery of year 2014. Subsequently, the most statistically promising predictive models were applied to the satellite image of 2013 and validation was conducted using in-situ data of 2013 as reference. Results showed a relatively variable statistical relationship between the in-situ and reflectances (R logchl-a: 0.58, R NH4+: 0.26, R chl-a: 0.44). Correlation coefficient (R) values reported of up to 0.7 for ammonium concentrations and also up to 0.5 and up to 0.4 for chl-a concentration and chl-a concentrations respectively. These results represent a higher accuracy of Landsat 8 in comparison to its predecessors in the Landsat satellites series, as evidenced in the literature. Our findings suggest that Landsat 8 has a promising capability in estimating water quality components in an oligotrophic freshwater body characterized by a complete absence of any quantitative, temporal and spatial variance, as is the case of Trichonis lake. Yet, even with the presence of a lot of ground information as was the case in our study, a quantitatively accurate estimation of water quality constituents in coastal/inland waters remains a great challenge. The launch of sophisticated spaceborne sensing systems, such as that of Landsat 8, can assist in improving our ability to estimate freshwater lake properties from space.

Description: Sensors, Vol. 18, Pages 2041: Visual Information Fusion through Bayesian Inference for Adaptive Probability-Oriented Feature Matching Sensors doi: 10.3390/s18072041 Authors: David Valiente Luis Payá Luis M. Jiménez Jose M. Sebastián Óscar Reinoso This work presents a visual information fusion approach for robust probability-oriented feature matching. It is sustained by omnidirectional imaging, and it is tested in a visual localization framework, in mobile robotics. General visual localization methods have been extensively studied and optimized in terms of performance. However, one of the main threats that jeopardizes the final estimation is the presence of outliers. In this paper, we present several contributions to deal with that issue. First, 3D information data, associated with SURF (Speeded-Up Robust Feature) points detected on the images, is inferred under the Bayesian framework established by Gaussian processes (GPs). Such information represents a probability distribution for the feature points’ existence, which is successively fused and updated throughout the robot’s poses. Secondly, this distribution can be properly sampled and projected onto the next 2D image frame in t+1, by means of a filter-motion prediction. This strategy permits obtaining relevant areas in the image reference system, from which probable matches could be detected, in terms of the accumulated probability of feature existence. This approach entails an adaptive probability-oriented matching search, which accounts for significant areas of the image, but it also considers unseen parts of the scene, thanks to an internal modulation of the probability distribution domain, computed in terms of the current uncertainty of the system. The main outcomes confirm a robust feature matching, which permits producing consistent localization estimates, aided by the odometer’s prior to estimate the scale factor. Publicly available datasets have been used to validate the design and operation of the approach. Moreover, the proposal has been compared, firstly with a standard feature matching and secondly with a localization method, based on an inverse depth parametrization. The results confirm the validity of the approach in terms of feature matching, localization accuracy, and time consumption.

Description: Sensors, Vol. 18, Pages 2037: An Exception Handling Approach for Privacy-Preserving Service Recommendation Failure in a Cloud Environment Sensors doi: 10.3390/s18072037 Authors: Lianyong Qi Shunmei Meng Xuyun Zhang Ruili Wang Xiaolong Xu Zhili Zhou Wanchun Dou Service recommendation has become an effective way to quickly extract insightful information from massive data. However, in the cloud environment, the quality of service (QoS) data used to make recommendation decisions are often monitored by distributed sensors and stored in different cloud platforms. In this situation, integrating these distributed data (monitored by remote sensors) across different platforms while guaranteeing user privacy is an important but challenging task, for the successful service recommendation in the cloud environment. Locality-Sensitive Hashing (LSH) is a promising way to achieve the abovementioned data integration and privacy-preservation goals, while current LSH-based recommendation studies seldom consider the possible recommendation failures and hence reduce the robustness of recommender systems significantly. In view of this challenge, we develop a new LSH variant, named converse LSH, and then suggest an exception handling approach for recommendation failures based on the converse LSH technique. Finally, we conduct several simulated experiments based on the well-known dataset, i.e., Movielens to prove the effectiveness and efficiency of our approach.

Description: Sensors, Vol. 18, Pages 2040: Rub-Impact Fault Diagnosis Using an Effective IMF Selection Technique in Ensemble Empirical Mode Decomposition and Hybrid Feature Models Sensors doi: 10.3390/s18072040 Authors: Alexander E. Prosvirin Manjurul Islam Jaeyoung Kim Jong-Myon Kim The complex nature of rubbing faults makes it difficult to use traditional signal analysis methods for feature extraction. Various time-frequency analysis approaches based on signal decomposition, such as empirical mode decomposition (EMD) and ensemble EMD (EEMD), have been widely utilized recently to analyze rub-impact faults. However, traditional EMD suffers from “mode-mixing”, and in both EMD and EEMD the relevance of the extracted components to rubbing processes must be determined. In this paper, we introduce a new informative intrinsic mode function (IMF) selection method for EEMD and a hybrid feature model for diagnosing rub-impact faults of various intensities. Our method uses a novel selection procedure that combines the degree-of-presence ratio of rub impact and a Kullback–Leibler divergence-based similarity measure into an IMF quality metric with adaptive threshold-based selection to pick the meaningful signal-dominant modes. Signals reconstructed using the selected IMFs contained explicit information about the rubbing faults and are used for hybrid feature extraction. Experimental results demonstrated that the proposed approach effectively defines meaningful IMFs for rubbing processes, and the presented hybrid feature model allows for the classification of rub-impact faults of various intensities with good accuracy.

Description: Sensors, Vol. 18, Pages 2042: Coumarin Probe for Selective Detection of Fluoride Ions in Aqueous Solution and Its Bioimaging in Live Cells Sensors doi: 10.3390/s18072042 Authors: Kantapat Chansaenpak Anyanee Kamkaew Oratai Weeranantanapan Khomson Suttisintong Gamolwan Tumcharern We have synthesized novel coumarin-based fluorescent chemosensors for detection of fluoride ions in aqueous solution. The detection mechanism relied on a fluoride-mediated desilylation triggering fluorogenic reaction and a strong interaction between fluoride and the silicon center. In this work, the hydroxyl-decorated coumarins containing oxysilyl moiety have been synthesized through the aldehyde-functionalized coumarins. The optical responses toward fluoride, as well as aqueous stability studies of both aldehyde and hydroxyl functionalized coumarins, have been investigated. Due to the highest fluorescence enhancement upon the addition of fluoride and good stability in aqueous solution, the hydroxyl-decorated coumarin connected with the bulky tert-butyldiphenyloxysilyl group (-OSitBuPh2) has been selected for further investigation of its potential as a fluoride sensor. This hydroxyl-decorated coumarin can selectively sense fluoride ions in aqueous media (contain 0.8% MeCN) with desirable response times (40 min). The limit of detection of this compound was determined as 0.043 ppm, satisfying the standard fluoride level (0.7 ppm) in drinking water recommended by U.S. Department of Health and Human Services. The application of this silyl-capped coumarin derivative for fluoride analysis in collected water samples displayed satisfactory analytical accuracy (<5% error). Finally, this compound was successfully employed in fluorescence bioimaging of fluoride ions in human liver cancer cells, indicating its excellent cell permeability, ability to retain inside the living cells, and good stability under physiological conditions.

Description: Forests, Vol. 9, Pages 383: Effect of Gap Position on the Heavy Metal Contents of Epiphytic Mosses and Lichens on the Fallen Logs and Standing Trees in an Alpine Forest Forests doi: 10.3390/f9070383 Authors: Zhuang Wang Fuzhong Wu Wanqin Yang Bo Tan Chenhui Chang Qin Wang Rui Cao Guoqing Tang To understand the role of the forest gaps and epiphytic mosses and lichens in the heavy metal cycles of forest ecosystems, the biomass, concentration, and storage of Cd, Pb, Cu, and Zn in epiphytic mosses and lichens on fallen logs and standing trees from the gap center to the closed canopy of an alpine forest ecosystem on the eastern Tibetan Plateau were investigated. Mosses were the dominant epiphytes on fallen logs and standing trees and contribute 82.1–95.1% of total epiphyte biomass in the alpine forest. A significantly higher biomass of epiphytic mosses and lichens was observed at the gap edge. The heavy metals concentration in mosses and lichens on fallen logs and standing trees varied widely with gap positions. Lower concentrations of Cd, Cu, and Pb were found in the mosses and lichens under the closed canopy, higher concentrations of Cd and Pb were detected in the mosses and lichens at the gap edge, and higher concentrations of Cu were found at the gap center. A significant difference in Zn concentration was observed between the mosses and lichens. No significant differences in Pb or Zn concentrations were observed in the mosses and lichens between the fallen log and standing tree substrates. Furthermore, the epiphytic mosses and lichens at the gap edge accumulated more Cd, Pb, and Cu, whereas the epiphytic lichens on the fallen logs and large shrubs at the gap center accumulated more Zn. In conclusion, gap regeneration accelerates the cycling of heavy metals in alpine forest ecosystems by promoting the growth of epiphytic mosses and lichens on fallen logs and standing trees at gap edges and increasing the concentration of heavy metals in these plants.

Description: Forests, Vol. 9, Pages 384: Investment Uncertainty Analysis in Eucalyptus Bole Biomass Production in Brazil Forests doi: 10.3390/f9070384 Authors: Danilo Simões Ailton Jesus Dinardi Magali Ribeiro da Silva Forestry investment projects for the biomass production of the eucalyptus bole can be characterized by uncertain environments, which result in economic risk to the forest producer, however that can be measured by applying probabilistic techniques. This was our motivation and justification for analyzing the economic-financial viability of different silvicultural practices for eucalyptus bole biomass production under conditions of uncertainties, running Monte Carlo method for risk management. The experiment was carried out in the state of São Paulo, Brazil, using 5 treatments with spacings of 3 × 2 m; 3 × 1 m; 1.5 × 2 m; 3 × 0.5 m; and 1.5 × 1.0 m, i.e., different spacings between planting lines and plants. These treatments were characterized as investment projects. To develop stochastic models, we relied on technical-economic deterministic variables. We evaluated the investment projects based on the cash flows under conditions of uncertainty, discounted at the attractiveness rate calculated by capital asset pricing model. With the results of these economic flows, the net present value, the modified internal rate of return and the profitability index values were estimated, commonly used in the analysis of investments in projects. The results showed that based on economic metrics, the three-year rotation cycle for forest stands for biomass production of the main bole of eucalyptus, with a spacing of three meters between rows and two meters between plants, had an 83% probability of economic success. The sensitivity analysis showed that the bole biomass of eucalyptus is the most important variable for determining the economic-financial feasibility of the investment project.

Description: Remote Sensing, Vol. 10, Pages 1022: Improving the Estimation of Daily Aerosol Optical Depth and Aerosol Radiative Effect Using an Optimized Artificial Neural Network Remote Sensing doi: 10.3390/rs10071022 Authors: Wenmin Qin Lunche Wang Aiwen Lin Ming Zhang Muhammad Bilal Aerosols can absorb and scatter surface solar radiation (SSR), which is called the aerosol radiative forcing effect (ARF). Great efforts have been made for the estimation of the aerosol optical depth (AOD), SSR and ARF using meteorological measurements and satellite observations. However, the accuracy, and spatial and temporal resolutions of these existing AOD, SSR and ARF models should be improved to meet the application requirements, due to the uncertainties and gaps of input parameters. In this study, an optimized back propagation (BP) artificial neural network (Genetic_BP) was developed for improving the estimation of the AOD values. The retrieved AOD values using the Genetic_BP model and meteorological measurements at China Meteorological Administration (CMA) stations were used to calculate SSR and bottom of the atmosphere (BOA) ARF (ARFB) using Yang’s Hybrid model (YHM). The result show that the Genetic_BP could be used for estimating AOD values with high accuracy (R = 0.866 for CASNET (China Aerosol Remote Sensing Network) stations and R = 0.865 for AERONET (Aerosol Robotic Network) stations). The estimated SSR also showed a good agreement with SSR measurements at 96 CMA radiation stations, with RMSE, MAE, R and R2 of 29.27%, 23.77%, 0.948, and 0.899, respectively. The estimated ARFB values are also highly correlated with the AERONET ARFB ones with RMSE, MAE, R and R2 of −35.47%, −25.33%, 0.843, and 0.711, respectively. Finally, the spatial and temporal variations of AOD, SSR, and ARFB values over Mainland China were investigated. Both AOD and SSR values are generally higher in summer than in other seasons. The ARFB are generally stronger in spring and summer than in other seasons. The ranges for the monthly mean AOD, SSR and ARFB values over Mainland China are 0.183–0.333, 10.218–24.196 MJ m−2day−1 and −2.986 to −1.244 MJ m−2day−1, respectively. The Qinghai-Tibetan Plateau has always been an area with the highest SSR, the lowest AOD and the weakest ARFB. In contrast, the Sichuan Basin has always been an area with low SSR, high AOD, and strong ARFB. The newly proposed AOD model may be of vital importance for improving the accuracy and computational efficiency of AOD, SSR and ARFB estimations for solar energy applications, ecological modeling, and energy policy.

Description: Sensors, Vol. 18, Pages 2059: Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images Sensors doi: 10.3390/s18072059 Authors: Xavier Soria Angel Sappa Riad Hammoud Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm). This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different scenarios and using different similarity metrics. Both of them improve the state of the art approaches.

Description: Sensors, Vol. 18, Pages 2048: Detection of Cattle Using Drones and Convolutional Neural Networks Sensors doi: 10.3390/s18072048 Authors: Alberto Rivas Pablo Chamoso Alfonso González-Briones Juan Manuel Corchado Multirotor drones have been one of the most important technological advances of the last decade. Their mechanics are simple compared to other types of drones and their possibilities in flight are greater. For example, they can take-off vertically. Their capabilities have therefore brought progress to many professional activities. Moreover, advances in computing and telecommunications have also broadened the range of activities in which drones may be used. Currently, artificial intelligence and information analysis are the main areas of research in the field of computing. The case study presented in this article employed artificial intelligence techniques in the analysis of information captured by drones. More specifically, the camera installed in the drone took images which were later analyzed using Convolutional Neural Networks (CNNs) to identify the objects captured in the images. In this research, a CNN was trained to detect cattle, however the same training process could be followed to develop a CNN for the detection of any other object. This article describes the design of the platform for real-time analysis of information and its performance in the detection of cattle.

Description: Algorithms, Vol. 11, Pages 94: Tensor Completion Based on Triple Tubal Nuclear Norm Algorithms doi: 10.3390/a11070094 Authors: Dongxu Wei Andong Wang Xiaoqin Feng Boyu Wang Bo Wang Many tasks in computer vision suffer from missing values in tensor data, i.e., multi-way data array. The recently proposed tensor tubal nuclear norm (TNN) has shown superiority in imputing missing values in 3D visual data, like color images and videos. However, by interpreting in a circulant way, TNN only exploits tube (often carrying temporal/channel information) redundancy in a circulant way while preserving the row and column (often carrying spatial information) relationship. In this paper, a new tensor norm named the triple tubal nuclear norm (TriTNN) is proposed to simultaneously exploit tube, row and column redundancy in a circulant way by using a weighted sum of three TNNs. Thus, more spatial-temporal information can be mined. Further, a TriTNN-based tensor completion model with an ADMM solver is developed. Experiments on color images, videos and LiDAR datasets show the superiority of the proposed TriTNN against state-of-the-art nuclear norm-based tensor norms.

Description: Algorithms, Vol. 11, Pages 93: Layered Graphs: Applications and Algorithms Algorithms doi: 10.3390/a11070093 Authors: Bhadrachalam Chitturi Srijith Balachander Sandeep Satheesh Krithic Puthiyoppil The computation of distances between strings has applications in molecular biology, music theory and pattern recognition. One such measure, called short reversal distance, has applications in evolutionary distance computation. It has been shown that this problem can be reduced to the computation of a maximum independent set on the corresponding graph that is constructed from the given input strings. The constructed graphs primarily fall into a class that we call layered graphs. In a layered graph, each layer refers to a subgraph containing, at most, some k vertices. The inter-layer edges are restricted to the vertices in adjacent layers. We study the MIS, MVC, MDS, MCV and MCD problems on layered graphs where MIS computes the maximum independent set; MVC computes the minimum vertex cover; MDS computes the minimum dominating set; MCV computes the minimum connected vertex cover; and MCD computes the minimum connected dominating set. The MIS, MVC and MDS are computed in polynomial time if k=Θ(log|V|). MCV and MCD are computed polynomial time if k=O((log|V|)α), where α<1. If k=Θ((log|V|)1+ϵ), for ϵ>0, then MIS, MVC and MDS are computed in quasi-polynomial time. If k=Θ(log|V|), then MCV and MCD are computed in quasi-polynomial time.

Description: Sensors, Vol. 18, Pages 2082: Recent Surface Water Extent of Lake Chad from Multispectral Sensors and GRACE Sensors doi: 10.3390/s18072082 Authors: Willibroad Gabila Buma Sang-Il Lee Jae Young Seo Consistent observations of lakes and reservoirs that comprise the majority of surface freshwater globally are limited, especially in Africa where water bodies are exposed to unfavorable climatic conditions and human interactions. Publicly available satellite imagery has increased the ability to monitor water bodies of various sizes without much financial hassle. Landsat 7 and 8 images were used in this study to estimate area changes around Lake Chad. The Automated Water Extraction Index (AWEI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Vegetation Index (NDVI) were compared for the remote sensing retrieval process of surface water. Otsu threshold method was used to separate water from non-water features. With an overall accuracy of ~96% and an inter-rater agreement (kappa coefficient) of 0.91, the MNDWI was a better indicator for mapping recent area changes in Lake Chad and was used to estimate the lake’s area changes from 2003–2016. Extracted monthly areas showed an increasing trend and ranged between ~1242 km2 and 2231 km2 indicating high variability within the 13-year period, 2003–2016. In addition, we combined Landsat measurements with Total Water Storage Anomaly (TWSA) data from the Gravity Recovery and Climate Experiment (GRACE) satellites. This combination is well matched with our estimated surface area trends. This work not only demonstrates the importance of remote sensing in sparsely gauged developing countries, it also suggests the use of freely available high-quality imagery data to address existing lake crisis.

Description: Sensors, Vol. 18, Pages 2073: Application and Extension of Vertical Intensity Lower-Mode in Methods for Target Depth-Resolution with a Single-Vector Sensor Sensors doi: 10.3390/s18072073 Authors: Anbang Zhao Xuejie Bi Juan Hui Caigao Zeng Lin Ma In this paper, based on the reactive component of the vertical intensity, the method for target depth resolution has been improved. In the previous existing research results, using the reactive component of vertical intensity, the research objects for target depth resolution in shallow water, can only be the targets whose frequencies can only excite the first two normal modes, and the depth of targets whose frequencies excite more than two normal modes cannot be correctly identified. The basic idea of the improved method is to classify targets on the foundation of the lower-mode correlation quantity of the vertical intensity. Based on the improved method, we can realize depth resolution of the targets whose frequency can excite the first three normal modes so as to effectively expand the working band useful for target depth resolution. Finally, we can realize the three-dimensional target depth resolution so as to distinguish the aerial, surface and underwater targets. The feasibility of the algorithm is verified by simulation and experimental data processing.

Description: Sensors, Vol. 18, Pages 2071: UAV Visual and Laser Sensors Fusion for Detection and Positioning in Industrial Applications Sensors doi: 10.3390/s18072071 Authors: Edmundo Guerra Rodrigo Munguía Antoni Grau This work presents a solution to localize Unmanned Autonomous Vehicles with respect to pipes and other cylindrical elements found in inspection and maintenance tasks both in industrial and civilian infrastructures. The proposed system exploits the different features of vision and laser based sensors, combining them to obtain accurate positioning of the robot with respect to the cylindrical structures. A probabilistic (RANSAC-based) procedure is used to segment possible cylinders found in the laser scans, and this is used as a seed to accurately determine the robot position through a computer vision system. The priors obtained from the laser scan registration help to solve the problem of determining the apparent contour of the cylinders. In turn this apparent contour is used in a degenerate quadratic conic estimation, enabling to visually estimate the pose of the cylinder.

Description: Sensors, Vol. 18, Pages 2067: Design and Evaluation of FBG-Based Tension Sensor in Laparoscope Surgical Robots Sensors doi: 10.3390/s18072067 Authors: Renfeng Xue Bingyin Ren Jiaqing Huang Zhiyuan Yan Zhijiang Du Due to the narrow space and a harsh chemical environment in the sterilization processes for the end-effector of surgical robots, it is difficult to install and integrate suitable sensors for the purpose of effective and precise force control. This paper presents an innovative tension sensor for estimation of grasping force in our laparoscope surgical robot. The proposed sensor measures the tension of cable using fiber gratings (FBGs) which are pasted in the grooves on the inclined cantilevers of the sensor. By exploiting the stain measurement characteristics of FBGs, the small deformation of the inclined cantilevers caused by the cable tension can be measured. The working principle and the sensor model are analyzed. Based on the sensor model, the dimensions of the sensor are designed and optimized. A dedicated experimental setup is established to calibrate and test the sensor. The results of experiments for estimation the grasping force validate the sensor.

Description: Sensors, Vol. 18, Pages 2061: Probe of Alcohol Structures in the Gas and Liquid States Using C–H Stretching Raman Spectroscopy Sensors doi: 10.3390/s18072061 Authors: Yuanqin Yu Wei Fan Yuxi Wang Xiaoguo Zhou Jin Sun Shilin Liu Vibrational spectroscopy is a powerful tool for probing molecular structures and dynamics since it offers a unique fingerprint that allows molecular identification. One of important aspects of applying vibrational spectroscopy is to develop the probes that can characterize the related properties of molecules such as the conformation and intermolecular interaction. Many examples of vibrational probes have appeared in the literature, including the azide group (–N3), amide group (–CONH2), nitrile groups (–CN), hydroxyl group (–OH), –CH group and so on. Among these probes, the –CH group is an excellent one since it is ubiquitous in organic and biological molecules and the C–H stretching vibrational spectrum is extraordinarily sensitive to the local molecular environment. However, one challenge encountered in the application of C–H probes arises from the difficulty in the accurate assignment due to spectral congestion in the C–H stretching region. In this paper, recent advances in the complete assignment of C–H stretching spectra of aliphatic alcohols and the utility of C–H vibration as a probe of the conformation and weak intermolecular interaction are outlined. These results fully demonstrated the potential of the –CH chemical group as a molecular probe.

Description: Sensors, Vol. 18, Pages 2418: Design and Analysis of Non-Binary LDPC-CPM System for Hybrid Check Matrix Construction Algorithm of WSN Sensors doi: 10.3390/s18082418 Authors: Jiahui Meng Danfeng Zhao Liang Zhang In order to enhance the reliability and anti-interference performance of wireless sensor network (WSN) data transmission, this paper designs the low power scheme of the WSN from the angle of error correction coding and proposes the hybrid check matrix construction (HC) algorithm based on iterative coding algorithms with linear coding complexity. The algorithm first improves the traditional iterative coding algorithm, making it suitable for non-binary low-density parity check (LDPC) codes. Then, the algorithm applies the backward iteration method to change the coding scheme and uses the check matrix construction method so that the progressive edge growth (PEG) algorithm has a lower triangular structure, which is used as a base matrix. An improved quasi-cyclic LDPC (QC-LDPC) algorithm, with a lower triangular structure, is used to generate a cyclic shift matrix and a finite domain coefficient matrix. Simultaneously, the short loop is eliminated and the optimal check matrix is selected for use in the channel coding process. The non-binary LDPC-CPM system is modeled and simulated. The simulation results show that the non-binary LDPC code constructed by the HC algorithm not only has linear coding and storage complexity but also has strong error correction capability. The design of non-binary LDPC-CPM system parameters can enhance the reliability, anti-jamming capability and reduce the complexity and reduce the complexity of the WSN.

Description: Sensors, Vol. 18, Pages 2413: UAV Based Relay for Wireless Sensor Networks in 5G Systems Sensors doi: 10.3390/s18082413 Authors: Shu Fu Lian Zhao Zhou Su Xin Jian Relay is one of the most significant issues in smart industrial wireless sensor networks (WSN) due to the low transmitting power of sensors. By relay, the signals of sensors can be concentrated at the relay and further transmitted to the base station for decreasing energy consumption in the system. In the past decades, the relay in WSN is generally one super sensor with large transmitting power. However, the placement of the super sensor is static, which leads to the instability of performance in WSN under the time-varying wireless environment. Fortunately, unmanned aerial vehicles (UAV) can provide an effective leverage to improve the environment-adaptation in WSN compared to the static relay in WSN. In this paper, we employ UAV as the relay in WSN, which can move in three-dimensional space to possess a better position to minimize the system power consumption. We use a simple case study to demonstrate the effectiveness of UAV in WSN. Extended simulations are also given to verify the preferable performance of the UAV based relay in WSN.

Description: Remote Sensing, Vol. 10, Pages 1205: Examining the Accuracy of GlobCurrent Upper Ocean Velocity Data Products on the Northwestern Atlantic Shelf Remote Sensing doi: 10.3390/rs10081205 Authors: Hui Feng Douglas Vandemark Julia Levin John Wilkin This study provides a regional coastal ocean assessment of global upper ocean current data developed by the GlobCurrent (GC) project. These gridded data synthesize multiple satellite altimeter and wind model inputs to estimate both Geostrophic and Ekman-layer velocities. While the GC product was mostly devised and intended for open ocean studies, the present objective is to assess whether its data quality nearer the coast is suitable for other applications. The key ground truth sources are long-term mean and time series observations on the Northwestern Atlantic (NWA) shelf derived from Acoustic Doppler Current Profilers (ADCP) and high frequency (HF) radar networks in both the Mid-Atlantic Bight (MAB) and the Gulf of Maine (GoM). Results indicate that mean geostrophic currents across the MAB and the offshore GoM agree to roughly 10% in speed and 10 degree in direction with the in situ depth-averaged currents, with correlation levels of 0.5–0.8 at seasonal and longer time scales. Interior GoM comparisons at 5 coastal buoys show much less agreement. One likely source of GoM error is shown to be the GC mean dynamic topography near the coast. Comparison to near-surface MAB HF radar current measurements on the MAB shelf shows significant GC data improvement when including the surface Ekman term. Overall, the study results imply that application of GlobCurrent data may prove useful in coastal seas with broad continental shelves such as the MAB or Scotian shelf, but that large inaccuracies inside the GoM diminish its utility there.

Description: Sensors, Vol. 18, Pages 2474: Detection of Talking in Respiratory Signals: A Feasibility Study Using Machine Learning and Wearable Textile-Based Sensors Sensors doi: 10.3390/s18082474 Authors: Andreas Ejupi Carlo Menon Social isolation and loneliness are major health concerns in young and older people. Traditional approaches to monitor the level of social interaction rely on self-reports. The goal of this study was to investigate if wearable textile-based sensors can be used to accurately detect if the user is talking as a future indicator of social interaction. In a laboratory study, fifteen healthy young participants were asked to talk while performing daily activities such as sitting, standing and walking. It is known that the breathing pattern differs significantly between normal and speech breathing (i.e., talking). We integrated resistive stretch sensors into wearable elastic bands, with a future integration into clothing in mind, to record the expansion and contraction of the chest and abdomen while breathing. We developed an algorithm incorporating machine learning and evaluated its performance in distinguishing between periods of talking and non-talking. In an intra-subject analysis, our algorithm detected talking with an average accuracy of 85%. The highest accuracy of 88% was achieved during sitting and the lowest accuracy of 80.6% during walking. Complete segments of talking were correctly identified with 96% accuracy. From the evaluated machine learning algorithms, the random forest classifier performed best on our dataset. We demonstrate that wearable textile-based sensors in combination with machine learning can be used to detect when the user is talking. In the future, this approach may be used as an indicator of social interaction to prevent social isolation and loneliness.

Description: Sensors, Vol. 18, Pages 2476: Visual Localizer: Outdoor Localization Based on ConvNet Descriptor and Global Optimization for Visually Impaired Pedestrians Sensors doi: 10.3390/s18082476 Authors: Shufei Lin Ruiqi Cheng Kaiwei Wang Kailun Yang Localization systems play an important role in assisted navigation. Precise localization renders visually impaired people aware of ambient environments and prevents them from coming across potential hazards. The majority of visual localization algorithms, which are applied to autonomous vehicles, are not adaptable completely to the scenarios of assisted navigation. Those vehicle-based approaches are vulnerable to viewpoint, appearance and route changes (between database and query images) caused by wearable cameras of assistive devices. Facing these practical challenges, we propose Visual Localizer, which is composed of ConvNet descriptor and global optimization, to achieve robust visual localization for assisted navigation. The performance of five prevailing ConvNets are comprehensively compared, and GoogLeNet is found to feature the best performance on environmental invariance. By concatenating two compressed convolutional layers of GoogLeNet, we use only thousands of bytes to represent image efficiently. To further improve the robustness of image matching, we utilize the network flow model as a global optimization of image matching. The extensive experiments using images captured by visually impaired volunteers illustrate that the system performs well in the context of assisted navigation.

Description: Sensors, Vol. 18, Pages 2473: A New Azimuth-Dependent Elevation Weight (ADEW) Model for Real-Time Deformation Monitoring in Complex Environment by Multi-GNSS Sensors doi: 10.3390/s18082473 Authors: Junqiang Han Guanwen Huang Qin Zhang Rui Tu Yuan Du Xiaolei Wang Global navigation satellite systems (GNSS) have provided an excellent way to monitor micro-deformation in real-time. However, at local sites where landslides frequently occur, the environment can include complex surroundings with mountains, dense vegetation, and human settlements, which can severely degrade the accuracy of positioning with the GNSS technique. In this study, we propose an azimuth-dependent elevation weight (ADEW) model using an azimuth-dependent elevation mask (ADEM) to reduce the effects of multipath errors and improve the accuracy of real-time deformation monitoring in such environments. We developed an adaptive fixed-elevation mask to serve as the outlier of low precision observations at lower elevations for the ADEM, and then, we applied the weighted phase observations into the mitigation process for the effects of multipath errors. The real numerical results indicate that the ADEM model performs better than the conventional model, and the average improvements were 18.91% and 34.93% in the horizontal and vertical direction, respectively. The ADEW model further improved upon the ADEM model results by an additional 21.9% and 29.8% in the horizontal and vertical direction, respectively. Therefore, we propose that the ADEW model can significantly mitigate the effects of multipath errors and improve the accuracy of micro-deformation monitoring via GNSS receivers.

Description: Algorithms, Vol. 11, Pages 116: A Robust and Energy-Efficient Weighted Clustering Algorithm on Mobile Ad Hoc Sensor Networks † Algorithms doi: 10.3390/a11080116 Authors: Huamei Qi Fengqi Liu Tailong Xiao Jiang Su In an Ad hoc sensor network, nodes have characteristics of limited battery energy, self-organization and low mobility. Due to the mobility and heterogeneity of the energy consumption in the hierarchical network, the cluster head and topology are changed dynamically. Therefore, topology control and energy consumption are growing to be critical in enhancing the stability and prolonging the lifetime of the network. In order to improve the survivability of Ad hoc network effectively, this paper proposes a new algorithm named the robust, energy-efficient weighted clustering algorithm (RE2WCA). For the homogeneous of the energy consumption; the proposed clustering algorithm takes the residual energy and group mobility into consideration by restricting minimum iteration times. In addition, a distributed fault detection algorithm and cluster head backup mechanism are presented to achieve the periodic and real-time topology maintenance to enhance the robustness of the network. The network is analyzed and the simulations are performed to compare the performance of this new clustering algorithm with the similar algorithms in terms of cluster characteristics, lifetime, throughput and energy consumption of the network. The result shows that the proposed algorithm provides better performance than others.

Description: Sensors, Vol. 18, Pages 2498: Analysis of Key Issues on GNSS-R Soil Moisture Retrieval Based on Different Antenna Patterns Sensors doi: 10.3390/s18082498 Authors: Fei Li Xuefeng Peng Xiuwan Chen Maolin Liu Liwen Xu GNSS-R (Global Navigation Satellite System-Reflectometry) has been demonstrated to be a new and powerful tool to sense soil moisture in recent years. Multi-antenna pattern and single-antenna pattern have been proposed regarding how to receive and process reflected signals. Great efforts have been made concerning ground-based and air-borne observations. Meanwhile, a number of satellite-based missions have also been implemented. For the in-depth study of soil moisture remote sensing by the technique of GNSS-R, regardless of the extraction methods of the reflected signals or the types of the observation platform, three key issues have to be determined: The specular reflection point, the spatial resolution and the detection depth in the soil. However, in current literatures, there are no comprehensive explanations of the above three key issues. This paper conducts theoretical analysis and formula derivation, aiming to systematically and quantitatively determine the extent of soil moisture being detected in three dimensions from the above-mentioned aspects. To further explain how the three factors behave in the specific application, the results of two application scenarios are shown: (1) a ground-based GPS measurement in Marshall, Colorado, US from the Plate Boundary Observatory, corresponding to single-antenna pattern. The relative location of the specular reflection points, the average area of the First Fresnel Ellipse Clusters and the sensing depth of the time-series soil moisture are analyzed, and (2) an aviation experiment conducted in Zhengzhou to retrieve soil moisture content, corresponding to the multi-antenna pattern. The spatial distribution of soil moisture estimation with a certain resolution based on the flight tracks and the relevant sensing depth are manifested. For remote sensing using GNSS reflected signals, BeiDou is different from GPS mainly in the carrier frequency. Therefore, the results of this study can provide references for China’s future development of the BeiDou-R technique.

Description: Sensors, Vol. 18, Pages 2494: A Resonant Pressure Microsensor Based on Double-Ended Tuning Fork and Electrostatic Excitation/Piezoresistive Detection Sensors doi: 10.3390/s18082494 Authors: Xiaoqing Shi Yulan Lu Bo Xie Yadong Li Junbo Wang Deyong Chen Jian Chen This paper presents a resonant pressure microsensor relying on electrostatic excitation and piezoresistive detection where two double-ended tuning forks were used as resonators, enabling differential outputs. Pressure under measurement caused the deformation of the pressure sensitive membrane, leading to stress buildup of the resonator under electrostatic excitation with a corresponding shift of the resonant frequency detected piezoresistively. The proposed microsensor was fabricated by simplified SOI-MEMS technologies and characterized by both open-loop and closed-loop circuits, producing a quality factor higher than 10,000, a sensitivity of 79.44 Hz/kPa and an accuracy rate of over 0.01% F.S. In comparison to the previously reported resonant piezoresistive sensors, the proposed device used single-crystal silicon as piezoresistors, which was featured with low DC biased voltages, simple sensing structures and fabrication steps. In addition, the two double-ended tuning forks were used as resonators, producing high quality factors and differential outputs, which further improved the sensor performances.

Description: Sensors, Vol. 18, Pages 2508: A Calibration Method for Acoustic Space Charge Measurements Using Multilayer Samples Sensors doi: 10.3390/s18082508 Authors: Guillermo Mier-Escurra Armando Rodrigo-Mor Peter Vaessen Nowadays, the use of several methods for the measurement of space charges in dielectrics has become more relevant. The availability of different space charge measurement methods brings the necessity of calibration and characterization of measurement equipment to reach standardized measurements. In this paper, a method is presented to apply known charges at built samples which can be used for acoustic and thermal methods to compare measured values with calibration purposes. Experimental tests for validation purposes were performed in flat samples by comparison of results between a single layer and multilayer samples. Moreover, this method can be used to measure the resolution and accuracy of space charge measurement systems.

Description: Sensors, Vol. 18, Pages 2499: Analytical Model for the Duty Cycle in Solar-Based EH-WSN for Environmental Monitoring Sensors doi: 10.3390/s18082499 Authors: Sebastià Galmés Soledad Escolar A technology drift is currently taking place from traditional battery-powered sensor networks, which exhibit limited lifetime, to the new Energy-Harvesting Wireless Sensor Networks (EH-WSN), which open the way towards self-sustained operation. However, this emergent modality also brings up new challenges, especially due to the time-varying nature and unpredictability of ambient energy sources. Most proposals for implementing EH-WSN rely on heuristic approaches to redesign the duty-cycling mechanism at the MAC layer, with the ultimate goal of optimizing network performance while preserving self-sustained and continuous operation. In contrast to the common system-wide reduced duty cycle of battery-powered sensor networks, the duty cycle in EH-WSN is much larger and adapted to the energy harvesting rate and traffic load of each node in the network. In this paper, we focus on solar-based EH-WSN devoted to environmental monitoring. In contrast to current works, we follow an analytical approach, which results into closed-form expressions for the duty cycle and initial energy storage that guarantee self-sustained operation to any node in a solar-based EH-WSN. To center the analysis, we consider TinyOS sensor nodes, though we postulate that the essential components of the obtained formulation will contribute to further develop duty cycle adaptation schemes for TinyOS and other software platforms.

Description: Sensors, Vol. 18, Pages 2495: On-The-Fly Ambiguity Resolution Based on Double-Differential Square Observation Sensors doi: 10.3390/s18082495 Authors: Tengfei Wang Zheng Yao Mingquan Lu Global navigation systems provide worldwide positioning, navigation and navigation services. However, in some challenging environments, especially when the satellite is blocked, the performance of GNSS is seriously degraded or even unavailable. Ground based positioning systems, including pseudolites and Locata, have shown their potentials in centimeter-level positioning accuracy using carrier phase measurements. Ambiguity resolution (AR) is a key issue for such high precision positioning. Current methods for the ground based systems need code measurements for initialization and/or approximating linearization. If the code measurements show relatively large errors, current methods might suffer from convergence difficulties in ground based positioning. In this paper, the concept of double-differential square observation (DDS) is proposed, and an on-the-fly ambiguity resolution (OTF-AR) method is developed for ground based navigation systems using two-way measurements. An important advantage of the proposed method is that only the carrier phase measurements are used, and code measurements are not necessary. The clock error is canceled out by two-way measurements between the rover and the base stations. The squared observations are then differenced between different rover positions and different base stations, and a linear model is then obtained. The floating integer values are easy to compute via this model, and there is no need to do approximate linearization. In this procedure, the rover’s approximate coordinates are also directly obtained from the carrier measurements, therefore code measurements are not necessary. As an OTF-AR method, the proposed method relies on geometric changes caused by the rover’s motion. As shown by the simulations, the geometric diversity of observations is the key factor for the AR success rate. Moreover, the fine floating solutions given by our method also have a fairly good accuracy, which is valuable when fixed solutions are not reliable. A real experiment is conducted to validate the proposed method. The results show that the fixed solution could achieve centimeter-level accuracy.

Description: Sensors, Vol. 18, Pages 2492: Real-Time Distributed Architecture for Remote Acoustic Elderly Monitoring in Residential-Scale Ambient Assisted Living Scenarios Sensors doi: 10.3390/s18082492 Authors: Joan Navarro Ester Vidaña-Vila Rosa Ma Alsina-Pagès Marcos Hervás Ambient Assisted Living (AAL) has become a powerful alternative to improving the life quality of elderly and partially dependent people in their own living environments. In this regard, tele-care and remote surveillance AAL applications have emerged as a hot research topic in this domain. These services aim to infer the patients’ status by means of centralized architectures that collect data from a set of sensors deployed in their living environment. However, when the size of the scenario and number of patients to be monitored increase (e.g., residential areas, retirement homes), these systems typically struggle at processing all associated data and providing a reasonable output in real time. The purpose of this paper is to present a fog-inspired distributed architecture to collect, analyze and identify up to nine acoustic events that represent abnormal behavior or dangerous health conditions in large-scale scenarios. Specifically, the proposed platform collects data from a set of wireless acoustic sensors and runs an automatic two-stage audio event classification process to decide whether or not to trigger an alarm. Conducted experiments over a labeled dataset of 7116 s based on the priorities of the Fundació Ave Maria health experts have obtained an overall accuracy of 94.6%.

Description: Sensors, Vol. 18, Pages 2496: Triple Flat-Type Inductive-Based Oil Palm Fruit Maturity Sensor Sensors doi: 10.3390/s18082496 Authors: Nor Aziana Aliteh Norhisam Misron Ishak Aris Roslina Mohd Sidek Kunihisa Tashiro Hiroyuki Wakiwaka This paper aims to study a triple flat-type air coil inductive sensor that can identify two maturity stages of oil palm fruits, ripe and unripe, based on the resonance frequency and fruitlet capacitance changes. There are two types of triple structure that have been tested, namely Triple I and II. Triple I is a triple series coil with a fixed number of turns (n = 200) with different length, and Triple II is a coil with fixed length (l = 5 mm) and a different number of turns. The peak comparison between Triple I and II is using the coefficient of variation cv, which is defined as the ratio of the standard deviation to the mean to express the precision and repeatability of data. As the fruit ripens, the resonance frequency peaks from an inductance–frequency curve and shifts closer to the peak curve of the air, and the fruitlet capacitance decreases. The coefficient of the variation of the inductive oil palm fruit sensor shows that Triple I is smaller and more consistent in comparison with Triple II, for both resonance frequency and fruitlet capacitance. The development of this sensor proves the capability of an inductive element such as a coil, to be used as a sensor so as to determine the ripeness of the oil palm fresh fruit bunch sample.

Description: Sensors, Vol. 18, Pages 2505: An Augmented Reality System Using Improved-Iterative Closest Point Algorithm for On-Patient Medical Image Visualization Sensors doi: 10.3390/s18082505 Authors: Ming-Long Wu Jong-Chih Chien Chieh-Tsai Wu Jiann-Der Lee In many surgery assistance systems, cumbersome equipment or complicated algorithms are often introduced to build the whole system. To build a system without cumbersome equipment or complicated algorithms, and to provide physicians the ability to observe the location of the lesion in the course of surgery, an augmented reality approach using an improved alignment method to image-guided surgery (IGS) is proposed. The system uses RGB-Depth sensor in conjunction with the Point Cloud Library (PCL) to build and establish the patient’s head surface information, and, through the use of the improved alignment algorithm proposed in this study, the preoperative medical imaging information obtained can be placed in the same world-coordinates system as the patient’s head surface information. The traditional alignment method, Iterative Closest Point (ICP), has the disadvantage that an ill-chosen starting position will result only in a locally optimal solution. The proposed improved para-alignment algorithm, named improved-ICP (I-ICP), uses a stochastic perturbation technique to escape from locally optimal solutions and reach the globally optimal solution. After the alignment, the results will be merged and displayed using Microsoft’s HoloLens Head-Mounted Display (HMD), and allows the surgeon to view the patient’s head at the same time as the patient’s medical images. In this study, experiments were performed using spatial reference points with known positions. The experimental results show that the proposed improved alignment algorithm has errors bounded within 3 mm, which is highly accurate.

Description: Sensors, Vol. 18, Pages 2491: A Survey of Anomaly Detection in Industrial Wireless Sensor Networks with Critical Water System Infrastructure as a Case Study Sensors doi: 10.3390/s18082491 Authors: Daniel Ramotsoela Adnan Abu-Mahfouz Gerhard Hancke The increased use of Industrial Wireless Sensor Networks (IWSN) in a variety of different applications, including those that involve critical infrastructure, has meant that adequately protecting these systems has become a necessity. These cyber-physical systems improve the monitoring and control features of these systems but also introduce several security challenges. Intrusion detection is a convenient second line of defence in case of the failure of normal network security protocols. Anomaly detection is a branch of intrusion detection that is resource friendly and provides broader detection generality making it ideal for IWSN applications. These schemes can be used to detect abnormal changes in the environment where IWSNs are deployed. This paper presents a literature survey of the work done in the field in recent years focusing primarily on machine learning techniques. Major research gaps regarding the practical feasibility of these schemes are also identified from surveyed work and critical water infrastructure is discussed as a use case.

Description: Sensors, Vol. 18, Pages 2497: Multiday EMG-Based Classification of Hand Motions with Deep Learning Techniques Sensors doi: 10.3390/s18082497 Authors: Muhammad Zia ur Rehman Asim Waris Syed Omer Gilani Mads Jochumsen Imran Khan Niazi Mohsin Jamil Dario Farina Ernest Nlandu Kamavuako Pattern recognition of electromyography (EMG) signals can potentially improve the performance of myoelectric control for upper limb prostheses with respect to current clinical approaches based on direct control. However, the choice of features for classification is challenging and impacts long-term performance. Here, we propose the use of EMG raw signals as direct inputs to deep networks with intrinsic feature extraction capabilities recorded over multiple days. Seven able-bodied subjects performed six active motions (plus rest), and EMG signals were recorded for 15 consecutive days with two sessions per day using the MYO armband (MYB, a wearable EMG sensor). The classification was performed by a convolutional neural network (CNN) with raw bipolar EMG samples as the inputs, and the performance was compared with linear discriminant analysis (LDA) and stacked sparse autoencoders with features (SSAE-f) and raw samples (SSAE-r) as inputs. CNN outperformed (lower classification error) both LDA and SSAE-r in the within-session, between sessions on same day, between the pair of days, and leave-out one-day evaluation (p < 0.001) analyses. However, no significant difference was found between CNN and SSAE-f. These results demonstrated that CNN significantly improved performance and increased robustness over time compared with standard LDA with associated handcrafted features. This data-driven features extraction approach may overcome the problem of the feature calibration and selection in myoelectric control.

Description: Sensors, Vol. 18, Pages 2493: Wheel Force Sensor-Based Techniques for Wear Detection and Analysis of a Special Road Sensors doi: 10.3390/s18082493 Authors: Huawen Yan Weigong Zhang Dong Wang Automobile proving ground is important for the research of vehicles which are used for vehicle dynamics, durability testing, braking testing, etc. However, the road in automobile proving grounds will inevitably be damaged with the extension of the service life. In most previous research, equipment similar to a laser profilometer was used to detect the quality of the road, the principle of which is to reflect the quality of the road by measuring the roughness of the pavement. This method ignores the elastic deformation of the road itself when the vehicle is traveling and it is difficult to compensate for the error. Therefore, this paper presents a new method based on a force sensor to reduce the impact of elastic deformation, such as tire deformation, pavement deformation, and wheel rim deformation. In this study, force sensors mounted on the wheels collect the three-dimensional dynamic force of the wheel. The presented method has been tested with two sets of cobblestone road loads, and the result shows that the load intensities imposed by the test vehicle on the target road are 88.3%, 91.0%, and 92.05% of the intensity of the load imposed by the test vehicle on a standard road in three respective dimensions. It is clear that the proposed method has strong potential effectiveness to be applied for wear detection and analysis of a special road.

Description: Sensors, Vol. 18, Pages 2487: A Collaborative Data Collection Scheme Based on Optimal Clustering for Wireless Sensor Networks Sensors doi: 10.3390/s18082487 Authors: Guorui Li Haobo Chen Sancheng Peng Xinguang Li Cong Wang Shui Yu Pengfei Yin In recent years, energy-efficient data collection has evolved into the core problem in the resource-constrained Wireless Sensor Networks (WSNs). Different from existing data collection models in WSNs, we propose a collaborative data collection scheme based on optimal clustering to collect the sensed data in an energy-efficient and load-balanced manner. After dividing the data collection process into the intra-cluster data collection step and the inter-cluster data collection step, we model the optimal clustering problem as a separable convex optimization problem and solve it to obtain the analytical solutions of the optimal clustering size and the optimal data transmission radius. Then, we design a Cluster Heads (CHs)-linking algorithm based on the pseudo Hilbert curve to build a CH chain with the goal of collecting the compressed sensed data among CHs in an accumulative way. Furthermore, we also design a distributed cluster-constructing algorithm to construct the clusters around the virtual CHs in a distributed manner. The experimental results show that the proposed method not only reduces the total energy consumption and prolongs the network lifetime, but also effectively balances the distribution of energy consumption among CHs. By comparing it o the existing compression-based and non-compression-based data collection schemes, the average reductions of energy consumption are 17.9% and 67.9%, respectively. Furthermore, the average network lifetime extends no less than 20-times under the same comparison.

Description: Sensors, Vol. 18, Pages 2486: Effectiveness of Virtual Reality for Children and Adolescents with Autism Spectrum Disorder: An Evidence-Based Systematic Review Sensors doi: 10.3390/s18082486 Authors: Patricia Mesa-Gresa Hermenegildo Gil-Gómez José-Antonio Lozano-Quilis José-Antonio Gil-Gómez Autism Spectrum Disorder (ASD) is a neurodevelopmental disease that is specially characterized by impairments in social communication and social skills. ASD has a high prevalence in children, affecting 1 in 160 subjects. Virtual reality (VR) has emerged as an effective tool for intervention in the health field. Different recent papers have reviewed the VR-based treatments in ASD, but they have an important limitation because they only use clinical databases and do not include important technical indexes such as the Web of Science index or the Scimago Journal & Country Rank. To our knowledge, this is the first contribution that has carried out an evidence-based systematic review including both clinical and technical databases about the effectiveness of VR-based intervention in ASD. The initial search identified a total of 450 records. After the exclusion of the papers that are not studies, duplicated articles, and the screening of the abstract and full text, 31 articles met the PICO (Population, Intervention, Comparison and Outcomes) criteria and were selected for analysis. The studies examined suggest moderate evidence about the effectiveness of VR-based treatments in ASD. VR can add many advantages to the treatment of ASD symptomatology, but it is necessary to develop consistent validations in future studies to state that VR can effectively complement the traditional treatments.

Description: Sensors, Vol. 18, Pages 2490: Adaptive and Economically-Robust Group Selling of Spectrum Slots for Cognitive Radio-Based Networks Sensors doi: 10.3390/s18082490 Authors: Maqbool Ahmad Muhammad Shafiq Azeem Irshad Muhammad Khalil Afzal Dae Wan Kim Jin-Ghoo Choi Auction theory has found vital application in cognitive radio to relieve spectrum scarcity by redistributing idle channels to those who value them most. However, countries have been slow to introduce spectrum auctions in the secondary market. This could be in part because a number of substantial conflicts could emerge for leasing the spectrum at the micro level. These representative conflicts include the lack of legislation, interference management, setting a reasonable price, etc. In addition, the heterogeneous nature of the spectrum precludes the true evaluation of non-identical channels. The information abstracted from the initial activity in terms of price paid for specific channels may not be a useful indicator for the valuation of another channel. Therefore, auction mechanisms to efficiently redistribute idle channels in the secondary market are of vital interest. In this paper, we first investigate such leading conflicts and then propose a novel Adaptive and Economically-Robust spectrum slot Group-selling scheme (AERG), for cognitive radio-based networks such as IoT, 5G and LTE-Advanced. This scheme enables group-selling behavior among the primary users to collectively sell their uplink slots that are individually not attractive to the buyers due to the auction overhead. AERG is based on two single-round sealed-bid reverse-auction mechanisms accomplished in three phases. In the first phase, participants adapt asks and bids to fairly evaluate uplink slots considering the dynamics of spectrum trading such as space and time. In the second phase, an inner-auction in each primary network is conducted to collect asks on group slots, and then, an outer-auction is held between primary and secondary networks. In the third phase, the winning primary network declares the winners of the inner-auction that can evenly share the revenue of the slots. Simulation results and logical proofs verify that AERG satisfies economic properties such as budget balance, truthfulness and individual rationality and improves the utilities of the participants.

Description: Sensors, Vol. 18, Pages 2489: Evaluation of the Effect of Fly Ash on Hydration Characterization in Self-Compacting Concrete (SCC) at Very Early Ages Using Piezoceramic Transducers Sensors doi: 10.3390/s18082489 Authors: Yu Zheng Dongdong Chen Lingzhu Zhou Linsheng Huo Hongwei Ma Gangbing Song Nowadays, the industrial waste, Fly Ash (FA), as a mineral admixture or a replacement of cement for the production of self-compacting concrete (SCC) has been increasingly used, because of its benefits in enhancing both fresh and long-term concrete properties and in promoting environmental-friendly construction. In this study, the conventional cement was replaced by FA at different rates (0%, 20%, 40%, 60% of the cement mass) for the SCC mixtures. The early-age (0–24 h) SCC hydration, which is a complicated chemical reaction in pozzolanic behavior, was characterized by using a pair of piezoceramic Smart Aggregates (SAs). One SA works as an actuator and the other works as a sensor. A sweep sine signal from 100 Hz to100 kHz was used as the excitation signal, which is helpful to understand the quantitative influence of fly ash on the kinetics of SCC hydration. During the hydration reaction, the received electrical signal was continuously detected by the sensor. The experimental results showed that increasing the volume of fly ash resulted in longer pozzolanic reaction time in SCCs, which successfully reveals the effect of fly ash volume on the hydration behavior in early age (0–24 h) hydration. In order to quantitatively evaluate the hydration in the 0–24 h, based on the wavelet packet energy analysis, the hydration completion index (HCI) and normalized hydration completion index (NHCI) were defined. The experimental results showed that the NHCI can clearly reveal the hydration completion progress during the early hydration age (0–24 h). To validate the accuracy of the test results based on SAs, a series of mechanical tests for penetration resistance of SCCs with different volumes of fly ash were carried out. The results predicted by the signal based on SAs gave reasonable agreement with the test results of penetration resistance. It can be concluded that a successful investigation of the influence of fly ash on early-age SCC hydration response can be achieved based on the analysis of the received electrical signal using the proposed method and the important hydration characteristics, such as initial and final setting time, and can be approximately predicted by NHCI values.

Description: Sensors, Vol. 18, Pages 2482: Generalized L-Shaped Nested Array Concept Based on the Fourth-Order Difference Co-Array Sensors doi: 10.3390/s18082482 Authors: Lei Zhang Shiwei Ren Xiangnan Li Guishan Ren Xiaohua Wang In this paper, a generalized L-shaped nested array based on the fourth-order difference co-array is proposed for two-dimensional (2D) directions’ estimation. The new structure framework makes full use of the physical sensor locations to form a virtual uniform rectangular array (URA) as large as possible. As it utilizes the fourth-order difference instead of the traditional second-order difference result, this structure framework can acquire a much higher degree-of-freedom (DOF) than the existing 2D sparse arrays. The proposed structures have two advantages. One is that the subarrays can be chosen as any nested-class arrays, which makes the sparse array design more flexible. We can choose arbitrary subarray structures for DOF enhancement purposes. Another advantage is that the relative position of two subarrays can be set as any integral multiple of half wavelength. This means that two subarrays can be located as far as possible so that the relative influence between two physical subarrays can be ignored. The DOFs of several typical generalized L-shaped nested arrays (GLNAs) are compared in this paper. By setting the subarrays as different types and the relative position as a special value, a special GLNA is presented. Simulations show that GLNAs have obvious superiority in 2D direction-of-arrival estimation.

Description: Sensors, Vol. 18, Pages 2483: A Hydrogen Gas Sensor Based on TiO2 Nanoparticles on Alumina Substrate Sensors doi: 10.3390/s18082483 Authors: Siti Amaniah Mohd Chachuli Mohd Nizar Hamidon Md. Shuhazlly Mamat Mehmet Ertugrul Nor Hapishah Abdullah High demand of semiconductor gas sensor works at low operating temperature to as low as 100 °C has led to the fabrication of gas sensor based on TiO2 nanoparticles. A sensing film of gas sensor was prepared by mixing the sensing material, TiO2 (P25) and glass powder, and B2O3 with organic binder. The sensing film was annealed at temperature of 500 °C in 30 min. The morphological and structural properties of the sensing film were characterized by field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX) and X-ray diffraction (XRD). The gas sensor was exposed to hydrogen with concentration of 100–1000 ppm and was tested at different operating temperatures which are 100 °C, 200 °C, and 300 °C to find the optimum operating temperature for producing the highest sensitivity. The gas sensor exhibited p-type conductivity based on decreased current when exposed to hydrogen. The gas sensor showed capability in sensing low concentration of hydrogen to as low as 100 ppm at 100 °C.

Description: Forests, Vol. 9, Pages 465: Qualitative Assessment of Forest Ecosystem Services: The Stakeholders’ Point of View in Support of Landscape Planning Forests doi: 10.3390/f9080465 Authors: Isabella De Meo Maria Giulia Cantiani Fabrizio Ferretti Alessandro Paletto In the last decades, the ecosystem services (ES) concept has become one of the main challenges of study and discussion in the scientific community. The quantitative and qualitative assessment of ES is as a tool to address forest management planning on a local scale. Forest landscape management planning is the most suitable level for integrating social needs and demands in the enhancement of different forest ES. Some regions in Italy have developed forest landscape management plans taking into account the social preferences for the different ES. In this paper, we refer to five case studies in three pilot areas in Italy. A survey collected and analyzed the opinions and preferences, from 362 stakeholders, for ten ES included in three categories (provisioning, regulating and cultural services). The main aim of this study is to understand what type of variables (study area, the groups of interest and socio-demographic characteristics of respondents) most influence stakeholder preferences for ES. The results show that for the sample of stakeholders involved in the survey, the most important ES category is regulating services followed by cultural services. In addition, the results show that the group of stakeholders’ interest is the most important variable influencing their preferences for ES.

Description: Remote Sensing, Vol. 10, Pages 1215: Filtering Stems and Branches from Terrestrial Laser Scanning Point Clouds Using Deep 3-D Fully Convolutional Networks Remote Sensing doi: 10.3390/rs10081215 Authors: Zhouxin Xi Chris Hopkinson Laura Chasmer Terrestrial laser scanning (TLS) can produce precise and detailed point clouds of forest environment, thus enabling quantitative structure modeling (QSM) for accurate tree morphology and wood volume allocation. Applying QSM to plot-scale wood delineation is highly dependent on wood visibility from forest scans. A common problem is to filter wood point from noisy leafy points in the crowns and understory. This study proposed a deep 3-D fully convolution network (FCN) to filter both stem and branch points from complex plot scans. To train the 3-D FCN, reference stem and branch points were delineated semi-automatically for 14 sampled areas and three common species. Among seven testing areas, agreements between reference and model prediction, measured by intersection over union (IoU) and overall accuracy (OA), were 0.89 (stem IoU), 0.54 (branch IoU), 0.79 (mean IoU), and 0.94 (OA). Wood filtering results were further incorporated to a plot-scale QSM to extract individual tree forms, isolated wood, and understory wood from three plot scans with visual assessment. The wood filtering experiment provides evidence that deep learning is a powerful tool in 3-D point cloud processing and parsing.

Description: Remote Sensing, Vol. 10, Pages 1208: Seabed Mapping in Coastal Shallow Waters Using High Resolution Multispectral and Hyperspectral Imagery Remote Sensing doi: 10.3390/rs10081208 Authors: Javier Marcello Francisco Eugenio Javier Martín Ferran Marqués Coastal ecosystems experience multiple anthropogenic and climate change pressures. To monitor the variability of the benthic habitats in shallow waters, the implementation of effective strategies is required to support coastal planning. In this context, high-resolution remote sensing data can be of fundamental importance to generate precise seabed maps in coastal shallow water areas. In this work, satellite and airborne multispectral and hyperspectral imagery were used to map benthic habitats in a complex ecosystem. In it, submerged green aquatic vegetation meadows have low density, are located at depths up to 20 m, and the sea surface is regularly affected by persistent local winds. A robust mapping methodology has been identified after a comprehensive analysis of different corrections, feature extraction, and classification approaches. In particular, atmospheric, sunglint, and water column corrections were tested. In addition, to increase the mapping accuracy, we assessed the use of derived information from rotation transforms, texture parameters, and abundance maps produced by linear unmixing algorithms. Finally, maximum likelihood (ML), spectral angle mapper (SAM), and support vector machine (SVM) classification algorithms were considered at the pixel and object levels. In summary, a complete processing methodology was implemented, and results demonstrate the better performance of SVM but the higher robustness of ML to the nature of information and the number of bands considered. Hyperspectral data increases the overall accuracy with respect to the multispectral bands (4.7% for ML and 9.5% for SVM) but the inclusion of additional features, in general, did not significantly improve the seabed map quality.

Description: Remote Sensing, Vol. 10, Pages 1209: Fog and Low Cloud Frequency and Properties from Active-Sensor Satellite Data Remote Sensing doi: 10.3390/rs10081209 Authors: Jan Cermak An analysis of fog and low cloud properties and distribution is performed using satellite-based LiDAR. Recent years have seen great progress in the remote sensing of fog and low clouds using passive satellite-based sensors. On this basis, maps of fog distribution and frequency as well as baseline climatologies have been constructed. However, no information on fog altitude and vertical extent is available in this way, and fog/low cloud below other clouds cannot be detected in most cases. In this study, ten years of observations by the LiDAR aboard the CALIPSO (Cloud-Aerosol LiDAR and Pathfinder Satellite Observations) platform are used to construct a map and statistical evaluations of fog/low cloud distribution and properties. For the purpose of evaluation, a comparison is made to an evaluation of fog/low cloud distribution in Europe, derived from Meteosat measurements using the Satellite-Based Operation Fog Observation Scheme (SOFOS). Both maps show good agreement in spatial patterns in this region with very diverse fog formation mechanisms. It is found that fog/low cloud layers display distinct spatial differences in terms of geometrical thickness and detection accuracy. The number of fog/low cloud instances missed by passive-sensor retrievals due to multi-layer cloud situations is considerable, with clear regional differences.

Description: Sensors, Vol. 18, Pages 2522: A High-Level Control Algorithm Based on sEMG Signalling for an Elbow Joint SMA Exoskeleton Sensors doi: 10.3390/s18082522 Authors: Dorin Copaci David Serrano Luis Moreno Dolores Blanco A high-level control algorithm capable of generating position and torque references from surface electromyography signals (sEMG) was designed. It was applied to a shape memory alloy (SMA)-actuated exoskeleton used in active rehabilitation therapies for elbow joints. The sEMG signals are filtered and normalized according to data collected online during the first seconds of a therapy session. The control algorithm uses the sEMG signals to promote active participation of patients during the therapy session. In order to generate the reference position pattern with good precision, the sEMG normalized signal is compared with a pressure sensor signal to detect the intention of each movement. The algorithm was tested in simulations and with healthy people for control of an elbow exoskeleton in flexion–extension movements. The results indicate that sEMG signals from elbow muscles, in combination with pressure sensors that measure arm–exoskeleton interaction, can be used as inputs for the control algorithm, which adapts the reference for exoskeleton movements according to a patient’s intention.

Description: Sensors, Vol. 18, Pages 2532: fPADnet: Small and Efficient Convolutional Neural Network for Presentation Attack Detection Sensors doi: 10.3390/s18082532 Authors: Thi Nguyen Eunsoo Park Xuenan Cui Van Nguyen Hakil Kim The rapid growth of fingerprint authentication-based applications makes presentation attack detection, which is the detection of fake fingerprints, become a crucial problem. There have been numerous attempts to deal with this problem; however, the existing algorithms have a significant trade-off between accuracy and computational complexity. This paper proposes a presentation attack detection method using Convolutional Neural Networks (CNN), named fPADnet (fingerprint Presentation Attack Detection network), which consists of Fire and Gram-K modules. Fire modules of fPADnet are designed following the structure of the SqueezeNet Fire module. Gram-K modules, which are derived from the Gram matrix, are used to extract texture information since texture can provide useful features in distinguishing between real and fake fingerprints. Combining Fire and Gram-K modules results in a compact and efficient network for fake fingerprint detection. Experimental results on three public databases, including LivDet 2011, 2013 and 2015, show that fPADnet can achieve an average detection error rate of 2.61%, which is comparable to the state-of-the-art accuracy, while the network size and processing time are significantly reduced.

Description: Sensors, Vol. 18, Pages 2520: High-Efficiency Output Pressure Performance Using Capacitive Micromachined Ultrasonic Transducers with Substrate-Embedded Springs Sensors doi: 10.3390/s18082520 Authors: Byung Chul Lee Amin Nikoozadeh Kwan Kyu Park Butrus T. Khuri-Yakub Capacitive micromachined ultrasonic transducers (CMUTs) with substrate-embedded springs offer highly efficient output pressure performance over conventional CMUTs, owing to their nonflexural parallel plate movement. The embedded silicon springs support thick Si piston plates, creating a large nonflexural average volume displacement efficiency in the operating frequency range from 1–3 MHz. Static and dynamic volume displacements of the nonflexural parallel plates were examined using white light interferometry and laser Doppler vibrometry. In addition, an output pressure measurement in immersion was performed using a hydrophone. The device showed a maximum transmission efficiency of 21 kPa/V, and an average volume displacement efficiency of 1.1 nm/V at 1.85 MHz with a low DC bias voltage of 55 V. The device element outperformed the lead zirconate titanate (PZT) ceramic HD3203, in the maximum transmission efficiency or the average volume displacement efficiency by 1.35 times. Furthermore, its average volume displacement efficiency reached almost 80% of the ideal state-of-the-art single-crystal relaxor ferroelectric materials PMN-0.33PT. Additionally, we confirmed that high-efficiency output pressure could be generated from the CMUT device, by quantitatively comparing the hydrophone measurement of a commercial PZT transducer.

Description: Sensors, Vol. 18, Pages 2527: Self-Sensing Nonlinear Ultrasonic Fatigue Crack Detection under Temperature Variation † Sensors doi: 10.3390/s18082527 Authors: Namgyu Kim Keunyoung Jang Yun-Kyu An This paper proposes a self-sensing nonlinear ultrasonic technique for fatigue crack detection under temperature variations. Fatigue cracks are identified from linear (α) and nonlinear (β) ultrasonic parameters recorded by a self-sensing piezoelectric transducer (PZT). The self-sensing PZT scheme minimizes the data acquisition system’s inherent nonlinearity, which often prevents the identification of fatigue cracks. Also, temperature-dependent false alarms are prevented based on the different behaviors of α and β. The proposed technique was numerically pre-validated with finite element method simulations to confirm the trends of α and β with changing temperature, and then was experimentally validated using an aluminum plate with an artificially induced fatigue crack. These validation tests reveal that fatigue cracks can be detected successfully in realistic conditions of unpredictable temperature and that positive false alarms of 0.12% occur.

Description: Sensors, Vol. 18, Pages 2528: Pendulum-Type Hetero-Core Fiber Optic Accelerometer for Low-Frequency Vibration Monitoring Sensors doi: 10.3390/s18082528 Authors: Hiroshi Yamazaki Ichiro Kurose Michiko Nishiyama Kazuhiro Watanabe In this paper, a novel pendulum-type accelerometer based on hetero-core fiber optics has been proposed for structural health monitoring targeting large-scale civil infrastructures. Vibration measurement is a non-destructive method for diagnosing the failure of structures by assessing natural frequencies and other vibration patterns. The hetero-core fiber optic sensor utilized in the proposed accelerometer can serve as a displacement sensor with robustness to temperature changes, in addition to immunity to electromagnetic interference and chemical corrosions. Thus, the hetero-core sensor inside the accelerometer measures applied acceleration by detecting the rotation of an internal pendulum. A series of experiments showed that the hetero-core fiber sensor linearly responded to the rotation angle of the pendulum ranging within (−6°, 4°), and furthermore the proposed accelerometer could reproduce the waveform of input vibration in a frequency band of several Hz order.

Description: Sensors, Vol. 18, Pages 2521: A Combined Deep Learning GRU-Autoencoder for the Early Detection of Respiratory Disease in Pigs Using Multiple Environmental Sensors Sensors doi: 10.3390/s18082521 Authors: Jake Cowton Ilias Kyriazakis Thomas Plötz Jaume Bacardit We designed and evaluated an assumption-free, deep learning-based methodology for animal health monitoring, specifically for the early detection of respiratory disease in growing pigs based on environmental sensor data. Two recurrent neural networks (RNNs), each comprising gated recurrent units (GRUs), were used to create an autoencoder (GRU-AE) into which environmental data, collected from a variety of sensors, was processed to detect anomalies. An autoencoder is a type of network trained to reconstruct the patterns it is fed as input. By training the GRU-AE using environmental data that did not lead to an occurrence of respiratory disease, data that did not fit the pattern of “healthy environmental data” had a greater reconstruction error. All reconstruction errors were labelled as either normal or anomalous using threshold-based anomaly detection optimised with particle swarm optimisation (PSO), from which alerts are raised. The results from the GRU-AE method outperformed state-of-the-art techniques, raising alerts when such predictions deviated from the actual observations. The results show that a change in the environment can result in occurrences of pigs showing symptoms of respiratory disease within 1–7 days, meaning that there is a period of time during which their keepers can act to mitigate the negative effect of respiratory diseases, such as porcine reproductive and respiratory syndrome (PRRS), a common and destructive disease endemic in pigs.

Description: Sensors, Vol. 18, Pages 2525: A Simple and Effective Colorimetric Assay for Glucose Based on MnO2 Nanosheets Sensors doi: 10.3390/s18082525 Authors: Zhengjun Huang Linlin Zheng Feng Feng Yuyuan Chen Zhenzhen Wang Zhen Lin Xinhua Lin Shaohuang Weng Simple and effective methods for the detection of the level of blood glucose are closely linked to the monitoring of people’s health. In the study, MnO2 nanosheets with absorption range of 300 nm~500 nm and obvious yellow color were easily prepared and applied to detect glucose through their absorbance and color. The proposed method is based on the fact that a specific concentration of glucose can be quantitatively transformed into hydrogen peroxide (H2O2) under the catalytic effect of glucose oxidase. Based on the redox reaction of MnO2 with H2O2, yellow MnO2 can be converted into colorless Mn2+ to monitor the concentration of glucose. Under optimal conditions, a simple and effective visual assay for the sensitive and reliable detection of glucose was developed. The linear range was estimated to the range from 0 μM to 100 μM, with a detection limit of 12.8 μM. Furthermore, the proposed colorimetric assay based on MnO2 nanosheets can effectively detect blood glucose of clinical serum samples with accuracy and convenience.

Description: Remote Sensing, Vol. 10, Pages 1216: UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health Remote Sensing doi: 10.3390/rs10081216 Authors: Jonathan P. Dash Grant D. Pearse Michael S. Watt The development of methods that can accurately detect physiological stress in forest trees caused by biotic or abiotic factors is vital for ensuring productive forest systems that can meet the demands of the Earth’s population. The emergence of new sensors and platforms presents opportunities to augment traditional practices by combining remotely-sensed data products to provide enhanced information on forest condition. We tested the sensitivity of multispectral imagery collected from time-series unmanned aerial vehicle (UAV) and satellite imagery to detect herbicide-induced stress in a carefully controlled experiment carried out in a mature Pinus radiata D. Don plantation. The results revealed that both data sources were sensitive to physiological stress in the study trees. The UAV data were more sensitive to changes at a finer spatial resolution and could detect stress down to the level of individual trees. The satellite data tested could only detect physiological stress in clusters of four or more trees. Resampling the UAV imagery to the same spatial resolution as the satellite imagery revealed that the differences in sensitivity were not solely the result of spatial resolution. Instead, vegetation indices suited to the sensor characteristics of each platform were required to optimise the detection of physiological stress from each data source. Our results define both the spatial detection threshold and the optimum vegetation indices required to implement monitoring of this forest type. A comparison between time-series datasets of different spectral indices showed that the two sensors are compatible and can be used to deliver an enhanced method for monitoring physiological stress in forest trees at various scales. We found that the higher resolution UAV imagery was more sensitive to fine-scale instances of herbicide induced physiological stress than the RapidEye imagery. Although less sensitive to smaller phenomena the satellite imagery was found to be very useful for observing trends in physiological stress over larger areas.

Description: Remote Sensing, Vol. 10, Pages 1219: Lidar Studies of Wind Turbulence in the Stable Atmospheric Boundary Layer Remote Sensing doi: 10.3390/rs10081219 Authors: Viktor A. Banakh Igor N. Smalikho The kinetic energy of turbulence, the dissipation rate of turbulent energy, and the integral scale of turbulence in the stable atmospheric boundary layer at the location heights of low-level jets (LLJs) have been measured with a coherent Doppler light detection and ranging (lidar) system. The turbulence is shown to be weak in the central part of LLJs. The kinetic energy of turbulence at the maximum velocity heights of the jet does not exceed 0.1 (m/s)2, while the dissipation rate is about 10−5 m2/s3. On average, the integral scale of turbulence in the central part of the jet is about 100 m, which is two to three times less than the effective vertical size of the LLJ.

Description: Remote Sensing, Vol. 10, Pages 1222: Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data Remote Sensing doi: 10.3390/rs10081222 Authors: Yanjun Wang Qi Chen Lin Liu Xiong Li Arun Kumar Sangaiah Kai Li Power lines classification is important for electric power management and geographical objects extraction using LiDAR (light detection and ranging) point cloud data. Many supervised classification approaches have been introduced for the extraction of features such as ground, trees, and buildings, and several studies have been conducted to evaluate the framework and performance of such supervised classification methods in power lines applications. However, these studies did not systematically investigate all of the relevant factors affecting the classification results, including the segmentation scale, feature selection, classifier variety, and scene complexity. In this study, we examined these factors systematically using airborne laser scanning and mobile laser scanning point cloud data. Our results indicated that random forest and neural network were highly suitable for power lines classification in forest, suburban, and urban areas in terms of the precision, recall, and quality rates of the classification results. In contrast to some previous studies, random forest yielded the best results, while Naïve Bayes was the worst classifier in most cases. Random forest was the more robust classifier with or without feature selection for various LiDAR point cloud data. Furthermore, the classification accuracies were directly related to the selection of the local neighborhood, classifier, and feature set. Finally, it was suggested that random forest should be considered in most cases for power line classification.

Description: Remote Sensing, Vol. 10, Pages 1220: Correction: Shao, Z.; et al. A Benchmark Dataset for Performance Evaluation of Multi-Label Remote Sensing Image Retrieval. Remote Sens. 2018, 10, 964 Remote Sensing doi: 10.3390/rs10081220 Authors: Zhenfeng Shao Ke Yang Weixun Zhou In our paper [1], we presented a dense labeling dataset that can be used for not only single-label and multi-label remote sensing image retrieval but also pixel-based problems such as semantic segmentation.[...]

Description: Sensors, Vol. 18, Pages 2549: Accurate Indoor Localization Based on CSI and Visibility Graph Sensors doi: 10.3390/s18082549 Authors: Zhefu Wu Lei Jiang Zhuangzhuang Jiang Bin Chen Kai Liu Qi Xuan Yun Xiang Passive indoor localization techniques can have many important applications. They are nonintrusive and do not require users carrying measuring devices. Therefore, indoor localization techniques are widely used in many critical areas, such as security, logistics, healthcare, etc. However, because of the unpredictable indoor environment dynamics, the existing nonintrusive indoor localization techniques can be quite inaccurate, which greatly limits their real-world applications. To address those problems, in this work, we develop a channel state information (CSI) based indoor localization technique. Unlike the existing methods, we employ both the intra-subcarrier statistics features and the inter-subcarrier network features. Specifically, we make the following contributions: (1) we design a novel passive indoor localization algorithm which combines the statistics and network features; (2) we modify the visibility graph (VG) technique to build complex networks for the indoor localization applications; and (3) we demonstrate the effectiveness of our technique using real-world deployments. The experimental results show that our technique can achieve about 96% accuracy on average and is more than 9% better than the state-of-the-art techniques.

Description: Sensors, Vol. 18, Pages 2546: Scoping Review of Systems to Train Psychomotor Skills in Hearing Impaired Children Sensors doi: 10.3390/s18082546 Authors: Victor M. Peñeñory Cristina Manresa-Yee Inmaculada Riquelme Cesar A. Collazos Habib M. Fardoun Objectives: The aim of this work is to provide a scoping review to compile and classify the systems helping train and enhance psychomotor skills in hearing impaired (HI) children. Methods: Based on an exhaustive review on psychomotor deficits in HI children, the procedure used to carry out a scoping review was: select keywords and identify synonyms, select databases and prepare the queries using keywords, analyze the quality of the works found using the PEDro Scale, classify the works based on psychomotor competences, analyze the interactive systems (e.g., sensors), and the achieved results. Results: Thirteen works were found. These works used a variety of sensors and input devices such as cameras, contact sensors, touch screens, mouse and keyboard, tangible objects, haptic and virtual reality (VR) devices. Conclusions: From the research it was possible to contextualize the deficits and psychomotor problems of HI children that prevent their normal development. Additionally, from the analysis of different proposals of interactive systems addressed to this population, it was possible to establish the current state of the use of different technologies and how they contribute to psychomotor rehabilitation.

Description: Sensors, Vol. 18, Pages 2571: Towards a Meaningful 3D Map Using a 3D Lidar and a Camera Sensors doi: 10.3390/s18082571 Authors: Jongmin Jeong Tae Sung Yoon Jin Bae Park Semantic 3D maps are required for various applications including robot navigation and surveying, and their importance has significantly increased. Generally, existing studies on semantic mapping were camera-based approaches that could not be operated in large-scale environments owing to their computational burden. Recently, a method of combining a 3D Lidar with a camera was introduced to address this problem, and a 3D Lidar and a camera were also utilized for semantic 3D mapping. In this study, our algorithm consists of semantic mapping and map refinement. In the semantic mapping, a GPS and an IMU are integrated to estimate the odometry of the system, and subsequently, the point clouds measured from a 3D Lidar are registered by using this information. Furthermore, we use the latest CNN-based semantic segmentation to obtain semantic information on the surrounding environment. To integrate the point cloud with semantic information, we developed incremental semantic labeling including coordinate alignment, error minimization, and semantic information fusion. Additionally, to improve the quality of the generated semantic map, the map refinement is processed in a batch. It enhances the spatial distribution of labels and removes traces produced by moving vehicles effectively. We conduct experiments on challenging sequences to demonstrate that our algorithm outperforms state-of-the-art methods in terms of accuracy and intersection over union.

Description: Sensors, Vol. 18, Pages 2569: An X-band Bi-Directional Transmit/Receive Module for a Phased Array System in 65-nm CMOS Sensors doi: 10.3390/s18082569 Authors: Van-Viet Nguyen Hyohyun Nam Young Joe Choe Bok-Hyung Lee Jung-Dong Park We present an X-band bi-directional transmit/receive module (TRM) for a phased array system utilized in radar-based sensor systems. The proposed module, comprising a 6-bit phase shifter, a 6-bit digital step attenuator, and bi-directional gain amplifiers, is fabricated using 65-nm CMOS technology. By constructing passive networks in the phase-shifter and the variable attenuator, the implemented TRM provides amplitude and phase control with 360° phase coverage and 5.625° as the minimum step size while the attenuation range varies from 0 to 31.5 dB with a step size of 0.5 dB. The fabricated T/R module in all of the phase shift states had RMS phase errors of less than 4° and an RMS amplitude error of less than 0.93 dB at 9–11 GHz. The output 1dB gain compression point (OP1dB) of the chip was 5.13 dBm at 10 GHz. The circuit occupies 3.92 × 2.44 mm2 of the chip area and consumes 170 mW of DC power.