ALBERT

Description: This paper developed a principal component analysis (PCA)-integrated algorithm for feature identification in manufacturing; this algorithm is based on an adaptive PCA-based scheme for identifying image features in vision-based inspection. PCA is a commonly used statistical method for pattern recognition tasks, but an effective PCA-based approach for identifying suitable image features in manufacturing has yet to be developed. Unsuitable image features tend to yield poor results when used in conventional visual inspections. Furthermore, research has revealed that the use of unsuitable or redundant features might influence the performance of object detection. To address these problems, the adaptive PCA-based algorithm developed in this study entails the identification of suitable image features using a support vector machine (SVM) model for inspecting of various object images; this approach can be used for solving the inherent problem of detection that occurs when the extraction contains challenging image features in manufacturing processes. The results of experiments indicated that the proposed algorithm can successfully be used to adaptively select appropriate image features. The algorithm combines image feature extraction and PCA/SVM classification to detect patterns in manufacturing. The algorithm was determined to achieve high-performance detection and to outperform the existing methods.

Description: Artificial vision systems (AVS) have become very important in precision agriculture applied to produce high-quality and low-cost foods with high functional characteristics generated through environmental care practices. This article reported the design and implementation of a new fuzzy classification architecture based on the RGB color model with descriptors. Three inputs were used that are associated with the average value of the color components of four views of the tomato; the number of triangular membership functions associated with the components and were three and four for the case of component . The amount of tomato samples used in training were forty and twenty for testing; the training was done using the Matlab© ANFISEDIT. The tomato samples were divided into six categories according to the US Department of Agriculture (USDA). This study focused on optimizing the descriptors of the color space to achieve high precision in the prediction results of the final classification task with an error of -6. The Computer Vision System (CVS) is integrated by an image isolation system with lighting; the image capture system uses a Raspberry Pi 3 and Camera Module Raspberry Pi 2 at a fixed distance and a black background. In the implementation of the CVS, three different color description methods for tomato classification were analyzed and their respective diffuse systems were also designed, two of them using the descriptors described in the literature.

Description: Traffic data plays a very important role in Intelligent Transportation Systems (ITS). ITS requires complete traffic data in transportation control, management, guidance, and evaluation. However, the traffic data collected from many different types of sensors often includes missing data due to sensor damage or data transmission error, which affects the effectiveness and reliability of ITS. In order to ensure the quality and integrity of traffic flow data, it is very important to propose a satisfying data imputation method. However, most of the existing imputation methods cannot fully consider the impact of sensor data with data missing and the spatiotemporal correlation characteristics of traffic flow on imputation results. In this paper, a traffic data imputation method is proposed based on improved low-rank matrix decomposition (ILRMD), which fully considers the influence of missing data and effectively utilizes the spatiotemporal correlation characteristics among traffic data. The proposed method uses not only the traffic data around the sensor including missing data, but also the sensor data with data missing. The information of missing data is reflected into the coefficient matrix, and the spatiotemporal correlation characteristics are applied in order to obtain more accurate imputation results. The real traffic data collected from the Caltrans Performance Measurement System (PeMS) are used to evaluate the imputation performance of the proposed method. Experiment results show that the average imputation accuracy with proposed method can be improved 87.07% compared with the SVR, ARIMA, KNN, DBN-SVR, WNN, and traditional MC methods, and it is an effective method for data imputation.

Description: This paper proposes the end-to-end detection of a deep network for far infrared small target detection. The problem of detecting small targets has been a subject of research for decades and has been applied mainly in the field of surveillance. Traditional methods focus on filter design for each environment, and several steps are needed to obtain the final detection result. Most of them work well in a given environment but are vulnerable to severe clutter or environmental changes. This paper proposes a novel deep learning-based far infrared small target detection method and a heterogeneous data fusion method to solve the lack of semantic information due to the small target size. Heterogeneous data consists of radiometric temperature data (14-bit) and gray scale data (8-bit), which includes the physical meaning of the target, and compares the effects of the normalization method to fuse heterogeneous data. Experiments were conducted using an infrared small target dataset built directly on the cloud backgrounds. The experimental results showed that there is a significant difference in performance according to the various fusion methods and normalization methods, and the proposed detector showed approximately 20% improvement in average precision (AP) compared to the baseline constant false alarm rate (CFAR) detector.

Description: The concentration dependence of a microwave frequency radiation from a solution of a functioning enzyme system (ES) (with the example of cytochrome P450 BM3 (CYP102A1) system during lauric acid (LA) hydroxylation) has been studied with a radiothermometric sensor. Registration of the radiation from the enzyme solution has been performed in the frequency range from 3.4 to 4.2 GHz at the enzyme concentrations from 10−10 М to 10−6 М. It has been demonstrated that the catalysis of LA hydroxylation in a reconstituted CYP102A1 system is accompanied by a generation of microwave radiation over the entire range of concentrations studied. It has been found that a transition from a multipulse mode (at nanomolar enzyme concentrations from 10−10М to 10−8М) to a single-pulse mode (at micromolar enzyme concentrations from 10−7М to 10−6М) is observed. This effect is discussed on the basis of assumptions considering possible realization of biomaser-like radiation in the enzyme system. The discovered concentration-based effect of the transition of an unsynchronized pulsed radiation into a synchronized one in ES can further be used in the development of novel methods of noninvasive diagnostics of diseases, in mathematical modeling of the functioning of living systems, and in the development of next-generation quantum computers.

Description: Closed-form expression of three-dimensional emitter location estimation using azimuth and elevation measurements at multiple locations is presented in this paper. The three-dimensional location estimate is obtained from three-dimensional sensor locations and the azimuth and elevation measurements at each sensor location. Since the formulation is not iterative, it is not computationally intensive and does not need initial location estimate. Numerical results are presented to show the validity of the proposed scheme.

Description: In this paper, a digital elevation model (DEM) was produced for Lop Nur playa produced with the data from TanDEM-X mission. The spatial resolution is 10 m. It covers an area of 38,000 km2 for orthometric height from 785 m to 900 m above sea level, which is composed of 42 interferometric synthetic aperture radar (InSAR) scenes. A least-square adjustment approach was used to reduce the systematic errors in each DEM scene. The DEM produced was validated with data from other sensors including Ice, Cloud, and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) and aerial Structure-from-Motion (SfM) DEM. The results show that global elevation root mean square error to GLAS is 0.57 m, and the relative height error to SfM DEM in complicated terrain is 3 m. The excellent height reliability of TanDEM InSAR DEM in Lop region was proved in this paper. A reliable high-resolution basic topographic dataset for researches of Lop Nur was provided.

Description: In this paper, distributed constant level detection in wireless sensor networks (WSNs) is investigated. The permuted linear model with a scalar parameter and additive heteroscedastic Gaussian noise is introduced, where the associations between the observations and the sensors are assumed to be unknown. Several detectors such as the approximations of the generalized likelihood ratio test (GLRT) detector, mean detector, and energy detector are proposed, and their receiver operating characteristics (ROCs) are evaluated. Numerical simulations are performed, and it is shown that the performance degradation of the GLRT detector is small, compared to the permutation known as Neyman-Pearson (NP) detector.

Description: Explicit information of tree species composition provides valuable materials for the management of forests and urban greenness. In recent years, scholars have employed multiple features in tree species classification, so as to identify them from different perspectives. Most studies use different features to classify the target tree species in a specific growth environment and evaluate the classification results. However, the data matching problems have not been discussed; besides, the contributions of different features and the performance of different classifiers have not been systematically compared. Remote sensing technology of the integrated sensors helps to realize the purpose with high time efficiency and low cost. Benefiting from an integrated system which simultaneously acquired the hyperspectral images, LiDAR waveform, and point clouds, this study made a systematic research on different features and classifiers in pixel-wised tree species classification. We extracted the crown height model (CHM) from the airborne LiDAR device and multiple features from the hyperspectral images, including Gabor textural features, gray-level co-occurrence matrix (GLCM) textural features, and vegetation indices. Different experimental schemes were tested at two study areas with different numbers and configurations of tree species. The experimental results demonstrated the effectiveness of Gabor textural features in specific tree species classification in both homogeneous and heterogeneous growing environments. The GLCM textural features did not improve the classification accuracy of tree species when being combined with spectral features. The CHM feature made more contributions to discriminating tree species than vegetation indices. Different classifiers exhibited similar performances, and support vector machine (SVM) produced the highest overall accuracy among all the classifiers.

Description: The health condition of rolling-element bearings is important for machine performance and operating safety. Due to external interferences, the impulse-related fault information is always buried in the raw vibration signal. To solve this problem, a hybrid time-frequency analysis method combining ensemble local mean decomposition (ELMD) and the Teager-Kaiser energy operator (TKEO) is proposed for the fault diagnosis of high-speed train bearings. The ELMD method is a significant improvement over local mean decomposition (LMD) for addressing the mode-mixing problem. The TKEO method is effective for separating amplitude-modulated (AM) and frequency-modulated (FM) signals from a raw signal. But it is only valid for monocomponent AM-FM signals. The proposed time-frequency method integrates the advantages of ELMD and TKEO to detect localized defects in rolling-element bearings. First, a raw signal is decomposed into an ensemble of PFs and a residual component using ELMD. A novel sensitive parameter (SP) is introduced to select the sensitive PF that contains the most fault-related information. Subsequently, the TKEO is applied to extract both the amplitude and frequency modulations from the selected PF. The experimental results of rolling element and outer race fault signals confirmed that the proposed method could effectively recover fault information from raw signals contaminated by strong noise and other interferences.

Description: In this work, a compact affordable and portable spectral imaging system is presented. The system is intended to be employed in general applications, such as material classification or determination of the concentration of chemical species together with colorimetric sensors. The imaging device is reduced to a small digital color detector with an active area of . This device provides a quantification of the incident emission in the form of four digital words corresponding to its averaged components blue, green, red, and near infrared. In this way, the size of the image is reduced to one pixel. The wavelength selection is carried out by means of a LED array disposed surrounding the color detector. The LEDs are selected to cover the wavelength range from 360 to 890 nm. A sequential measurement protocol is followed, and the generated data is transmitted to an external portable device via a Bluetooth link where a classification protocol is implemented in a custom-developed Android™ application. The presented system has been applied in three different scenarios involving material classification, meat freshness monitoring, and chemical analysis. The analysis of the data using principal components shows that it is possible to find a set of wavelengths where the classification of the samples is optimal.

Description: Acoustic emission (AE) is an effective nondestructive evaluation method for assessing damage in materials; however, few works in the literature have focused on one quantification method of damage in concrete under fatigue loading by using AE for characterizing the entire three main deterioration behaviors simultaneously. These deterioration behaviors include Young’s modulus degradation, fatigue total strain, and residual strain development. In this work, an AE quantification method of fatigue damage in concrete was developed, by combining AE and a fiber bundle-based statistical damage model (fiber bundle-irreversible chain model). By establishing a relationship between normalized AE counts and the damage variable based on the fiber bundle-irreversible chain model, the method was proposed. Additionally, this method was verified against the experimental results. It is able to capture the mechanisms of damage accumulation and characterize the three deterioration behaviors simultaneously.

Description: This paper reviews the practical process of designing the electrodes on electroless immersion gold-plated test strips for glucose measurement. We have found that great care must be taken on determining the width and length of the working and reference electrodes, to yield the optimum performance on electroless immersion gold-plated glucose test strips for glucose measurement. Preferably, a width of at least 0.6 mm and a length of at least 24 mm are recommended to prevent variance in electrode resistance during mass production of electroless immersion gold-plated glucose test strips, in order to enhance the accuracy of glucose measurement. This paper also recommended that, if two different geometric sizes of test strips are going to be used on the same glucose meter, both test strips must use different lengths and widths on the working and reference electrodes to yield the same electrical resistance and hence similar electrochemical performance.

Description: In wireless sensor networks, an improved throughput capacity region can be achieved by equipping multiple channels. However, such approach inevitably brings the issue of solving the coupled channel assignment and scheduling problem. This paper put forward a low-complexity distributed channel assignment and scheduling policy for multichannel wireless sensor networks with single-hop traffic flows, named LDCS, as well as its multihop multipath extension. Under the proposed algorithms, random access and backoff time techniques are introduced to keep the complexity low and independent of the number of links and channels. Through theoretical analysis and simulation experiments, it is proved that the proposed algorithms are throughput guaranteed, and in some network scenarios, the achieved capacity region can be larger than that of other comparable distributed algorithms.

Description: In directional sensor networks research, target event detection is currently an active research area, with applications in underwater target monitoring, forest fire warnings, border areas, and other important activities. Previous studies have often discussed target coverage in two-dimensional sensor networks, but these studies cannot be extensively applied to three-dimensional networks. Additionally, most of the previous target coverage detection models are based on a circular or omnidirectional sensing model. More importantly, if the directional sensor network does not design a better coverage algorithm in the coverage-monitoring process, its nodes’ energy consumption will increase and the network lifetime will be significantly shortened. With the objective of addressing three-dimensional target coverage in applications, this study proposes a dynamic adjustment optimisation algorithm for three-dimensional directional sensor networks based on a spherical sector coverage model, which improves the lifetime and coverage ratio of the network. First, we redefine the directional nodes’ sensing model and use the three-dimensional Voronoi method to divide the regions where the nodes are located. Then, we introduce a correlation force between the target and the sensor node to optimise the algorithm’s coverage mechanism, so that the sensor node can accurately move to the specified position for target coverage. Finally, by verifying the feasibility and accuracy of the proposed algorithm, the simulation experiments demonstrate that the proposed algorithm can effectively improve the network coverage and node utilisation.

Description: Pedestrians, motorist, and cyclist remain the victims of poor vision and negligence of human drivers, especially in the night. Millions of people die or sustain physical injury yearly as a result of traffic accidents. Detection and recognition of road markings play a vital role in many applications such as traffic surveillance and autonomous driving. In this study, we have trained a nighttime road-marking detection model using NIR camera images. We have modified the VGG-16 base network of the state-of-the-art faster R-CNN algorithm by using a multilayer feature fusion technique. We have demonstrated another promising feature fusion technique of concatenating all the convolutional layers within a stage to extract image features. The modification boosts the overall detection performance of the model by utilizing the advantages of the shallow layers and the deep layers of the VGG-16 network. The training samples were augmented using random rotation and translation to enhance the heterogeneity of the detection algorithm. We have achieved a mean average precision (mAP) of 89.48% and 92.83% for the baseline faster R-CNN and our modified method, respectively.

Description: This paper presents highly robust, novel approaches to solving the forward and inverse problems of an Electrical Capacitance Tomography (ECT) system for imaging conductive materials. ECT is one of the standard tomography techniques for industrial imaging. An ECT technique is nonintrusive and rapid and requires a low burden cost. However, the ECT system still suffers from a soft-field problem which adversely affects the quality of the reconstructed images. Although many image reconstruction algorithms have been developed, still the generated images are inaccurate and poor. In this work, the Capacitance Artificial Neural Network (CANN) system is presented as a solver for the forward problem to calculate the estimated capacitance measurements. Moreover, the Metal Filled Fuzzy System (MFFS) is proposed as a solver for the inverse problem to construct the metal images. To assess the proposed approaches, we conducted extensive experiments on image metal distributions in the lost foam casting (LFC) process to light the reliability of the system and its efficiency. The experimental results showed that the system is sensible and superior.

Description: Due to its efficiency in end-to-end communication, wireless sensor network based on the Internet protocol (IP-WSN) is used for monitoring purposes. Nowadays, the concerned agencies are giving their highest priority to monitor its critical infrastructure. Infrastructure health monitoring is the measure of estimating the state of infrastructure health or detecting the changes in structures that affect its performance. The traditional approach to monitor the infrastructure health is done by using centralized data acquisition hub. Installation and commissioning of these systems represent significant concerns, thus moving toward IP-WSN. As cost effectiveness and energy efficiency are major concerns, our proposed approach is to reduce the amount of overhead while keeping the infrastructure health monitoring system accurate. Our contribution in this paper is to reduce the amount of data to be transmitted by compressing the payload of the packets. Thus, we have proposed a double compression algorithm. In this way, the capacity of the sensor node will be increased since less time will be taken to transmit data between the intermediate node as well as the coordinator node. As a consequence, it will also extend the lifetime of the battery.

Description: Considering the advances in building monitoring and control through networks of interconnected devices, effective handling of the associated rich data streams is becoming an important challenge. In many situations, the application of conventional system identification or approximate grey-box models, partly theoretic and partly data driven, is either unfeasible or unsuitable. The paper discusses and illustrates an application of black-box modelling achieved using data mining techniques with the purpose of smart building ventilation subsystem control. We present the implementation and evaluation of a data mining methodology on collected data from over one year of operation. The case study is carried out on four air handling units of a modern campus building for preliminary decision support for facility managers. The data processing and learning framework is based on two steps: raw data streams are compressed using the Symbolic Aggregate Approximation method, followed by the resulting segments being input into a Support Vector Machine algorithm. The results are useful for deriving the behaviour of each equipment in various modi of operation and can be built upon for fault detection or energy efficiency applications. Challenges related to online operation within a commercial Building Management System are also discussed as the approach shows promise for deployment.

Description: A method of locating a magnetic target based on geomagnetic total field is proposed. In the method, a conjugate gradient algorithm is introduced to eliminate the time-varying and uneven spatial distribution of geomagnetic total field. Then a structure of the measuring array of geomagnetic total field is designed. In the measuring array, the array aperture is a primary factor for the conjugate gradient algorithm. To determine an optimal aperture, we analyze the relationship between the array aperture and the localization accuracy. According to the localization theory based on geomagnetic total field, we simulate the process of determining an optimum array aperture. Based on the simulation, we propose the basis and principle of determining the optimum array aperture. To prove it, we use optically pumped magnetometers with different array apertures to carry out the experiments of locating a car in a suburb. Through the experiment, we get the experimental relationship between apertures and location accuracy. And the relationship agrees with the theory. The result shows that the method is feasible to determine the optimum aperture.

Description: This paper reports a review about microelectromechanical system (MEMS) microphones. The focus of this review is to identify the issues in MEMS microphone designs and thoroughly discuss the state-of-the-art solutions that have been presented by the researchers to improve performance. Considerable research work has been carried out in capacitive MEMS microphones, and this field has attracted the research community because these designs have high sensitivity, flat frequency response, and low noise level. A detailed overview of the omnidirectional microphones used in the applications of an audio frequency range has been presented. Since the microphone membrane is made of a thin film, it has residual stress that degrades the microphone performance. An in-depth detailed review of research articles containing solutions to relieve these stresses has been presented. The comparative analysis of fabrication processes of single- and dual-chip omnidirectional microphones, in which the membranes are made up of single-crystal silicon, polysilicon, and silicon nitride, has been done, and articles containing the improved performance in these two fabrication processes have been explained. This review will serve as a starting guide for new researchers in the field of capacitive MEMS microphones.

Description: To eliminate the jump points of multipole angle values after subdivision at low temperature, the magnetic field and temperature field characteristics of a multipole magnetic encoder are analyzed in this study, and the effect of changes in magnetic field strength and temperature field on the precision of angle values is studied. To eliminate the jump point of multipole angle values caused by changes in the temperature field, the suppression method based on single-pole angle value fitting is proposed. The error between the single-pole and multipole angle values is tabulated by the oversampling linear interpolation method, and the precision of fitting single-pole to multipole angle values is effectively improved. The error of the angle value caused by changes in the temperature field is studied and analyzed, and the relationship between the jump angle values and the pole number of the multipole magnetic encoder is obtained. Furthermore, the jump point is compensated for by the jump range of the multipole angle values. Finally, the angle accuracy of the multipole magnetic encoder in a cryogenic chamber is experimentally verified. The experimental results show that the low-temperature jump point compensation method proposed for the multipole magnetic encoder in this paper can effectively suppress the jump of the angle values.

Description: The demand for the development of swift, simple, and ultrasensitive biosensors has been increasing after the introduction of innovative approaches such as bioelectronics, nanotechnology, and electrochemistry. The possibility to correlate changes in electrical parameters with the concentration of protein biomarkers in biological samples is appealing to improve sensitivity, reliability, and repeatability of the biochemical assays currently available for protein investigation. Potentiostats are the required instruments to ensure the proper cell conditioning and signal processing in accurate electrochemical biosensing applications. In this light, this review is aimed at analyzing design considerations, electrical specifications, and measurement characteristics of potentiostats, specifically customized for protein detection. This review demonstrates how a proper potentiostat for protein quantification should be able to supply voltages in a range between few mV to few V, with high resolution in terms of readable current (in the order of 100 pA). To ensure a reliable quantification of clinically relevant protein concentrations (〉1 ng/mL), the accuracy of the measurement (

Description: With the popularization of Internet applications and the rapid development of e-commerce, online shopping has become a widespread and important pattern of consumption. Online user comments are an important data asset on e-commerce sites and have a great potential value for online users and merchants. However, accurate and effective extraction of the characteristics of products and users’ sentiment evaluation from a tremendous amount of comments is a significant challenge. Based on the concept of the LinLog energy model, this paper proposes an online review attribute-sentiment pair correlation model that evaluates user comments. After preprocessing the comment data of mobile phones and constructing an attribute dictionary, the proposed model conducts a clustering analysis of attributes and sentiment pairs to gain accurate assessment of attributes in order to explore potential information from user comments. Experiments conducted on one real-world dataset with comprehensive measurements verify the efficacy of the proposed model.

Description: Particulate matter (PM) has been revealed to have detrimental effects on public health, social economy, agriculture, and so forth. Thus, it became one of the major concerns in terms of a factor that can reduce “quality of life” over East Asia, where the concentration is significantly high. In this regard, it is imperative to develop affordable and efficient prediction models to monitor real-time changes in PM concentration levels using digital images, which are readily available for many individuals (e.g., via mobile phone). Previous studies (i.e., DeepHaze) were limited in scope to priorly collected data and thereby less practical in providing real-time information (i.e., undermined interprediction). This drawback led us to hardly capture drastic changes caused by weather or regions of interests. To address this challenge, we propose a new method called Deep Q-haze, whose inference scheme is built on an online learning-based method in collaboration with reinforcement learning and deep learning (i.e., Deep Q-learning), making it possible to improve testing accuracy and model flexibility in virtue of real-time basis inference. Taking into account various experiment scenarios, the proposed method learns a binary decision rule on the basis of video sequences to predict, in real time, whether the level of PM10 (particles smaller than 10 in aerodynamic diameter) concentration is harmful (80/) or not. The proposed model shows superior accuracy compared to existing algorithms. Deep Q-haze effectively accounts for unexpected environmental changes in essence (e.g., weather) and facilitates monitoring of real-time PM10 concentration levels, showing implications for better understanding of characteristics of airborne particles.

Description: A general approach is proposed to determine occupant behavior (occupancy and activity) in offices and residential buildings in order to use these estimates for improved energy management. Occupant behavior is modelled with a Bayesian network in an unsupervised manner. This algorithm makes use of domain knowledge gathered via questionnaires and recorded sensor data for motion detection, power, and hot water consumption as well as indoor CO2 concentration. Different case studies have been investigated with diversity according to their context (available sensors, occupancy or activity feedback, complexity of the environment, etc.). Furthermore, experiments integrating occupancy estimation and hot water production control show that energy efficiency can be increased by roughly 5% over known optimal control techniques and more than 25% over rule-based control while maintaining the same occupant comfort.

Description: More and more synthetic aperture radar (SAR) satellites in orbit provide abundant data for remote sensing applications. In August 2016, China launched a new Earth observation SAR satellite, Gaofen-3 (GF-3). In this paper, we utilize a small stack of GF-3 differential interferograms to map land subsidence in Beijing (China) using the time-series SAR interferometry (InSAR) technique. The small stack of differential interferograms is generated with 5 GF-3 SAR images from March 2017 to January 2018. Orbit errors are carefully addressed and removed during differential InSAR (DInSAR) processing. Truncated singular-value decomposition (TSVD) is applied to strengthen the robustness of deformation rate estimation. To validate the results of GF-3 data, an additional deformation measurement using 26 Sentinel-1B images from March 2017 to February 2018 is carried out using the persistent scatterer interferometry (PSI) technique. By implementing a cross-comparison, we find that the retrieved results from GF-3 images and Sentinel-1 images are spatially consistent. The standard deviation of vertical deformation rate differences between two data stacks is 11.24 mm/y in the study area. The results shown in this paper demonstrate the reasonable potential of GF-3 SAR images to monitor land subsidence.

Description: Two-dimensional direction-of-arrival (DOA) estimation in coprime planar array involves problems that the complexity of spectral peak search is huge and the noncircular feature of signals is not considered. Considering that unitary estimating signal parameters via rotational invariance techniques (Unitary-ESPRIT) is a low complexity subspace algorithm, an approach to estimate DOA fast for multiple signals is proposed in this paper. We first apply Unitary-ESPRIT to solve one possible value of each signal. Given the relationship between the ambiguous values and real values, we then have all possible values belonging to each subarray. Through finding the common values of two subarrays, we finally obtain the highly precise true DOAs. Moreover, when the signals are noncircular, we present an improved method using noncircular Unitary-ESPRIT, which is favorable in terms of accuracy and degree of freedom. Simulation results demonstrate the effectiveness of our proposed methods.

Description: Here we described an aptasensor based on graphene oxide (GO) decorated with gold nanoparticles (AuNPs) able to detect the AR released by prostate cells, in order to provide new insights about the relationship between AR and prostate cancer for a more precise diagnosis for cancer. Characterization of the surface modified GE was carried out by cyclic voltammetry and electrochemical impedance spectroscopy using ferrocene as a redox probe. Under the optimized experimental conditions, a detection limit of 0.5 ng/mL (3σ/slope) was obtained by the present electrochemical biosensor, along with a linear range of 0-110 ng/mL. By virtue of excellent sensitivity, specificity, and repeatability, the present electrochemical biosensor provides a potential application in clinical diagnosis.

Description: A novel all-silica fiber optic Fabry-Perot (FP) pressure sensor with pressure leading-in tube based on microbubble structure is developed and experimentally demonstrated. The FP cavity is formed by fixing the end face of the single-mode fiber (SMF) parallel to the outer surface of the microbubble, in which the microbubble with a diameter of about 318 μm is constructed at the end of silica hollow tube. When external pressure is transmitted on the inner surface of the microbubble by the pressure leading-in tube, the FP cavity length changes with the diameter of microbubble. Experimental results show that such a sensor has a linear sensitivity of approximately 4.84 nm/MPa at room temperature over the pressure range of 1.1 MPa; the sensor has a very low temperature coefficient of approximately 2 pm/°C from room temperature to 600°C. The sensor has advantages of extremely low temperature coefficient, compact structure, and small size, which has potential applications for measuring pressure in high-temperature environment.

Description: A high precision and small size Fiber Bragg Grating (FBG) inclination sensor which is used for slope monitoring is proposed in this paper. The FBG inclination sensor has a pendulum structure for high precision and a circular body for easy installation in the common inclinometer pipe. The sensitivity of the FBG inclination sensor can reach 400 pm/deg by optimizing its parameters. The test results show the superior performance of this FBG inclination sensor in terms of static characteristics, and its sensitivity is 406.6 pm/deg. The comparison experiment shows the static characteristics of the FBG inclination sensor are better than the traditional electrical inclination sensor.

Description: A geostatistical framework for spatial quality assessment framework of coarse resolution remote sensing products is presented that can account for either the scale difference or the uncertainty of reference value prediction at coarse resolutions. A set of multiple reference field realizations is first generated at a fine spatial resolution using geostatistical simulation to explore the uncertainty in the true unknown reference field. The upscaling of multiple reference field realizations to coarse resolution is then followed to match the spatial resolution of the target remote sensing product and create coarse resolution reference fields. The simulated reference values at each coarse pixel are compared to the corresponding reported value from the coarse resolution remote sensing product, yielding alternative error values, from which several location-dependent statistics such as mean error, mean absolute error, and probability of overestimation can be computed. An experiment involving monthly Tropical Rainfall Measuring Mission (TRMM) precipitation products and point-level rain gauge data over South Korea illustrates the applicability of the proposed approach. The spatially distributed error statistics are useful to identify areas with larger errors and the degree of overestimation in the study area, leading to the identification of areas with unreliable estimates within the TRMM precipitation products. Therefore, it is expected that the geostatistical assessment framework presented in this paper can be effectively used to evaluate the spatial quality of coarse resolution remote sensing products.

Description: Light detection and ranging (LiDAR) data collected from airborne laser scanner system is one of the major sources to reconstruct Earth’s surface features. This paper presents a method for detecting model key points (MKPs) of the buildings using LiDAR point clouds. The proposed approach utilizes shaded relief images (SRIs) derived from the LiDAR data. The SRIs based on the concept of the shape from shading could provide unique information about individual surface patches of the building roofs. The main advantage of the proposed approach is to detect directly MKPs, which are primitives for 3D building modeling, without segmenting point clouds. Depending on the location of the light source, the SRIs are created differently. Therefore, integration of the multidirectional SRIs created from different locations of the light source could provide more reliable results. In addition, the vertical exaggeration (i.e., scaling Z-coordinates) is also beneficial because constituent surface patches of the roofs in the SRIs created with vertically exaggerated LiDAR data are more distinguishable. To determine the MKPs of the roofs, building data was separated from other objects using modified marker-controlled watershed algorithm in accordance with criteria to specify buildings such as area, height, and standard deviation. This process could remove the unnecessary objects such as trees, vegetation, and cars. The curvature scale space (CSS) corner detector was used to determine MKP since this method is robust to geometric changes such as rotation, translation, and scale. The proposed method was applied to simulated and real LiDAR datasets with various roof types. The experimental results show that the proposed method is effective in determining MKPs of various roof types with high level of detail (LoD).

Description: A novel coumarin-based compound DPAC with two dipicolylamine (DPA) arms as the chelator sites was designed and synthesized. The compound DPAC exhibits a highly selective response to Cu2+ ions with a distinctly emission-quenching phenomenon. Moreover, the in situ formed complex DPAC-Cu2+ was used for the detection of pyrophosphate (PPi). The binding manner of probe DPAC-Cu2+ with PPi in 1 : 1 stoichiometry was supported by the Benesi-Hildebrand fitting, ESI-MS and HPLC analysis. The linear range of PPi concentration was 1-4 μM, and the detection limit was 0.53 μM. The competing experiments illustrated that the probe DPAC-Cu2+ had good sensitivity and selectivity for PPi than other anions, including ATP, ADP, AMP, and Pi in CH3CN : HEPES (3 : 2, ,) buffer. Further, cell fluorescence imaging experiments indicated that the probe DPAC-Cu2+ had a potential to be used to detect PPi in vivo.

Description: Fatigue driving is becoming a dangerous and common situation for drivers and represents a significant factor for fatal car crashes. Machine learning researchers utilized various sources of information to detect driver’s drowsiness. This study integrated the morphological features of both the eye and mouth regions and extensively investigated the fatigue detection problem from the aspects of feature numbers, classifiers, and modeling parameters. The proposed algorithm REcognizing the Drowsy Expression (REDE) achieved the 10-fold cross-validation accuracy 96.07% and took about 21 milliseconds to process one image. REDE outperformed the existing four studies on both fatigue detection accuracy and running time and is fast enough to handle the task of real-time fatigue monitoring captured at the rate of 30 frames per second. To further facilitate the research of fatigue detection, the raw data and the feature matrix were also released.

Description: Nowadays, the use of sensor nodes for the IoT is widespread. At the same time, cyberattacks on these systems have become a relevant design consideration in the practical deployment of wireless sensor networks (WSNs). However, there are some types of attacks that have to be prevented or detected as fast as possible, like, for example, attacks that put lives in danger. In this regard, a primary user emulation (PUE) attack in a structural health monitoring (SHM) system falls inside this category since nodes failing to report structural damages may cause a collapse of the building with no warning to people inside it. Building on this, we mathematically model an energy and resource utilization-efficient WSN based on the cognitive radio (CR) technique to monitor the SHM of buildings when a seismic activity occurs, making efficient use of scarce bandwidth when a PUE attack is in progress. The main performance metrics considered in this work are average packet delay and average energy consumption. The proposed model allows an additional tool for the prompt identification of such attacks in order to implement effective countermeasures.

Description: Using high-precision sensors to monitor and predict the deformation trend of supertall buildings is a hot research topic for a long time. And in terms of deformation trend prediction, the main way to realized deformation trend prediction is the deep learning algorithm, but the accuracy of prediction result needs to be improved. To solve the problem described above, firstly, based on the conditional deep belief network (CDBN) model, the levenberg-marquardt (LM) was used to optimize the CDBN model; the LM-CDBN model has been constructed. Then taking CITIC tower, the tallest building in Beijing as the research object, the real-time monitoring data of the shape acceleration array (SAA) as an example, we used LM-CDBN model to analyse and predict the building deformation. Finally, to verify the accuracy and robustness of LM-CDBN model, the prediction results of the LM-CDBN model are compared with the prediction results of the CDBN model, the extreme learning machine (ELM) model, and the unscented Kalman filter-support vector regression (UKF-SVR) model, and we evaluated the result from three aspects: training error, fitness, and stability of prediction results. The results show that the LM-CDBN model has higher precision and fitting degree in the prediction of deformation trend of supertall buildings. And the MRE, MAE, and RMSE of the LM-CDBN model prediction results are only 0.0060, 0.0023mm, and 0.0031mm, and the prediction result was more in line with the actual deformation trend.

Description: Inspired by the biological immune system, many researchers apply artificial immune principles to intrusion detection in wireless sensor networks, such as negative selection algorithms, danger theory, and dendritic cell algorithms. When applying the negative selection algorithm to wireless sensor networks, the characteristics of wireless sensor networks, such as frequent changes in network topology and limited resources, are not considered too much, which makes the detection effect to need improvement. In this paper, a negative selection algorithm based on spatial partition is proposed and applied to hierarchical wireless sensor networks. The algorithm first analyzes the distribution of self-set in the real-valued space then divides the real-valued space, and several subspaces are obtained. Selves are filled into different subspaces. We implement the negative selection algorithm in the subspace. The randomly generated candidate detector only needs to be tolerated with selves in the subspace where the detector is located, not all the selves. This operation reduces the time cost of distance calculation. In the detection process of detectors, the antigen which is to be detected only needs to match the mature detectors in the subspace where the antigen is located, rather than all the detectors. This operation speeds up the antigen detection process. Theoretical analysis and experimental results show that the algorithm has better time efficiency and quality of detectors, saves sensor node resources and reduces the energy consumption, and is an effective algorithm for wireless sensor network intrusion detection.

Description: This paper presents a piezoresistive barometric pressure sensor fabricated by using a Silicon-on-Nothing (SON) technology. Array of silicon trenches were annealed in hydrogen environment to form continuing crystalline silicon membrane over a vacuum cavity. Epitaxial growth on the silicon membrane is then completed for the desired thickness. All processes are CMOS compatible and performed on the front side of the silicon wafer. The piezoresistive barometric pressure sensor has been demonstrated with pressure hysteresis as low as 0.007%.

Description: A fast and reliable photoacoustic (PA) sensor for trace gas detection is reported. The sensor is based on a 3D-printed resonant cell in combination with a continuous wave mode-hop-free external cavity quantum cascade laser to rapidly acquire gas absorption data in the midinfrared range. The cell is designed so as to minimize the window PA background at a selected acoustic resonance. The goal is a resonant PA cell capable of detecting the traces of gases using wavelength modulation of the laser source and second harmonic detection. The versatility and enhancement of the limit of detection at sub-ppm levels are investigated by monitoring specific lines of hydrogen sulfide (H2S). The noise-equivalent absorption normalized to laser-beam power and detection bandwidth is -8 W cm-1 Hz-1/2 for H2S targeting the absorption line at 1247.2 cm−1. These properties make the sensor suitable for various practical sensors for water quality applications.

Description: The Chinese Gaofen-3 (GF-3) synthetic aperture radar (SAR) satellite launched by the China Academy of Space Technology is under continuous operating mode at C-band since September 2016. The main objective of this study was to present a preliminary evaluation of GF-3 quad-polarized SAR imagery for protected wetland classification. Four scenes of GF-3 quad-polarized SAR imageries were collected in this study and analyzed systematically. Besides, the GF-2 optical imagery and in situ survey results were also collocated as reference data. It was verified that the polarized features including double bounce scattering component , odd bounce scattering component , and volume scattering component from Freeman decomposition algorithm were well applied in distinguishing typical coverage of Longbao protected plateau wetland. In consideration of robustness of classifier, the method of Maximum Likelihood Classification was investigated for classification based on selected polarized features. The Kappa coefficient and overall accuracy of classification mapping are 0.75 and 82.16%, respectively, which revealed that the GF-3 quad-polarized SAR can provide a satisfying mapping for classification of Longbao protected plateau wetland.

Description: This work develops a thorough review of bioimpedance systems for healthcare applications. The basis and fundamentals of bioimpedance measurements are described covering issues ranging from the hardware diagrams to the configurations and designs of the electrodes and from the mathematical models that describe the frequency behavior of the bioimpedance to the sources of noise and artifacts. Bioimpedance applications such as body composition assessment, impedance cardiography (ICG), transthoracic impedance pneumography, electrical impedance tomography (EIT), and skin conductance are described and analyzed. A breakdown of recent advances and future challenges of bioimpedance is also performed, addressing topics such as transducers for biosensors and Lab-on-Chip technology, measurements in implantable systems, characterization of new parameters and substances, and novel bioimpedance applications.

Description: With the development and progress of technology, people’s requirements for living quality are increasingly higher. This study builds an indoor thermal comfort environmental monitoring system through the Internet of Things (IoT) architecture to explore the thermal comfort of people in indoor environments. Then, the applicable indicators are selected from a series of thermal comfort pointers, and the controllable indoor environmental parameters are analyzed and simulated on MATLAB to obtain the impact on the thermal comfort indicators, which can serve as important data to set up the fuzzy rule base. Next, according to the ISO7730 comfort standard and energy saving, three ways to control thermal comfort are proposed. With Arduino UNO as the development substrate, the sensing nodes for the indoor environment are set up, and the wireless sensing network is configured with ESP8266 to transmit the sensing data to the terminal. Monitored by the C# human-machine interface, the controllable load is controlled by wireless remote mode. Finally, the data is stored in the database for follow-up experimentation and analysis. Through actual measurement experiments, the thermal comfort and energy saving effects, under comfort, general, and energy-saving modes, as proposed in this study, are verified to achieve a balance between thermal comfort and energy saving.

Description: The force is one of the parameters very often measured in our life. Force sensing resistors (FSRs) can be successfully used for measuring force, especially that they can be applied in dentistry for measuring bite forces. However, it is very difficult to apply commercial FSRs for accurate measurement of bite forces and to ensure personalized approach to each patient. Because of that, design, fabrication, and characterization of tailor-made force sensing resistors intended for application in dental medicine are presented in this paper. We designed two FSRs, one with two active areas and one with four active areas (for teeth of higher volume–molars). Two different fabrication processes were applied: first additive, using inkjet printer and silver as material for conductive segments, and second subtractive, using cutter, and gold as a material for manufacturing of interdigitated structure of FSR. Performances of these FSRs have been compared, measuring resistance as a function of applied force, using in-house developed experimental set-up with an articulator.

Description: Underwater Acoustic Networks (UANs) use acoustic communication. UANs are characterized by narrow bandwidth, long delay, limited energy, high bit error rate, and dynamic network topology. Therefore, UANs call for energy-efficient and latency-minimized routing protocol. In this paper, the shortest path routing protocol based on the vertical angle (SPRVA) is proposed. In SPRVA, the forwarding node determines the best next-hop according to main priority. When the main priorities of candidate nodes are the same, the alternative priority is used. The main priority is denoted by the residual energy and angle between propagation direction and depth direction, and the alternative priority is indicated by the link quality. SPRVA selects the node along the depth direction with more residual energy and better link quality as the best next-hop. In addition, a recovery algorithm is designed to avoid nodes in void areas as forwarding nodes. Simulation results show that SPRVA improves energy efficiency and decreases end-to-end communication delay.

Description: In this paper, a method to monitor the growth of bean plants from images taken in their vegetative stage is presented. Through a fuzzy system and the RGB image components, the growth stages of the plant are classified to achieve a powerful tool in the precision agricultural field, which can reduce cost and increase system portability. This also serves as a reference for the growth of the plant according to its age, vigor, and healthy that could help to create the necessary environmental conditions. To carry out this research, the development of twenty bean plants was periodically monitored from their germinal to the first trifoliate leaf stage. Images were obtained with controlled background and lighting; later they were segmented by color to count the pixel’s average for each case. These data were used to propose six different fuzzy systems to choose the best one within them. Finally, it was found that the artificial vision system can identify the vegetative stages of germination, emergence, primary leaves, and first trifoliate leaf.

Description: Wireless monitoring systems are currently a common part of both industrial and domestic solutions. In case of industrial use, it may be a sensory system at a point where it is not possible to implement a fixed line, or it is necessary to repeatedly change the position, respectively, the location of the measuring probes. This is, in particular, the monitoring of environmental parameters (temperature, humidity) in bulk materials, biomass, or agricultural crops in storage facilities. For domestic use, local weather devices and systems are part of smart households such as lock control (entrance gate, apartment entrance), light control, and HVAC control. Devices that utilize wireless technology for communication include IoT devices as well. This article describes the use of the VirtualWire communication protocol using radio frequency waves with a carrier frequency of 433MHz-900MHz. The main topic of the article is the design and implementation of a secure one-way communication channel. Such a solution consists of a transmitting device with implemented desired sensors and a receiving device that aggregates data from multiple transmitters.

Description: We propose a flexible pressure sensor based on polydimethylsiloxane (PDMS) and transparent electrodes. The transmittance of the total device is 82% and the minimum bending radius is 18 mm. Besides, the effect of annealing temperature on the mechanical properties of PDMS is reported here. The results show that the PDMS film under lower annealing temperature of 80°C has good compression property but poor dynamic response. While for higher temperatures, the compression property of PDMS films significantly reduced. The best compromise of annealing temperature between compression property and dynamic response is found for PDMS film of about 110°C. The pressure sensor under 110°C curing temperature shows a good sensitivity of 0.025 kPa−1 and robust response property. The device shows a promising route for future intelligent transparent sensing applications.

Description: In this paper we propose a global positioning algorithm of multiple assets based on Received Signal Strength (RSS) measurements that takes into account the gain uncertainties of each hardware transceiver involved in the system, as well as the uncertainties on the Log-Distance Path Loss (LDPL) parameters. Such a statistical model is established and its Maximum Likelihood Estimator (MLE) is given with the analytic expression of the Cramér-Rao Lower Bound (CRLB). Typical values of those uncertainties are given considering whether calibration is done in production, in situ, or if hardware is used uncalibrated, in order to know what is the expected accuracy in function of the calibration setup. Results are tested by numerical simulations and confronted to real measurements in different room configurations, showing that the theoretical bound can be reached by the proposed MLE algorithm.

Description: Self-positioning of submerged Autonomous Underwater Vehicles (AUVs) is a challenging task due to nonavailability of GPS signals. One of the most recent solutions for this is the use of surface vehicles (sensors) for cooperative localization of the underwater vehicles (targets) by measuring their relative positions. However, correct placement of the surface sensors is very critical as their geometric configuration affects their observability and hence availability of their relative positions information to the targets. In this paper, a comparative survey of sensors’ optimal formation techniques for cooperative localization of AUVs has been presented. Introduction to the basic cooperative localization techniques and background theory of optimal sensor placements have been provided. This paper can also serve as a fundamental reading material for students and researchers pursuing research on optimal sensor placement for cooperative localization.

Description: An easier method for the calibration of differential drive robots is presented. Most calibration is done on-board and it is not necessary to spend too much time taking note of the robot’s position. The calibration method does not need a large free space to perform the tests. The bigger space is merely in a straight line, which is easy to find. The results with the method presented are compared with those from UMB for reference, and they show very little deviation while the proposed calibration is much simpler.

Description: The purpose of this research was to analyze the quality and characteristics of four digital elevation models (DEMs) generated using in-track Korea Multi-Purpose Satellite (KOMPSAT)-3 stereo images. The sensor modeling methods were based on ground control points (GCPs), the initial rational polynomial coefficients (RPCs), relative adjustment, and the automatic bias-compensation method. The GCPs and check points (CPs) were extracted from the 0.25 m aerial orthoimage and the 5 m DEM provided by the National Geographic Information Institute (NGII). The DEMs had the same resolution as the reference DEM (5 m) and comparative analysis was carried out. The results indicate that when relative adjustment was applied alone (DEM 3), the percentage of matched points with a correlation of 0.8 or more was improved by at least 17% compared to the case where only initial RPCs were used (DEM 2). Although the absolute horizontal position error of DEM 3 could not be eliminated, the relative elevation error at the same position was reduced significantly. Therefore, if the relative positions of DEMs produced at different times can be corrected, they can be used for the detection of changes in altitude. When applying the automatic bias-compensation method (DEM 4) without GCPs, the percentage of matched points with a correlation of 0.8 or more was 70.1%. When GCPs were used (DEM 1), the value was 70.2%, i.e., almost identical to that of DEM 4. The mean difference in resolution among DEMs 1 and 4 was -1.8 ± 3.4 m (median, -1.0 m). The results show that DEMs of sufficient quality can be generated without GCPs. Furthermore, although discrepancies among the DEMs were noted in forest and shadow areas, it is possible to produce a 5~10 m resolution DEM by using additional image processing techniques, such as shadow removal.

Description: Medical studies have intensively demonstrated that sports activity can enhance both the mental and the physical health of practitioners. In recent years, fitness activity became the most common way to motivate and engage people in sports activity. Recently, there have been multiple attempts to elaborate on the “ideal” IoT-based solution to track and assess these fitness activities. Most fitness activities (except aerobic activities like running) involve one or multiple interactions between the athlete’s hand palms and body or between the hand palms and the workout materials. In this work, we present our idea to exploit these biomechanical interactions of the hand palms to track fitness activities via a smart glove. Our smart glove-based system integrates force-sensitive resistor (FSR) sensors into wearable fitness gloves to identify and count fitness activity, by analyzing the time series of the pressure distribution in the hand palms observed during fitness sessions. To assess the performance of our proposed system, we conducted an experimental study with 10 participants over 10 common fitness activities. For the user-dependent activity recognition case, the experimental results showed 88.90% of the score for overall activity recognition. The result of leave-one-participant-out cross-validation showed an score ranging from 58.30% to 100%, with an average of 82.00%. For the exercise repetition count, the system achieved an average counting error of 9.85%, with a standard deviation of 1.38.

Description: We developed a high-sensitivity magnetoimpedance magnetic field sensor using a FeCoSiB amorphous wire and a coil wound around it. The amorphous wire had the diameter of 0.1 mm and the length of 5 mm. The magnetic field resolution of about 20 pT/√Hz was achieved. But the dynamic range of the magnetoimpedance magnetic field sensor was only about ±0.7 Gauss, which was not enough for some applications, such as the defect evaluation of steel plate. The linearity of the system was also not good when big magnetic field was applied, which will cause some noise when the system is used in unshielded environment. We developed a feedback method to improve the dynamic range and the linearity of the magnetic field sensor. The operation point of the magnetic field sensor was fixed by sending a feedback current to the coil. Using the feedback method, the dynamic range was improved from ±0.7 Gauss to ±10 Gauss and the linearity was also improved over 100 times better. An eddy current testing system using the magnetic sensor was developed, and the crack defects in steel plate and in 3D-printed titanium alloy plate were evaluated.

Description: A wireless sensor network consists of numerous low-power microsensor devices that can be deployed in a geographical area for remote sensing, surveillance, control, and monitoring applications. The advancements of wireless devices in terms of user-friendly interface, size, and deployment cost have given rise to many smart applications of wireless sensor networks (WSNs). However, certain issues like energy efficiency, long lifetime, and communication reliability restrict their large scale utilization. In WSNs, the cluster-based routing protocols assist nodes to collect, aggregate, and forward sensed data from event regions towards the sink node through minimum cost links. A clustering method helps to improve data transmission efficiency by dividing the sensor nodes into small groups. However, improper cluster head (CH) selection may affect the network lifetime, average network energy, and other quality of service (QoS) parameters. In this paper, a multiobjective clustering strategy is proposed to optimize the energy consumption, network lifetime, network throughput, and network delay. A fitness function has been formulated for heterogenous and homogenous wireless sensor networks. This fitness function is utilized to select an optimum CH for energy minimization and load balancing of cluster heads. A new hybrid clustered routing protocol is proposed based on fitness function. The simulation results conclude that the proposed protocol achieves better efficiency in increasing the network lifetime by 63%, 26%, and 10% compared with three well-known heterogeneous protocols: DEEC, EDDEEC, and ATEER, respectively. The proposed strategy also attains better network stability than a homogenous LEACH protocol.

Description: In this work, a low-cost resistive gas sensor based on graphene grown by CVD was fabricated and its sensitivity was studied in terms of defect density. CVD graphene was transferred using Polyurethane as sacrifice layer with low contamination and defect-free results. An atmospheric plasma etching system was used to homogeneously induce defects on the sensor’s active area, as investigated through Raman spectroscopy. Device sensing properties were significantly enhanced for greater defect density for both NH3 and NO2. The modified sensors were submitted to different concentrations of both target gas to assess detection limits and overall behavior. It was revealed that defective CVD graphene devices possess sensitivity up to ppm range with linear dependence in the range of values measured. The fabricated sensors presented little to no signal degradation after months of atmospheric exposure.

Description: The structural health monitoring (SHM) of large and complex infrastructures as well as laboratory tests of new structures and materials resorts to strain gauge measurements to check mechanical stress. A wireless measurement of the strain gauge response is desirable in many practical applications to avoid the cost and the difficulty of wiring, particularly in large structures requiring several sensors and in complex objects where the measurement points are difficult to access. In this paper, a wireless strain gauge which is a hybrid between an RFID tag and a usual thin-film resistive strain gauge is experimented. Installation and maintenance problems of the wireless sensor networks are overcome allowing a high level of measurement accuracy, comparable to that of wired strain sensors, together with a long measurement distance. A large set of measurements has been performed using reference specimens and readings in order to validate the sensor and to develop a calibration procedure that makes the sensor suitable for a large number of different applications in civil engineering.

Description: Sun position and the optimum inclination of a solar panel to the sun vary over time throughout the day. A simple but accurate solar position measurement system is essential for maximizing the output power from a solar panel in order to increase the panel efficiency while minimizing the system cost. Solar position can be measured either by a sensor (active/passive) or through the sun position monitoring algorithm. Sensor-based sun position measuring systems fail to measure the solar position in a cloudy or intermittent day, and they require precise installation and periodic calibrations. In contrast, the sun position algorithms use mathematical formula or astronomical data to obtain the station of the sun at a particular geographical location and time. A standalone low-cost but high-precision dual-axis closed-loop sun-tracking system using the sun position algorithm was implemented in an 8-bit microcontroller platform. The Astronomical Almanac’s (AA) algorithm was used for its simplicity, reliability, and fast computation capability of the solar position. Results revealed that incorporation of the sun position algorithm into a solar tracking system helps in outperforming the fixed system and optical tracking system by 13.9% and 2.1%, respectively. In summary, even for a small-scale solar tracking system, the algorithm-based closed-loop dual-axis tracking system can increase overall system efficiency.

Description: Los Angeles has undergone tremendous deformations over the past few decades, mainly due to human factors such as natural disasters and earthquakes, urban construction, overexploitation of groundwater, and oil extraction. The purpose of this study is to map the temporal and spatial variations of land subsidence in Los Angeles and to use the improved SBAS (small baseline subset) technique and multisensor SAR datasets to analyze the causes of deformations in this area from October 2003 to October 2017. At the same time, the deformation results of SBAS inversion are compared with the GPS measurements and the multisensor SAR dataset deformation, and the results are highly consistent. During the period from 2003 to 2017, there were several subsidence regions and one uplift region in Los Angeles. The cumulative subsidence was -266.8 mm at the maximum, and the average annual subsidence velocity was -19 mm/yr, which was mainly caused by groundwater overexploitation. The maximum amount of accumulated lift is +104.8 mm, and the average annual lifting velocity can reach +7.5 mm/yr. Our results have very strong practical application value and can provide a significant basis for local government services in disaster prevention and mitigation decision-making.

Description: This paper proposes a proof-of-concept, low-cost, and easily deployable Bluetooth low energy- (BLE-) based localization system which actively scans and localizes BLE beacons attached to mobile subjects in a room. Using the received signal strength (RSS) of a BLE signal and the uniqueness of BLE hardware addresses, mobile subjects can be identified and localized within the hospital room. The RSS measurement of the BLE signal from a wearable BLE beacon varies with distance to the wall-anchored BLE scanner. In order to understand and demonstrate the practicality of the relationship between RSS of a BLE beacon and the distance of a beacon from a scanner, the first part of the paper presents the analysis of the experiments conducted in a low-noise and nonreflective environment. Based on the analysis conducted in an ideal environment, the second half of the paper proposes a data-driven localization process for pinpointing the movements of the subject within the experimental room. In order to ensure higher accuracy like fingerprinting techniques and handle the increased number of BLE-anchored scanners like geometric techniques, the proposed algorithm was designed to combine the best aspects of these two techniques for better localization. The paper evaluates the effects of the number of BLE wall-mounted scanners and the number of packets on the performance of the proposed algorithm. The proposed algorithm locates the patient within the room with error less than 1.8 m. It also performs better than other classical approaches used in localization.

Description: During the pigging process, a pig labeled with magnets can be effectively detected by monitoring the magnetic fluctuation (MF) introduced when the pig passes by. In order to analyze the influence of various factors on the MF, the principle for magnetic fluctuation detection (MFD) is described, and a detection model is established here. The influence of model parameters, such as pipeline geometries, geomagnetic characteristics, as well as permeability of pipeline, on the magnetic anomaly distribution along the measured line is analyzed. The study reveals that with the increase in pipeline parameters (thickness, permeability, and outer diameter), the MF detected decreases. The pipeline length will have little influence on the MF. With an increased number of magnets, the MF increases while remaining almost unchanged at two ends of the measuring line. Attention should be paid to the installation of magnets to ensure the consistency of the magnetic moments. With the increase in geomagnetic intensity and declination, the MF will be almost unaffected, while the change of geomagnetic inclination will introduce an obvious change to the MF. In field application, measuring points can be set along and above the pipeline with a certain interval. From the magnetic anomaly measured, it can be determined whether the pig has passed by the point or not.

Description: Immunosensor sensitivity and stability depend on a number of parameters such as the orientation, the surface density, and the antigen-binding efficiency of antibodies following their immobilization onto functionalized surfaces. A number of techniques have been developed to improve the performance of an immunosensor that targets one or both of the parameters mentioned above. Herein, two widely employed techniques are compared for the first time, which do not require any complex engineering of neither the antibodies nor the surfaces onto which the former get immobilized. To optimize the different surface functionalization protocols and compare their efficiency, a model antibody-antigen system was employed that resembles the complex matrices immunosensors are frequently faced with in real conditions. The obtained results reveal that protein A/G is much more efficient in increasing antibody loading onto the surfaces in comparison to boronate ester chemistry. Despite the fact, therefore, that both contribute towards the orientation-specific immobilization of antibodies and hence enhance their antigen-binding efficiency, it is the increased antibody surface density attained with the use of protein A/G that plays a critical role in achieving maximal antigen recognition.

Description: In this study, a novel gold-coated test strip for blood glucose measurement has been designed. Such gold-coated test strip is feasible for mass production to achieve economies of scale. Cyclic voltammetry was applied to test strips to undergo electrochemical reaction under a potential range of ±0.4 V. Glucose oxidase (GOD) was added into K3[Fe(CN)6]. When glucose oxidase undergoes electrochemical reaction, the medium, K3[Fe(CN)6], will act as an electron acceptor, causing the electrodes on the test strip to generate a pair of clear anodic and reductive peaks. The maximum of the anodic and reductive peaks can be used as reference to adjust the resistance of the blood glucose meter. The experimental results show that by adjusting the resistance of the blood glucose meter, the accuracy of blood glucose meter reading can be tuned and blood glucose reading can be stabilized. Therefore, when the resistance of the blood glucose meter is at 2.4 KΩ, the standard deviation (STD) and coefficient of variation (CV) of the test strip are lower than those of the test strips measured at resistances of 2.2 KΩ and 2.6 KΩ. It has been proved in this study that adjusting the resistance of the blood glucose meter can optimize the chemical reaction on gold-coated test strips as well as its reading. This method can also be applied to tune the accuracy of readings for test strips coated with other materials.

Description: Image-based measurement has received increasing attention as it can substantially reduce the cost of labor, measurement equipment, and installation process. Instead of using optical flow, pattern, or marker tracking to extract a displacement signal, in this study, a novel noncontact machine learning-based system was proposed to directly predict vibration frequency with high accuracy and good reliability by using image sequences acquired from a single camera. The performance of the proposed method was demonstrated through experiments conducted in a laboratory and under real-field conditions and compared with those obtained using a contacted sensor. The vibration frequency prediction results of the proposed method are compared with industry-level vibration sensor results in the frequency domain, demonstrating that the proposed method could predict the target-object-vibration frequency as accurately as an industry-level vibration sensor, even under uncontrollable real-field conditions with no additional enhancement or extra signal processing techniques. However, only the principal vibration frequency of a measurement target is predicted, and the measurement range is limited by the trained model. Nonetheless, if these limitations are resolved, this method can potentially be used in real engineering applications in mechanical or civil structural health monitoring thanks to the simple deployment and concise pipeline of this method.

Description: The paper presents the results of design, manufacturing, and characterization of a hybrid broad band in-line fiber-optic device. It uses nematic liquid crystal as cladding with electro-steering properties in a biconical optical fiber taper structure. Liquid crystal mixtures denoted as 6CHBT and E7 are designed for electric as well as temperature control of electromagnetic wave propagation in a broad wavelength range. The applied taper with  μm diameters has losses lower than 0.5 dB in whole investigated spectrum range. Three kinds of initial liquid crystal molecules’ orientations (parallel, orthogonal, and twist) in relation to the light beam propagating in a taper were applied. The performance of a tuned cladding was studied at an electric field of the range of 0–190 V and the temperature range from 20°C up to 42°C and 59°C for 6CHBT and E7, respectively. The induced reorientation of liquid crystal molecules was measured at a broad wavelength range (550-1550 nm).

Description: Event-driven monitoring policies enable to significantly reduce the power consumption of wireless sensor networks by reducing the recording period to those time intervals that provide valuable data. The resulting longer operation lifetime increase discloses fields of application that require long monitoring periods. This paper presents a structural monitoring system that uses specialized sentinel nodes for detecting possibly heavy road vehicles and for alarming monitoring nodes, which are specialized on strain sensing. Heavy vehicles are identified by estimating nearly in real time height and length of vehicles of a traffic flow by processing data recorded from low-cost ultrasonic and magnetic displacement sensors. Field tests demonstrated that while height detection is very reliable, length detection is too imprecise to discriminate with high success rates between trucks and delivery vans.

Description: As portable spectrometers have been developed, the research of spectral analysis has evolved from a traditional laboratory-based closed environment to a network-connected open environment. Consequently, its application areas are expanding in combination with machine learning techniques. The device-to-device variation in the spectral response of portable spectrometers is a critical issue in a machine learning-based service scenario since the classification performance is highly dependent on the consistency of spectral responses from each spectrometer. To minimize device-to-device variation, a cuboid prism is employed instead of a combination of mirrors and prism to construct an optical system for the spectrometer. The spectral responses are calibrated to correct pixel shift on the image sensor. Experimental results show that the proposed method can minimize the device-to-device variation in spectral response of portable spectrometers.

Description: Precision of guided projectiles depends equally on the accuracy in determining the coordinates of the objective and on the exactness of the measurement devices utilized for position and attitude calculation of the projectile. Development of algorithms for low-cost high-precision terminal guidance systems is a cornerstone in research in this field. Semiactive laser (SAL) kits, and particularly quadrant detector devices, have been developed to improve precision in guided weapons. Photodetection system can be functionally divided into two main parts: sensing and processing. The sensed signal is processed to estimate the spot coordinates, i.e., the laser footprint, which provides some information regarding projectile-target relative position, to obtain the needed information for the navigation and guidance algorithms. The electrical intensities that a real sensor provides under laboratory conditions are compared to a mathematical model based on area intersection calculations to simulate the intensities on real flights. Then, four different processing algorithms, two of them rational, and the other two logarithmic, are tested for different spot sizes, which are nonlinear. Proposing an interpolation algorithm based on the four electrical intensities obtained in a semiactive laser quadrant photodetector, laser footprint center estimation is improved for artillery applications. Finally, an example illustrating a projectile flight is employed to compare real and calculated laser footprints in order to select the best algorithm for artillery applications.

Description: Biosensors can play an important role in early disease detection, a reason why they are gaining more attention in the world of biomedicine. Functionalization of the material used in the detector is of a great importance since it maintains the molecule’s structure of interest with minimal changes. We report on sensing BSA molecules, solutions, and concentrations using a functionalized commercial resistor in a simple electric circuit. Our results demonstrate the outstanding utility of functionalization in biosensing devices; while sensing is not possible with a naked resistor, a BSA covered resistor can detect a very low solution concentration around 0.1 fM. A smaller molecule like tryptophan was also used in order to functionalize the resistor. After proving that tryptophan is more effective in covering the resistor before sensing, BSA molecules in other solvent conditions were detected, and a threshold of 1 μM was obtained. This can prove that sensing depends on the choice of the functionalizations of the material used for sensing and on the conformation of the molecule of interest in its solutions. This method of detection may be of great interest in triggering and sensing biological molecules using simple-based devices.

Description: The development and the corresponding evaluation of a multidirectional thermal flow sensor are presented in this work. The sensor was fabricated on a flexible substrate, allowing for new applications, since it provides the possibility of installation in nonplanar surfaces such as pipelines. Furthermore, the sensing elements are not in direct contact with the fluid, which increases the device reliability, extends its application range, and allows the noncontact monitoring of fluids. This was achieved by utilizing the substrate as a protective layer between the sensing elements and the fluid under measurement. The operation principle is based on the determination of the flow-induced temperature profile variations. A dedicated experimental setup was designed and used for the device evaluation. Both flow velocity value and direction were successfully extracted, while the results were consistent with the predicted theoretical values. A single-layer back propagation neural network that correlates the sensors’ readouts to the angle of rotation was implemented, which leads to a mean absolute direction estimation error in the order of 2.7 degrees independent to the training procedure datasets.

Description: Node location is one of the most important problems to be solved in practical application of WSN. As a typical location algorithm without ranging, DV-Hop is widely used in node localization of wireless sensor networks. However, in the third phase of DV-Hop, a least square method is used to solve the nonlinear equations. Using this method to locate the unknown nodes will produce large coordinate errors, poor stability of positioning accuracy, low location coverage, and high energy consumption. An improved localization algorithm based on hybrid chaotic strategy (MGDV-Hop) is proposed in this paper. Firstly, a glowworm swarm optimization of hybrid chaotic strategy based on chaotic mutation and chaotic inertial weight updating (MC-GSO) is proposed. The MC-GSO algorithm is used to control the moving distance of each firefly by chaos mutation and chaotic inertial weight when the firefly falls into a local optimum. The experimental results show that MC-GSO has better convergence and higher accuracy and avoids the premature convergence. Then, MC-GSO is used to replace the least square method in estimating node coordinates to solve the problem that the localization accuracy of the DV-Hop algorithm is not high. By establishing the error fitness function, the linear solution of coordinates is transformed into a two-dimensional combinatorial optimization problem. The simulation results and analysis confirm that the improved algorithm (MGDV-Hop) reduces the average location error, increases the location coverage, and decreases and balances the energy consumption as compared to DV-Hop and the location algorithm based on classical GSO (GSDV-Hop).

Description: In this paper, we propose an adaptive triangular deployment algorithm that can adjust sensor distribution depending on the variation in communication performance in an underwater environment. To predict the distance between sensor nodes, a performance surface model is implemented by estimating the communication performance based on spatio-temporal environment factors affecting the communication performance of the underwater sensor node. Subsequently, the performance surface model is applied to the adaptive triangular deployment algorithm and is used to control the distance between nodes. Therefore, underwater wireless sensor networks deployed with adaptive triangular deployment algorithms can achieve a maximum connectivity rate with an optimal number of nodes.

Description: Low back pain affects around 50% of pregnant women and presents significant morbidity and persistence for years in 20% of postpartum women who report that pain. Numerous studies have documented gait alterations during pregnancy and postpartum. Therefore, an analysis of the relationship between certain gait parameters and low back pain was attempted using low-cost validated instrumented insoles. This work presents a longitudinal cohort study carried out during routine gynecological follow-up visits in the first, second, and third trimesters of pregnancy at an Obstetrics and Gynecology Service. Sample size was 62. Plantar pressure data were collected with specially designed instrumented insoles containing four force sensors to measure peak pressure, center of pressure, and stance phase time in each foot and in each pregnancy trimester. The analysis was carried out on a two-dimensional level, simultaneously considering the data from both feet using Hotelling’s test. This longitudinal study detected relationships between certain gait parameter changes and low back pain during pregnancy. It revealed a cyclic tendency of low back pain prevalence with a maximum in the second trimester and a decrease in the third trimester, which was correlated with alterations of the pregnant gait: excessive foot pronation and rearfoot pressure increase.

Description: Over the past few years, there has been a growing awareness regarding the concept of Internet of Things (IoT), which involves connecting to the Internet various objects surrounding us in everyday life. The main purpose of this concept closely connected to the smart city issue is increasing the quality of life by contributing to streamlining resource consumption and protecting the environment. The LoRa communication mechanism is a physical layer of the LoRaWAN protocol, defined by the LoRa Alliance. Compared to other existing technologies, LoRa is a modulation technique enabling the transfer of information over a range of tens of kilometers. The main contribution this paper brings to the field is analyzing the scalability of the LoRa technology and determining the maximum number of sensors which can be integrated into this type of monitoring and control architecture. The sensor architecture is specific to the smart city concept that involves the integration of a large number of high-density sensors distributed on a large-scale geographic area. The reason behind this study is the need to assess the scalability of the LoRa technology, taking into consideration other factors, such as the packet payload size, the duty circle parameter, the spreading factor, and the number of nodes. The experimental results reveal that the maximum number of LoRa sensors that can communicate on the same channel is 1,500; furthermore, in order to obtain a high performance level, it is necessary to schedule and plan the network as carefully as possible. The spreading factor must be allocated according to the distance at which the sensor is placed from the gateway.

Description: Seeding absence detection is essential during seeding operation, since it affects the subsequent crop performance. Existing methods cannot detect the seeding absence position immediately after planting without soil disturbance. In this paper, a nondestructive detection method for finding out the seeding absence position of drills is put forward. It focuses on the echo energy reflected by the circle energy inside the tilled cropland soil, to which the sensor is attached directly on the soil surface, not on ultrasonic waves that penetrate the soil-seed medium below tilled soil. Firstly, the energy circle is used to analyze the sound field distribution characteristics of the sensor in cropland soil. According to the size difference of the seeding absence length value and energy circle diameter, the total energy for three different cases with eight steps for each case is discussed in detail, and in order to find the left and right boundary lines and the length value of seeding absence, a program is designed to help with calculating four base positions automatically. At last, the nondestructive detection method is evaluated by the experiments, and the results demonstrate that the proposed method is accurate, efficient, and convenient in finding the seeding absence position of drilling seeds on the soil surface without soil disturbance.

Description: Mobile Robot Indoor Positioning System has wide application in the industry and home automation field. Unfortunately, existing mobile robot indoor positioning methods often suffer from poor positioning accuracy, system instability, and need for extra installation efforts. In this paper, we propose a novel positioning system which applies the centralized positioning method into the mobile robot, in which real-time positioning is achieved via interactions between ARM and computer. We apply the Kernel extreme learning machine (K-ELM) algorithm as our positioning algorithm after comparing four different algorithms in simulation experiments. Real-world indoor localization experiments are conducted, and the results demonstrate that the proposed system can not only improve positioning accuracy but also greatly reduce the installation efforts since our system solely relies on Wi-Fi devices.

Description: When the longitudinal mode guided waves based on magnetostrictive effect were employed to inspect the bridge cables, we found that there was a large difference in the signal’s amplitude of the same specification cable under different tensile force. This difference would affect the test results and the identification of defects. It is necessary to study the effect of tensile force on the signal for the reliability of detection. Firstly, the effective field theory is employed to take the force as an additional bias magnetic field. Then, the effect of the tensile force on generating and receiving longitudinal mode guided waves based on magnetostrictive effect is obtained by the relationship between the bias magnetic field and the magnetostrictive coupling coefficient. Finally, the experiment of the magnetostrictive sensor is carried out on a  mm steel wire under different force. The experimental results are in good agreement with the theoretical results. The results show that the existence of the tensile force would change the operation point for generating and receiving the longitudinal mode guided waves based on magnetostrictive effect, which associated with the coupling coefficient. In order to obtain the optimal conversion efficiency for the force state wire and cable, the applied bias magnetic field should be set smaller than the bias magnetic field for the force-free state.

Description: A digital mode-matching control system based on feedback calibration, where two pilot tones are applied to actuate the sense mode by the robust feedback controller, is presented for a MEMS gyroscope in this paper. A dual-mass decoupled MEMS gyroscope with the integrated electrostatic frequency tuning mechanisms, the quadrature correction electrode, and the feedback electrode is adopted to implement mode-matching control. Compared with the previous mode-matching method of forward excitation calibration, the proposed mode-matching scheme based on feedback calibration has better adaptability to the variation in the frequency of calibration pilot tones and the quality factor of the sense mode. The influences of calibration pilot tone frequency and the amplitude ratio on tuning performance are studied in theory and simulation. The simulation results demonstrate that the tuning error due to the amplitude asymmetry of the sense mode increases with a frequency split between pilot tones and the drive mode and is significantly reduced by the amplitude correction technology of pilot tones. In addition, the influence of key parameters on the stability of the mode-matching system is deduced by using the average analysis method. The MATLAB simulation of the mode-matching control system illustrates that simulation results have a good consistency with theoretical analysis, which verifies the effectiveness of the closed-loop mode-matching control system. The entire mode-matching control system based on a FPGA device is implemented combined with a closed-loop self-excitation drive, closed-loop force feedback control, and quadrature error correction control. Experimental results demonstrate that the mode-matching prototype has a bias instability of 0.63°/h and ARW of 0.0056°/h1/2. Compared with the mode-mismatched MEMS gyroscope, the performances of bias instability and ARW are improved by 3.81 times and 4.20 times, respectively.

Description: As IoT systems spread, transmissions of all data from various sensing devices to a remote OM (Operation and Management) server through the Internet can lead to many problems, such as an explosion of network traffic and delayed responses to data. Fog computing is a good means of resolving these problems in an IoT system environment. In this paper, a management method for sensor data in a fog computing node is proposed. The monitoring node monitors data from sensor devices using a data pattern from the OM server, which dynamically generates and updates the pattern. The monitoring node reports only the data beyond the normal range of the pattern to the OM server rather than sending all data to the OM server. The monitoring node can control the operations of sensor devices remotely according to the requests of the OM server.

Description: Human communication relies on several aspects beyond the speech. One of them is gestures as they express intentions, interests, feelings, or ideas and complement the speech. Social robots need to interpret these messages to allow a more natural Human-Robot Interaction. In this sense, our aim is to study the effect of position and speed features in dynamic gesture recognition. We use 3D information to extract the user’s skeleton and calculate the normalized position for all of its joints, and using the temporal variation of such positions, we calculate their speeds. Our three datasets are composed of 1355 samples from 30 users. We consider 14 common gestures in HRI involving upper body movements. A set of classification techniques is evaluated testing these three datasets to find what features perform better. Results indicate that the union of both speed and position achieves the best results among the three possibilities, 0.999 of -score. The combination that performs better to detect dynamic gestures in real time is finally integrated in our social robot with a simple HRI application to run a proof of concept test to check how the proposal behaves in a realistic scenario.

Description: Rural traffic network (RTN), as a complex network, plays a significant role in the field of resisting natural disasters and emergencies. In this paper, we analyze the vulnerability of RTN via three traffic network models (i.e., No-power Traffic Network Model (NTNM), Distance Weight Traffic Network Model (DWTNM), and Road Level Weight Traffic Network Model (RLWTNM)). Firstly, based on the complex network theory, RTN is constructed by using road mapping method, according to the topological features. Secondly, Random Attack (RA) and Deliberate Attack (DA) strategies are used to analyze network vulnerability in three rural traffic network models. By analyzing the attack tolerance of RTN under the condition of different attack patterns, we find that the road level weight traffic network has a good performance to represent the vulnerability of RTN.

Description: Thaw slumps are well-developed within a 10 km wide zone along the Qinghai-Tibet engineering corridor, especially along the Qinghai-Tibet highway and railway. Previous studies have focused on thaw slump instability such as its origin development, headwall retrogression rate, failure scale, and thermal regime, yet the intrinsic dynamic process of surface movement is relatively less known. In this study, we used InSAR based on the L-band ALOS PALSAR images acquired from January 2007 to October 2010 to investigate the distribution of thaw-induced slope failures containing retrogressive thaw slumps and active layer detachment failures along the Qinghai-Tibet highway (QTH). Our InSAR analysis reveals that the maximum annual average sedimentation rates are even up to -35 mm·yr−1 in the slope direction to the K3035 thaw slump, and the K3035W active layer detachment failure developed on the west side of K3035. The distribution, failure extent, and stability of the slope failures obtained by our InSAR analysis all agree well with the field investigations. Our study illustrates that InSAR is an effective tool for studying the distribution and processes of the thaw slump-derived thermokarst and provides useful references for evaluating permafrost degradation in response to climate warming and external disturbance on the Qinghai-Tibet plateau.

Description: Autonomous energy harvesting sensors present one of the most attractive areas of microelectronics at the moment. They are a part of Internet of Things (IoT) systems so the data need to be protected across transmission. One way for data protection is encryption and the other way is digital signature. However, energy consumption of those systems is increased using protections algorithms, and it should be considered because these are energy harvesting systems. The paper describes the ways in which data encryption and digital signature algorithms can be implemented in resource limited systems based on an 8-bit microcontroller. Alongside the implementation method, the paper deals with the energy demands of the selected encryption algorithms and digital signatures. The execution time, energy consumption, and memory consumption will be considered.

Description: Plant disease is one of the primary causes of crop yield reduction. With the development of computer vision and deep learning technology, autonomous detection of plant surface lesion images collected by optical sensors has become an important research direction for timely crop disease diagnosis. In this paper, an anthracnose lesion detection method based on deep learning is proposed. Firstly, for the problem of insufficient image data caused by the random occurrence of apple diseases, in addition to traditional image augmentation techniques, Cycle-Consistent Adversarial Network (CycleGAN) deep learning model is used in this paper to accomplish data augmentation. These methods effectively enrich the diversity of training data and provide a solid foundation for training the detection model. In this paper, on the basis of image data augmentation, densely connected neural network (DenseNet) is utilized to optimize feature layers of the YOLO-V3 model which have lower resolution. DenseNet greatly improves the utilization of features in the neural network and enhances the detection result of the YOLO-V3 model. It is verified in experiments that the improved model exceeds Faster R-CNN with VGG16 NET, the original YOLO-V3 model, and other three state-of-the-art networks in detection performance, and it can realize real-time detection. The proposed method can be well applied to the detection of anthracnose lesions on apple surfaces in orchards.

Description: The ripening process in bananas causes the waste of a significant part of the production of this fruit. The aim of this research is to find a new technique useful for identifying, registering, and quantifying the ripening process of a banana (Musa AAB Simmonds) at the seventh stage of the growing process. This quantification is proposed with a nondestructive technique based on processing multispectral images. This experiment used a set of multispectral imagery registered in a range of 270-1000 nm (from UV to IR) with the aid of a monochromatic camera and a set of 10 optical filters. Multispectral images were analyzed with three different techniques: Fourier fractal analysis, Hotelling transform, and homogeneity texture analysis based on cooccurrence matrix. First, a characteristic index was computed for each technique for a daily set of multispectral imagery. These indexes are slope index, for Fourier fractals; the average of the computed eigenvalues, with Hotelling transform; and the texture homogeneity value. These indexes were evaluated using the behavior of the resulting graphs for a seven-day period, being preferred those graphs with a tendency of decreasing values. Finally, the repeatability of each technique was evaluated by reproducing similar values for each day during the evaluation period. These three methods will be compared in this article in order to select the one with the best performance for measuring the ripening process in bananas. The obtained results show that it is possible to effectively isolate the brown spots from the banana peel with the Hotelling transform by using only 2 optical filters: visible (410-690 nm) and Near-IR (820-910 nm). With the resulting spectral image fusion it was possible to effectively describe the evolution of the brown spots present in the ripening process through the texture homogeneity criteria.

Description: Light detection and ranging (LiDAR) data collected from airborne laser scanning systems are one of the major sources of spatial data. Airborne laser scanning systems have the capacity for rapid and direct acquisition of accurate 3D coordinates. Use of LiDAR data is increasing in various applications, such as topographic mapping, building and city modeling, biomass measurement, and disaster management. Segmentation is a crucial process in the extraction of meaningful information for applications such as 3D object modeling and surface reconstruction. Most LiDAR processing schemes are based on digital image processing and computer vision algorithms. This paper introduces a shape descriptor method for segmenting LiDAR point clouds using a “multilevel cube code” that is an extension of the 2D chain code to 3D space. The cube operator segments point clouds into roof surface patches, including superstructures, removes unnecessary objects, detects the boundaries of buildings, and determines model key points for building modeling. Both real and simulated LiDAR data were used to verify the proposed approach. The experiments demonstrated the feasibility of the method for segmenting LiDAR data from buildings with a wide range of roof types. The method was found to segment point cloud data effectively.

Description: Vehicle detection is one of the most important environment perception tasks for autonomous vehicles. The traditional vision-based vehicle detection methods are not accurate enough especially for small and occluded targets, while the light detection and ranging- (lidar-) based methods are good in detecting obstacles but they are time-consuming and have a low classification rate for different target types. Focusing on these shortcomings to make the full use of the advantages of the depth information of lidar and the obstacle classification ability of vision, this work proposes a real-time vehicle detection algorithm which fuses vision and lidar point cloud information. Firstly, the obstacles are detected by the grid projection method using the lidar point cloud information. Then, the obstacles are mapped to the image to get several separated regions of interest (ROIs). After that, the ROIs are expanded based on the dynamic threshold and merged to generate the final ROI. Finally, a deep learning method named You Only Look Once (YOLO) is applied on the ROI to detect vehicles. The experimental results on the KITTI dataset demonstrate that the proposed algorithm has high detection accuracy and good real-time performance. Compared with the detection method based only on the YOLO deep learning, the mean average precision (mAP) is increased by 17%.

Description: Background. Obesity in children is highly prevalent in Mexican population. Adipose tissue has been related to specific pro- and anti-inflammatory cytokine and inflammasome gene and protein expression patterns. Actually, there is no existing biosensor for detecting gene expression patterns in children with obesity. The quartz crystal microbalance with dissipation monitoring (QCM-D) has been used as a transducer for DNA biosensor design. Results. In this study, the gene expression pattern of IL-1β, NLRP3, and CASPASE-1 in children with obesity was successfully determined by means of QCM-D. Gene expression patterns were validated with those obtained by quantitative polymerase chain reaction (qPCR), a validated molecular biology technique for gene expression quantification. QCM-D analysis of the detected mass corresponding results for each of the genes showed a major detected mass for IL-1β, followed by similar NLRP3 and constitutive gene 18S deposited mass and a smaller deposited mass for CASPASE-1. Surprisingly, when comparing mRNA gene expression results for NLRP3, IL-1β, and CASPASE-1 obtained with qPCR and QCM-D, similar patterns were found, revealing greatest expression of IL-1β, followed by NLRP3, with CASPASE-1 being the molecule of least expression in the group of children with obesity. AFM images illustrate the step-by-step changes that took place on the quartz surface. Conclusions. QCM-D proved successfully for determining the gene transcripts and expression of NLRP3, IL-1β, and CASPASE-1 in children with obesity, with similar results validated by qPCR. “QCM-D decreases detection costs compared with a validated molecular biology technique.” The QCM-D biosensor developed by our group was successful for gene expression determination; in the future, it can be used for molecular diagnosis.

Description: A frozen or wet road surface is a cause of skidding and accidents, so road surface condition is important information for driving safety. Some instruments and methods have been developed to investigate road surface conditions based on optical imagery, although an active sensor is needed, regardless of the time of day. Recently, the laser scanner, which acquires backscattering intensity data related to reflectivity, has become popular in various fields. There is a need to investigate road surface conditions (frozen, wet, or dry) using laser backscattering intensity. This study tries to analyze signal characteristics of laser backscattering intensity to detect frozen and wet surfaces on roads. An ice target with a 7 cm thickness was placed on a road surface, and a wet surface was made due to the melting ice. The ice target, wet surface, dry surface, and roadside vegetation were scanned using a laser scanner. As a result, backscattering signals from the top surface of the ice target were missing due to its smoothness. Dry and wet asphalt surfaces showed distinguishable intensity ranges in their signals. The thick sidewall of the ice target and vegetation at the roadside showed overlapping intensity ranges. An ice sheet is only a few millimeters thick on a real road surface, and the roadside vegetation might be easily distinguished by using texture or auxiliary data. Therefore, laser backscattering intensity can be used to detect frozen, wet, and dry road surfaces, regardless of the time of day. The laser scanner can be installed to acquire information about road surface conditions from observation stations and vehicles in an application for transportation.

Description: Capacitive micromachined ultrasonic transducers (CMUTs) were reported to own high potential in air-coupled ultrasonic applications such as noncontact nondestructive examination and gas flow measurement. The unsealed CMUTs which utilized the squeeze film effect were reported to overcome the narrow output pressure bandwidth of the conventional sealed CMUTs in air operation. This kind of unsealed CMUTs can also be regarded as Helmholtz resonators. In this work, we present the air-coupled unsealed Helmholtz structural CMUTs which utilize both the squeeze film effect and the Helmholtz resonant effect to enhance the output pressure bandwidth. Based on the mechanism of vibration coupling between membrane and air pistons in membrane holes, we propose an analytical model to aid the design process of this kind of CMUTs. We also use finite element method (FEM) to investigate this kind of CMUTs for our analytical model validation. The FEM results show that the significant bandwidth enhancement can be achieved when the Helmholtz resonant frequency is designed close to the fundamental resonant frequency of the CMUT membrane. Compared with the conventional sealed CMUT cell, the 4-hole unsealed Helmholtz structural CMUT cell improves both the 3-dB fractional bandwidth and SPL-bandwidth product around 35 times. Furthermore, it is found that, with more holes under the same hole area ratio or with a smaller ratio of the cavity height to the viscous boundary layer thickness, the Helmholtz resonant effect becomes weaker and thus the output pressure bandwidth decreases.

Description: The coal mine working face overlying strata is often disturbed by multiple mining, leading to adverse effects on the working face’s safe production and ground surface movement. In the three-dimensional physical model test with the size of , after the overburden gets stable when the first working face had been extracted, by using three vertical distributed optical fibers based on the BOTDA principle, the deformation law of the overburden caused by the contiguous coal face mining is studied. Results show that, before the working face advanced to 840 mm (near the fiber), the stress law of the overburden was as follows: the middle of the model was under pressure state and the remaining part was under tension state, and the key stratum produced stress concentration phenomena caused by the secondary mining; when the face advanced to 840 mm (through the fiber), the frequency shift curve of the key stratum and the strata on it combined, and the stress concentration in the key stratum disappeared, indicating that the bearing structure of the key stratum gets unstable; compared with the previous monitoring data, when the working face far away from the fiber, the information reflected by the frequency shift data gradually gets single when the working face is far away from the fiber compared with the previous measurement data. The overburden deformation increased dramatically after the key stratum gets unstable. The surrounding rock and fiber will detach when the stratum goes though large deformation such as abscission layer, fracture, and collapse, and the frequency shift monitored by BOTDA cannot characterize the rock deformation in this situation. The experimental method and the results of this paper serve as useful reference for the application of BOTDA technology in geotechnical engineering.

Description: This paper describes the design and real-time implementation of a proposed algorithm for deriving an accurate heading system by fusing data from various inexpensive sensor devices that is comparable to more expensive maritime navigation systems. The proposed algorithm is a 3-Stage Classification N’ Weighing (CnW) Heading System with forward azimuth (FAz) and extended Kalman filter (EKF). Data from three Global Positioning System devices, an inertial measurement unit, and an electronic compass were fed into the algorithm that can be generally described as Classification N’ Weighing-Stage 1 → forward azimuth → Classification N’ Weighing-Stage 2 → extended Kalman filter → Classification N’ Weighing-Stage 3. The proposed algorithm is shown to be comparably accurate as an expensive marine navigation system, and it has less processing time compared to our previous work. The Qt-anywhere-based system developed on a Linux desktop was successfully downloaded onto an Ubuntu Linux-embedded board for real-time implementation. Important notes related to device naming problems when deploying the system on a Linux-embedded board are also given as reference for those interested to address it.

Description: A prestressed bolt connection is one of the crucial connection types in timber structures. The daily checking and maintenance of bolt connections have to be carried out in order to avoid the collapse of timber structures due to bolt looseness. Real-time health monitoring of bolt connections can not only reduce the daily maintenance cost of timber structures, but it can also avoid property loss and casualties by giving early warning if the bolt connection is loosened in timber structures. This paper proposes a method of prestress monitoring of bolt joints in timber structures by pasting lead zirconate titanate (PZT) patches on the surface of timber structures, and the time-reversal method is applied to denote the connection status of bolts in timber structures. The prestress loss index of timber structural bolts based on wavelet analysis is designed to quantify the bolt looseness of the timber structure. The experimental timber specimen was fabricated consisting of two timber panels, one bolt, and two PZT patches. One of the PZT patches acted as an actuator to emit the stress waves, and another one acted as a sensor to receive the stress wave propagating through the connection interface. The experimental results showed that the amplitude of the focused signal increases significantly with the increase of the prestress value of the bolts, which verify that the proposed method can be utilized to monitor the looseness of bolts in timber structures. The analysis results of the focused signal is proof that the prestress loss index of timber structural bolts designed based on wavelet analysis can reflect the looseness of timber structural bolts.

Description: This paper describes the development of a lab-on-chip (LOC) device that can perform reliable online detection in continuous-flow systems for microorganisms. The objective of this work was to examine the performance of a fibre optic detection system integrated into a LOC device. The microfluidic system was fabricated using dry film resist (DFR), integrated with multimode fibre pigtails in the LOC. Subsequently, the performance of the fibre optic detection was evaluated by its absorbance spectra, detection limit, repeatability and reproducibility, and response time. The analysis was carried out using a constant flow rate for three different types of microorganisms which are Escherichia coli, Saccharomyces cerevisiae, and Aeromonas hydrophila. Under the experimental conditions used in this study, the detection limit of cells/mL for both A. hydrophila and E. coli, while a detection limit of cells/mL for S. cerevisiae cells were measured. The results also revealed that the device showed good repeatability with standard deviations less than 0.2 for A. hydrophila and E. coli, while standard deviations for S. cerevisiae were larger than 1.0. The response times for A. hydrophila, E. coli, and S. cerevisiae were 104 s, 122 s, and 78 s, respectively, although significant errors were recorded for all three species for reproducibility experiment. It was found that the device showed generally good sensitivity, with the highest sensitivity towards S. cerevisiae. These findings suggest that an integrated LOC device, with embedded multimode fibre pigtails, can be a reliable instrument for microorganism detection.