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Sensors, Volume 22, Issue 12 (June-2 2022) – 346 articles

Cover Story (view full-size image): In this work, we suggested an innovative non-invasive microwave method for biosensing adherent cancer cells with different aggressiveness by measuring their dielectric properties. The sensor was designed according to its application with culture dishes and realized to test two groups of different cancer cell lines with different aggressiveness. Experimental results showed that the proposed sensor exhibited high sensitivity in the measurement of resonant frequency, which allowed discriminating between low- and high-metastatic cells, even from different types of cancer, paving the way to the development of more complex systems for non-invasive cancer tissue detection and characterization. View this paper
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18 pages, 6144 KiB  
Article
An Embedded Portable Lightweight Platform for Real-Time Early Smoke Detection
by Bowen Liu, Bingjian Sun, Pengle Cheng and Ying Huang
Sensors 2022, 22(12), 4655; https://doi.org/10.3390/s22124655 - 20 Jun 2022
Cited by 4 | Viewed by 2493
Abstract
The advances in developing more accurate and fast smoke detection algorithms increase the need for computation in smoke detection, which demands the involvement of personal computers or workstations. Better detection results require a more complex network structure of the smoke detection algorithms and [...] Read more.
The advances in developing more accurate and fast smoke detection algorithms increase the need for computation in smoke detection, which demands the involvement of personal computers or workstations. Better detection results require a more complex network structure of the smoke detection algorithms and higher hardware configuration, which disqualify them as lightweight portable smoke detection for high detection efficiency. To solve this challenge, this paper designs a lightweight portable remote smoke front-end perception platform based on the Raspberry Pi under Linux operating system. The platform has four modules including a source video input module, a target detection module, a display module, and an alarm module. The training images from the public data sets will be used to train a cascade classifier characterized by Local Binary Pattern (LBP) using the Adaboost algorithm in OpenCV. Then the classifier will be used to detect the smoke target in the following video stream and the detected results will be dynamically displayed in the display module in real-time. If smoke is detected, warning messages will be sent to users by the alarm module in the platform for real-time monitoring and warning on the scene. Case studies showed that the developed system platform has strong robustness under the test datasets with high detection accuracy. As the designed platform is portable without the involvement of a personal computer and can efficiently detect smoke in real-time, it provides a potential affordable lightweight smoke detection option for forest fire monitoring in practice. Full article
(This article belongs to the Topic Applications in Image Analysis and Pattern Recognition)
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26 pages, 12941 KiB  
Article
Conceptual Modeling of Extended Collision Warning System from the Perspective of Smart Product-Service System
by Chunlong Wu, Hanyu Lv, Tianming Zhu, Yunhe Liu and Marcus Vinicius Pereira Pessôa
Sensors 2022, 22(12), 4654; https://doi.org/10.3390/s22124654 - 20 Jun 2022
Cited by 4 | Viewed by 1722
Abstract
While Product-Service Systems (PSS) have a potential sustainability impact by increasing a product’s life and reducing resource consumption, the lack of ownership might lead to less responsible user behavior. Smart PSS can overcome this obstacle and guarantee correct and safe PSS use. In [...] Read more.
While Product-Service Systems (PSS) have a potential sustainability impact by increasing a product’s life and reducing resource consumption, the lack of ownership might lead to less responsible user behavior. Smart PSS can overcome this obstacle and guarantee correct and safe PSS use. In this context, intelligent connected vehicles (ICVs) with PSS can effectively reduce traffic accidents and ensure the safety of vehicles and pedestrians by guaranteeing optimal and safe vehicle operation. A core subsystem to support that is the collision-warning system (CWS). Existing CWSs are, however, limited to in-car warning; users have less access to the warning information, so the result of CWS for collision avoidance is insufficient. Therefore, CWS needs to be extended to include more elements and stakeholders in the collision scenario. This paper aims to provide a novel understanding of extended CWS (ECWS), outline the conceptual framework of ECWS, and contribute a conceptual modeling approach of ECWS from the smart PSS perspective at the functional level. It defines an integrated solution of intelligent products and warning services. The function is modeled based on the Theory of Inventive Problem Solving (TRIZ). Functions of an ECWS from the perspective of smart PSS can be comprehensively expressed to form an overall solution of integrated intelligent products, electronic services, and stakeholders. Based on the case illustration, the proposed method can effectively help function modeling and development of the ECWS at a conceptual level. This can effectively avoid delays due to traffic accidents and ensure the safety of vehicles and pedestrians. Full article
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17 pages, 3259 KiB  
Review
Recent Advances in Flexible Sensors and Their Applications
by Bouchaib Zazoum, Khalid Mujasam Batoo and Muhammad Azhar Ali Khan
Sensors 2022, 22(12), 4653; https://doi.org/10.3390/s22124653 - 20 Jun 2022
Cited by 42 | Viewed by 11529
Abstract
Flexible sensors are low cost, wearable, and lightweight, as well as having a simple structure as per the requirements of engineering applications. Furthermore, for many potential applications, such as human health monitoring, robotics, wearable electronics, and artificial intelligence, flexible sensors require high sensitivity [...] Read more.
Flexible sensors are low cost, wearable, and lightweight, as well as having a simple structure as per the requirements of engineering applications. Furthermore, for many potential applications, such as human health monitoring, robotics, wearable electronics, and artificial intelligence, flexible sensors require high sensitivity and stretchability. Herein, this paper systematically summarizes the latest progress in the development of flexible sensors. The review briefly presents the state of the art in flexible sensors, including the materials involved, sensing mechanisms, manufacturing methods, and the latest development of flexible sensors in health monitoring and soft robotic applications. Moreover, this paper provides perspectives on the challenges in this field and the prospect of flexible sensors. Full article
(This article belongs to the Special Issue Wearable Sensors for Physical Activity Monitoring and Motion Control)
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13 pages, 4709 KiB  
Communication
High-Resolution Hyperspectral Imaging Using Low-Cost Components: Application within Environmental Monitoring Scenarios
by Mary B. Stuart, Matthew Davies, Matthew J. Hobbs, Tom D. Pering, Andrew J. S. McGonigle and Jon R. Willmott
Sensors 2022, 22(12), 4652; https://doi.org/10.3390/s22124652 - 20 Jun 2022
Cited by 23 | Viewed by 6111
Abstract
High-resolution hyperspectral imaging is becoming indispensable, enabling the precise detection of spectral variations across complex, spatially intricate targets. However, despite these significant benefits, currently available high-resolution set-ups are typically prohibitively expensive, significantly limiting their user base and accessibility. These limitations can have wider [...] Read more.
High-resolution hyperspectral imaging is becoming indispensable, enabling the precise detection of spectral variations across complex, spatially intricate targets. However, despite these significant benefits, currently available high-resolution set-ups are typically prohibitively expensive, significantly limiting their user base and accessibility. These limitations can have wider implications, limiting data collection opportunities, and therefore our knowledge, across a wide range of environments. In this article we introduce a low-cost alternative to the currently available instrumentation. This instrument provides hyperspectral datasets capable of resolving spectral variations in mm-scale targets, that cannot typically be resolved with many existing low-cost hyperspectral imaging alternatives. Instrument metrology is provided, and its efficacy is demonstrated within a mineralogy-based environmental monitoring application highlighting it as a valuable addition to the field of low-cost hyperspectral imaging. Full article
(This article belongs to the Special Issue Hyperspectral Imaging Sensing and Analysis)
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19 pages, 3372 KiB  
Article
Estimation of Knee Extension Force Using Mechanomyography Signals Based on GRA and ICS-SVR
by Zebin Li, Lifu Gao, Wei Lu, Daqing Wang, Huibin Cao and Gang Zhang
Sensors 2022, 22(12), 4651; https://doi.org/10.3390/s22124651 - 20 Jun 2022
Cited by 3 | Viewed by 1871
Abstract
During lower-extremity rehabilitation training, muscle activity status needs to be monitored in real time to adjust the assisted force appropriately, but it is a challenging task to obtain muscle force noninvasively. Mechanomyography (MMG) signals offer unparalleled advantages over sEMG, reflecting the intention of [...] Read more.
During lower-extremity rehabilitation training, muscle activity status needs to be monitored in real time to adjust the assisted force appropriately, but it is a challenging task to obtain muscle force noninvasively. Mechanomyography (MMG) signals offer unparalleled advantages over sEMG, reflecting the intention of human movement while being noninvasive. Therefore, in this paper, based on MMG, a combined scheme of gray relational analysis (GRA) and support vector regression optimized by an improved cuckoo search algorithm (ICS-SVR) is proposed to estimate the knee joint extension force. Firstly, the features reflecting muscle activity comprehensively, such as time-domain features, frequency-domain features, time–frequency-domain features, and nonlinear dynamics features, were extracted from MMG signals, and the relational degree was calculated using the GRA method to obtain the correlation features with high relatedness to the knee joint extension force sequence. Then, a combination of correlated features with high relational degree was input into the designed ICS-SVR model for muscle force estimation. The experimental results show that the evaluation indices of the knee joint extension force estimation obtained by the combined scheme of GRA and ICS-SVR were superior to other regression models and could estimate the muscle force with higher estimation accuracy. It is further demonstrated that the proposed scheme can meet the need of muscle force estimation required for rehabilitation devices, powered prostheses, etc. Full article
(This article belongs to the Topic Human–Machine Interaction)
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13 pages, 2858 KiB  
Article
Real-Time Sound Source Localization for Low-Power IoT Devices Based on Multi-Stream CNN
by Jungbeom Ko, Hyunchul Kim and Jungsuk Kim
Sensors 2022, 22(12), 4650; https://doi.org/10.3390/s22124650 - 20 Jun 2022
Cited by 7 | Viewed by 2920
Abstract
Voice-activated artificial intelligence (AI) technology has advanced rapidly and is being adopted in various devices such as smart speakers and display products, which enable users to multitask without touching the devices. However, most devices equipped with cameras and displays lack mobility; therefore, users [...] Read more.
Voice-activated artificial intelligence (AI) technology has advanced rapidly and is being adopted in various devices such as smart speakers and display products, which enable users to multitask without touching the devices. However, most devices equipped with cameras and displays lack mobility; therefore, users cannot avoid touching them for face-to-face interactions, which contradicts the voice-activated AI philosophy. In this paper, we propose a deep neural network-based real-time sound source localization (SSL) model for low-power internet of things (IoT) devices based on microphone arrays and present a prototype implemented on actual IoT devices. The proposed SSL model delivers multi-channel acoustic data to parallel convolutional neural network layers in the form of multiple streams to capture the unique delay patterns for the low-, mid-, and high-frequency ranges, and estimates the fine and coarse location of voices. The model adapted in this study achieved an accuracy of 91.41% on fine location estimation and a direction of arrival error of 7.43° on noisy data. It achieved a processing time of 7.811 ms per 40 ms samples on the Raspberry Pi 4B. The proposed model can be applied to a camera-based humanoid robot that mimics the manner in which humans react to trigger voices in crowded environments. Full article
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25 pages, 1829 KiB  
Article
Degradation Detection in a Redundant Sensor Architecture
by Amer Kajmakovic, Konrad Diwold, Kay Römer, Jesus Pestana and Nermin Kajtazovic
Sensors 2022, 22(12), 4649; https://doi.org/10.3390/s22124649 - 20 Jun 2022
Cited by 3 | Viewed by 2606
Abstract
Safety-critical automation often requires redundancy to enable reliable system operation. In the context of integrating sensors into such systems, the one-out-of-two (1oo2) sensor architecture is one of the common used methods used to ensure the reliability and traceability of sensor readings. In taking [...] Read more.
Safety-critical automation often requires redundancy to enable reliable system operation. In the context of integrating sensors into such systems, the one-out-of-two (1oo2) sensor architecture is one of the common used methods used to ensure the reliability and traceability of sensor readings. In taking such an approach, readings from two redundant sensors are continuously checked and compared. As soon as the discrepancy between two redundant lines deviates by a certain threshold, the 1oo2 voter (comparator) assumes that there is a fault in the system and immediately activates the safe state. In this work, we propose a novel fault prognosis algorithm based on the discrepancy signal. We analyzed the discrepancy changes in the 1oo2 sensor configuration caused by degradation processes. Several publicly available databases were checked, and the discrepancy between redundant sensors was analyzed. An initial analysis showed that the discrepancy between sensor values changes (increases or decreases) over time. To detect an increase or decrease in discrepancy data, two trend detection methods are suggested, and the evaluation of their performance is presented. Moreover, several models were trained on the discrepancy data. The models were then compared to determine which of the models can be best used to describe the dynamics of the discrepancy changes. In addition, the best-fitting models were used to predict the future behavior of the discrepancy and to detect if, and when, the discrepancy in sensor readings will reach a critical point. Based on the prediction of the failure date, the customer can schedule the maintenance system accordingly and prevent its entry into the safe state—or being shut down. Full article
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17 pages, 30663 KiB  
Article
Algorithms and Methods for the Fault-Tolerant Design of an Automated Guided Vehicle
by Ralf Stetter
Sensors 2022, 22(12), 4648; https://doi.org/10.3390/s22124648 - 20 Jun 2022
Cited by 5 | Viewed by 2102
Abstract
Researchers around the globe have contributed for many years to the research field of fault-tolerant control; the importance of this field is ever increasing as a consequence of the rising complexity of technical systems, the enlarging importance of electronics and software as well [...] Read more.
Researchers around the globe have contributed for many years to the research field of fault-tolerant control; the importance of this field is ever increasing as a consequence of the rising complexity of technical systems, the enlarging importance of electronics and software as well as the widening share of interconnected and cloud solutions. This field was supplemented in recent years by fault-tolerant design. Two main goals of fault-tolerant design can be distinguished. The first main goal is the improvement of the controllability and diagnosability of technical systems through intelligent design. The second goal is the enhancement of the fault-tolerance of technical systems by means of inherently fault-tolerant design characteristics. Inherently fault-tolerant design characteristics are, for instance, redundancy or over-actuation. This paper describes algorithms, methods and tools of fault-tolerant design and an application of the concept to an automated guided vehicle (AGV). This application took place on different levels ranging from conscious requirements management to redundant elements, which were consciously chosen, on the most concrete level of a technical system, i.e., the product geometry. The main scientific contribution of the paper is a methodical framework for fault-tolerant design, as well as certain algorithms and methods within this framework. The underlying motivation is to support engineers in design and control trough product development process transparency and appropriate algorithms and methods. Full article
(This article belongs to the Special Issue Sensors and Fault-Tolerant Systems for Automated Guided Vehicles)
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16 pages, 4513 KiB  
Article
Video Anomaly Detection Based on Convolutional Recurrent AutoEncoder
by Bokun Wang and Caiqian Yang
Sensors 2022, 22(12), 4647; https://doi.org/10.3390/s22124647 - 20 Jun 2022
Cited by 9 | Viewed by 2721
Abstract
As an essential task in computer vision, video anomaly detection technology is used in video surveillance, scene understanding, road traffic analysis and other fields. However, the definition of anomaly, scene change and complex background present great challenges for video anomaly detection tasks. The [...] Read more.
As an essential task in computer vision, video anomaly detection technology is used in video surveillance, scene understanding, road traffic analysis and other fields. However, the definition of anomaly, scene change and complex background present great challenges for video anomaly detection tasks. The insight that motivates this study is that the reconstruction error for normal samples would be lower since they are closer to the training data, while the anomalies could not be reconstructed well. In this paper, we proposed a Convolutional Recurrent AutoEncoder (CR-AE), which combines an attention-based Convolutional Long–Short-Term Memory (ConvLSTM) network and a Convolutional AutoEncoder. The ConvLSTM network and the Convolutional AutoEncoder could capture the irregularity of the temporal pattern and spatial irregularity, respectively. The attention mechanism was used to obtain the current output characteristics from the hidden state of each Covn-LSTM layer. Then, a convolutional decoder was utilized to reconstruct the input video clip and the testing video clip with higher reconstruction error, which were further judged to be anomalies. The proposed method was tested on two popular benchmarks (UCSD ped2 Dataset and Avenue Dataset), and the experimental results demonstrated that CR-AE achieved 95.6% and 73.1% frame-level AUC on two public datasets, respectively. Full article
(This article belongs to the Section Optical Sensors)
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20 pages, 15513 KiB  
Article
A Single-Phase High-Impedance Ground Faulty Feeder Detection Method for Small Resistance to Ground Systems Based on Current-Voltage Phase Difference
by Zequan Hou, Zhihua Zhang, Yizhao Wang, Jiandong Duan, Wanying Yan and Wenchao Lu
Sensors 2022, 22(12), 4646; https://doi.org/10.3390/s22124646 - 20 Jun 2022
Cited by 5 | Viewed by 1551
Abstract
At present, the small resistance to ground system (SRGS) is mainly protected by fixed-time zero-sequence overcurrent protection, but its ability to detect transition resistance is only about 100 Ω, which is unable to detect single-phase high resistance grounding fault (SPHIF). This paper analyzes [...] Read more.
At present, the small resistance to ground system (SRGS) is mainly protected by fixed-time zero-sequence overcurrent protection, but its ability to detect transition resistance is only about 100 Ω, which is unable to detect single-phase high resistance grounding fault (SPHIF). This paper analyzes the zero-sequence characteristics of SPHIF for SRGS and proposes a SPHIF feeder detection method that uses the current–voltage phase difference. The proposed method is as follows: first, the zero-sequence current phase of each feeder is calculated. Second, the phase voltage root mean square (RMS) value is used to determine the fault phase and obtain its initial phase as the reference value. The introduction of the initial phase of the fault phase voltage can highlight the fault characteristics and improve the sensitivity and reliability of feeder detection, and then CVPD is the difference between each feeder ZSC phase and the reference value. Finally, the magnitude of CVPD is judged. If the CVPD of a particular feeder meets the condition, the feeder is detected as the faulted feeder. Combining the theoretical and practical constraints, the specific adjustment principle and feeder detection logic are given. A large number of simulations show that the proposed method can be successfully detected under the conditions of 5000 Ω transition resistance, –1 dB noise interference, and 40% data missing. Compared with existing methods, the proposed method uses phase voltages that are easy to measure to construct SPHIF feeder detection criteria, without adding additional measurement and communication devices, and can quickly achieve local isolation of SPHIF with better sensitivity, reliability, and immunity to interference. Full article
(This article belongs to the Section Electronic Sensors)
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18 pages, 1916 KiB  
Article
Secure IIoT Information Reinforcement Model Based on IIoT Information Platform Using Blockchain
by Yoon-Su Jeong
Sensors 2022, 22(12), 4645; https://doi.org/10.3390/s22124645 - 20 Jun 2022
Cited by 7 | Viewed by 1813
Abstract
Data created at industrial sites through industrial internet of things devices are now being processed automatically or in real-time in the industrial structure, due to the application of artificial intelligence technology to industrial sites. However, the expenses of autonomous or real-time data processing [...] Read more.
Data created at industrial sites through industrial internet of things devices are now being processed automatically or in real-time in the industrial structure, due to the application of artificial intelligence technology to industrial sites. However, the expenses of autonomous or real-time data processing and steady data processing (analysis, prediction, prescription, and implementation) necessitate a new processing method. We propose a blockchain-based industrial internet of things information reinforcement model in this work that may reliably ensure the integrity of industrial internet of things data produced at industrial locations. The proposed model processes industrial internet of things data that may occur at endpoints at industrial sites into the blockchain by processing data generated by the same industrial internet of things device independently. As a result, the IIoT data sent to the industrial internet of things server can be evaluated more readily, and production accuracy may be enhanced. The proposed model optimizes industrial internet of things information linkage by stochastically reflecting the information based on attribute value frequency. By dynamically aggregating the related data of industrial internet of things information acquired as a seed through hierarchical subnets, the proposed model increases stability and accuracy. Furthermore, the proposed model may be used to enhance an organizations’ operational efficiency (consulting and training, for example) and strategic decision-making by utilizing fundamental knowledge about items produced at industrial locations. Furthermore, the proposed model allows for information sharing and system connectivity between industrial locations, allowing for close collaboration between industrial internet of things features. As a result of the performance evaluation, the proposed model included an industrial internet of things sensor to the blockchain, eliminating the need for an extra function in the manufacturing process and reducing the time required to validate the integrity of industrial internet of things data. In addition, as a result of analyzing industrial internet of things data by an algorithm according to the number of simulated clouds, the accuracy of industrial internet of things information was improved by 2.5% to 3%, on average. Full article
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17 pages, 1214 KiB  
Article
Assessment of Soil Fertility Using Induced Fluorescence and Machine Learning
by Louis Longchamps, Dipankar Mandal and Raj Khosla
Sensors 2022, 22(12), 4644; https://doi.org/10.3390/s22124644 - 20 Jun 2022
Cited by 2 | Viewed by 2415
Abstract
Techniques such as proximal soil sampling are investigated to increase the sampling density and hence the resolution at which nutrient prescription maps are developed. With the advent of a commercial mobile fluorescence sensor, this study assessed the potential of fluorescence to estimate soil [...] Read more.
Techniques such as proximal soil sampling are investigated to increase the sampling density and hence the resolution at which nutrient prescription maps are developed. With the advent of a commercial mobile fluorescence sensor, this study assessed the potential of fluorescence to estimate soil chemical properties and fertilizer recommendations. This experiment was conducted over two years at nine sites on 168 soil samples and used random forest regression to estimate soil properties, fertility classes, and recommended N rates for maize production based on induced fluorescence of air-dried soil samples. Results showed that important soil properties such as soil organic matter, pH, and CEC can be estimated with a correlation of 0.74, 0.75, and 0.75, respectively. When attempting to predict fertility classes, this approach yielded an overall accuracy of 0.54, 0.78, and 0.69 for NO3-N, SOM, and Zn, respectively. The N rate recommendation for maize can be directly estimated by fluorescence readings of the soil with an overall accuracy of 0.78. These results suggest that induced fluorescence is a viable approach for assessing soil fertility. More research is required to transpose these laboratory-acquired soil analysis results to in situ readings successfully. Full article
(This article belongs to the Section Environmental Sensing)
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13 pages, 3321 KiB  
Article
Chlorine Gas Sensor with Surface Temperature Control
by Andrzej Krajewski, Shadi Houshyar, Lijing Wang and Rajiv Padhye
Sensors 2022, 22(12), 4643; https://doi.org/10.3390/s22124643 - 20 Jun 2022
Cited by 1 | Viewed by 1808
Abstract
The work describes the design, manufacturing, and user interface of a thin-film gas transducer platform that is able to provide real-time detection of toxic vapor. This proof-of-concept system has applications in the field of real-time detection of hazardous gaseous agents that are harmful [...] Read more.
The work describes the design, manufacturing, and user interface of a thin-film gas transducer platform that is able to provide real-time detection of toxic vapor. This proof-of-concept system has applications in the field of real-time detection of hazardous gaseous agents that are harmful to the person exposed to the environment. The small-size gas sensor allows for integration with an unmanned aerial vehicle, thus combining high-level mobility with the ability for the real-time detection of hazardous/toxic chemicals or use as a standalone system in industries that deal with harmful gaseous substances. The sensor was designed based on the ability of thin-film metal oxide sensors to detect chlorine gas in real time. Specifically, a concentration of 10 ppm of Cl2 was tested. Full article
(This article belongs to the Special Issue Advances in Nanosensors and Nanogenerators)
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15 pages, 3920 KiB  
Article
Pilot Feasibility Study of a Multi-View Vision Based Scoring Method for Cervical Dystonia
by Chen Ye, Yuhao Xiao, Ruoyu Li, Hongkai Gu, Xinyu Wang, Tianyang Lu and Lingjing Jin
Sensors 2022, 22(12), 4642; https://doi.org/10.3390/s22124642 - 20 Jun 2022
Cited by 1 | Viewed by 2513
Abstract
Abnormal movement of the head and neck is a typical symptom of Cervical Dystonia (CD). Accurate scoring on the severity scale is of great significance for treatment planning. The traditional scoring method is to use a protractor or contact sensors to calculate the [...] Read more.
Abnormal movement of the head and neck is a typical symptom of Cervical Dystonia (CD). Accurate scoring on the severity scale is of great significance for treatment planning. The traditional scoring method is to use a protractor or contact sensors to calculate the angle of the movement, but this method is time-consuming, and it will interfere with the movement of the patient. In the recent outbreak of the coronavirus disease, the need for remote diagnosis and treatment of CD has become extremely urgent for clinical practice. To solve these problems, we propose a multi-view vision based CD severity scale scoring method, which detects the keypoint positions of the patient from the frontal and lateral images, and finally scores the severity scale by calculating head and neck motion angles. We compared the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) subscale scores calculated by our vision based method with the scores calculated by a neurologist trained in dyskinesia. An analysis of the correlation coefficient was then conducted. Intra-class correlation (ICC)(3,1) was used to measure absolute accuracy. Our multi-view vision based CD severity scale scoring method demonstrated sufficient validity and reliability. This low-cost and contactless method provides a new potential tool for remote diagnosis and treatment of CD. Full article
(This article belongs to the Topic Human Movement Analysis)
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21 pages, 13115 KiB  
Article
Simulation and Analysis of an FMCW Radar against the UWB EMP Coupling Responses on the Wires
by Kaibai Chen, Shaohua Liu, Min Gao and Xiaodong Zhou
Sensors 2022, 22(12), 4641; https://doi.org/10.3390/s22124641 - 20 Jun 2022
Cited by 3 | Viewed by 2234
Abstract
An ultra-wideband electromagnetic pulse (UWB EMP) can be coupled to an FMCW system through metal wires, causing electronic equipment disturbance or damage. In this paper, a hybrid model is proposed to carry out the interference analysis of UWB EMP coupling responses on the [...] Read more.
An ultra-wideband electromagnetic pulse (UWB EMP) can be coupled to an FMCW system through metal wires, causing electronic equipment disturbance or damage. In this paper, a hybrid model is proposed to carry out the interference analysis of UWB EMP coupling responses on the wires to the FMCW radar. First, a field simulation model of the radar is constructed and the wire coupling responses are calculated. Then, the responses are injected into an FMCW circuit model via data format modification. Finally, we use the FFT transform to identify the spectral peak of the intermediate frequency (IF) output signal, which corresponds to the radar’s detection range. The simulation results show that the type of metal wire has the greatest influence on the amplitude of coupling responses. The spectral peak of the IF output changes to the wrong frequency with the increase of injection power. Applying interference at the end of the circuit can more effectively interfere with the detection of the radar. The investigation provides a theoretical basis for the electromagnetic protection design of the radar. Full article
(This article belongs to the Special Issue Radar Sensors for Target Tracking and Localization)
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14 pages, 1390 KiB  
Article
FDG-PET to T1 Weighted MRI Translation with 3D Elicit Generative Adversarial Network (E-GAN)
by Farideh Bazangani, Frédéric J. P. Richard, Badih Ghattas and Eric Guedj
Sensors 2022, 22(12), 4640; https://doi.org/10.3390/s22124640 - 20 Jun 2022
Cited by 9 | Viewed by 2607
Abstract
Objective: With the strengths of deep learning, computer-aided diagnosis (CAD) is a hot topic for researchers in medical image analysis. One of the main requirements for training a deep learning model is providing enough data for the network. However, in medical images, due [...] Read more.
Objective: With the strengths of deep learning, computer-aided diagnosis (CAD) is a hot topic for researchers in medical image analysis. One of the main requirements for training a deep learning model is providing enough data for the network. However, in medical images, due to the difficulties of data collection and data privacy, finding an appropriate dataset (balanced, enough samples, etc.) is quite a challenge. Although image synthesis could be beneficial to overcome this issue, synthesizing 3D images is a hard task. The main objective of this paper is to generate 3D T1 weighted MRI corresponding to FDG-PET. In this study, we propose a separable convolution-based Elicit generative adversarial network (E-GAN). The proposed architecture can reconstruct 3D T1 weighted MRI from 2D high-level features and geometrical information retrieved from a Sobel filter. Experimental results on the ADNI datasets for healthy subjects show that the proposed model improves the quality of images compared with the state of the art. In addition, the evaluation of E-GAN and the state of art methods gives a better result on the structural information (13.73% improvement for PSNR and 22.95% for SSIM compared to Pix2Pix GAN) and textural information (6.9% improvements for homogeneity error in Haralick features compared to Pix2Pix GAN). Full article
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17 pages, 3199 KiB  
Article
Severity Estimation for Interturn Short-Circuit and Demagnetization Faults through Self-Attention Network
by Hojin Lee, Hyeyun Jeong, Seongyun Kim and Sang Woo Kim
Sensors 2022, 22(12), 4639; https://doi.org/10.3390/s22124639 - 20 Jun 2022
Cited by 3 | Viewed by 1636
Abstract
This study presents a novel interturn short-circuit fault (ISCF) and demagnetization fault (DF) diagnosis strategy based on a self-attention-based severity estimation network (SASEN). We analyze the effects of the ISCF and DF in a permanent-magnet synchronous machine and select appropriate inputs for estimating [...] Read more.
This study presents a novel interturn short-circuit fault (ISCF) and demagnetization fault (DF) diagnosis strategy based on a self-attention-based severity estimation network (SASEN). We analyze the effects of the ISCF and DF in a permanent-magnet synchronous machine and select appropriate inputs for estimating the fault severities, i.e., a positive-sequence voltage and current and negative-sequence voltage and current. The chosen inputs are fed into the SASEN to estimate fault indicators for quantifying the fault severities of the ISCF and DF. The SASEN comprises an encoder and decoder based on a self-attention module. The self-attention mechanism enhances the high-dimensional feature extraction and regression ability of the network by concentrating on specific sequence representations, thereby supporting the estimation of the fault severities. The proposed strategy can diagnose a hybrid fault in which the ISCF and DF occur simultaneously and does not require the exact model and parameters essential for the existing method for estimating the fault severity. The effectiveness and feasibility of the proposed fault diagnosis strategy are demonstrated through experimental results based on various fault cases and load torque conditions. Full article
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14 pages, 2511 KiB  
Article
Biosensors for Klebsiella pneumoniae with Molecularly Imprinted Polymer (MIP) Technique
by Chuchart Pintavirooj, Naphatsawan Vongmanee, Wannisa Sukjee, Chak Sangma and Sarinporn Visitsattapongse
Sensors 2022, 22(12), 4638; https://doi.org/10.3390/s22124638 - 20 Jun 2022
Cited by 12 | Viewed by 2536
Abstract
Nosocomial infection is one of the most important problems that occurs in hospitals, as it directly affects susceptible patients or patients with immune deficiency. Klebsiella pneumoniae (K. pneumoniae) is the most common cause of nosocomial infections in hospitals. K. pneumoniae can cause [...] Read more.
Nosocomial infection is one of the most important problems that occurs in hospitals, as it directly affects susceptible patients or patients with immune deficiency. Klebsiella pneumoniae (K. pneumoniae) is the most common cause of nosocomial infections in hospitals. K. pneumoniae can cause various diseases such as pneumonia, urinary tract infections, septicemias, and soft tissue infections, and it has also become highly resistant to antibiotics. The principal routes for the transmission of K. pneumoniae are via the gastrointestinal tract and the hands of hospital personnel via healthcare workers, patients, hospital equipment, and interventional procedures. These bacteria can spread rapidly in the hospital environment and tend to cause nosocomial outbreaks. In this research, we developed a MIP-based electrochemical biosensor to detect K. pneumoniae. Quantitative detection was performed using an electrochemical technique to measure the changes in electrical signals in different concentrations of K. pneumoniae ranging from 10 to 105 CFU/mL. Our MIP-based K. pneumoniae sensor was found to achieve a high linear response, with an R2 value of 0.9919. A sensitivity test was also performed on bacteria with a similar structure to that of K. pneumoniae. The sensitivity results show that the MIP-based K. pneumoniae biosensor with a gold electrode was the most sensitive, with a 7.51 (% relative current/log concentration) when compared with the MIP sensor applied with Pseudomonas aeruginosa and Enterococcus faecalis, where the sensitivity was 2.634 and 2.226, respectively. Our sensor was also able to achieve a limit of detection (LOD) of 0.012 CFU/mL and limit of quantitation (LOQ) of 1.61 CFU/mL. Full article
(This article belongs to the Section Biosensors)
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18 pages, 5805 KiB  
Article
Method for Continuous Integration and Deployment Using a Pipeline Generator for Agile Software Projects
by Ionut-Catalin Donca, Ovidiu Petru Stan, Marius Misaros, Dan Gota and Liviu Miclea
Sensors 2022, 22(12), 4637; https://doi.org/10.3390/s22124637 - 20 Jun 2022
Cited by 12 | Viewed by 4879
Abstract
Lately, the software development industry is going through a slow but real transformation. Software is increasingly a part of everything, and, software developers, are trying to cope with this exploding demand through more automation. The pipelining technique of continuous integration (CI) and continuous [...] Read more.
Lately, the software development industry is going through a slow but real transformation. Software is increasingly a part of everything, and, software developers, are trying to cope with this exploding demand through more automation. The pipelining technique of continuous integration (CI) and continuous delivery (CD) has developed considerably due to the overwhelming demand for the deployment and deliverability of new features and applications. As a result, DevOps approaches and Agile principles have been developed, in which developers collaborate closely with infrastructure engineers to guarantee that their applications are deployed quickly and reliably. Thanks to pipeline approach thinking, the efficiency of projects has greatly improved. Agile practices represent the introduction to the system of new features in each sprint delivery. Those practices may contain well-developed features or can contain bugs or failures which impact the delivery. The pipeline approach, depicted in this paper, overcomes the problems of delivery, improving the delivery timeline, the test load steps, and the benchmarking tasks. It decreases system interruption by integrating multiple test steps and adds stability and deliverability to the entire process. It provides standardization which means having an established, time-tested process to use, and can also decrease ambiguity and guesswork, guarantee quality and boost productivity. This tool is developed with an interpreted language, namely Bash, which offers an easier method to integrate it into any platform. Based on the experimental results, we demonstrate the value that this solution currently creates. This solution provides an effective and efficient way to generate, manage, customize, and automate Agile-based CI and CD projects through automated pipelines. The suggested system acts as a starting point for standard CI/CD processes, caches Docker layers for subsequent usage, and implements highly available deliverables in a Kubernetes cluster using Helm. Changing the principles of this solution and expanding it into multiple platforms (windows) will be addressed in a future discussion. Full article
(This article belongs to the Special Issue Intelligent Control and Testing Systems and Applications)
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13 pages, 3409 KiB  
Article
Thermo-Optical Sensitivity of Whispering Gallery Modes in As2S3 Chalcogenide Glass Microresonators
by Alexey V. Andrianov, Maria P. Marisova and Elena A. Anashkina
Sensors 2022, 22(12), 4636; https://doi.org/10.3390/s22124636 - 20 Jun 2022
Cited by 7 | Viewed by 2063
Abstract
Glass microresonators with whispering gallery modes (WGMs) have a lot of diversified applications, including applications for sensing based on thermo-optical effects. Chalcogenide glass microresonators have a noticeably higher temperature sensitivity compared to silica ones, but only a few works have been devoted to [...] Read more.
Glass microresonators with whispering gallery modes (WGMs) have a lot of diversified applications, including applications for sensing based on thermo-optical effects. Chalcogenide glass microresonators have a noticeably higher temperature sensitivity compared to silica ones, but only a few works have been devoted to the study of their thermo-optical properties. We present experimental and theoretical studies of thermo-optical effects in microspheres made of an As2S3 chalcogenide glass fiber. We investigated the steady-state and transient temperature distributions caused by heating due to the partial thermalization of the pump power and found the corresponding wavelength shifts of the WGMs. The experimental measurements of the thermal response time, thermo-optical shifts of the WGMs, and heat power sensitivity in microspheres with diameters of 80–380 µm are in a good agreement with the theoretically predicted dependences. The calculated temperature sensitivity of 42 pm/K does not depend on diameter for microspheres made of commercially available chalcogenide fiber, which may play an important role in the development of temperature sensors. Full article
(This article belongs to the Special Issue State-of-the-Art Optical Sensors Technology in Russia 2021-2022)
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13 pages, 3282 KiB  
Article
Three-Dimensional (3D) Imaging Technology to Monitor Growth and Development of Holstein Heifers and Estimate Body Weight, a Preliminary Study
by Yannick Le Cozler, Elodie Brachet, Laurianne Bourguignon, Laurent Delattre, Thibaut Luginbuhl and Philippe Faverdin
Sensors 2022, 22(12), 4635; https://doi.org/10.3390/s22124635 - 19 Jun 2022
Cited by 6 | Viewed by 1899
Abstract
The choice of rearing strategy for dairy cows can have an effect on production yield, at least during the first lactation. For this reason, it is important to closely monitor the growth and development of young heifers. Unfortunately, current methods for evaluation can [...] Read more.
The choice of rearing strategy for dairy cows can have an effect on production yield, at least during the first lactation. For this reason, it is important to closely monitor the growth and development of young heifers. Unfortunately, current methods for evaluation can be costly, time-consuming, and dangerous because of the need to physically manipulate animals, and as a result, this type of monitoring is seldom performed on farms. One potential solution may be the use of tools based on three-dimensional (3D) imaging, which has been studied in adult cows but not yet in growing individuals. In this study, an imaging approach that was previously validated for adult cows was tested on a pilot population of five randomly selected growing Holstein heifers, from 5 weeks of age to the end of the first gestation. Once a month, all heifers were weighed and an individual 3D image was recorded. From these images, we estimated growth trends in morphological traits such as heart girth or withers height (188.1 ± 3.7 cm and 133.5 ± 6.0 cm on average at one year of age, respectively). From other traits, such as body surface area and volume (5.21 ± 0.32 m2 and 0.43 ± 0.05 m3 on average at one year of age, respectively), we estimated body weight based on volume (402.4 ± 37.5 kg at one year of age). Body weight estimates from images were on average 9.7% higher than values recorded by the weighing scale (366.8 ± 47.2 kg), but this difference varied with age (19.1% and 1.8% at 6 and 20 months of age, respectively). To increase accuracy, the predictive model developed for adult cows was adapted and completed with complementary data on young heifers. Using imaging data, it was also possible to analyze changes in the surface-to-volume ratio that occurred as body weight and age increased. In sum, 3D imaging technology is an easy-to-use tool for following the growth and management of heifers and should become increasingly accurate as more data are collected on this population. Full article
(This article belongs to the Section Smart Agriculture)
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17 pages, 3680 KiB  
Article
Micro-Expression-Based Emotion Recognition Using Waterfall Atrous Spatial Pyramid Pooling Networks
by Marzuraikah Mohd Stofa, Mohd Asyraf Zulkifley and Muhammad Ammirrul Atiqi Mohd Zainuri
Sensors 2022, 22(12), 4634; https://doi.org/10.3390/s22124634 - 19 Jun 2022
Cited by 5 | Viewed by 2191
Abstract
Understanding a person’s attitude or sentiment from their facial expressions has long been a straightforward task for humans. Numerous methods and techniques have been used to classify and interpret human emotions that are commonly communicated through facial expressions, with either macro- or micro-expressions. [...] Read more.
Understanding a person’s attitude or sentiment from their facial expressions has long been a straightforward task for humans. Numerous methods and techniques have been used to classify and interpret human emotions that are commonly communicated through facial expressions, with either macro- or micro-expressions. However, performing this task using computer-based techniques or algorithms has been proven to be extremely difficult, whereby it is a time-consuming task to annotate it manually. Compared to macro-expressions, micro-expressions manifest the real emotional cues of a human, which they try to suppress and hide. Different methods and algorithms for recognizing emotions using micro-expressions are examined in this research, and the results are presented in a comparative approach. The proposed technique is based on a multi-scale deep learning approach that aims to extract facial cues of various subjects under various conditions. Then, two popular multi-scale approaches are explored, Spatial Pyramid Pooling (SPP) and Atrous Spatial Pyramid Pooling (ASPP), which are then optimized to suit the purpose of emotion recognition using micro-expression cues. There are four new architectures introduced in this paper based on multi-layer multi-scale convolutional networks using both direct and waterfall network flows. The experimental results show that the ASPP module with waterfall network flow, which we coined as WASPP-Net, outperforms the state-of-the-art benchmark techniques with an accuracy of 80.5%. For future work, a high-resolution approach to multi-scale approaches can be explored to further improve the recognition performance. Full article
(This article belongs to the Topic Applications in Image Analysis and Pattern Recognition)
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22 pages, 8960 KiB  
Article
Emotion Recognition for Partial Faces Using a Feature Vector Technique
by Ratanak Khoeun, Ponlawat Chophuk and Krisana Chinnasarn
Sensors 2022, 22(12), 4633; https://doi.org/10.3390/s22124633 - 19 Jun 2022
Cited by 8 | Viewed by 2529
Abstract
Wearing a facial mask is indispensable in the COVID-19 pandemic; however, it has tremendous effects on the performance of existing facial emotion recognition approaches. In this paper, we propose a feature vector technique comprising three main steps to recognize emotions from facial mask [...] Read more.
Wearing a facial mask is indispensable in the COVID-19 pandemic; however, it has tremendous effects on the performance of existing facial emotion recognition approaches. In this paper, we propose a feature vector technique comprising three main steps to recognize emotions from facial mask images. First, a synthetic mask is used to cover the facial input image. With only the upper part of the image showing, and including only the eyes, eyebrows, a portion of the bridge of the nose, and the forehead, the boundary and regional representation technique is applied. Second, a feature extraction technique based on our proposed rapid landmark detection method employing the infinity shape is utilized to flexibly extract a set of feature vectors that can effectively indicate the characteristics of the partially occluded masked face. Finally, those features, including the location of the detected landmarks and the Histograms of the Oriented Gradients, are brought into the classification process by adopting CNN and LSTM; the experimental results are then evaluated using images from the CK+ and RAF-DB data sets. As the result, our proposed method outperforms existing cutting-edge approaches and demonstrates better performance, achieving 99.30% and 95.58% accuracy on CK+ and RAF-DB, respectively. Full article
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18 pages, 4920 KiB  
Article
Development of a Non-Contacting Muscular Activity Measurement System for Evaluating Knee Extensors Training in Real-Time
by Zixi Gu, Shengxu Liu, Sarah Cosentino and Atsuo Takanishi
Sensors 2022, 22(12), 4632; https://doi.org/10.3390/s22124632 - 19 Jun 2022
Cited by 3 | Viewed by 1966
Abstract
To give people more specific information on the quality of their daily motion, it is necessary to continuously measure muscular activity during everyday occupations in an easy way. The traditional methods to measure muscle activity using a combination of surface electromyography (sEMG) sensors [...] Read more.
To give people more specific information on the quality of their daily motion, it is necessary to continuously measure muscular activity during everyday occupations in an easy way. The traditional methods to measure muscle activity using a combination of surface electromyography (sEMG) sensors and optical motion capture system are expensive and not suitable for non-technical users and unstructured environment. For this reason, in our group we are researching methods to estimate leg muscle activity using non-contact wearable sensors, improving ease of movement and system usability. In a previous study, we developed a method to estimate muscle activity via only a single inertial measurement unit (IMU) on the shank. In this study, we describe a method to estimate muscle activity during walking via two IMU sensors, using an original sensing system and specifically developed estimation algorithms based on ANN techniques. The muscle activity estimation results, estimated by the proposed algorithm after optimization, showed a relatively high estimation accuracy with a correlation efficient of R2 = 0.48 and a standard deviation STD = 0.10, with a total system average delay of 192 ms. As the average interval between different gait phases in human gait is 250–1000 ms, a 192 ms delay is still acceptable for daily walking requirements. For this reason, compared with the previous study, the newly proposed system presents a higher accuracy and is better suitable for real-time leg muscle activity estimation during walking. Full article
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17 pages, 3651 KiB  
Article
Voltammetric Behaviour of Rhodamine B at a Screen-Printed Carbon Electrode and Its Trace Determination in Environmental Water Samples
by Kevin C. Honeychurch
Sensors 2022, 22(12), 4631; https://doi.org/10.3390/s22124631 - 19 Jun 2022
Cited by 6 | Viewed by 2441
Abstract
The voltammetric behaviour of Rhodamine B was studied at a screen-printed carbon electrode (SPCE), by cyclic and differential pulse voltammetry. Cyclic voltammograms exhibited two reduction peaks (designated R1 and R2) generated from the reduction of the parent compound through, first, one electron reduction [...] Read more.
The voltammetric behaviour of Rhodamine B was studied at a screen-printed carbon electrode (SPCE), by cyclic and differential pulse voltammetry. Cyclic voltammograms exhibited two reduction peaks (designated R1 and R2) generated from the reduction of the parent compound through, first, one electron reduction (R1) to give a radical species, and then a further one-electron, one-proton reduction to give a neutral molecule (R2). On the reverse positive-going scan, two oxidation peaks were observed. The first, O1, resulted from the oxidation of the species generated at R2, and the second, O2, through the one-electron oxidation of the amine group. The nature of the redox reactions was further investigated by observing the effect of scan rate and pH on the voltammetric behaviour. The developed SPCE method was evaluated by carrying out Rhodamine B determinations on a spiked and unspiked environmental water sample. A mean recovery of 94.3% with an associated coefficient of variation of 2.9% was obtained. The performance characteristics indicated that reliable data may be obtained for Rhodamine B measurements in environmental water samples using this approach. Full article
(This article belongs to the Special Issue Screen-Printed Sensors)
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18 pages, 1439 KiB  
Article
Hybrid Technique for Cyber-Physical Security in Cloud-Based Smart Industries
by Deepak Garg, Shalli Rani, Norbert Herencsar, Sahil Verma, Marcin Wozniak and Muhammad Fazal Ijaz
Sensors 2022, 22(12), 4630; https://doi.org/10.3390/s22124630 - 19 Jun 2022
Cited by 11 | Viewed by 1999
Abstract
New technologies and trends in industries have opened up ways for distributed establishment of Cyber-Physical Systems (CPSs) for smart industries. CPSs are largely based upon Internet of Things (IoT) because of data storage on cloud servers which poses many constraints due to the [...] Read more.
New technologies and trends in industries have opened up ways for distributed establishment of Cyber-Physical Systems (CPSs) for smart industries. CPSs are largely based upon Internet of Things (IoT) because of data storage on cloud servers which poses many constraints due to the heterogeneous nature of devices involved in communication. Among other challenges, security is the most daunting challenge that contributes, at least in part, to the impeded momentum of the CPS realization. Designers assume that CPSs are themselves protected as they cannot be accessed from external networks. However, these days, CPSs have combined parts of the cyber world and also the physical layer. Therefore, cyber security problems are large for commercial CPSs because the systems move with one another and conjointly with physical surroundings, i.e., Complex Industrial Applications (CIA). Therefore, in this paper, a novel data security algorithm Dynamic Hybrid Secured Encryption Technique (DHSE) is proposed based on the hybrid encryption scheme of Advanced Encryption Standard (AES), Identity-Based Encryption (IBE) and Attribute-Based Encryption (ABE). The proposed algorithm divides the data into three categories, i.e., less sensitive, mid-sensitive and high sensitive. The data is distributed by forming the named-data packets (NDPs) via labelling the names. One can choose the number of rounds depending on the actual size of a key; it is necessary to perform a minimum of 10 rounds for 128-bit keys in DHSE. The average encryption time taken by AES (Advanced Encryption Standard), IBE (Identity-based encryption) and ABE (Attribute-Based Encryption) is 3.25 ms, 2.18 ms and 2.39 ms, respectively. Whereas the average time taken by the DHSE encryption algorithm is 2.07 ms which is very much less when compared to other algorithms. Similarly, the average decryption times taken by AES, IBE and ABE are 1.77 ms, 1.09 ms and 1.20 ms and the average times taken by the DHSE decryption algorithms are 1.07 ms, which is very much less when compared to other algorithms. The analysis shows that the framework is well designed and provides confidentiality of data with minimum encryption and decryption time. Therefore, the proposed approach is well suited for CPS-IoT. Full article
(This article belongs to the Section Intelligent Sensors)
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15 pages, 2870 KiB  
Article
Noise Reduction in Human Motion-Captured Signals for Computer Animation based on B-Spline Filtering
by Mehdi Memar Ardestani and Hong Yan
Sensors 2022, 22(12), 4629; https://doi.org/10.3390/s22124629 - 19 Jun 2022
Cited by 4 | Viewed by 2109
Abstract
Motion capturing is used to record the natural movements of humans for a particular task. The motions recorded are extensively used to produce animation characters with natural movements and for virtual reality (VR) devices. The raw captured motion signals, however, contain noises introduced [...] Read more.
Motion capturing is used to record the natural movements of humans for a particular task. The motions recorded are extensively used to produce animation characters with natural movements and for virtual reality (VR) devices. The raw captured motion signals, however, contain noises introduced during the capturing process. Therefore, the signals should be effectively processed before they can be applied to animation characters. In this study, we analyzed several common methods used for smoothing signals. The smoothed signals were then compared based on the smoothness metrics defined. It was concluded that the filtering based on the B-spline-based least square method could achieve high-quality outputs with predetermined continuity and minimal parameter adjustments for a variety of motion signals. Full article
(This article belongs to the Section Biosensors)
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22 pages, 49074 KiB  
Article
LiDAR Echo Gaussian Decomposition Algorithm for FPGA Implementation
by Guoqing Zhou, Xiang Zhou, Jinlong Chen, Guoshuai Jia and Qiang Zhu
Sensors 2022, 22(12), 4628; https://doi.org/10.3390/s22124628 - 19 Jun 2022
Cited by 11 | Viewed by 1953
Abstract
As the existing processing algorithms for LiDAR echo decomposition are time-consuming, this paper proposes an FPGA-based improved Gaussian full-waveform decomposition method. The proposed FPGA architecture consists of three modules: (i) a pre-processing module, which is used to pipeline data reading and Gaussian filtering, [...] Read more.
As the existing processing algorithms for LiDAR echo decomposition are time-consuming, this paper proposes an FPGA-based improved Gaussian full-waveform decomposition method. The proposed FPGA architecture consists of three modules: (i) a pre-processing module, which is used to pipeline data reading and Gaussian filtering, (ii) the inflection point coordinate solution module, applied to the second-order differential operation and to calculate inflection point coordinates, and (iii) the Gaussian component parameter solution and echo component positioning module, which is utilized to calculate the Gaussian component and echo time parameters. Finally, two LiDAR datasets, covering the Congo and Antarctic regions, are used to verify the accuracy and speed of the proposed method. The experimental results show that (i) the accuracy of the FPGA-based processing is equivalent to that of PC-based processing, and (ii) the processing speed of the FPGA-based processing is 292 times faster than that of PC-based processing. Full article
(This article belongs to the Section Radar Sensors)
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26 pages, 6924 KiB  
Article
InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users’ Feedback
by Muhammad Munir Ud Din, Nasser Alshammari, Saad Awadh Alanazi, Fahad Ahmad, Shahid Naseem, Muhammad Saleem Khan and Hafiz Syed Imran Haider
Sensors 2022, 22(12), 4627; https://doi.org/10.3390/s22124627 - 19 Jun 2022
Cited by 4 | Viewed by 2237
Abstract
Cloud Computing (CC) provides a combination of technologies that allows the user to use the most resources in the least amount of time and with the least amount of money. CC semantics play a critical role in ranking heterogeneous data by using the [...] Read more.
Cloud Computing (CC) provides a combination of technologies that allows the user to use the most resources in the least amount of time and with the least amount of money. CC semantics play a critical role in ranking heterogeneous data by using the properties of different cloud services and then achieving the optimal cloud service. Regardless of the efforts made to enable simple access to this CC innovation, in the presence of various organizations delivering comparative services at varying cost and execution levels, it is far more difficult to identify the ideal cloud service based on the user’s requirements. In this research, we propose a Cloud-Services-Ranking Agent (CSRA) for analyzing cloud services using end-users’ feedback, including Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Software as a Service (SaaS), based on ontology mapping and selecting the optimal service. The proposed CSRA possesses Machine-Learning (ML) techniques for ranking cloud services using parameters such as availability, security, reliability, and cost. Here, the Quality of Web Service (QWS) dataset is used, which has seven major cloud services categories, ranked from 0–6, to extract the required persuasive features through Sequential Minimal Optimization Regression (SMOreg). The classification outcomes through SMOreg are capable and demonstrate a general accuracy of around 98.71% in identifying optimum cloud services through the identified parameters. The main advantage of SMOreg is that the amount of memory required for SMO is linear. The findings show that our improved model in terms of precision outperforms prevailing techniques such as Multilayer Perceptron (MLP) and Linear Regression (LR). Full article
(This article belongs to the Special Issue Cloud/Edge/Fog Computing for Network and IoT)
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26 pages, 4484 KiB  
Article
HARNU-Net: Hierarchical Attention Residual Nested U-Net for Change Detection in Remote Sensing Images
by Haojin Li, Liejun Wang and Shuli Cheng
Sensors 2022, 22(12), 4626; https://doi.org/10.3390/s22124626 - 19 Jun 2022
Cited by 3 | Viewed by 1919
Abstract
Change detection (CD) is a particularly important task in the field of remote sensing image processing. It is of practical importance for people when making decisions about transitional situations on the Earth’s surface. The existing CD methods focus on the design of feature [...] Read more.
Change detection (CD) is a particularly important task in the field of remote sensing image processing. It is of practical importance for people when making decisions about transitional situations on the Earth’s surface. The existing CD methods focus on the design of feature extraction network, ignoring the strategy fusion and attention enhancement of the extracted features, which will lead to the problems of incomplete boundary of changed area and missing detection of small targets in the final output change map. To overcome the above problems, we proposed a hierarchical attention residual nested U-Net (HARNU-Net) for remote sensing image CD. First, the backbone network is composed of a Siamese network and nested U-Net. We remold the convolution block in nested U-Net and proposed ACON-Relu residual convolution block (A-R), which reduces the missed detection rate of the backbone network in small change areas. Second, this paper proposed the adjacent feature fusion module (AFFM). Based on the adjacency fusion strategy, the module effectively integrates the details and semantic information of multi-level features, so as to realize the feature complementarity and spatial mutual enhancement between adjacent features. Finally, the hierarchical attention residual module (HARM) is proposed, which locally filters and enhances the features in a more fine-grained space to output a much better change map. Adequate experiments on three challenging benchmark public datasets, CDD, LEVIR-CD and BCDD, show that our method outperforms several other state-of-the-art methods and performs excellent in F1, IOU and visual image quality. Full article
(This article belongs to the Section Sensing and Imaging)
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