Sensors

8 pages, 1324 KiB

Open AccessArticle

Air-Coupled Ultrasound Sealing Integrity Inspection Using Leaky Lamb Waves in a Simplified Model of a Lithium-Ion Pouch Battery: Feasibility Study

by Hyunwoo Cho, Eunwoo Kil, Jihun Jang, Jinbum Kang, Ilseob Song and Yangmo Yoo

Sensors 2022, 22(17), 6718; https://doi.org/10.3390/s22176718 - 05 Sep 2022

Cited by 4 | Viewed by 2213

Abstract

Inspecting the sealing integrity of lead tabs is an important means of ensuring the reliability and safety of pouch-type lithium-ion (Li-ion) batteries with a thin multi-layered aluminum (Al) laminated film. This paper presents a new air-coupled ultrasonic non-destructive testing (NDT) inspection method based [...] Read more.

Inspecting the sealing integrity of lead tabs is an important means of ensuring the reliability and safety of pouch-type lithium-ion (Li-ion) batteries with a thin multi-layered aluminum (Al) laminated film. This paper presents a new air-coupled ultrasonic non-destructive testing (NDT) inspection method based on leaky Lamb wave transmission; and reception for evaluating the sealing integrity between the lead tab and the Al pouch film. The proposed method uses the critical incidence angle between the air and the layer with the fastest Lamb wave velocity to maximize the signal-to-noise ratio in the through-transmission mode. To determine the critical incidence angle, phantom experiments with two test pieces (i.e., an Al tab and an Al tab sealed with an Al pouch film) are conducted. In addition, 2D scans are performed at various incidence angles for an inhouse pouch-type Li-ion battery with a 1-mm-wide foreign material inserted as a defect. At the critical incidence angle (i.e., 22°), the proposed air-coupled ultrasonic NDT method in through-transmission mode successfully identifies the shape and location of the defect through c-scan image reconstruction. These preliminary results indicate that the proposed air-coupled ultrasonic NDT method with leaky Lamb waves can be used to inspect the sealing integrity of Li-ion pouch batteries in dry test conditions. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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17 pages, 1448 KiB

Open AccessArticle

Optimization of Edge Resources for Deep Learning Application with Batch and Model Management

by Seungwoo Kum, Seungtaek Oh, Jeongcheol Yeom and Jaewon Moon

Sensors 2022, 22(17), 6717; https://doi.org/10.3390/s22176717 - 05 Sep 2022

Cited by 6 | Viewed by 2564

Abstract

As deep learning technology paves its way, real-world applications that make use of it become popular these days. Edge computing architecture is one of the service architectures to realize the deep learning based service, which makes use of the resources near the data [...] Read more.

As deep learning technology paves its way, real-world applications that make use of it become popular these days. Edge computing architecture is one of the service architectures to realize the deep learning based service, which makes use of the resources near the data source or client. In Edge computing architecture it becomes important to manage resource usage, and there is research on optimization of deep learning, such as pruning or binarization, which makes deep learning models more lightweight, along with the research for the efficient distribution of workloads on cloud or edge resources. Those are to reduce the workload on edge resources. In this paper, a usage optimization method with batch and model management is proposed. The proposed method is to increase the utilization of GPU resource by modifying the batch size of the input of an inference application. To this end, the inference pipelines are identified to see how the different kinds of resources are used, and then the effect of batch inference on GPU is measured. The proposed method consists of a few modules, including a tool for batch size management which is able to change a batch size with respect to the available resources, and another one for model management which supports on-the-fly update of a model. The proposed methods are implemented on a real-time video analysis application and deployed in the Kubernetes cluster as a Docker container. The result shows that the proposed method can optimize the usage of edge resources for real-time video analysis deep learning applications. Full article

(This article belongs to the Special Issue Convolutional Neural Networks and Edge Computing Application)

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16 pages, 3732 KiB

Open AccessArticle

A Method of Calibration for the Distortion of LiDAR Integrating IMU and Odometer

by Qiuxuan Wu, Qinyuan Meng, Yangyang Tian, Zhongrong Zhou, Cenfeng Luo, Wandeng Mao, Pingliang Zeng, Botao Zhang and Yanbin Luo

Sensors 2022, 22(17), 6716; https://doi.org/10.3390/s22176716 - 05 Sep 2022

Cited by 3 | Viewed by 2240

Abstract

To improve the motion distortion caused by LiDAR data at low and medium frame rates when moving, this paper proposes an improved algorithm for scanning matching of estimated velocity that combines an IMU and odometer. First, the information of the IMU and the [...] Read more.

To improve the motion distortion caused by LiDAR data at low and medium frame rates when moving, this paper proposes an improved algorithm for scanning matching of estimated velocity that combines an IMU and odometer. First, the information of the IMU and the odometer is fused, and the pose of the LiDAR is obtained using the linear interpolation method. The ICP method is used to scan and match the LiDAR data. The data fused by the IMU and the odometer provide the optimal initial value for the ICP. The estimated speed of the LiDAR is introduced as the termination condition of the ICP method iteration to realize the compensation of the LiDAR data. The experimental comparative analysis shows that the algorithm is better than the ICP algorithm and the VICP algorithm in matching accuracy. Full article

(This article belongs to the Special Issue Efficient Intelligence with Applications in Embedded Sensing)

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32 pages, 6055 KiB

Open AccessArticle

A Blockchain-Enabled Incentive Trust Management with Threshold Ring Signature Scheme for Traffic Event Validation in VANETs

by Waheeb Ahmed, Wu Di and Daniel Mukathe

Sensors 2022, 22(17), 6715; https://doi.org/10.3390/s22176715 - 05 Sep 2022

Cited by 7 | Viewed by 2605

Abstract

As a part of the intelligent transportation system, vehicular ad hoc networks (VANETs) provide timely information about road events and traffic to improve road safety and traffic efficiency. However, VANETs face many challenges, such as attacks from malicious vehicles, identity privacy leakage, and [...] Read more.

As a part of the intelligent transportation system, vehicular ad hoc networks (VANETs) provide timely information about road events and traffic to improve road safety and traffic efficiency. However, VANETs face many challenges, such as attacks from malicious vehicles, identity privacy leakage, and the absence of trust between vehicular nodes. In addition, vehicles nearby an event usually lack the motivation to participate in the traffic event validation whenever it occurs, which requires the cooperation of vehicles on the network. To solve these problems, a blockchain-enabled incentive trust model with a privacy-preserving threshold ring signature scheme for VANETs is proposed. Firstly, a threshold ring signature scheme is designed in order to allow participants in the non-trusted environment to anonymously witness the message’s authenticity and reliability while guaranteeing the vehicle’s privacy. Second, a blockchain-enabled incentive trust management model is presented to enable the roadside units (RSUs) to thwart various attacks and guarantee the trustworthiness of event messages transmitted in VANETs and also motivate the senders of the traffic information and their witnesses with incentives. Finally, to improve efficiency, a practical Byzantine fault-tolerant consensus mechanism is used. Our proposed system is demonstrated to be effective and secure for VANETs, according to both security analysis and performance evaluation. Full article

(This article belongs to the Section Sensor Networks)

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11 pages, 1924 KiB

Open AccessArticle

Establishment of an Improved ELONA Method for Detecting Fumonisin B₁ Based on Aptamers and Hemin-CDs Conjugates

by Xinyue Zhao, Jiale Gao, Yuzhu Song, Jinyang Zhang and Qinqin Han

Sensors 2022, 22(17), 6714; https://doi.org/10.3390/s22176714 - 05 Sep 2022

Cited by 3 | Viewed by 1754

Abstract

Fumonisin B₁ (FB₁) is a strong mycotoxin that is ubiquitous in agricultural products. The establishment of rapid detection methods is an important means to prevent and control FB₁ contamination. In this study, an improved enzyme-linked oligonucleotide assay (ELONA) method [...] Read more.

Fumonisin B₁ (FB₁) is a strong mycotoxin that is ubiquitous in agricultural products. The establishment of rapid detection methods is an important means to prevent and control FB₁ contamination. In this study, an improved enzyme-linked oligonucleotide assay (ELONA) method was designed and tested to detect the contents of FB₁ in maize (corn) samples. F10 modified with biotin was bound to an enzyme label plate that was coated with streptavidin (SA) in advance, and carbon dots (CDs) were used to catalyze the color of tetramethylbenzidine (TMB). The complementary chain of F10 was modified with an amino group and coupled with CDs to obtain conjugates. The sample and conjugates were then added to the enzyme plate coated with F10 (an FB₁ aptamer). Upon completion of the color reaction, the absorbance was measured at 450 nm. The LOD of this method was 4.30 ng/mL and the LOQ was 13.03 ng/mL. We observed a linear relationship in the FB₁ concentration range of 0–100 ng/mL. The standard curve was y = −0.001482 × x + 0.3463, R² = 0.9918, and the experimental results could be directly measured visually. The recovery of the maize sample was 97.5–99.23% and 94.54–99.25%, and the total detection time was 1 h. Full article

(This article belongs to the Special Issue Recent Advances in Apta-Biosensors)

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25 pages, 5473 KiB

Open AccessArticle

An Anonymous Authentication and Key Update Mechanism for IoT Devices Based on EnOcean Protocol

by Yi Wu and Tao Feng

Sensors 2022, 22(17), 6713; https://doi.org/10.3390/s22176713 - 05 Sep 2022

Cited by 3 | Viewed by 2112

Abstract

EnOcean, a commonly used control protocol in smart lighting systems, provides authentication, as well as message integrity verification services, and can resist replay attack and tamper attack. However, since the device identity information transmitted between sensors in smart lighting control systems is easily [...] Read more.

EnOcean, a commonly used control protocol in smart lighting systems, provides authentication, as well as message integrity verification services, and can resist replay attack and tamper attack. However, since the device identity information transmitted between sensors in smart lighting control systems is easily accessible by malicious attackers, attackers can analyze users’ habits based on the intercepted information. This paper analyzed the security of the EnOcean protocol using a formal analysis method based on the colored Petri net (CPN) theory and the Dolev–Yao attacker model and found that the protocol did not anonymize the device identity information and did not have a communication key update mechanism, so an attacker could easily initiate a key compromise impersonation attack (KCIA) after breaking the pre-shared communication key. To address the above security issues, this paper proposed an EnOcean-A protocol with higher security based on the EnOcean protocol. The EnOcean-A protocol introduced a trusted third-party server to send communication keys to communication devices because devices must obtain different communication keys from the trusted third-party server each time they communicated. Thus, this protocol could resist a KCIA and achieve forward security. Meanwhile, the device identity information was anonymized using a homomorphic hash function in the EnOcean-A protocol, and the dynamic update mechanism of the device identity information was added so that an attacker could not obtain the real identity information of the device. Finally, the formal analysis of the EnOcean-A protocol showed that the new protocol could resist a KCIA and ensure the anonymity and untraceability of the communication device, which had higher security compared with the EnOcean protocol. Full article

(This article belongs to the Special Issue Cryptography and Security Protocol in Internet of Things)

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20 pages, 6444 KiB

Open AccessArticle

Transportation Mode Detection Using Temporal Convolutional Networks Based on Sensors Integrated into Smartphones

by Pu Wang and Yongguo Jiang

Sensors 2022, 22(17), 6712; https://doi.org/10.3390/s22176712 - 05 Sep 2022

Cited by 3 | Viewed by 2602

Abstract

In recent years, with the development of science and technology, people have more and more choices for daily travel. However, assisting with various mobile intelligent services by transportation mode detection has become more urgent for the refinement of human activity identification. Although much [...] Read more.

In recent years, with the development of science and technology, people have more and more choices for daily travel. However, assisting with various mobile intelligent services by transportation mode detection has become more urgent for the refinement of human activity identification. Although much work has been done on transportation mode detection, accurate and reliable transportation mode detection remains challenging. In this paper, we propose a novel transportation mode detection algorithm, namely T2Trans, based on a temporal convolutional network (i.e., TCN), which employs multiple lightweight sensors integrated into a phone. The feature representation learning of multiple preprocessed sensor data using temporal convolutional networks can improve transportation mode detection accuracy and enhance learning efficiency. Extensive experimental results demonstrated that our algorithm attains a macro F1-score of 86.42% on the real-world SHL dataset and 88.37% on the HTC dataset, with an average accuracy of 86.37% on the SHL dataset and 89.13% on the HTC dataset. Our model can better identify eight transportation modes, including stationary, walking, running, cycling, car, bus, subway, and train, with better transportation mode detection accuracy, and outperform other benchmark algorithms. Full article

(This article belongs to the Section Sensor Networks)

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13 pages, 7062 KiB

Open AccessArticle

Localization of Cracks in Concrete Structures Using an Unmanned Aerial Vehicle

by Hyun-Jung Woo, Dong-Min Seo, Min-Seok Kim, Min-San Park, Won-Hwa Hong and Seung-Chan Baek

Sensors 2022, 22(17), 6711; https://doi.org/10.3390/s22176711 - 05 Sep 2022

Cited by 10 | Viewed by 2112

Abstract

Active research on crack detection technology for structures based on unmanned aerial vehicles (UAVs) has attracted considerable attention. Most of the existing research on localization of cracks using UAVs mounted the Global Positioning System (GPS)/Inertial Measurement Unit (IMU) on the UAVs to obtain [...] Read more.

Active research on crack detection technology for structures based on unmanned aerial vehicles (UAVs) has attracted considerable attention. Most of the existing research on localization of cracks using UAVs mounted the Global Positioning System (GPS)/Inertial Measurement Unit (IMU) on the UAVs to obtain location information. When such absolute position information is used, several studies confirmed that positioning errors of the UAVs were reflected and were in the order of a few meters. To address these limitations, in this study, without using the absolute position information, localization of cracks was defined using relative position between objects in UAV-captured images to significantly reduce the error level. Through aerial photography, a total of 97 images were acquired. Using the point cloud technique, image stitching, and homography matrix algorithm, 5 cracks and 3 reference objects were defined. Importantly, the comparative analysis of estimated relative position values and ground truth values through field measurement revealed that errors in the range 24–84 mm and 8–48 mm were obtained on the x- and y-directions, respectively. Also, RMSE errors of 37.95–91.24 mm were confirmed. In the future, the proposed methodology can be utilized for supplementing and improving the conventional methods for visual inspection of infrastructures and facilities. Full article

(This article belongs to the Topic Recent Advances in Structural Health Monitoring)

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20 pages, 8263 KiB

Open AccessArticle

A Thermal Infrared Pedestrian-Detection Method for Edge Computing Devices

by Shuai You, Yimu Ji, Shangdong Liu, Chaojun Mei, Xiaoliang Yao and Yujian Feng

Sensors 2022, 22(17), 6710; https://doi.org/10.3390/s22176710 - 05 Sep 2022

Cited by 3 | Viewed by 1916

Abstract

The thermal imaging pedestrian-detection system has excellent performance in different lighting scenarios, but there are problems regarding weak texture, object occlusion, and small objects. Meanwhile, large high-performance models have higher latency on edge devices with limited computing power. To solve the above problems, [...] Read more.

The thermal imaging pedestrian-detection system has excellent performance in different lighting scenarios, but there are problems regarding weak texture, object occlusion, and small objects. Meanwhile, large high-performance models have higher latency on edge devices with limited computing power. To solve the above problems, in this paper, we propose a real-time thermal imaging pedestrian-detection method for edge computing devices. Firstly, we utilize multi-scale mosaic data augmentation to enhance the diversity and texture of objects, which alleviates the impact of complex environments. Then, the parameter-free attention mechanism is introduced into the network to enhance features, which barely increases the computing cost of the network. Finally, we accelerate multi-channel video detection through quantization and multi-threading techniques on edge computing devices. Additionally, we create a high-quality thermal infrared dataset to facilitate the research. The comparative experiments on the self-built dataset, YDTIP, and three public datasets, with other methods show that our method also has certain advantages. Full article

(This article belongs to the Section Intelligent Sensors)

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12 pages, 2513 KiB

Open AccessArticle

Machine Learning Model Based on Radiomic Features for Differentiation between COVID-19 and Pneumonia on Chest X-ray

by Young Jae Kim

Sensors 2022, 22(17), 6709; https://doi.org/10.3390/s22176709 - 05 Sep 2022

Cited by 6 | Viewed by 2066

Abstract

Machine learning approaches are employed to analyze differences in real-time reverse transcription polymerase chain reaction scans to differentiate between COVID-19 and pneumonia. However, these methods suffer from large training data requirements, unreliable images, and uncertain clinical diagnosis. Thus, in this paper, we used [...] Read more.

Machine learning approaches are employed to analyze differences in real-time reverse transcription polymerase chain reaction scans to differentiate between COVID-19 and pneumonia. However, these methods suffer from large training data requirements, unreliable images, and uncertain clinical diagnosis. Thus, in this paper, we used a machine learning model to differentiate between COVID-19 and pneumonia via radiomic features using a bias-minimized dataset of chest X-ray scans. We used logistic regression (LR), naive Bayes (NB), support vector machine (SVM), k-nearest neighbor (KNN), bagging, random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGBM) to differentiate between COVID-19 and pneumonia based on training data. Further, we used a grid search to determine optimal hyperparameters for each machine learning model and 5-fold cross-validation to prevent overfitting. The identification performances of COVID-19 and pneumonia were compared with separately constructed test data for four machine learning models trained using the maximum probability, contrast, and difference variance of the gray level co-occurrence matrix (GLCM), and the skewness as input variables. The LGBM and bagging model showed the highest and lowest performances; the GLCM difference variance showed a high overall effect in all models. Thus, we confirmed that the radiomic features in chest X-rays can be used as indicators to differentiate between COVID-19 and pneumonia using machine learning. Full article

(This article belongs to the Section Sensing and Imaging)

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16 pages, 4212 KiB

Open AccessArticle

Investigating Stroke Effects on Respiratory Parameters Using a Wearable Device: A Pilot Study on Hemiplegic Patients

by Joshua Di Tocco, Daniela Lo Presti, Martina Zaltieri, Marco Bravi, Michelangelo Morrone, Silvia Sterzi, Emiliano Schena and Carlo Massaroni

Sensors 2022, 22(17), 6708; https://doi.org/10.3390/s22176708 - 05 Sep 2022

Cited by 2 | Viewed by 1832

Abstract

Quantitatively assessing personal health status is gaining increasing attention due to the improvement of diagnostic technology and the increasing occurrence of chronic pathologies. Monitoring physiological parameters allows for retrieving a general overview of the personal health status. Respiratory activity can provide relevant information, [...] Read more.

Quantitatively assessing personal health status is gaining increasing attention due to the improvement of diagnostic technology and the increasing occurrence of chronic pathologies. Monitoring physiological parameters allows for retrieving a general overview of the personal health status. Respiratory activity can provide relevant information, especially when pathologies affect the muscles and organs involved in breathing. Among many technologies, wearables may represent a valid solution for continuous and remote monitoring of respiratory activity, thus reducing healthcare costs. The most popular wearables used in this arena are based on detecting the breathing-induced movement of the chest wall. Therefore, their use in patients with impaired chest wall motion and abnormal respiratory kinematics can be challenging, but literature is still in its infancy. This study investigates the performance of a custom wearable device for respiratory monitoring in post-stroke patients. We tested the device on six hemiplegic patients under different respiratory regimes. The estimated respiratory parameters (i.e., respiratory frequency and the timing of the respiratory phase) demonstrated good agreement with the ones provided by a gold standard device. The promising results of this pilot study encourage the exploitation of wearables on these patients that may strongly impact the treatment of chronic diseases, such as hemiplegia. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

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14 pages, 4718 KiB

Open AccessArticle

Development and Validation of a Tunable Diode Laser Absorption Spectroscopy System for Hot Gas Flow and Small-Scale Flame Measurement

by Ran Tu, Junqing Gu, Yi Zeng, Xuejin Zhou, Kai Yang, Jiaojiao Jing, Zhihong Miao and Jianhong Yang

Sensors 2022, 22(17), 6707; https://doi.org/10.3390/s22176707 - 05 Sep 2022

Cited by 4 | Viewed by 1967

Abstract

TDLAS (tunable diode laser absorption spectroscopy) is an important gas analysis method that can be employed to obtain characteristic parameters non-invasively by the infrared absorption spectra of tracer molecules such as CH₄, H₂O and O₂. In this [...] Read more.

TDLAS (tunable diode laser absorption spectroscopy) is an important gas analysis method that can be employed to obtain characteristic parameters non-invasively by the infrared absorption spectra of tracer molecules such as CH₄, H₂O and O₂. In this study, a portable H₂O-based TDLAS system with a dual optical path was developed with the aim of assessing the combustion characteristics of flammable gases. Firstly, a calculation method of gas characteristics including temperature and velocity combining absorption spectra and a HITRAN database was provided. Secondly, to calibrate and validate this TDLAS system precisely, a pressure vessel and a shock tube were introduced innovatively to generate static or steady flow fields with preset constant temperatures, pressures, or velocities. Static tests within environment pressures up to 2 MPa and steady flow field tests with temperatures up to 1600 K and flow velocities up to 950 m/s were performed for verification. It was proved that this system can provide an accurate values for high temperature and velocity gas flows. Finally, an experimental investigation of CH₄/air flames was conducted to test the effectiveness of the system when applied to small diffusion flames. This TDLAS system gave satisfactory flame temperature and velocity data owing to the dual optical path design and high frequency scanning, which compensated for scale effects and pulsation of the flame. This work demonstrates a valuable new approach to thermal hazard analysis in specific environments. Full article

(This article belongs to the Special Issue Spectral Detection Technology, Sensors and Instruments)

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24 pages, 10393 KiB

Open AccessArticle

Design of FOPID Controller for Pneumatic Control Valve Based on Improved BBO Algorithm

by Min Zhu, Zihao Xu, Zhaoyu Zang and Xueping Dong

Sensors 2022, 22(17), 6706; https://doi.org/10.3390/s22176706 - 05 Sep 2022

Cited by 4 | Viewed by 2170

Abstract

Aiming at the problems of nonlinearity and inaccuracy in the model of the pneumatic control valve position in the industrial control process, a valve position control method based on a fractional-order PID controller is proposed. The working principle of the pneumatic control valve [...] Read more.

Aiming at the problems of nonlinearity and inaccuracy in the model of the pneumatic control valve position in the industrial control process, a valve position control method based on a fractional-order PID controller is proposed. The working principle of the pneumatic control valve is analyzed, and its mathematical model is established. In order to improve the accuracy of the model, an improved biogeography-based optimization algorithm is proposed to tune the parameters of the fractional-order PID controller in view of the wide range and high complexity of the fractional-order PID controller. The initialization of the chaotic graph, the adjustment of the migration model, and the improvement of the migration operator and the mutation operator are introduced to improve the algorithm optimization ability, which is used for the model identification of the control valve control system. The simulation and experimental results clearly show that, compared with the integer-order PID controller, the designed fractional-order PID controller has faster response speed and control accuracy, which can better meet the requirements of pneumatic control valve position control. Full article

(This article belongs to the Special Issue Intelligent Sensing, Control and Optimization of Networks)

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16 pages, 3876 KiB

Open AccessArticle

Research on an Algorithm of Express Parcel Sorting Based on Deeper Learning and Multi-Information Recognition

by Xing Xu, Zhenpeng Xue and Yun Zhao

Sensors 2022, 22(17), 6705; https://doi.org/10.3390/s22176705 - 05 Sep 2022

Cited by 7 | Viewed by 2178

Abstract

With the development of smart logistics, current small distribution centers have begun to use intelligent equipment to indirectly read bar code information on courier sheets to carry out express sorting. However, limited by the cost, most of them choose relatively low-end sorting equipment [...] Read more.

With the development of smart logistics, current small distribution centers have begun to use intelligent equipment to indirectly read bar code information on courier sheets to carry out express sorting. However, limited by the cost, most of them choose relatively low-end sorting equipment in a warehouse environment that is complex. This single information identification method leads to a decline in the identification rate of sorting, affecting efficiency of the entire express sorting. Aimed at the above problems, an express recognition method based on deeper learning and multi-information fusion is proposed. The method is mainly aimed at bar code information and three segments of code information on the courier sheet, which is divided into two parts: target information detection and recognition. For the detection of target information, we used a method of deeper learning to detect the target, and to improve speed and precision we designed a target detection network based on the existing YOLOv4 network, Experiments show that the detection accuracy and speed of the redesigned target detection network were much improved. Next for recognition of two kinds of target information we first intercepted the image after positioning and used a ZBAR algorithm to decode the barcode image after interception. The we used Tesseract-OCR technology to identify the intercepted three segments code picture information, and finally output the information in the form of strings. This deeper learning-based multi-information identification method can help logistics centers to accurately obtain express sorting information from the database. The experimental results show that the time to detect a picture was 0.31 s, and the recognition accuracy was 98.5%, which has better robustness and accuracy than single barcode information positioning and recognition alone. Full article

(This article belongs to the Special Issue Deep Learning for Information Fusion and Pattern Recognition)

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16 pages, 7855 KiB

Open AccessArticle

Research on a Defecation Pre-Warning Algorithm for the Disabled Elderly Based on a Semi-Supervised Generative Adversarial Network

by Yanbiao Zou, Shenghong Wu, Tie Zhang and Yuanhang Yang

Sensors 2022, 22(17), 6704; https://doi.org/10.3390/s22176704 - 05 Sep 2022

Cited by 1 | Viewed by 1763

Abstract

The elderly population in China is continuously increasing, and the disabled account for a large proportion of the elderly population. An effective solution is urgently needed for incontinence among disabled elderly people. Compared with disposable adult diapers, artificial sphincter implantation and medication for [...] Read more.

The elderly population in China is continuously increasing, and the disabled account for a large proportion of the elderly population. An effective solution is urgently needed for incontinence among disabled elderly people. Compared with disposable adult diapers, artificial sphincter implantation and medication for incontinence, the defecation pre-warning method is more flexible and convenient. However, due to the complex human physiology and individual differences, its development is limited. Based on the aging trend of the population and clinical needs, this paper proposes a bowel sound acquisition system and a defecation pre-warning method and system based on a semi-supervised generative adversarial network. A network model was established to predict defecation using bowel sounds. The experimental results show that the proposed method can effectively classify bowel sounds with or without defecation tendency, and the accuracy reached 94.4%. Full article

(This article belongs to the Special Issue E-health System Based on Sensors and Artificial Intelligence)

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15 pages, 29614 KiB

Open AccessArticle

Potential Obstacle Detection Using RGB to Depth Image Encoder–Decoder Network: Application to Unmanned Aerial Vehicles

by Tomasz Hachaj

Sensors 2022, 22(17), 6703; https://doi.org/10.3390/s22176703 - 05 Sep 2022

Cited by 3 | Viewed by 2313

Abstract

In this work, a new method is proposed that allows the use of a single RGB camera for the real-time detection of objects that could be potential collision sources for Unmanned Aerial Vehicles. For this purpose, a new network with an encoder–decoder architecture [...] Read more.

In this work, a new method is proposed that allows the use of a single RGB camera for the real-time detection of objects that could be potential collision sources for Unmanned Aerial Vehicles. For this purpose, a new network with an encoder–decoder architecture has been developed, which allows rapid distance estimation from a single image by performing RGB to depth mapping. Based on a comparison with other existing RGB to depth mapping methods, the proposed network achieved a satisfactory trade-off between complexity and accuracy. With only 6.3 million parameters, it achieved efficiency close to models with more than five times the number of parameters. This allows the proposed network to operate in real time. A special algorithm makes use of the distance predictions made by the network, compensating for measurement inaccuracies. The entire solution has been implemented and tested in practice in an indoor environment using a micro-drone equipped with a front-facing RGB camera. All data and source codes and pretrained network weights are available to download. Thus, one can easily reproduce the results, and the resulting solution can be tested and quickly deployed in practice. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Object Tracking—2nd Edition)

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18 pages, 8371 KiB

Open AccessArticle

Workshop Safety Helmet Wearing Detection Model Based on SCM-YOLO

by Bin Zhang, Chuan-Feng Sun, Shu-Qi Fang, Ye-Hai Zhao and Song Su

Sensors 2022, 22(17), 6702; https://doi.org/10.3390/s22176702 - 05 Sep 2022

Cited by 10 | Viewed by 2718

Abstract

In order to overcome the problems of object detection in complex scenes based on the YOLOv4-tiny algorithm, such as insufficient feature extraction, low accuracy, and low recall rate, an improved YOLOv4-tiny safety helmet-wearing detection algorithm SCM-YOLO is proposed. Firstly, the Spatial Pyramid Pooling [...] Read more.

In order to overcome the problems of object detection in complex scenes based on the YOLOv4-tiny algorithm, such as insufficient feature extraction, low accuracy, and low recall rate, an improved YOLOv4-tiny safety helmet-wearing detection algorithm SCM-YOLO is proposed. Firstly, the Spatial Pyramid Pooling (SPP) structure is added after the backbone network of the YOLOv4-tiny model to improve its adaptability of different scale features and increase its effective features extraction capability. Secondly, Convolutional Block Attention Module (CBAM), Mish activation function, K-Means++ clustering algorithm, label smoothing, and Mosaic data enhancement are introduced to improve the detection accuracy of small objects while ensuring the detection speed. After a large number of experiments, the proposed SCM-YOLO algorithm achieves a mAP of 93.19%, which is 4.76% higher than the YOLOv4-tiny algorithm. Its inference speed reaches 22.9FPS (GeForce GTX 1050Ti), which meets the needs of the real-time and accurate detection of safety helmets in complex scenes. Full article

(This article belongs to the Special Issue Pattern Recognition Using Neural Networks)

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18 pages, 1054 KiB

Open AccessArticle

Drone Detection and Classification Using Physical-Layer Protocol Statistical Fingerprint

by Louis Morge-Rollet, Denis Le Jeune, Frédéric Le Roy, Charles Canaff and Roland Gautier

Sensors 2022, 22(17), 6701; https://doi.org/10.3390/s22176701 - 05 Sep 2022

Cited by 3 | Viewed by 2905

Abstract

We propose a novel approach for drone detection and classification based on RF communication link analysis. Our approach analyses large signal record including several packets and can be decomposed of two successive steps: signal detection and drone classification. On one hand, the signal [...] Read more.

We propose a novel approach for drone detection and classification based on RF communication link analysis. Our approach analyses large signal record including several packets and can be decomposed of two successive steps: signal detection and drone classification. On one hand, the signal detection step is based on Power Spectral Entropy (PSE), a measure of the energy distribution uniformity in the frequency domain. It consists of detecting a structured signal such as a communication signal with a lower PSE than a noise one. On the other hand, the classification step is based on a so-called physical-layer protocol statistical fingerprint (PLSPF). This method extracts the packets at the physical layer using hysteresis thresholding, then computes statistical features for classification based on extracted packets. It consists of performing traffic analysis of communication link between the drone and its controller. Conversely to classic drone traffic analysis working at data link layer (or at upper layers), it performs traffic analysis directly from the corresponding I/Q signal, i.e., at the physical layer. The approach shows interesting properties such as scale invariance, frequency invariance, and noise robustness. Furthermore, the classification method allows us to distinguish WiFi drones from other WiFi devices due to underlying requirement of drone communications such as good reactivity in control. Finally, we propose different experiments to highlight theses properties and performances. The physical-layer protocol statistical fingerprint exploiting communication specificities could also be used in addition of RF fingerprinting method to perform authentication of devices at the physical-layer. Full article

(This article belongs to the Special Issue Physical-Layer Security for Wireless Communications)

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12 pages, 6812 KiB

Open AccessArticle

IoT-Based Fish Farm Water Quality Monitoring System

by Chiung-Hsing Chen, Yi-Chen Wu, Jia-Xiang Zhang and Ying-Hsiu Chen

Sensors 2022, 22(17), 6700; https://doi.org/10.3390/s22176700 - 05 Sep 2022

Cited by 13 | Viewed by 15876

Abstract

Typhoons in summer and cold snaps during winter in Taiwan often cause huge aquaculture losses. Simultaneously, the lack of human resources is a problem. Therefore, we used wireless transmission technology with various sensors to transmit the temperature, pH value, dissolved oxygen, water level, [...] Read more.

Typhoons in summer and cold snaps during winter in Taiwan often cause huge aquaculture losses. Simultaneously, the lack of human resources is a problem. Therefore, we used wireless transmission technology with various sensors to transmit the temperature, pH value, dissolved oxygen, water level, and life expectancy of the sensor in the fish farm to the server. The integrated data are transmitted to mobile devices through the Internet of Things, enabling administrators to monitor the water quality in a fish farm through mobile devices. Because the current pH sensors cannot be submerged in the liquid for a long time for measurements, human resources and time are required to take the instrument to each fish farm for testing at a fixed time. Therefore, a robotic arm was developed to complete automatic measurement and maintenance actions. We designed this arm with a programmable logic controller, a single chip combined with a wireless transmission module, and an embedded system. This system is divided into control, measurement, server, and mobility. The intelligent measurement equipment designed in this study can work 24 h per day, which effectively reduces the losses caused by personnel, material resources, and data errors. Full article

(This article belongs to the Special Issue Selected Papers from the 2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2021))

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11 pages, 4061 KiB

Open AccessArticle

Over-Two-Octave Supercontinuum Generation of Light-Carrying Orbital Angular Momentum in Germania-Doped Ring-Core Fiber

by Jian Yang, Yingning Wang, Yuxi Fang, Wenpu Geng, Wenqian Zhao, Changjing Bao, Yongxiong Ren, Zhi Wang, Yange Liu, Zhongqi Pan and Yang Yue

Sensors 2022, 22(17), 6699; https://doi.org/10.3390/s22176699 - 05 Sep 2022

Cited by 3 | Viewed by 1932

Abstract

In this paper, we design a silica-cladded Germania-doped ring-core fiber (RCF) that supports orbital angular momentum (OAM) modes. By optimizing the fiber structure parameters, the RCF possesses a near-zero flat dispersion with a total variation of <±30 ps/nm/km over 1770 nm bandwidth from [...] Read more.

In this paper, we design a silica-cladded Germania-doped ring-core fiber (RCF) that supports orbital angular momentum (OAM) modes. By optimizing the fiber structure parameters, the RCF possesses a near-zero flat dispersion with a total variation of <±30 ps/nm/km over 1770 nm bandwidth from 1040 to 2810 nm for the OAM_1,1 mode. A beyond-two-octave supercontinuum spectrum of the OAM_1,1 mode is generated numerically by launching a 40 fs 120 kW pulse train centered at 1400 nm into a 12 cm long designed 50 mol% Ge-doped fiber, which covers 2130 nm bandwidth from 630 nm to 2760 nm at −40 dB of power level. This design can serve as an efficient way to extend the spectral coverage of beams carrying OAM modes for various applications. Full article

(This article belongs to the Special Issue Optical Signal Processing Technologies for Communication, Computing, and Sensing Applications)

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19 pages, 3585 KiB

Open AccessArticle

Remove Artifacts from a Single-Channel EEG Based on VMD and SOBI

by Changrui Liu and Chaozhu Zhang

Sensors 2022, 22(17), 6698; https://doi.org/10.3390/s22176698 - 04 Sep 2022

Cited by 7 | Viewed by 2760

Abstract

With the development of portable EEG acquisition systems, the collected EEG has gradually changed from being multi-channel to few-channel or single-channel, thus the removal of single-channel EEG signal artifacts is extremely significant. For the artifact removal of single-channel EEG signals, the current mainstream [...] Read more.

With the development of portable EEG acquisition systems, the collected EEG has gradually changed from being multi-channel to few-channel or single-channel, thus the removal of single-channel EEG signal artifacts is extremely significant. For the artifact removal of single-channel EEG signals, the current mainstream method is generally a combination of the decomposition method and the blind source separation (BSS) method. Between them, a combination of empirical mode decomposition (EMD) and its derivative methods and ICA has been used in single-channel EEG artifact removal. However, EMD is prone to modal mixing and it has no relevant theoretical basis, thus it is not as good as variational modal decomposition (VMD) in terms of the decomposition effect. In the ICA algorithm, the implementation method based on high-order statistics is widely used, but it is not as effective as the implementation method based on second order statistics in processing EMG artifacts. Therefore, aiming at the main artifacts in single-channel EEG signals, including EOG and EMG artifacts, this paper proposed a method of artifact removal combining variational mode decomposition (VMD) and second order blind identification (SOBI). Semi-simulation experiments show that, compared with the existing EEMD-SOBI method, this method has a better removal effect on EOG and EMG artifacts, and can preserve useful information to the greatest extent. Full article

(This article belongs to the Section Electronic Sensors)

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14 pages, 2782 KiB

Open AccessArticle

Nonlinear Tactile Estimation Model Based on Perceptibility of Mechanoreceptors Improves Quantitative Tactile Sensing

by Momoko Sagara, Lisako Nobuyama and Kenjiro Takemura

Sensors 2022, 22(17), 6697; https://doi.org/10.3390/s22176697 - 04 Sep 2022

Cited by 2 | Viewed by 1578

Abstract

Tactile sensing has attracted significant attention as a tactile quantitative evaluation method because the tactile sensation is an important factor while evaluating consumer products. Although the human tactile perception mechanism has nonlinearity, previous studies have often developed linear regression models. In contrast, this [...] Read more.

Tactile sensing has attracted significant attention as a tactile quantitative evaluation method because the tactile sensation is an important factor while evaluating consumer products. Although the human tactile perception mechanism has nonlinearity, previous studies have often developed linear regression models. In contrast, this study proposes a nonlinear tactile estimation model that can estimate sensory evaluation scores from physical measurements. We extracted features from the vibration data obtained by a tactile sensor based on the perceptibility of mechanoreceptors. In parallel, a sensory evaluation test was conducted using 10 evaluation words. Then, the relationship between the extracted features and the tactile evaluation results was modeled using linear/nonlinear regressions. The best model was concluded by comparing the mean squared error between the model predictions and the actual values. The results imply that there are multiple evaluation words suitable for adopting nonlinear regression models, and the average error was 43.8% smaller than that of building only linear regression models. Full article

(This article belongs to the Section Physical Sensors)

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14 pages, 4964 KiB

Open AccessArticle

Substrateless Packaging for a D-Band MMIC Based on a Waveguide with a Glide-Symmetric EBG Hole Configuration

by Weihua Yu, Abbas Vosoogh, Bowu Wang and Zhongxia Simon He

Sensors 2022, 22(17), 6696; https://doi.org/10.3390/s22176696 - 04 Sep 2022

Cited by 2 | Viewed by 2146

Abstract

This paper presents a novel substrateless packaging solution for the D-band active e mixer MMIC module, using a waveguide line with a glide-symmetric periodic electromagnetic bandgap (EBG) hole configuration. The proposed packaging concept has the benefit of being able to control signal propagation [...] Read more.

This paper presents a novel substrateless packaging solution for the D-band active e mixer MMIC module, using a waveguide line with a glide-symmetric periodic electromagnetic bandgap (EBG) hole configuration. The proposed packaging concept has the benefit of being able to control signal propagation behavior by using a cost-effective EBG hole configuration for millimeter-wave- and terahertz (THz)-frequency-band applications. Moreover, the mixer MMIC is connected to the proposed hollow rectangular waveguide line via a novel wire-bond wideband transition without using any intermediate substrate. A simple periodical nail structure is utilized to suppress the unwanted modes in the transition. Additionally, the presented solution does not impose any limitations on the chip’s dimensions or shape. The packaged mixer module shows a return loss lower than 10 dB for LO (70–85 GHz) and RF (150–170 GHz) ports, achieving a better performance than that of traditional waveguide transitions. The module could be used as a transmitter or receiver, and the conversion loss shows good agreement in multiple samples. The proposed packaging solution has the advantages of satisfactory frequency performance, broadband adaptability, low production costs, and excellent repeatability for millimeter-wave- and THz-band systems, which would facilitate the commercialization of millimeter-wave and THz products. Full article

(This article belongs to the Special Issue mm Wave Integrated Circuits Based Sensing Systems and Applications)

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15 pages, 4676 KiB

Open AccessArticle

Novel Method for Estimating Propulsive Force Generated by Swimmers’ Hands Using Inertial Measurement Units and Pressure Sensors

by Tomoya Kadi, Tomohito Wada, Kenzo Narita, Takaaki Tsunokawa, Hirotoshi Mankyu, Hiroyuki Tamaki and Futoshi Ogita

Sensors 2022, 22(17), 6695; https://doi.org/10.3390/s22176695 - 04 Sep 2022

Cited by 4 | Viewed by 2051

Abstract

Propulsive force is a determinant of swimming performance. Several methods have been proposed to estimate the propulsive force in human swimming; however, their practical use in coaching is limited. Herein, we propose a novel method for estimating the propulsive force generated by swimmers’ [...] Read more.

Propulsive force is a determinant of swimming performance. Several methods have been proposed to estimate the propulsive force in human swimming; however, their practical use in coaching is limited. Herein, we propose a novel method for estimating the propulsive force generated by swimmers’ hands using an inertial measurement unit (IMU) and pressure sensors. In Experiment 1, we use a hand model to examine the effect of a hand-mounted IMU on pressure around the hand model at several flow velocities and water flow directions. In Experiment 2, we compare the propulsive force estimated using the IMU and pressure sensors (

F_{IMU}

) via an underwater motion-capture system and pressure sensors (

F_{Mocap}

). Five swimmers had markers, pressure sensors, and IMUs attached to their hands and performed front crawl swimming for 25 m twice at each of nine different swimming speeds. The results show that the hand-mounted IMU affects the resultant force; however, the effect of the hand-mounted IMU varies with the flow direction. The mean values of

F_{Mocap}

and

F_{IMU}

are similar (19.59 ± 7.66 N and 19.36 ± 7.86 N, respectively; intraclass correlation coefficient_(2,1) = 0.966), and their waveforms are similar (coefficient of multiple correlation = 0.99). These results indicate that the IMU can estimate the same level of propulsive force as an underwater motion-capture system. Full article

(This article belongs to the Special Issue Applications, Wearables and Sensing Technology in Sports and Physical Activity)

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18 pages, 3074 KiB

Open AccessArticle

Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain

by Trung C. Phan, Adrian Pranata, Joshua Farragher, Adam Bryant, Hung T. Nguyen and Rifai Chai

Sensors 2022, 22(17), 6694; https://doi.org/10.3390/s22176694 - 04 Sep 2022

Cited by 8 | Viewed by 2149

Abstract

This paper proposes an innovative methodology for finding how many lifting techniques people with chronic low back pain (CLBP) can demonstrate with camera data collected from 115 participants. The system employs a feature extraction algorithm to calculate the knee, trunk and hip range [...] Read more.

This paper proposes an innovative methodology for finding how many lifting techniques people with chronic low back pain (CLBP) can demonstrate with camera data collected from 115 participants. The system employs a feature extraction algorithm to calculate the knee, trunk and hip range of motion in the sagittal plane, Ward’s method, a combination of K-means and Ensemble clustering method for classification algorithm, and Bayesian neural network to validate the result of Ward’s method and the combination of K-means and Ensemble clustering method. The classification results and effect size show that Ward clustering is the optimal method where precision and recall percentages of all clusters are above 90, and the overall accuracy of the Bayesian Neural Network is 97.9%. The statistical analysis reported a significant difference in the range of motion of the knee, hip and trunk between each cluster, F (9, 1136) = 195.67, p < 0.0001. The results of this study suggest that there are four different lifting techniques in people with CLBP. Additionally, the results show that even though the clusters demonstrated similar pain levels, one of the clusters, which uses the least amount of trunk and the most knee movement, demonstrates the lowest pain self-efficacy. Full article

(This article belongs to the Special Issue Wearable Sensors Applied in Movement Analysis)

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17 pages, 4209 KiB

Open AccessArticle

Super-Resolution Reconstruction of Speckle Images of Engineered Bamboo Based on an Attention-Dense Residual Network

by Wei Yu, Zheng Liu, Zilong Zhuang, Ying Liu, Xu Wang, Yutu Yang and Binli Gou

Sensors 2022, 22(17), 6693; https://doi.org/10.3390/s22176693 - 04 Sep 2022

Cited by 6 | Viewed by 1664

Abstract

With the global population surge, the consumption of nonrenewable resources and pollution emissions have reached an alarming level. Engineered bamboo is widely used in construction, mechanical and electrical product packaging, and other industries. Its main damage is the material fracture caused by the [...] Read more.

With the global population surge, the consumption of nonrenewable resources and pollution emissions have reached an alarming level. Engineered bamboo is widely used in construction, mechanical and electrical product packaging, and other industries. Its main damage is the material fracture caused by the expansion of initial cracks. In order to accurately detect the length of crack propagation, digital image correlation technology can be used for calculation. At present, the traditional interpolation method is still used in the reconstruction of engineered bamboo speckle images for digital correlation technology, and the performance is relatively lagging. Therefore, this paper proposes a super-resolution reconstruction method of engineering-bamboo speckle images based on an attention-dense residual network. In this study, the residual network is improved by removing the BN layer, using the

L_{1}

loss function, introducing the attention model, and designing an attention-intensive residual block. An image super-resolution model based on the attention-dense residual network is proposed. Finally, the objective evaluation indexes PSNR and SSIM and subjective evaluation index MOS were used to evaluate the performance of the model. The ADRN method was 29.19 dB, 0.938, and 3.19 points in PSNR, SSIM, and MOS values. Compared to the traditional BICUBIC B-spline interpolation method, the speckle images reconstructed by this model increased by 8.55 dB, 0.323, and 1.43 points, respectively. Compared to the SRResNet method, the speckle images reconstructed by this model were increased by 4.53 dB, 0.111, and 0.14 points, respectively. The reconstructed speckle images of engineered bamboo were clearer, and the image features were more obvious, which could better identify the tip crack position of the engineered bamboo. The results show that the super-resolution reconstruction effect of engineered-bamboo speckle images can be effectively improved by adding the attention mechanism to the residual network. This method has great application value. Full article

(This article belongs to the Section Sensor Networks)

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20 pages, 1086 KiB

Open AccessArticle

An Efficient Distributed Approach for Cooperative Spectrum Sensing in Varying Interests Cognitive Radio Networks

by Maria Trigka and Elias Dritsas

Sensors 2022, 22(17), 6692; https://doi.org/10.3390/s22176692 - 04 Sep 2022

Cited by 3 | Viewed by 1536

Abstract

The rapid growth in wireless communications, coupled with insufficient utilization of the spectrum, led to the development of new wireless services and the promising technology of cognitive radio (CR) networks, which facilitate periodic access to the unoccupied spectrum bands and thus increases spectral [...] Read more.

The rapid growth in wireless communications, coupled with insufficient utilization of the spectrum, led to the development of new wireless services and the promising technology of cognitive radio (CR) networks, which facilitate periodic access to the unoccupied spectrum bands and thus increases spectral efficiency. A fundamental task in CR networks is spectrum sensing, through which unauthorized secondary users (SUs) detect unoccupied bands in the spectrum. To achieve this, an accurate estimate of the power spectrum is necessary. From this perspective, and given that many other factors can affect individual detection, such as pathloss and receiver uncertainty, we aim to improve its estimate by exploiting the spatial diversity in the SUs’ observations. Spectrum sensing is treated as a parameters estimation problem, assuming that the parameters’ vector of each SU consists of some global and partially common parameters. To exploit this modeling, distributed and cooperative spectrum sensing is the subject of interest in this study. Diffusion techniques, and especially the Adapt-Then-Combine (ATC) method will be exploited, where each SU cooperates with a group of nodes in its neighborhood that share the same parameters of interest. We consider a network of three static PUs with overlapping power spectrums, and thus, frequency bands. The performance of the employed method will be evaluated under two scenarios: (i) when the PUs spectrum varies, since some frequency bands are not yet utilized, and (ii) when the frequency bands of the PUs are fixed, but there is a mobile SU in the network, changing regions and parameters of interest. Experimental results and performance analysis reveal the ATC algorithm robustness and efficiency. Full article

(This article belongs to the Special Issue Wireless Sensor Networks Technology and Its Application in Distributed Intelligent System)

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12 pages, 2014 KiB

Open AccessArticle

Fault Restoration of Six-Axis Force/Torque Sensor Based on Optimized Back Propagation Networks

by Xuhao Li, Lifu Gao, Xiaohui Li, Huibin Cao and Yuxiang Sun

Sensors 2022, 22(17), 6691; https://doi.org/10.3390/s22176691 - 04 Sep 2022

Viewed by 1502

Abstract

Six-axis force/torque sensors are widely installed in manipulators to help researchers achieve closed-loop control. When manipulators work in comic space and deep sea, the adverse ambient environment will cause various degrees of damage to F/T sensors. If the disability of one or two [...] Read more.

Six-axis force/torque sensors are widely installed in manipulators to help researchers achieve closed-loop control. When manipulators work in comic space and deep sea, the adverse ambient environment will cause various degrees of damage to F/T sensors. If the disability of one or two dimensions is restored by self-restoration methods, the robustness and practicality of F/T sensors can be considerably enhanced. The coupling effect is an important characteristic of multi-axis F/T sensors, which implies that all dimensions of F/T sensors will influence each other. We can use this phenomenon to speculate the broken dimension by other regular dimensions. Back propagation neural network (BPNN) is a classical feedforward neural network, which consists of several layers and adopts the back-propagation algorithm to train networks. Hyperparameters of BPNN cannot be updated by training, but they impact the network performance directly. Hence, the particle swarm optimization (PSO) algorithm is adopted to tune the hyperparameters of BPNN. In this work, each dimension of a six-axis F/T sensor is regarded as an element in the input vector, and the relationships among six dimensions can be obtained using optimized BPNN. The average MSE of restoring one dimension and two dimensions over the testing data is

1.1693 \times 10^{- 5}

and

3.4205 \times 10^{- 5}

, respectively. Furthermore, the average quote error of one restored dimension and two restored dimensions are

8.800 {\times 10}^{- 3}

and

8.200 {\times 10}^{- 3}

, respectively. The analysis of experimental results illustrates that the proposed fault restoration method based on PSO-BPNN is viable and practical. The F/T sensor restored using the proposed method can reach the original measurement precision. Full article

(This article belongs to the Special Issue Artificial Intelligence for Fault Diagnostics and Prognostics)

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14 pages, 1910 KiB

Open AccessArticle

Distinguishing Nanoparticle Aggregation from Viscosity Changes in MPS/MSB Detection of Biomarkers

by Dhrubo Jyoti, Scott W. Gordon-Wylie, Daniel B. Reeves, Keith D. Paulsen and John B. Weaver

Sensors 2022, 22(17), 6690; https://doi.org/10.3390/s22176690 - 04 Sep 2022

Cited by 5 | Viewed by 1704

Abstract

Magnetic particle spectroscopy (MPS) in the Brownian relaxation regime, also termed magnetic spectroscopy of Brownian motion (MSB), can detect and quantitate very low, sub-nanomolar concentrations of molecular biomarkers. MPS/MSB uses the harmonics of the magnetization induced by a small, low-frequency oscillating magnetic field [...] Read more.

Magnetic particle spectroscopy (MPS) in the Brownian relaxation regime, also termed magnetic spectroscopy of Brownian motion (MSB), can detect and quantitate very low, sub-nanomolar concentrations of molecular biomarkers. MPS/MSB uses the harmonics of the magnetization induced by a small, low-frequency oscillating magnetic field to provide quantitative information about the magnetic nanoparticles’ (mNPs’) microenvironment. A key application uses antibody-coated mNPs to produce biomarker-mediated aggregation that can be detected using MPS/MSB. However, relaxation changes can also be caused by viscosity changes. To address this challenge, we propose a metric that can distinguish between aggregation and viscosity. Viscosity changes scale the MPS/MSB harmonic ratios with a constant multiplier across all applied field frequencies. The change in viscosity is exactly equal to the multiplier with generality, avoiding the need to understand the signal explicitly. This simple scaling relationship is violated when particles aggregate. Instead, a separate multiplier must be used for each frequency. The standard deviation of the multipliers over frequency defines a metric isolating viscosity (zero standard deviation) from aggregation (non-zero standard deviation). It increases monotonically with biomarker concentration. We modeled aggregation and simulated the MPS/MSB signal changes resulting from aggregation and viscosity changes. MPS/MSB signal changes were also measured experimentally using 100 nm iron-oxide mNPs in solutions with different viscosities (modulated by glycerol concentration) and with different levels of aggregation (modulated by concanavalin A linker concentrations). Experimental and simulation results confirmed that viscosity changes produced small changes in the standard deviation and aggregation produced larger values of standard deviation. This work overcomes a key barrier to using MPS/MSB to detect biomarkers in vivo with variable tissue viscosity. Full article

(This article belongs to the Special Issue Advanced Nanomaterial-Based Sensors for Biomedical Applications)

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24 pages, 3579 KiB

Open AccessArticle

Electrodeless Heart and Respiratory Rate Estimation during Sleep Using a Single Fabric Band and Event-Based Edge Processing

by Titus Jayarathna, Gaetano D. Gargiulo, Gough Y. Lui and Paul P. Breen

Sensors 2022, 22(17), 6689; https://doi.org/10.3390/s22176689 - 04 Sep 2022

Cited by 3 | Viewed by 1993

Abstract

Heart rate (HR) and respiratory rate (RR) are two vital parameters of the body medically used for diagnosing short/long-term illness. Out-of-the-body, non-skin-contact HR/RR measurement remains a challenge due to imprecise readings. “Invisible” wearables integrated into day-to-day garments have the potential to produce precise [...] Read more.

Heart rate (HR) and respiratory rate (RR) are two vital parameters of the body medically used for diagnosing short/long-term illness. Out-of-the-body, non-skin-contact HR/RR measurement remains a challenge due to imprecise readings. “Invisible” wearables integrated into day-to-day garments have the potential to produce precise readings with a comfortable user experience. Sleep studies and patient monitoring benefit from “Invisibles” due to longer wearability without significant discomfort. This paper suggests a novel method to reduce the footprint of sleep monitoring devices. We use a single silver-coated nylon fabric band integrated into a substrate of a standard cotton/nylon garment as a resistive elastomer sensor to measure air and blood volume change across the chest. We introduce a novel event-based architecture to process data at the edge device and describe two algorithms to calculate real-time HR/RR on ARM Cortex-M3 and Cortex-M4F microcontrollers. RR estimations show a sensitivity of 99.03% and a precision of 99.03% for identifying individual respiratory peaks. The two algorithms used for HR calculation show a mean absolute error of 0.81 ± 0.97 and

0.86 \pm 0.61

beats/min compared with a gold standard ECG-based HR. The event-based algorithm converts the respiratory/pulse waveform into instantaneous events, therefore reducing the data size by 40–140 times and requiring 33% less power to process and transfer data. Furthermore, we show that events hold enough information to reconstruct the original waveform, retaining pulse and respiratory activity. We suggest fabric sensors and event-based algorithms would drastically reduce the device footprint and increase the performance for HR/RR estimations during sleep studies, providing a better user experience. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

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