Sensors

11 pages, 2082 KiB

Open AccessArticle

Using Resistance-Band Tests to Evaluate Trunk Muscle Strength in Chronic Low Back Pain: A Test–Retest Reliability Study

by Francisco Franco-López, Krzysztof Durkalec-Michalski, Jesús Díaz-Morón, Enrique Higueras-Liébana, Alejandro Hernández-Belmonte and Javier Courel-Ibáñez

Sensors 2024, 24(13), 4131; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134131 (registering DOI) - 25 Jun 2024

Viewed by 60

Abstract

Exercise is a front-line intervention to increase functional capacity and reduce pain and disability in people with low strength levels or disorders. However, there is a lack of validated field-based tests to check the initial status and, more importantly, to control the process [...] Read more.

Exercise is a front-line intervention to increase functional capacity and reduce pain and disability in people with low strength levels or disorders. However, there is a lack of validated field-based tests to check the initial status and, more importantly, to control the process and make tailored adjustments in load, intensity, and recovery. We aimed to determine the test–retest reliability of a submaximal, resistance-band test to evaluate the strength of the trunk stability muscles using a portable force sensor in middle-aged adults (48 ± 13 years) with medically diagnosed chronic low back pain and healthy peers (n = 35). Participants completed two submaximal progressive tests of two resistance-band exercises (unilateral row and Pallof press), consisting of 5 s maintained contraction, progressively increasing the load. The test stopped when deviation from the initial position by compensation movements occurred. Trunk muscle strength (CORE muscles) was monitored in real time using a portable force sensor (strain gauge). Results revealed that both tests were highly reliable (intra-class correlation [ICC] > 0.901) and presented low errors and coefficients of variation (CV) in both groups. In particular, people with low back pain had errors of 14–19 N (CV = 9–12%) in the unilateral row test and 13–19 N (CV = 8–12%) in the Pallof press. No discomfort or pain was reported during or after the tests. These two easy-to-use and technology-based tests result in a reliable and objective screening tool to evaluate the strength and trunk stability in middle-aged adults with chronic low back pain, considering an error of measurement < 20 N. This contribution may have an impact on improving the individualization and control of rehabilitation or physical training in people with lumbar injuries or disorders. Full article

(This article belongs to the Special Issue Sports Sensors for Athlete Motion Tracking and Physiological Monitoring)

21 pages, 1903 KiB

Open AccessArticle

BinVPR: Binary Neural Networks towards Real-Valued for Visual Place Recognition

by Junshuai Wang, Junyu Han, Ruifang Dong and Jiangming Kan

Sensors 2024, 24(13), 4130; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134130 (registering DOI) - 25 Jun 2024

Viewed by 60

Abstract

Visual Place Recognition (VPR) aims to determine whether a robot or visual navigation system locates in a previously visited place using visual information. It is an essential technology and challenging problem in computer vision and robotic communities. Recently, numerous works have demonstrated that [...] Read more.

Visual Place Recognition (VPR) aims to determine whether a robot or visual navigation system locates in a previously visited place using visual information. It is an essential technology and challenging problem in computer vision and robotic communities. Recently, numerous works have demonstrated that the performance of Convolutional Neural Network (CNN)-based VPR is superior to that of traditional methods. However, with a huge number of parameters, large memory storage is necessary for these CNN models. It is a great challenge for mobile robot platforms equipped with limited resources. Fortunately, Binary Neural Networks (BNNs) can reduce memory consumption by converting weights and activation values from 32-bit into 1-bit. But current BNNs always suffer from gradients vanishing and a marked drop in accuracy. Therefore, this work proposed a BinVPR model to handle this issue. The solution is twofold. Firstly, a feature restoration strategy was explored to add features into the latter convolutional layers to further solve the gradient-vanishing problem during the training process. Moreover, we identified two principles to address gradient vanishing: restoring basic features and restoring basic features from higher to lower layers. Secondly, considering the marked drop in accuracy results from gradient mismatch during backpropagation, this work optimized the combination of binarized activation and binarized weight functions in the Larq framework, and the best combination was obtained. The performance of BinVPR was validated on public datasets. The experimental results show that it outperforms state-of-the-art BNN-based approaches and full-precision networks of AlexNet and ResNet in terms of both recognition accuracy and model size. It is worth mentioning that BinVPR achieves the same accuracy with only 1% and 4.6% model sizes of AlexNet and ResNet. Full article

(This article belongs to the Section Navigation and Positioning)

22 pages, 1710 KiB

Open AccessArticle

Waveform Design for the Integrated Sensing, Communication, and Simultaneous Wireless Information and Power Transfer System

by Qilong Miao, Weimin Shi, Chenfei Xie, Yong Gao and Lu Chen

Sensors 2024, 24(13), 4129; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134129 (registering DOI) - 25 Jun 2024

Viewed by 87

Abstract

Next-generation communication systems demand the integration of sensing, communication, and power transfer (PT) capabilities, requiring high spectral efficiency, energy efficiency, and low cost while also necessitating robustness in high-speed scenarios. Integrated sensing and communication systems (ISACSs) exhibit the ability to simultaneously perform communication [...] Read more.

Next-generation communication systems demand the integration of sensing, communication, and power transfer (PT) capabilities, requiring high spectral efficiency, energy efficiency, and low cost while also necessitating robustness in high-speed scenarios. Integrated sensing and communication systems (ISACSs) exhibit the ability to simultaneously perform communication and sensing tasks using a single RF signal, while simultaneous wireless information and power transfer (SWIPT) systems can handle simultaneous information and energy transmission, and orthogonal time frequency space (OTFS) signals are adept at handling high Doppler scenarios. Combining the advantages of these three technologies, a novel cyclic prefix (CP) OTFS-based integrated simultaneous wireless sensing, communication, and power transfer system (ISWSCPTS) framework is proposed in this work. Within the ISWSCPTS, the CP-OTFS matched filter (MF)-based target detection and parameter estimation (MF-TDaPE) algorithm is proposed to endow the system with sensing capabilities. To enhance the system’s sensing capability, a waveform design algorithm based on CP-OTFS ambiguity function shaping (AFS) is proposed, which is solved by an iterative method. Furthermore, to maximize the system’s sensing performance under communication and PT quality of service (QoS) constraints, a semidefinite relaxation (SDR) beamforming design (SDR-BD) algorithm is proposed, which is solved using through the SDR technique. The simulation results demonstrate that the ISWSCPTS exhibits stronger parameter estimation performance in high-speed scenarios compared to orthogonal frequency division multiplexing (OFDM), the waveform designed by CP-OTFS AFS demonstrates superior interference resilience, and the beamforming designed by SDR-BD strikes a balance in the overall performance of the ISWSCPTS. Full article

(This article belongs to the Section Sensor Networks)

16 pages, 919 KiB

Open AccessArticle

Deterministic Localization for Fully Automatic Operation: A Survey and Experiments

by Wan-Ning He and Xin-Lin Huang

Sensors 2024, 24(13), 4128; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134128 (registering DOI) - 25 Jun 2024

Viewed by 78

Abstract

With the rapid development of fully automatic operation (FAO) and location-based services, the evaluation criteria of average localization accuracy can no longer meet our demands, in favor of deterministic localization. However, most localization researches modeled localization performance function and enhanced it by minimizing [...] Read more.

With the rapid development of fully automatic operation (FAO) and location-based services, the evaluation criteria of average localization accuracy can no longer meet our demands, in favor of deterministic localization. However, most localization researches modeled localization performance function and enhanced it by minimizing average localization root mean square error (RMSE). The performance degradation in a small region was not considered. In this paper, we present a survey of deterministic localization and analyze the relationship between accuracy and certainty. In this paper, two common solutions of localization enhancement are presented and their localization certainties are discussed. Furthermore, we carry out related localization enhancement experiments in rail transit line and analyze their improvement on deterministic localization. The experimental results show that the overall localization performance is improved, while the deterministic localization requires the stricter solution to promote. Full article

(This article belongs to the Special Issue Moving Object Detection and Control Using Remote Sensing and Artificial Intelligence II)

17 pages, 15715 KiB

Open AccessArticle

An Optimization Method for Lightweight Rock Classification Models: Transferred Rich Fine-Grained Knowledge

by Mingshuo Ma, Zhiming Gui, Zhenji Gao and Bin Wang

Sensors 2024, 24(13), 4127; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134127 (registering DOI) - 25 Jun 2024

Viewed by 80

Abstract

Rock image classification represents a challenging fine-grained image classification task characterized by subtle differences among closely related rock categories. Current contrastive learning methods prevalently utilized in fine-grained image classification restrict the model’s capacity to discern critical features contrastively from image pairs, and are [...] Read more.

Rock image classification represents a challenging fine-grained image classification task characterized by subtle differences among closely related rock categories. Current contrastive learning methods prevalently utilized in fine-grained image classification restrict the model’s capacity to discern critical features contrastively from image pairs, and are typically too large for deployment on mobile devices used for in situ rock identification. In this work, we introduce an innovative and compact model generation framework anchored by the design of a Feature Positioning Comparison Network (FPCN). The FPCN facilitates interaction between feature vectors from localized regions within image pairs, capturing both shared and distinctive features. Further, it accommodates the variable scales of objects depicted in images, which correspond to differing quantities of inherent object information, directing the network’s attention to additional contextual details based on object size variability. Leveraging knowledge distillation, the architecture is streamlined, with a focus on nuanced information at activation boundaries to master the precise fine-grained decision boundaries, thereby enhancing the small model’s accuracy. Empirical evidence demonstrates that our proposed method based on FPCN improves the classification accuracy mobile lightweight models by nearly 2% while maintaining the same time and space consumption. Full article

(This article belongs to the Special Issue Advanced Sensors for Monitoring and Detection in Geotechnical Engineering)

17 pages, 1024 KiB

Open AccessArticle

E-Nose: Time–Frequency Attention Convolutional Neural Network for Gas Classification and Concentration Prediction

by Minglv Jiang, Na Li, Mingyong Li, Zhou Wang, Yuan Tian, Kaiyan Peng, Haoran Sheng, Haoyu Li and Qiang Li

Sensors 2024, 24(13), 4126; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134126 (registering DOI) - 25 Jun 2024

Viewed by 89

Abstract

In the electronic nose (E-nose) systems, gas type recognition and accurate concentration prediction are some of the most challenging issues. This study introduced an innovative pattern recognition method of time–frequency attention convolutional neural network (TFA-CNN). A time–frequency attention block was designed in the [...] Read more.

In the electronic nose (E-nose) systems, gas type recognition and accurate concentration prediction are some of the most challenging issues. This study introduced an innovative pattern recognition method of time–frequency attention convolutional neural network (TFA-CNN). A time–frequency attention block was designed in the network, aiming to excavate and effectively integrate the temporal and frequency domain information in the E-nose signals to enhance the performance of gas classification and concentration prediction tasks. Additionally, a novel data augmentation strategy was developed, manipulating the feature channels and time dimensions to reduce the interference of sensor drift and redundant information, thereby enhancing the model’s robustness and adaptability. Utilizing two types of metal-oxide-semiconductor gas sensors, this research conducted qualitative and quantitative analysis on five target gases. The evaluation results showed that the classification accuracy could reach 100%, and the coefficient of the determination (R²) score of the regression task was up to 0.99. The Pearson correlation coefficient (r) was 0.99, and the mean absolute error (MAE) was 1.54 ppm. The experimental test results were almost consistent with the system predictions, and the MAE was 1.39 ppm. This study provides a method of network learning that combines time–frequency domain information, exhibiting high performance in gas classification and concentration prediction within the E-nose system. Full article

(This article belongs to the Special Issue Electronic Sensors for Industrial or Environmental Monitoring Applications)

17 pages, 1820 KiB

Open AccessArticle

Wireless Mouth Motion Recognition System Based on EEG-EMG Sensors for Severe Speech Impairments

by Kee S. Moon, John S. Kang, Sung Q. Lee, Jeff Thompson and Nicholas Satterlee

Sensors 2024, 24(13), 4125; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134125 (registering DOI) - 25 Jun 2024

Viewed by 107

Abstract

This study aims to demonstrate the feasibility of using a new wireless electroencephalography (EEG)–electromyography (EMG) wearable approach to generate characteristic EEG-EMG mixed patterns with mouth movements in order to detect distinct movement patterns for severe speech impairments. This paper describes a method for [...] Read more.

This study aims to demonstrate the feasibility of using a new wireless electroencephalography (EEG)–electromyography (EMG) wearable approach to generate characteristic EEG-EMG mixed patterns with mouth movements in order to detect distinct movement patterns for severe speech impairments. This paper describes a method for detecting mouth movement based on a new signal processing technology suitable for sensor integration and machine learning applications. This paper examines the relationship between the mouth motion and the brainwave in an effort to develop nonverbal interfacing for people who have lost the ability to communicate, such as people with paralysis. A set of experiments were conducted to assess the efficacy of the proposed method for feature selection. It was determined that the classification of mouth movements was meaningful. EEG-EMG signals were also collected during silent mouthing of phonemes. A few-shot neural network was trained to classify the phonemes from the EEG-EMG signals, yielding classification accuracy of 95%. This technique in data collection and processing bioelectrical signals for phoneme recognition proves a promising avenue for future communication aids. Full article

(This article belongs to the Special Issue Advances in Mobile Sensing for Smart Healthcare)

16 pages, 734 KiB

Open AccessArticle

Tiny-Machine-Learning-Based Supply Canal Surface Condition Monitoring

by Chengjie Huang, Xinjuan Sun and Yuxuan Zhang

Sensors 2024, 24(13), 4124; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134124 (registering DOI) - 25 Jun 2024

Viewed by 136

Abstract

The South-to-North Water Diversion Project in China is an extensive inter-basin water transfer project, for which ensuring the safe operation and maintenance of infrastructure poses a fundamental challenge. In this context, structural health monitoring is crucial for the safe and efficient operation of [...] Read more.

The South-to-North Water Diversion Project in China is an extensive inter-basin water transfer project, for which ensuring the safe operation and maintenance of infrastructure poses a fundamental challenge. In this context, structural health monitoring is crucial for the safe and efficient operation of hydraulic infrastructure. Currently, most health monitoring systems for hydraulic infrastructure rely on commercial software or algorithms that only run on desktop computers. This study developed for the first time a lightweight convolutional neural network (CNN) model specifically for early detection of structural damage in water supply canals and deployed it as a tiny machine learning (TinyML) application on a low-power microcontroller unit (MCU). The model uses damage images of the supply canals that we collected as input and the damage types as output. With data augmentation techniques to enhance the training dataset, the deployed model is only 7.57 KB in size and demonstrates an accuracy of 94.17 ± 1.67% and a precision of 94.47 ± 1.46%, outperforming other commonly used CNN models in terms of performance and energy efficiency. Moreover, each inference consumes only 5610.18

μ

J of energy, allowing a standard 225 mAh button cell to run continuously for nearly 11 years and perform approximately 4,945,055 inferences. This research not only confirms the feasibility of deploying real-time supply canal surface condition monitoring on low-power, resource-constrained devices but also provides practical technical solutions for improving infrastructure security. Full article

(This article belongs to the Special Issue Sensing Technologies and Deep Learning Methods for Structural Health Monitoring Systems)

20 pages, 3993 KiB

Open AccessArticle

Diagnosis of Rotor Component Shedding in Rotating Machinery: A Data-Driven Approach

by Sikai Zhang, Qizhe Lin and Jiayao Lin

Sensors 2024, 24(13), 4123; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134123 (registering DOI) - 25 Jun 2024

Viewed by 106

Abstract

The potential for rotor component shedding in rotating machinery poses significant risks, necessitating the development of an early and precise fault diagnosis technique to prevent catastrophic failures and reduce maintenance costs. This study introduces a data-driven approach to detect rotor component shedding at [...] Read more.

The potential for rotor component shedding in rotating machinery poses significant risks, necessitating the development of an early and precise fault diagnosis technique to prevent catastrophic failures and reduce maintenance costs. This study introduces a data-driven approach to detect rotor component shedding at its inception, thereby enhancing operational safety and minimizing downtime. Utilizing frequency analysis, this research identifies harmonic amplitudes within rotor vibration data as key indicators of impending faults. The methodology employs principal component analysis (PCA) to orthogonalize and reduce the dimensionality of vibration data from rotor sensors, followed by k-fold cross-validation to select a subset of significant features, ensuring the detection algorithm’s robustness and generalizability. These features are then integrated into a linear discriminant analysis (LDA) model, which serves as the diagnostic engine to predict the probability of rotor component shedding. The efficacy of the approach is demonstrated through its application to 16 industrial compressors and turbines, proving its value in providing timely fault warnings and enhancing operational reliability. Full article

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

19 pages, 829 KiB

Open AccessArticle

Malware Detection for Internet of Things Using One-Class Classification

by Tongxin Shi, Roy A. McCann, Ying Huang, Wei Wang and Jun Kong

Sensors 2024, 24(13), 4122; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134122 (registering DOI) - 25 Jun 2024

Viewed by 121

Abstract

The increasing usage of interconnected devices within the Internet of Things (IoT) and Industrial IoT (IIoT) has significantly enhanced efficiency and utility in both personal and industrial settings but also heightened cybersecurity vulnerabilities, particularly through IoT malware. This paper explores the use of [...] Read more.

The increasing usage of interconnected devices within the Internet of Things (IoT) and Industrial IoT (IIoT) has significantly enhanced efficiency and utility in both personal and industrial settings but also heightened cybersecurity vulnerabilities, particularly through IoT malware. This paper explores the use of one-class classification, a method of unsupervised learning, which is especially suitable for unlabeled data, dynamic environments, and malware detection, which is a form of anomaly detection. We introduce the TF-IDF method for transforming nominal features into numerical formats that avoid information loss and manage dimensionality effectively, which is crucial for enhancing pattern recognition when combined with n-grams. Furthermore, we compare the performance of multi-class vs. one-class classification models, including Isolation Forest and deep autoencoder, that are trained with both benign and malicious NetFlow samples vs. trained exclusively on benign NetFlow samples. We achieve 100% recall with precision rates above 80% and 90% across various test datasets using one-class classification. These models show the adaptability of unsupervised learning, especially one-class classification, to the evolving malware threats in the IoT domain, offering insights into enhancing IoT security frameworks and suggesting directions for future research in this critical area. Full article

(This article belongs to the Special Issue AI Technology for Cybersecurity and IoT Applications)

22 pages, 4224 KiB

Open AccessArticle

Vision-Based UAV Detection and Localization to Indoor Positioning System

by Kheireddine Choutri, Mohand Lagha, Souham Meshoul, Hadil Shaiba, Akram Chegrani and Mohamed Yahiaoui

Sensors 2024, 24(13), 4121; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134121 (registering DOI) - 25 Jun 2024

Viewed by 99

Abstract

In recent years, the technological landscape has undergone a profound metamorphosis catalyzed by the widespread integration of drones across diverse sectors. Essential to the drone manufacturing process is comprehensive testing, typically conducted in controlled laboratory settings to uphold safety and privacy standards. However, [...] Read more.

In recent years, the technological landscape has undergone a profound metamorphosis catalyzed by the widespread integration of drones across diverse sectors. Essential to the drone manufacturing process is comprehensive testing, typically conducted in controlled laboratory settings to uphold safety and privacy standards. However, a formidable challenge emerges due to the inherent limitations of GPS signals within indoor environments, posing a threat to the accuracy of drone positioning. This limitation not only jeopardizes testing validity but also introduces instability and inaccuracies, compromising the assessment of drone performance. Given the pivotal role of precise GPS-derived data in drone autopilots, addressing this indoor-based GPS constraint is imperative to ensure the reliability and resilience of unmanned aerial vehicles (UAVs). This paper delves into the implementation of an Indoor Positioning System (IPS) leveraging computer vision. The proposed system endeavors to detect and localize UAVs within indoor environments through an enhanced vision-based triangulation approach. A comparative analysis with alternative positioning methodologies is undertaken to ascertain the efficacy of the proposed system. The results obtained showcase the efficiency and precision of the designed system in detecting and localizing various types of UAVs, underscoring its potential to advance the field of indoor drone navigation and testing. Full article

(This article belongs to the Section Navigation and Positioning)

31 pages, 16331 KiB

Open AccessArticle

A Common Knowledge-Driven Generic Vision Inspection Framework for Adaptation to Multiple Scenarios, Tasks, and Objects

by Delong Zhao, Feifei Kong, Nengbin Lv, Zhangmao Xu and Fuzhou Du

Sensors 2024, 24(13), 4120; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134120 (registering DOI) - 25 Jun 2024

Viewed by 102

Abstract

The industrial manufacturing model is undergoing a transformation from a product-centric model to a customer-centric one. Driven by customized requirements, the complexity of products and the requirements for quality have increased, which pose a challenge to the applicability of traditional machine vision technology. [...] Read more.

The industrial manufacturing model is undergoing a transformation from a product-centric model to a customer-centric one. Driven by customized requirements, the complexity of products and the requirements for quality have increased, which pose a challenge to the applicability of traditional machine vision technology. Extensive research demonstrates the effectiveness of AI-based learning and image processing on specific objects or tasks, but few publications focus on the composite task of the integrated product, the traceability and improvability of methods, as well as the extraction and communication of knowledge between different scenarios or tasks. To address this problem, this paper proposes a common, knowledge-driven, generic vision inspection framework, targeted for standardizing product inspection into a process of information decoupling and adaptive metrics. Task-related object perception is planned into a multi-granularity and multi-pattern progressive alignment based on industry knowledge and structured tasks. Inspection is abstracted as a reconfigurable process of multi-sub-pattern space combination mapping and difference metric under appropriate high-level strategies and experiences. Finally, strategies for knowledge improvement and accumulation based on historical data are presented. The experiment demonstrates the process of generating a detection pipeline for complex products and continuously improving it through failure tracing and knowledge improvement. Compared to the (, 69.802 mm) and 0.883 obtained by state-of-the-art deep learning methods, the generated pipeline achieves a pose estimation ranging from (, 153.584 mm) to (, 52.308 mm) and a detection rate ranging from 0.462 to 0.927. Through verification of other imaging methods and industrial tasks, we prove that the key to adaptability lies in the mining of inherent commonalities of knowledge, multi-dimensional accumulation, and reapplication. Full article

(This article belongs to the Special Issue Advanced Sensing and Fault Diagnosis for Complex Manufacturing Processes)

17 pages, 13097 KiB

Open AccessCase Report

Method for Underground Mining Shaft Sensor Data Collection

by Artur Adamek, Janusz Będkowski, Paweł Kamiński, Rafał Pasek, Michał Pełka and Jan Zawiślak

Sensors 2024, 24(13), 4119; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134119 (registering DOI) - 25 Jun 2024

Viewed by 125

Abstract

The motivation behind this research is the lack of an underground mining shaft data set in the literature in the form of open access. For this reason, our data set can be used for many research purposes such as shaft inspection, 3D measurements, [...] Read more.

The motivation behind this research is the lack of an underground mining shaft data set in the literature in the form of open access. For this reason, our data set can be used for many research purposes such as shaft inspection, 3D measurements, simultaneous localization and mapping, artificial intelligence, etc. The data collection method incorporates rotated Velodyne VLP-16, Velodyne Ultra Puck VLP-32c, Livox Tele-15, IMU Xsens MTi-30 and Faro Focus 3D. The ground truth data were acquired with a geodetic survey including 15 ground control points and 6 Faro Focus 3D terrestrial laser scanner stations of a total 273,784,932 of 3D measurement points. This data set provides an end-user case study of realistic applications in mobile mapping technology. The goal of this research was to fill the gap in the underground mining data set domain. The result is the first open-access data set for an underground mining shaft (shaft depth −300 m). Full article

(This article belongs to the Section Physical Sensors)

18 pages, 23482 KiB

Open AccessArticle

Robust Detection of Critical Events in the Context of Railway Security Based on Multimodal Sensor Data Fusion

by Michael Hubner, Kilian Wohlleben, Martin Litzenberger, Stephan Veigl, Andreas Opitz, Stefan Grebien, Franz Graf, Andreas Haderer, Susanne Rechbauer and Sebastian Poltschak

Sensors 2024, 24(13), 4118; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134118 (registering DOI) - 25 Jun 2024

Viewed by 120

Abstract

Effective security surveillance is crucial in the railway sector to prevent security incidents, including vandalism, trespassing, and sabotage. This paper discusses the challenges of maintaining seamless surveillance over extensive railway infrastructure, considering both technological advances and the growing risks posed by terrorist attacks. [...] Read more.

Effective security surveillance is crucial in the railway sector to prevent security incidents, including vandalism, trespassing, and sabotage. This paper discusses the challenges of maintaining seamless surveillance over extensive railway infrastructure, considering both technological advances and the growing risks posed by terrorist attacks. Based on previous research, this paper discusses the limitations of current surveillance methods, particularly in managing information overload and false alarms that result from integrating multiple sensor technologies. To address these issues, we propose a new fusion model that utilises Probabilistic Occupancy Maps (POMs) and Bayesian fusion techniques. The fusion model is evaluated on a comprehensive dataset comprising three use cases with a total of eight real life critical scenarios. We show that, with this model, the detection accuracy can be increased while simultaneously reducing the false alarms in railway security surveillance systems. This way, our approach aims to enhance situational awareness and reduce false alarms, thereby improving the effectiveness of railway security measures. Full article

(This article belongs to the Special Issue Sensor Data Fusion Analysis for Broad Applications: 2nd Edition)

18 pages, 928 KiB

Open AccessArticle

A Multi-Task Joint Learning Model Based on Transformer and Customized Gate Control for Predicting Remaining Useful Life and Health Status of Tools

by Chunming Hou and Liaomo Zheng

Sensors 2024, 24(13), 4117; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134117 (registering DOI) - 25 Jun 2024

Viewed by 100

Abstract

Previous studies have primarily focused on predicting the remaining useful life (RUL) of tools as an independent process. However, the RUL of a tool is closely related to its wear stage. In light of this, a multi-task joint learning model based on a [...] Read more.

Previous studies have primarily focused on predicting the remaining useful life (RUL) of tools as an independent process. However, the RUL of a tool is closely related to its wear stage. In light of this, a multi-task joint learning model based on a transformer encoder and customized gate control (TECGC) is proposed for simultaneous prediction of tool RUL and tool wear stages. Specifically, the transformer encoder is employed as the backbone of the TECGC model for extracting shared features from the original data. The customized gate control (CGC) is utilized to extract task-specific features relevant to tool RUL prediction and tool wear stage and shared features. Finally, by integrating these components, the tool RUL and the tool wear stage can be predicted simultaneously by the TECGC model. In addition, a dynamic adaptive multi-task learning loss function is proposed for the model’s training to enhance its calculation efficiency. This approach avoids unsatisfactory prediction performance of the model caused by unreasonable selection of trade-off parameters of the loss function. The effectiveness of the TECGC model is evaluated using the PHM2010 dataset. The results demonstrate its capability to accurately predict tool RUL and tool wear stages. Full article

(This article belongs to the Section Industrial Sensors)

27 pages, 2333 KiB

Open AccessArticle

Design and Construction of a Portable IoT Station

by Mario A. Trape, Ali Hellany, Syed K. H. Shah, Jamal Rizk, Mahmood Nagrial and Tosin Famakinwa

Sensors 2024, 24(13), 4116; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134116 (registering DOI) - 25 Jun 2024

Viewed by 126

Abstract

This paper discusses the design and implementation of a portable IoT station. Communication and data synchronization issues in several installations are addressed here, making possible a detailed analysis of the entire system during its operation. The system operator requires a synchronized data stream, [...] Read more.

This paper discusses the design and implementation of a portable IoT station. Communication and data synchronization issues in several installations are addressed here, making possible a detailed analysis of the entire system during its operation. The system operator requires a synchronized data stream, combining multiple communication protocols into one single time stamp. The hardware selected for the portable IoT station complies with the International Electrotechnical Commission (IEC) industrial standards. A short discussion regarding interface customization shows how easily the hardware can be modified so that it is integrated with almost any system. A programmable logic controller enables the Node-RED to be utilized. This open-source middleware defines operations for each global variable nominated in the Modbus register. Two applications are presented and discussed in this paper; each application has a distinct methodology utilized to publish and visualize the acquired data. The portable IoT station is highly customizable, consisting of a modular structure and providing the best platform for future research and development of dedicated algorithms. This paper also demonstrates how the portable IoT station can be implemented in systems where time-based data synchronization is essential while introducing a seamless implementation and operation. Full article

(This article belongs to the Special Issue Recent Development and Applications of Sensing Technology in Resilient and Sustainable Infrastructure)

18 pages, 2037 KiB

Open AccessArticle

Information System Model and Key Technologies of High-Definition Maps in Autonomous Driving Scenarios

by Zhiqi Qian, Zhirui Ye and Xiaomeng Shi

Sensors 2024, 24(13), 4115; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134115 (registering DOI) - 25 Jun 2024

Viewed by 100

Abstract

Background: High-definition maps can provide necessary prior data for autonomous driving, as well as the corresponding beyond-line-of-sight perception, verification and positioning, dynamic planning, and decision control. It is a necessary element to achieve L4/L5 unmanned driving at the current stage. However, currently, high-definition [...] Read more.

Background: High-definition maps can provide necessary prior data for autonomous driving, as well as the corresponding beyond-line-of-sight perception, verification and positioning, dynamic planning, and decision control. It is a necessary element to achieve L4/L5 unmanned driving at the current stage. However, currently, high-definition maps still have problems such as a large amount of data, a lot of data redundancy, and weak data correlation, which make autonomous driving fall into difficulties such as high data query difficulty and low timeliness. In order to optimize the data quality of high-definition maps, enhance the degree of data correlation, and ensure that they better assist vehicles in safe driving and efficient passage in the autonomous driving scenario, it is necessary to clarify the information system thinking of high-definition maps, propose a complete and accurate model, determine the content and functions of each level of the model, and continuously improve the information system model. Objective: The study aimed to put forward a complete and accurate high-definition map information system model and elaborate in detail the content and functions of each component in the data logic structure of the system model. Methods: Through research methods such as the modeling method and literature research method, we studied the high-definition map information system model in the autonomous driving scenario and explored the key technologies therein. Results: We put forward a four-layer integrated high-definition map information system model, elaborated in detail the content and functions of each component (map, road, vehicle, and user) in the data logic structure of the model, and also elaborated on the mechanism of the combined information of each level of the model to provide services in perception, positioning, decision making, and control for autonomous driving vehicles. This article also discussed two key technologies that can support autonomous driving vehicles to complete path planning, navigation decision making, and vehicle control in different autonomous driving scenarios. Conclusions: The four-layer integrated high-definition map information model proposed by this research institute has certain application feasibility and can provide references for the standardized production of high-definition maps, the unification of information interaction relationships, and the standardization of map data associations. Full article

(This article belongs to the Section Vehicular Sensing)

24 pages, 2217 KiB

Open AccessArticle

Design, Fabrication, and Evaluation of 3D Biopotential Electrodes and Intelligent Garment System for Sports Monitoring

by Deyao Shen, Jianping Wang, Vladan Koncar, Krittika Goyal and Xuyuan Tao

Sensors 2024, 24(13), 4114; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134114 (registering DOI) - 25 Jun 2024

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Abstract

This study presents the development and evaluation of an innovative intelligent garment system, incorporating 3D knitted silver biopotential electrodes, designed for long-term sports monitoring. By integrating advanced textile engineering with wearable monitoring technologies, we introduce a novel approach to real-time physiological signal acquisition, [...] Read more.

This study presents the development and evaluation of an innovative intelligent garment system, incorporating 3D knitted silver biopotential electrodes, designed for long-term sports monitoring. By integrating advanced textile engineering with wearable monitoring technologies, we introduce a novel approach to real-time physiological signal acquisition, focusing on enhancing athletic performance analysis and fatigue detection. Utilizing low-resistance silver fibers, our electrodes demonstrate significantly reduced skin-to-electrode impedance, facilitating improved signal quality and reliability, especially during physical activities. The garment system, embedded with these electrodes, offers a non-invasive, comfortable solution for continuous ECG and EMG monitoring, addressing the limitations of traditional Ag/AgCl electrodes, such as skin irritation and signal degradation over time. Through various experimentation, including impedance measurements and biosignal acquisition during cycling activities, we validate the system’s effectiveness in capturing high-quality physiological data. Our findings illustrate the electrodes’ superior performance in both dry and wet conditions. This study not only advances the field of intelligent garments and biopotential monitoring, but also provides valuable insights for the application of intelligent sports wearables in the future. Full article

(This article belongs to the Section Wearables)

22 pages, 54909 KiB

Open AccessArticle

DriveLLaVA: Human-Level Behavior Decisions via Vision Language Model

by Rui Zhao, Qirui Yuan, Jinyu Li, Yuze Fan, Yun Li and Fei Gao

Sensors 2024, 24(13), 4113; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134113 (registering DOI) - 25 Jun 2024

Viewed by 114

Abstract

Human-level driving is the ultimate goal of autonomous driving. As the top-level decision-making aspect of autonomous driving, behavior decision establishes short-term driving behavior strategies by evaluating road structures, adhering to traffic rules, and analyzing the intentions of other traffic participants. Existing behavior decisions [...] Read more.

Human-level driving is the ultimate goal of autonomous driving. As the top-level decision-making aspect of autonomous driving, behavior decision establishes short-term driving behavior strategies by evaluating road structures, adhering to traffic rules, and analyzing the intentions of other traffic participants. Existing behavior decisions are primarily implemented based on rule-based methods, exhibiting insufficient generalization capabilities when faced with new and unseen driving scenarios. In this paper, we propose a novel behavior decision method that leverages the inherent generalization and commonsense reasoning abilities of visual language models (VLMs) to learn and simulate the behavior decision process in human driving. We constructed a novel instruction-following dataset containing a large number of image–text instructions paired with corresponding driving behavior labels, to support the learning of the Drive Large Language and Vision Assistant (DriveLLaVA) and enhance the transparency and interpretability of the entire decision process. DriveLLaVA is fine-tuned on this dataset using the Low-Rank Adaptation (LoRA) approach, which efficiently optimizes the model parameter count and significantly reduces training costs. We conducted extensive experiments on a large-scale instruction-following dataset, and compared with state-of-the-art methods, DriveLLaVA demonstrated excellent behavior decision performance. DriveLLaVA is capable of handling various complex driving scenarios, showing strong robustness and generalization abilities. Full article

(This article belongs to the Section Vehicular Sensing)

26 pages, 12729 KiB

Open AccessArticle

Study on Mechanical and Acoustic Emission Characteristics of Backfill–Rock Instability under Different Stress Conditions

by Longjun Dong, Mingchun Yan, Yongchao Chen, Longbin Yang and Daoyuan Sun

Sensors 2024, 24(13), 4112; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134112 (registering DOI) - 25 Jun 2024

Viewed by 108

Abstract

Unveiling the mechanical properties and damage mechanism of the complex composite structure, comprising backfill and surrounding rock, is crucial for ensuring the safe development of the downward-approach backfill mining method. This work conducts biaxial compression tests on backfill–rock under various loading conditions. The [...] Read more.

Unveiling the mechanical properties and damage mechanism of the complex composite structure, comprising backfill and surrounding rock, is crucial for ensuring the safe development of the downward-approach backfill mining method. This work conducts biaxial compression tests on backfill–rock under various loading conditions. The damage process is analyzed using DIC and acoustic emission (AE) techniques, while the distribution of AE events at different loading stages is explored. Additionally, the dominant failure forms of specimens are studied through multifractal analysis. The damage evolution law of backfill–rock combinations is elucidated. The results indicate that DIC and AE provide consistent descriptions of specimen damage, and the damage evolution of backfill–rock composite specimens varies notably under different loading conditions, offering valuable insights for engineering site safety protection. Full article

(This article belongs to the Section Navigation and Positioning)

12 pages, 1224 KiB

Open AccessArticle

Speech Emotion Recognition Incorporating Relative Difficulty and Labeling Reliability

by Youngdo Ahn, Sangwook Han, Seonggyu Lee and Jong Won Shin

Sensors 2024, 24(13), 4111; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134111 (registering DOI) - 25 Jun 2024

Viewed by 101

Abstract

Emotions in speech are expressed in various ways, and the speech emotion recognition (SER) model may perform poorly on unseen corpora that contain different emotional factors from those expressed in training databases. To construct an SER model robust to unseen corpora, regularization approaches [...] Read more.

Emotions in speech are expressed in various ways, and the speech emotion recognition (SER) model may perform poorly on unseen corpora that contain different emotional factors from those expressed in training databases. To construct an SER model robust to unseen corpora, regularization approaches or metric losses have been studied. In this paper, we propose an SER method that incorporates relative difficulty and labeling reliability of each training sample. Inspired by the Proxy-Anchor loss, we propose a novel loss function which gives higher gradients to the samples for which the emotion labels are more difficult to estimate among those in the given minibatch. Since the annotators may label the emotion based on the emotional expression which resides in the conversational context or other modality but is not apparent in the given speech utterance, some of the emotional labels may not be reliable and these unreliable labels may affect the proposed loss function more severely. In this regard, we propose to apply label smoothing for the samples misclassified by a pre-trained SER model. Experimental results showed that the performance of the SER on unseen corpora was improved by adopting the proposed loss function with label smoothing on the misclassified data. Full article

(This article belongs to the Special Issue Sensors Applications on Emotion Recognition)

27 pages, 1731 KiB

Open AccessArticle

Calibration and Inter-Unit Consistency Assessment of an Electrochemical Sensor System Using Machine Learning

by Ioannis D. Apostolopoulos, Silas Androulakis, Panayiotis Kalkavouras, George Fouskas and Spyros N. Pandis

Sensors 2024, 24(13), 4110; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134110 (registering DOI) - 25 Jun 2024

Viewed by 100

Abstract

This paper addresses the challenges of calibrating low-cost electrochemical sensor systems for air quality monitoring. The proliferation of pollutants in the atmosphere necessitates efficient monitoring systems, and low-cost sensors offer a promising solution. However, issues such as drift, cross-sensitivity, and inter-unit consistency have [...] Read more.

This paper addresses the challenges of calibrating low-cost electrochemical sensor systems for air quality monitoring. The proliferation of pollutants in the atmosphere necessitates efficient monitoring systems, and low-cost sensors offer a promising solution. However, issues such as drift, cross-sensitivity, and inter-unit consistency have raised concerns about their accuracy and reliability. The study explores the following three calibration methods for converting sensor signals to concentration measurements: utilizing manufacturer-provided equations, incorporating machine learning (ML) algorithms, and directly applying ML to voltage signals. Experiments were performed in three urban sites in Greece. High-end instrumentation provided the reference concentrations for training and evaluation of the model. The results reveal that utilizing voltage signals instead of the manufacturer’s calibration equations diminishes variability among identical sensors. Moreover, the latter approach enhances calibration efficiency for CO, NO, NO₂, and O₃ sensors while incorporating voltage signals from all sensors in the ML algorithm, taking advantage of cross-sensitivity to improve calibration performance. The Random Forest ML algorithm is a promising solution for calibrating similar devices for use in urban areas. Full article

(This article belongs to the Special Issue Advanced Sensors for Gas Monitoring)

28 pages, 16083 KiB

Open AccessArticle

Enhancing Coordination Efficiency with Fuzzy Monte Carlo Uncertainty Analysis for Dual-Setting Directional Overcurrent Relays Amid Distributed Generation

by Faraj Al-Bhadely and Aslan İnan

Sensors 2024, 24(13), 4109; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134109 (registering DOI) - 25 Jun 2024

Viewed by 109

Abstract

In the contemporary context of power network protection, acknowledging uncertainties in safeguarding recent power networks integrated with distributed generation (DG) is imperative to uphold the dependability, security, and efficiency of the grid amid the escalating integration of renewable energy sources and evolving operational [...] Read more.

In the contemporary context of power network protection, acknowledging uncertainties in safeguarding recent power networks integrated with distributed generation (DG) is imperative to uphold the dependability, security, and efficiency of the grid amid the escalating integration of renewable energy sources and evolving operational conditions. This study delves into the optimization of relay settings within distribution networks, presenting a novel approach aimed at augmenting coordination while accounting for the dynamic presence of DG resources and the uncertainties inherent in their generation outputs and load consumption—factors previously overlooked in existing research. Departing from conventional methodologies, the study proposes a dual-setting characteristic for directional overcurrent relays (DOCRs). Initially, a meticulous modeling of a power network featuring distributed generation is undertaken, integrating Weibull probability functions for each resource to capture their probabilistic behavior. Subsequently, the second stage employs the fuzzy Monte Carlo method to address generation and consumption uncertainties. The optimization conundrum is addressed using the ant lion optimizer (ALO) algorithm in the MATLAB environment. This thorough analysis was conducted on IEEE 14-bus and IEEE 30-bus power distribution systems, showcasing a notable reduction in the total DOCR operating time compared to conventional characteristics. The proposed characteristic not only achieves resilient coordination across a spectrum of uncertainties in both distributed generation outputs and load consumption, but also strengthens the resilience of distribution networks overall. Full article

(This article belongs to the Topic Power System Protection)

21 pages, 3773 KiB

Open AccessArticle

Mangrove Classification from Unmanned Aerial Vehicle Hyperspectral Images Using Object-Oriented Methods Based on Feature Combination and Optimization

by Fankai Ye and Baoping Zhou

Sensors 2024, 24(13), 4108; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134108 - 24 Jun 2024

Viewed by 266

Abstract

Accurate and timely acquisition of the spatial distribution of mangrove species is essential for conserving ecological diversity. Hyperspectral imaging sensors are recognized as effective tools for monitoring mangroves. However, the spatial complexity of mangrove forests and the spectral redundancy of hyperspectral images pose [...] Read more.

Accurate and timely acquisition of the spatial distribution of mangrove species is essential for conserving ecological diversity. Hyperspectral imaging sensors are recognized as effective tools for monitoring mangroves. However, the spatial complexity of mangrove forests and the spectral redundancy of hyperspectral images pose challenges to fine classification. Moreover, finely classifying mangrove species using only spectral information is difficult due to spectral similarities among species. To address these issues, this study proposes an object-oriented multi-feature combination method for fine classification. Specifically, hyperspectral images were segmented using multi-scale segmentation techniques to obtain different species of objects. Then, a variety of features were extracted, including spectral, vegetation indices, fractional order differential, texture, and geometric features, and a genetic algorithm was used for feature selection. Additionally, ten feature combination schemes were designed to compare the effects on mangrove species classification. In terms of classification algorithms, the classification capabilities of four machine learning classifiers were evaluated, including K-nearest neighbor (KNN), support vector machines (SVM), random forests (RF), and artificial neural networks (ANN) methods. The results indicate that SVM based on texture features achieved the highest classification accuracy among single-feature variables, with an overall accuracy of 97.04%. Among feature combination variables, ANN based on raw spectra, first-order differential spectra, texture features, vegetation indices, and geometric features achieved the highest classification accuracy, with an overall accuracy of 98.03%. Texture features and fractional order differentiation are identified as important variables, while vegetation index and geometric features can further improve classification accuracy. Object-based classification, compared to pixel-based classification, can avoid the salt-and-pepper phenomenon and significantly enhance the accuracy and efficiency of mangrove species classification. Overall, the multi-feature combination method and object-based classification strategy proposed in this study provide strong technical support for the fine classification of mangrove species and are expected to play an important role in mangrove restoration and management. Full article

(This article belongs to the Special Issue Advancing Land Monitoring through Synergistic Harmonization of Optical, Radar and Lidar Satellite Technologies)

27 pages, 3854 KiB

Open AccessArticle

Reliability Assessment of Wireless Sensor Networks by Strain-Based Region Analysis for Redundancy Estimation in Measurements on the Example of an Aircraft Wing Box

by Sören Meyer zu Westerhausen, Gurubaran Raveendran, Thorben-Hendrik Lauth, Ole Meyer, Daniel Rosemann, Max Leo Wawer, Timo Stauß, Johanna Wurst and Roland Lachmayer

Sensors 2024, 24(13), 4107; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134107 - 24 Jun 2024

Viewed by 160

Abstract

Wireless sensor networks (WSNs) are attracting increasing research interest due to their ability to monitor large areas independently. Their reliability is a crucial issue, as it is influenced by hardware, data, and energy-related factors such as loading conditions, signal attenuation, and battery lifetime. [...] Read more.

Wireless sensor networks (WSNs) are attracting increasing research interest due to their ability to monitor large areas independently. Their reliability is a crucial issue, as it is influenced by hardware, data, and energy-related factors such as loading conditions, signal attenuation, and battery lifetime. Proper selection of sensor node positions is essential to maximise system reliability during the development of products equipped with WSNs. For this purpose, this paper presents an approach to estimate WSN system reliability during the development phase based on the analysis of measurements, using strain measurements in finite element (FE) models as an example. The approach involves dividing the part under consideration into regions with similar strains using a region growing algorithm (RGA). The WSN configuration is then analysed for reliability based on data paths and measurement redundancy resulting from the sensor positions in the identified measuring regions. This methodology was tested on an exemplary WSN configuration at an aircraft wing box under bending load and found to effectively estimate the hardware perspective on system reliability. Therefore, the methodology and algorithm show potential for optimising sensor node positions to achieve better reliability results. Full article

(This article belongs to the Section Sensor Networks)

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13 pages, 2744 KiB

Open AccessArticle

An Embedded Electromyogram Signal Acquisition Device

by Changjia Lu, Xin Xu, Yingjie Liu, Dan Li, Yue Wang, Wenhao Xian, Changbing Chen, Baichun Wei and Jin Tian

Sensors 2024, 24(13), 4106; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134106 - 24 Jun 2024

Viewed by 228

Abstract

In this study, we design an embedded surface EMG acquisition device to conveniently collect human surface EMG signals, pursue more intelligent human–computer interactions in exoskeleton robots, and enable exoskeleton robots to synchronize with or even respond to user actions in advance. The device [...] Read more.

In this study, we design an embedded surface EMG acquisition device to conveniently collect human surface EMG signals, pursue more intelligent human–computer interactions in exoskeleton robots, and enable exoskeleton robots to synchronize with or even respond to user actions in advance. The device has the characteristics of low cost, miniaturization, and strong compatibility, and it can acquire eight-channel surface EMG signals in real time while retaining the possibility of expanding the channel. This paper introduces the design and function of the embedded EMG acquisition device in detail, which includes the use of wired transmission to adapt to complex electromagnetic environments, light signals to indicate signal strength, and an embedded processing chip to reduce signal noise and perform filtering. The test results show that the device can effectively collect the original EMG signal, which provides a scheme for improving the level of human–computer interactions and enhancing the robustness and intelligence of exoskeleton equipment. The development of this device provides a new possibility for the intellectualization of exoskeleton systems and reductions in their cost. Full article

(This article belongs to the Special Issue Sensing, Estimating, and Analyzing Human Movements for Human–Robot Interaction)

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24 pages, 2195 KiB

Open AccessArticle

Energy-Efficient, Cluster-Based Routing Protocol for Wireless Sensor Networks Using Fuzzy Logic and Quantum Annealing Algorithm

by Hongzhi Wang, Ke Liu, Chuhang Wang and Huangshui Hu

Sensors 2024, 24(13), 4105; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134105 - 24 Jun 2024

Viewed by 123

Abstract

The main limitation of wireless sensor networks (WSNs) lies in their reliance on battery power. Therefore, the primary focus of the current research is to determine how to transmit data in a rational and efficient way while simultaneously extending the network’s lifespan. In [...] Read more.

The main limitation of wireless sensor networks (WSNs) lies in their reliance on battery power. Therefore, the primary focus of the current research is to determine how to transmit data in a rational and efficient way while simultaneously extending the network’s lifespan. In this paper, a hybrid of a fuzzy logic system and a quantum annealing algorithm-based clustering and routing protocol (FQA) is proposed to improve the stability of the network and minimize energy consumption. The protocol uses a fuzzy inference system (FIS) to select appropriate cluster heads (CHs). In the routing phase, we used the quantum annealing algorithm to select the optimal route from the CHs and the base station (BS). Furthermore, we defined an energy threshold to filter candidate CHs in order to save computation time. Unlike with periodic clustering, we adopted an on-demand re-clustering mechanism to perform global maintenance of the network, thereby effectively reducing the computation and overhead. The FQA was compared with FRNSEER, BOA-ACO, OAFS-IMFO, and FC-RBAT in different scenarios from the perspective of energy consumption, alive nodes, network lifetime, and throughput. According to the simulation results, the FQA outperformed all the other methods in all scenarios. Full article

(This article belongs to the Section Sensor Networks)

16 pages, 860 KiB

Open AccessArticle

Multi-Armed Bandit-Based User Network Node Selection

by Qinyan Gao and Zhidong Xie

Sensors 2024, 24(13), 4104; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134104 - 24 Jun 2024

Viewed by 188

Abstract

In the scenario of an integrated space–air–ground emergency communication network, users encounter the challenge of rapidly identifying the optimal network node amidst the uncertainty and stochastic fluctuations of network states. This study introduces a Multi-Armed Bandit (MAB) model and proposes an optimization algorithm [...] Read more.

In the scenario of an integrated space–air–ground emergency communication network, users encounter the challenge of rapidly identifying the optimal network node amidst the uncertainty and stochastic fluctuations of network states. This study introduces a Multi-Armed Bandit (MAB) model and proposes an optimization algorithm leveraging dynamic variance sampling (DVS). The algorithm posits that the prior distribution of each node’s network state conforms to a normal distribution, and by constructing the distribution’s expected value and variance, it maximizes the utilization of sample data, thereby maintaining an equilibrium between data exploitation and the exploration of the unknown. Theoretical substantiation is provided to illustrate that the Bayesian regret associated with the algorithm exhibits sublinear growth. Empirical simulations corroborate that the algorithm in question outperforms traditional ε-greedy, Upper Confidence Bound (UCB), and Thompson sampling algorithms in terms of higher cumulative rewards, diminished total regret, accelerated convergence rates, and enhanced system throughput. Full article

(This article belongs to the Section Physical Sensors)

13 pages, 1266 KiB

Open AccessArticle

High Power Pulsed LED Driver for Vibration Measurements

by Paolo Neri, Gabriele Ciarpi and Bruno Neri

Sensors 2024, 24(13), 4103; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134103 - 24 Jun 2024

Viewed by 193

Abstract

Vibration measurements pose specific experimental challenges to be faced. In particular, optical methods can be used to obtain full-field vibration information. In this scenario, stereo-camera systems can be developed to obtain 3D displacement measurements. As vibration frequency increases, the common approach is to [...] Read more.

Vibration measurements pose specific experimental challenges to be faced. In particular, optical methods can be used to obtain full-field vibration information. In this scenario, stereo-camera systems can be developed to obtain 3D displacement measurements. As vibration frequency increases, the common approach is to reduce camera exposure time to avoid blurred images, which can lead to under-exposed images and data loss, as well as issues with the synchronization of the stereo pair. Both of these problems can be solved by using high-intensity light pulses, which can produce high-quality images and guarantee camera synchronization since data is saved by both cameras only during the short-time light pulse. To this extent, high-power Light-Emitting Diodes (LEDs) can be used, but even if the LED itself can have a fast response time, specific electronic drivers are needed to ensure the desired timing of the light pulse. In this paper, a circuit is specifically designed to achieve high-intensity short-time light pulses in the range of 1 µs. A prototype of the designed board was assembled and tested to check its capability to respect the specification. Three different measurement methods are proposed and validated to achieve short-time light pulse measurements: shunt voltage measurement, direct photodiode measurement with a low-cost sensor, and indirect pulse measurement through a low-frame-rate digital camera. Full article

(This article belongs to the Section Sensing and Imaging)

12 pages, 856 KiB

Open AccessArticle

Self-Sensing Electromechanical System Integrated with the Embedded Displacement Sensor

by Shuxian Wang, Shiyou Liu, Zuqiang Su, Linlin Liu and Zhi Tang

Sensors 2024, 24(13), 4102; https://0-doi-org.brum.beds.ac.uk/10.3390/s24134102 - 24 Jun 2024

Viewed by 203

Abstract

Conventionally, the electromechanical system requires the installation of auxiliary displacement sensors and only the amount on the drive part and motion end, which increases volume, cost, and measurement error in the system. This paper presents an integrated measurement method with a sensing head, [...] Read more.

Conventionally, the electromechanical system requires the installation of auxiliary displacement sensors and only the amount on the drive part and motion end, which increases volume, cost, and measurement error in the system. This paper presents an integrated measurement method with a sensing head, which takes the equal division characteristics of mechanical structures as part of the sensor, thus, the so-called self-sensing system. Moreover, the displacement is measured by counting the time pulses. The sensing head is integrated with the entire electromechanical system, including the driving, transmitting, and moving parts. Thus, the integration of the sensing part is greatly improved. Taking the rotary table as a special example, and the sensing head embedded into each part of the system, displacement information is obtained by the common processing system and fused by the adaptive weighted average method. The results of the experiment show that the fusion precision of each component is higher than only the motor position information as the feedback. The proposed method is a practical self-sensing technology with significant volume reduction and intelligent control benefits in the industry, especially suitable for extremely small and narrow spaces. Full article

(This article belongs to the Section Chemical Sensors)

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