Sensors

24 pages, 9829 KiB

Open AccessArticle

Multi-Camera Calibration Using Far-Range Dual-LED Wand and Near-Range Chessboard Fused in Bundle Adjustment

by Prayook Jatesiktat, Guan Ming Lim and Wei Tech Ang

Sensors 2024, 24(23), 7416; https://doi.org/10.3390/s24237416 (registering DOI) - 21 Nov 2024

This paper presents a calibration approach for multiple synchronized global-shutter RGB cameras surrounding a large capture volume for 3D application. The calibration approach uses an active wand with two LED-embedded markers waved manually within the target capture volume. Data from the waving wand [...] Read more.

This paper presents a calibration approach for multiple synchronized global-shutter RGB cameras surrounding a large capture volume for 3D application. The calibration approach uses an active wand with two LED-embedded markers waved manually within the target capture volume. Data from the waving wand are combined with chessboard images taken at close range during each camera’s intrinsic calibration, optimizing camera parameters via our proposed bundle adjustment method. These additional constraints from the chessboard are developed to overcome an overfitting issue of wand-based calibration discovered by benchmarking its 3D triangulation accuracy in an independent record against a ground-truth trajectory and not on the record used for calibration itself. Addressing this overfitting issue in bundle adjustment leads to significant improvements in both 3D accuracy and result consistency. As a by-product of this development, a new benchmarking workflow and our calibration dataset that reflects realistic 3D accuracy are proposed and made publicly available to allow for fair comparisons of various calibration methods in the future. Additionally, our experiment highlights a significant benefit of a ray distance-based (RDB) triangulation formula over the popular direct linear transformation (DLT) method. Full article

(This article belongs to the Special Issue Sensing and Processing for 3D Computer Vision: 3rd Edition)

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17 pages, 11373 KiB

Open AccessArticle

The Fluorescent Sensing of BF₃ and Amines: A Dual Approach with Hydrazone Ligands

by Haichao Ye, Liqin Liu, Dagang Shen, Chang Song and Huanhuan Wang

Sensors 2024, 24(23), 7415; https://doi.org/10.3390/s24237415 (registering DOI) - 21 Nov 2024

Abstract

BF₃, volatile amines (VOAs), and biogenic amines (BAs) are the key indicators in chemical reaction catalysis and food quality monitoring. In this study, we present two types of fluorescent sensors, a hydrazone ligand (HL)-based fluorescent sensor for BF₃ detection and [...] Read more.

BF₃, volatile amines (VOAs), and biogenic amines (BAs) are the key indicators in chemical reaction catalysis and food quality monitoring. In this study, we present two types of fluorescent sensors, a hydrazone ligand (HL)-based fluorescent sensor for BF₃ detection and a novel sensor array using six boron difluoride (BF₂) hydrazone complexes (BFHs) for monitoring VOAs and BAs. Spectral research indicates that the interaction mechanism between the HLs and BF₃ is based on intramolecular charge transfer (ICT). The HLs for the monitoring of BF₃ showed good sensitivity, selectivity, and anti-interference and have the characteristics of a visible color change. Additionally, the HL probe demonstrates reversibility in the presence of triethylamine, making it a candidate for “ON-OFF-ON” mode sensing. BF₃ detection can also be efficiently performed using test strips for convenient, air-based applications. The BFH sensor array successfully differentiates histamine from the other typical non-volatile BAs in solution; in comparison, the VOAs are analyzed through recognition patterns and statistical analysis. The array’s color changes enable the practical, on-site detection of shrimp spoilage, with principal component analysis distinguishing various ageing intervals. In summary, this sensor array demonstrates high selectivity for VOAs and BAs, with significant potential for application in real-world sample analysis. Full article

(This article belongs to the Special Issue Application of Intelligent Flavor Sensing System for the Quality Detection of Foods)

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15 pages, 2414 KiB

Open AccessArticle

Interaction of Sensitivity, Emotions, and Motivations During Visual Perception

by Sergey Lytaev

Sensors 2024, 24(22), 7414; https://doi.org/10.3390/s24227414 (registering DOI) - 20 Nov 2024

Abstract

When an organism is exposed to environmental stimuli of varying intensity, the adaptive changes in the CNS can be explained by several conceptual provisions: the law of motivation–activation by Yerkes and Dodson, the laws of force and pessimal protective inhibition, and the theory [...] Read more.

When an organism is exposed to environmental stimuli of varying intensity, the adaptive changes in the CNS can be explained by several conceptual provisions: the law of motivation–activation by Yerkes and Dodson, the laws of force and pessimal protective inhibition, and the theory of emotion activation. Later, reinforcement sensitivity theory was developed in the fields of psychology and psychophysics. At the same time, cortical prepulse inhibition (PPI), the prepulse inhibition of perceived stimulus intensity (PPIPSI), and the augmentation/reduction phenomenon were proposed in sensory neurophysiology, which expanded our understanding of consciousness. The aim of this study was to identify markers of levels of activity of cognitive processes under normal and in psychopathological conditions while amplifying the information stimulus. For this purpose, we changed the contrast level of reversible checkerboard patterns and mapped the visual evoked potentials (VEPs) in 19 monopolar channels among 52 healthy subjects and 39 patients with a mental illness without an active productive pathology. Their cognitive functions were assessed by presenting visual tests to assess invariant pattern recognition, short-term visual memory, and Gestalt perception. The personalities of the healthy subjects were assessed according to Cattell’s 16-factor questionnaire, linking the data from neurophysiological and cognitive studies to factors Q4 (relaxation/tension) and C (emotional stability). According to the N₇₀ and N₁₅₀ VEP waves, the healthy subjects and the patients were divided into two groups. In some, there was a direct relationship between VEP amplitude and contrast intensity (21 patients and 29 healthy persons), while in the others, there was an inverse relationship, with a reduction in VEP amplitude (18 patients and 23 healthy persons). The relationship and mechanisms of subjects’ cognitive abilities and personality traits are discussed based on the data acquired from the responses to information stimuli of varied intensity. Full article

(This article belongs to the Section Intelligent Sensors)

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18 pages, 6146 KiB

Open AccessArticle

A Near-Infrared Imaging System for Robotic Venous Blood Collection

by Zhikang Yang, Mao Shi, Yassine Gharbi, Qian Qi, Huan Shen, Gaojian Tao, Wu Xu, Wenqi Lyu and Aihong Ji

Sensors 2024, 24(22), 7413; https://doi.org/10.3390/s24227413 (registering DOI) - 20 Nov 2024

Abstract

Venous blood collection is a widely used medical diagnostic technique, and with rapid advancements in robotics, robotic venous blood collection has the potential to replace traditional manual methods. The success of this robotic approach is heavily dependent on the quality of vein imaging. [...] Read more.

Venous blood collection is a widely used medical diagnostic technique, and with rapid advancements in robotics, robotic venous blood collection has the potential to replace traditional manual methods. The success of this robotic approach is heavily dependent on the quality of vein imaging. In this paper, we develop a vein imaging device based on the simulation analysis of vein imaging parameters and propose a U-Net+ResNet18 neural network for vein image segmentation. The U-Net+ResNet18 neural network integrates the residual blocks from ResNet18 into the encoder of the U-Net to form a new neural network. ResNet18 is pre-trained using the Bootstrap Your Own Latent (BYOL) framework, and its encoder parameters are transferred to the U-Net+ResNet18 neural network, enhancing the segmentation performance of vein images with limited labelled data. Furthermore, we optimize the AD-Census stereo matching algorithm by developing a variable-weight version, which improves its adaptability to image variations across different regions. Results show that, compared to U-Net, the BYOL+U-Net+ResNet18 method achieves an 8.31% reduction in Binary Cross-Entropy (BCE), a 5.50% reduction in Hausdorff Distance (HD), a 15.95% increase in Intersection over Union (IoU), and a 9.20% increase in the Dice coefficient (Dice), indicating improved image segmentation quality. The average error of the optimized AD-Census stereo matching algorithm is reduced by 25.69%, and the improvement of the image stereo matching performance is more obvious. Future research will explore the application of the vein imaging system in robotic venous blood collection to facilitate real-time puncture guidance. Full article

(This article belongs to the Section Sensors and Robotics)

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17 pages, 1619 KiB

Open AccessArticle

An Entropy-Based Clustering Algorithm for Real-Time High-Dimensional IoT Data Streams

by Ibrahim Mutambik

Sensors 2024, 24(22), 7412; https://doi.org/10.3390/s24227412 (registering DOI) - 20 Nov 2024

Abstract

The rapid growth of data streams, propelled by the proliferation of sensors and Internet of Things (IoT) devices, presents significant challenges for real-time clustering of high-dimensional data. Traditional clustering algorithms struggle with high dimensionality, memory and time constraints, and adapting to dynamically evolving [...] Read more.

The rapid growth of data streams, propelled by the proliferation of sensors and Internet of Things (IoT) devices, presents significant challenges for real-time clustering of high-dimensional data. Traditional clustering algorithms struggle with high dimensionality, memory and time constraints, and adapting to dynamically evolving data. Existing dimensionality reduction methods often neglect feature ranking, leading to suboptimal clustering performance. To address these issues, we introduce E-Stream, a novel entropy-based clustering algorithm for high-dimensional data streams. E-Stream performs real-time feature ranking based on entropy within a sliding time window to identify the most informative features, which are then utilized with the DenStream algorithm for efficient clustering. We evaluated E-Stream using the NSL-KDD dataset, comparing it against DenStream, CluStream, and MR-Stream. The evaluation metrics included the average F-Measure, Jaccard Index, Fowlkes–Mallows Index, Purity, and Rand Index. The results show that E-Stream outperformed the baseline algorithms in both clustering accuracy and computational efficiency while effectively reducing dimensionality. E-Stream also demonstrated significantly less memory consumption and fewer computational requirements, highlighting its suitability for real-time processing of high-dimensional data streams. Despite its strengths, E-Stream requires manual parameter adjustment and assumes a consistent number of active features, which may limit its adaptability to diverse datasets. Future work will focus on developing a fully autonomous, parameter-free version of the algorithm, incorporating mechanisms to handle missing features and improving the management of evolving clusters to enhance robustness and adaptability in dynamic IoT environments. Full article

(This article belongs to the Special Issue Advances in Big Data and Internet of Things)

18 pages, 13718 KiB

Open AccessArticle

BG-YOLO: A Bidirectional-Guided Method for Underwater Object Detection

by Ruicheng Cao, Ruiteng Zhang, Xinyue Yan and Jian Zhang

Sensors 2024, 24(22), 7411; https://doi.org/10.3390/s24227411 (registering DOI) - 20 Nov 2024

Abstract

Degraded underwater images decrease the accuracy of underwater object detection. Existing research uses image enhancement methods to improve the visual quality of images, which may not be beneficial in underwater image detection and lead to serious degradation in detector performance. To alleviate this [...] Read more.

Degraded underwater images decrease the accuracy of underwater object detection. Existing research uses image enhancement methods to improve the visual quality of images, which may not be beneficial in underwater image detection and lead to serious degradation in detector performance. To alleviate this problem, we proposed a bidirectional guided method for underwater object detection, referred to as BG-YOLO. In the proposed method, a network is organized by constructing an image enhancement branch and an object detection branch in a parallel manner. The image enhancement branch consists of a cascade of an image enhancement subnet and object detection subnet. The object detection branch only consists of a detection subnet. A feature-guided module connects the shallow convolution layers of the two branches. When training the image enhancement branch, the object detection subnet in the enhancement branch guides the image enhancement subnet to be optimized towards the direction that is most conducive to the detection task. The shallow feature map of the trained image enhancement branch is output to the feature-guided module, constraining the optimization of the object detection branch through consistency loss and prompting the object detection branch to learn more detailed information about the objects. This enhances the detection performance. During the detection tasks, only the object detection branch is reserved so that no additional computational cost is introduced. Extensive experiments demonstrate that the proposed method significantly improves the detection performance of the YOLOv5s object detection network (the mAP is increased by up to 2.9%) and maintains the same inference speed as YOLOv5s (132 fps). Full article

(This article belongs to the Special Issue Machine Learning in Image/Video Processing and Sensing)

16 pages, 6297 KiB

Open AccessArticle

Numerical Simulation Study on Predicting the Critical Icing Conditions of Aircraft Pitot Tubes

by Qixi Chen, Lifen Zhang, Chengxin Zhou, Zhengang Liu and Yaguo Lyu

Sensors 2024, 24(22), 7410; https://doi.org/10.3390/s24227410 (registering DOI) - 20 Nov 2024

Abstract

Aircraft pitot tubes are sophisticated instruments designed to detect airflow pressure and relay this information to onboard computers and flight instruments, enabling the calculation of airspeed through the measurement of total-static pressure differences. The formation of ice on aircraft pitot tubes can compromise [...] Read more.

Aircraft pitot tubes are sophisticated instruments designed to detect airflow pressure and relay this information to onboard computers and flight instruments, enabling the calculation of airspeed through the measurement of total-static pressure differences. The formation of ice on aircraft pitot tubes can compromise the acquisition of airspeed data, misguide pilots, and potentially cause catastrophic flight control failures. This article introduces a predictive methodology for identifying critical conditions that lead to icing on aircraft pitot tubes. Utilizing numerical simulation techniques, the methodology calculates the critical conditions for pitot tube icing across cruise flight regimes and atmospheric conditions, resulting in the generation of a critical condition envelope surface. By comparing these critical conditions against actual sensor data, a predictive danger zone can be established, offering an advanced warning system to ensure flight safety. Full article

(This article belongs to the Section Intelligent Sensors)

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15 pages, 5384 KiB

Open AccessArticle

Gradual Failure of a Rainfall-Induced Creep-Type Landslide and an Application of Improved Integrated Monitoring System: A Case Study

by Jun Guo, Fanxing Meng and Jingwei Guo

Sensors 2024, 24(22), 7409; https://doi.org/10.3390/s24227409 (registering DOI) - 20 Nov 2024

Abstract

Landslides cause severe damage to life and property with a wide-ranging impact. Infiltration of rainfall is one of the significant factors leading to landslides. This paper reports on a phase creep landslide caused by long-term rainfall infiltration. A detailed geological survey of the [...] Read more.

Landslides cause severe damage to life and property with a wide-ranging impact. Infiltration of rainfall is one of the significant factors leading to landslides. This paper reports on a phase creep landslide caused by long-term rainfall infiltration. A detailed geological survey of the landslide was conducted, and the deformation development pattern and mechanism of the landslide were analyzed in conjunction with climatic characteristics. Furthermore, reinforcement measures specific to the landslide area were proposed. To monitor the stability of the reinforced slope, a Beidou intelligent monitoring and warning system suitable for remote mountainous areas was developed. The system utilizes LoRa Internet of Things (IoT) technology to connect various monitoring components, integrating surface displacement, deep deformation, structural internal forces, and rainfall monitoring devices into a local IoT network. A data processing unit was established on site to achieve preliminary processing and automatic handling of monitoring data. The monitoring results indicate that the reinforced slope has generally stabilized, and the improved intelligent monitoring system has been able to continuously and accurately reflect the real-time working conditions of the slope. Over the two-year monitoring period, 13 early warnings were issued, with more than 90% of the warnings accurately corresponding to actual conditions, significantly improving the accuracy of early warnings. The research findings provide valuable experience and reference for the monitoring and warning of high slopes in mountainous areas. Full article

(This article belongs to the Section Internet of Things)

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23 pages, 938 KiB

Open AccessArticle

An Efficient Flow-Based Anomaly Detection System for Enhanced Security in IoT Networks

by Ibrahim Mutambik

Sensors 2024, 24(22), 7408; https://doi.org/10.3390/s24227408 (registering DOI) - 20 Nov 2024

Abstract

The growing integration of Internet of Things (IoT) devices into various sectors like healthcare, transportation, and agriculture has dramatically increased their presence in everyday life. However, this rapid expansion has exposed new vulnerabilities within computer networks, creating security challenges. These IoT devices, often [...] Read more.

The growing integration of Internet of Things (IoT) devices into various sectors like healthcare, transportation, and agriculture has dramatically increased their presence in everyday life. However, this rapid expansion has exposed new vulnerabilities within computer networks, creating security challenges. These IoT devices, often limited by their hardware constraints, lack advanced security features, making them easy targets for attackers and compromising overall network integrity. To counteract these security issues, Behavioral-based Intrusion Detection Systems (IDS) have been proposed as a potential solution for safeguarding IoT networks. While Behavioral-based IDS have demonstrated their ability to detect threats effectively, they encounter practical challenges due to their reliance on pre-labeled data and the heavy computational power they require, limiting their practical deployment. This research introduces the IoT-FIDS (Flow-based Intrusion Detection System for IoT), a lightweight and efficient anomaly detection framework tailored for IoT environments. Instead of employing traditional machine learning techniques, the IoT-FIDS focuses on identifying unusual behaviors by examining flow-based representations that capture standard device communication patterns, services used, and packet header details. By analyzing only benign traffic, this network-based IDS offers a streamlined and practical approach to securing IoT networks. Our experimental results reveal that the IoT-FIDS can accurately detect most abnormal traffic patterns with minimal false positives, making it a feasible security solution for real-world IoT implementations. Full article

(This article belongs to the Special Issue IoT Cybersecurity)

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19 pages, 4058 KiB

Open AccessArticle

Enhanced Fault Detection in Photovoltaic Panels Using CNN-Based Classification with PyQt5 Implementation

by Younes Ledmaoui, Adila El Maghraoui, Mohamed El Aroussi and Rachid Saadane

Sensors 2024, 24(22), 7407; https://doi.org/10.3390/s24227407 (registering DOI) - 20 Nov 2024

Abstract

Solar photovoltaic systems have increasingly become essential for harvesting renewable energy. However, as these systems grow in prevalence, the issue of the end of life of modules is also increasing. Regular maintenance and inspection are vital to extend the lifespan of these systems, [...] Read more.

Solar photovoltaic systems have increasingly become essential for harvesting renewable energy. However, as these systems grow in prevalence, the issue of the end of life of modules is also increasing. Regular maintenance and inspection are vital to extend the lifespan of these systems, minimize energy losses, and protect the environment. This paper presents an innovative explainable AI model for detecting anomalies in solar photovoltaic panels using an enhanced convolutional neural network (CNN) and the VGG16 architecture. The model effectively identifies physical and electrical changes, such as dust and bird droppings, and is implemented using the PyQt5 Python tool to create a user-friendly interface that facilitates decision-making for users. Key processes included dataset balancing through oversampling and data augmentation to expand the dataset. The model achieved impressive performance metrics: 91.46% accuracy, 98.29% specificity, and an F1 score of 91.67%. Overall, it enhances power generation efficiency and prolongs the lifespan of photovoltaic systems, while minimizing environmental risks. Full article

(This article belongs to the Special Issue Sensor Enabled Smart Energy Solutions)

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13 pages, 2588 KiB

Open AccessArticle

Construction of Flexible Deterministic Sparse Measurement Matrix in Compressed Sensing Using Legendre Sequences

by Haiqiang Liu, Ming Li and Caiping Hu

Sensors 2024, 24(22), 7406; https://doi.org/10.3390/s24227406 (registering DOI) - 20 Nov 2024

Abstract

Compressed sensing (CS) is an innovative signal acquisition and reconstruction technology that has broken through the limit of the Nyquist sampling theory. It is widely employed to optimize the measurement processes in various applications. One of the core challenges of CS is the [...] Read more.

Compressed sensing (CS) is an innovative signal acquisition and reconstruction technology that has broken through the limit of the Nyquist sampling theory. It is widely employed to optimize the measurement processes in various applications. One of the core challenges of CS is the construction of a measurement matrix. However, traditional random measurement matrices are often impractical. Additionally, many existing deterministic binary measurement matrices fail to provide the required flexibility for practical applications. In this study, inspired by the observation that pseudo-random sequences share similar properties with random sequences, we constructed a deterministic sparse measurement matrix with a flexible measurement number based on an pseudo-random sequence—the Legendre sequence. Empirical analysis of the phase transition and an assessment of the practical features of the proposed measurement matrix were conducted. We validated the effectiveness of the proposed measurement matrix on randomly synthesized signals and images. The results of our simulations reveal that our proposed measurement matrix performs better than several other measurement matrices, particularly in terms of accuracy and efficiency. Full article

(This article belongs to the Section Physical Sensors)

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19 pages, 414 KiB

Open AccessArticle

Quantum Privacy-Preserving Range Query Protocol for Encrypted Data in IoT Environments

by Chong-Qiang Ye, Jian Li and Xiao-Yu Chen

Sensors 2024, 24(22), 7405; https://doi.org/10.3390/s24227405 (registering DOI) - 20 Nov 2024

Abstract

With the rapid development of IoT technology, securely querying sensitive data collected by devices within a specific range has become a focal concern for users. This paper proposes a privacy-preserving range query scheme based on quantum encryption, along with circuit simulations and performance [...] Read more.

With the rapid development of IoT technology, securely querying sensitive data collected by devices within a specific range has become a focal concern for users. This paper proposes a privacy-preserving range query scheme based on quantum encryption, along with circuit simulations and performance analysis. We first propose a quantum private set similarity comparison protocol and then construct a privacy-preserving range query scheme for IoT environments. By leveraging the properties of quantum homomorphic encryption, the proposed scheme enables encrypted data comparisons, effectively preventing the leakage of sensitive data. The correctness and security analysis demonstrates that the designed protocol guarantees users receive the correct query results while resisting both external and internal attacks. Moreover, the protocol requires only simple quantum states and operations, and does not require users to bear the cost of complex quantum resources, making it feasible under current technological conditions. Full article

(This article belongs to the Special Issue IoT Network Security (Second Edition))

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25 pages, 3415 KiB

Open AccessArticle

An Innovative Neighbor Attention Mechanism Based on Coordinates for the Recognition of Facial Expressions

by Cheng Peng, Bohao Li, Kun Zou, Bowen Zhang, Genan Dai and Ah Chung Tsoi

Sensors 2024, 24(22), 7404; https://doi.org/10.3390/s24227404 (registering DOI) - 20 Nov 2024

Abstract

For solving the facial expression recognition (FER) problem, we introduce a novel feature extractor called the coordinate-based neighborhood attention mechanism (CNAM), which uses the coordinate attention (CA) method to capture the directional relationships in separate horizontal and vertical directions, the input features from [...] Read more.

For solving the facial expression recognition (FER) problem, we introduce a novel feature extractor called the coordinate-based neighborhood attention mechanism (CNAM), which uses the coordinate attention (CA) method to capture the directional relationships in separate horizontal and vertical directions, the input features from a preprocessing unit, and then passes this to two residual blocks, one consisting of the neighborhood attention (NA) mechanism, which captures the local interaction of features within the neighborhood of a feature vector, while the other one contains a channel attention implemented by a multilayer perceptron (MLP). We apply the feature extractor, the CNAM module, to four FER benchmark datasets, namely, RAF-DB, AffectNet(7cls), AffectNet(8cls), and CK+, and through qualitative and quantitative analysis techniques, we conclude that the insertion of the CNAM module could decrease the intra-cluster distances and increase the inter-cluster distances among the high-dimensional feature vectors. The CNAM compares well with other state-of-the-art (SOTA) methods, being the best-performing method for the AffectNet(7cls) and CK+ datasets, while for the RAF-DB and AffectNet(8cls) datasets, its performance is among the top-performing SOTA methods. Full article

(This article belongs to the Section Sensing and Imaging)

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16 pages, 2949 KiB

Open AccessArticle

Development of a Conceptual Scheme for Controlling Tool Wear During Cutting, Based on the Interaction of Virtual Models of a Digital Twin and a Vibration Monitoring System

by Lapshin Viktor, Turkin Ilya, Gvindzhiliya Valeriya, Dudinov Ilya and Gamaleev Denis

Sensors 2024, 24(22), 7403; https://doi.org/10.3390/s24227403 - 20 Nov 2024

Abstract

This article discusses the issue of the joint use of neural network algorithms for data processing and deterministic mathematical models. The use of a new approach is proposed, to determine the discrepancy between data from a vibration monitoring system of the cutting process [...] Read more.

This article discusses the issue of the joint use of neural network algorithms for data processing and deterministic mathematical models. The use of a new approach is proposed, to determine the discrepancy between data from a vibration monitoring system of the cutting process and the calculated data obtained by modeling mathematical models of the digital twin system of the cutting process. This approach is justified by the fact that some coordinates for the state of the cutting process cannot be measured, and the vibration signals measured by the vibration monitoring system (the vibration acceleration of the tip of the cutting tool) are subject to external disturbing influences. Both the experimental method and the Matlab 2022b simulation method were used as research methods. The experimental research method is based on the widespread use of modern analog vibration transducers, the signals from which undergo the process of digitalization and further processing in order to identify arrays of additional information required for virtual digital twin models. The results obtained allow us to formulate a new conceptual approach to the construction of systems for determining the degree of cutting tool wear, based on the joint use of computational virtual models of the digital twin system and data obtained from the vibration monitoring system of the cutting process. Full article

(This article belongs to the Section Physical Sensors)

20 pages, 1628 KiB

Open AccessReview

Energy Efficiency for 5G and Beyond 5G: Potential, Limitations, and Future Directions

by Adrian Ichimescu, Nirvana Popescu, Eduard C. Popovici and Antonela Toma

Sensors 2024, 24(22), 7402; https://doi.org/10.3390/s24227402 - 20 Nov 2024

Abstract

Energy efficiency constitutes a pivotal performance indicator for 5G New Radio (NR) networks and beyond, and achieving optimal efficiency necessitates the meticulous consideration of trade-offs against other performance parameters, including latency, throughput, connection densities, and reliability. Energy efficiency assumes it is of paramount [...] Read more.

Energy efficiency constitutes a pivotal performance indicator for 5G New Radio (NR) networks and beyond, and achieving optimal efficiency necessitates the meticulous consideration of trade-offs against other performance parameters, including latency, throughput, connection densities, and reliability. Energy efficiency assumes it is of paramount importance for both User Equipment (UE) to achieve battery prologue and base stations to achieve savings in power and operation cost. This paper presents an exhaustive review of power-saving research conducted for 5G and beyond 5G networks in recent years, elucidating the advantages, disadvantages, and key characteristics of each technique. Reinforcement learning, heuristic algorithms, genetic algorithms, Markov Decision Processes, and the hybridization of various standard algorithms inherent to 5G and 5G NR represent a subset of the available solutions that shall undergo scrutiny. In the final chapters, this work identifies key limitations, namely, computational expense, deployment complexity, and scalability constraints, and proposes a future research direction by theoretically exploring online learning, the clustering of the network base station, and hard HO to lower the consumption of networks like 2G or 4G. In lowering carbon emissions and lowering OPEX, these three additional features could help mobile network operators achieve their targets. Full article

(This article belongs to the Special Issue Disruptive Technologies and Wireless Sensor Network Communication Algorithms (2nd Edition))

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17 pages, 88951 KiB

Open AccessArticle

Sec-CLOCs: Multimodal Back-End Fusion-Based Object Detection Algorithm in Snowy Scenes

by Rui Gong, Xiangsuo Fan, Dengsheng Cai and You Lu

Sensors 2024, 24(22), 7401; https://doi.org/10.3390/s24227401 - 20 Nov 2024

Abstract

LiDAR and cameras, often regarded as the “eyes” of intelligent driving vehicles, are vulnerable to adverse weather conditions like haze, rain, and snow, compromising driving safety. In order to solve this problem and enhance the environmental sensing capability under severe weather conditions, this [...] Read more.

LiDAR and cameras, often regarded as the “eyes” of intelligent driving vehicles, are vulnerable to adverse weather conditions like haze, rain, and snow, compromising driving safety. In order to solve this problem and enhance the environmental sensing capability under severe weather conditions, this paper proposes a multimodal back-end fusion object detection method, Sec-CLOCs, which is specifically optimized for vehicle detection under heavy snow. This method achieves object detection by integrating an improved YOLOv8s 2D detector with a SECOND 3D detector. First, the quality of image data is enhanced through the Two-stage Knowledge Learning and Multi-contrastive Regularization (TKLMR) image processing algorithm. Additionally, the DyHead detection head and Wise-IOU loss function are introduced to optimize YOLOv8s and improve 2D detection performance.The LIDROR algorithm preprocesses point cloud data for the SECOND detector, yielding 3D object detection results. The CLOCs back-end fusion algorithm is then employed to merge the 2D and 3D detection outcomes, thereby enhancing overall object detection capabilities. The experimental results show that the Sec-CLOCs algorithm achieves a vehicle detection accuracy of 82.34% in moderate mode (30–100 m) and 81.76% in hard mode (more than 100 m) under heavy snowfall, which demonstrates the algorithm’s high detection performance and robustness. Full article

(This article belongs to the Section Sensing and Imaging)

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20 pages, 1947 KiB

Open AccessArticle

Pressure Control of Multi-Mode Variable Structure Electro–Hydraulic Load Simulation System

by He Hao, Hao Yan, Qi Zhang and Haoyu Li

Sensors 2024, 24(22), 7400; https://doi.org/10.3390/s24227400 - 20 Nov 2024

Abstract

During the loading process, significant external position disturbances occur in the electro–hydraulic load simulation system. To address these position disturbances and effectively mitigate the impact of uncertainty on system performance, this paper first treats model parameter uncertainty and external disturbances as lumped disturbances. [...] Read more.

During the loading process, significant external position disturbances occur in the electro–hydraulic load simulation system. To address these position disturbances and effectively mitigate the impact of uncertainty on system performance, this paper first treats model parameter uncertainty and external disturbances as lumped disturbances. The various states of the servo valve and the pressures within the hydraulic cylinder chambers are then examined. Building on this foundation, the paper proposes a nonlinear multi-mode variable structure independent load port electro–hydraulic load simulation system that is tailored for specific loading conditions. Secondly, in light of the significant motion disturbances present, this paper proposes an integral sliding mode active disturbance rejection composite control strategy that is based on fixed-time convergence. Based on the structure of the active disturbance rejection control framework, the fixed-time integral sliding mode and active disturbance rejection control algorithms are integrated. An extended state observer is designed to accurately estimate the lumped disturbance, effectively compensating for it to achieve precise loading of the independent load port electro–hydraulic load simulation system. The stability of the designed controller is also demonstrated. The results of the simulation research indicate that when the command input is a step signal, the pressure control accuracy under the composite control strategy is 99.94%, 99.86%, and 99.76% for disturbance frequencies of 1 Hz, 3 Hz, and 5 Hz, respectively. Conversely, when the command input is a sinusoidal signal, the pressure control accuracy remains high, measuring 99.94%, 99.8%, and 99.6% under the same disturbance frequencies. Furthermore, the simulation demonstrates that the influence of sensor random noise on the system remains within acceptable limits, highlighting the effective filtering capabilities of the extended state observer. This research establishes a solid foundation for the collaborative control of load ports and the engineering application of the independent load port electro–hydraulic load simulation system. Full article

(This article belongs to the Section Electronic Sensors)

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18 pages, 6536 KiB

Open AccessArticle

Strong Interference Elimination in Seismic Data Using Multivariate Variational Mode Extraction

by Zhichao Yu, Yuyang Tan and Yiran Lv

Sensors 2024, 24(22), 7399; https://doi.org/10.3390/s24227399 - 20 Nov 2024

Abstract

Seismic data acquired in the presence of mechanical vibrations or power facilities may be contaminated by strong interferences, significantly decreasing the data signal-to-noise ratio (S/N). Conventional methods, such as the notch filter and time-frequency transform method, are usually inadequate for suppressing non-stationary interference [...] Read more.

Seismic data acquired in the presence of mechanical vibrations or power facilities may be contaminated by strong interferences, significantly decreasing the data signal-to-noise ratio (S/N). Conventional methods, such as the notch filter and time-frequency transform method, are usually inadequate for suppressing non-stationary interference noises, and may distort effective signals if overprocessing. In this study, we propose a method for eliminating mechanical vibration interferences in seismic data. In our method, we extended the variational mode extraction (VME) technique to a multivariate form, called multivariate variational mode extraction (MVME), for synchronous analysis of multitrace seismic data. The interference frequencies are determined via synchrosqueezing-based time-frequency analysis of process recordings; their corresponding modes are extracted and removed from seismic data using MVME with optimal balancing factors. We used synthetic data to investigate the effectiveness of the method and the influence of tuning parameters on processing results, and then applied the method to field datasets. The results have demonstrated that, compared with the conventional methods, the proposed method could effectively suppress the mechanical vibration interferences, improve the S/Ns and enhance polarization analysis of seismic signals. Full article

(This article belongs to the Special Issue Sensor Technologies for Seismic Monitoring)

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17 pages, 4619 KiB

Open AccessArticle

Efficient Video Compression Using Afterimage Representation

by Minseong Jeon and Kyungjoo Cheoi

Sensors 2024, 24(22), 7398; https://doi.org/10.3390/s24227398 - 20 Nov 2024

Abstract

Recent advancements in large-scale video data have highlighted the growing need for efficient data compression techniques to enhance video processing performance. In this paper, we propose an afterimage-based video compression method that significantly reduces video data volume while maintaining analytical performance. The proposed [...] Read more.

Recent advancements in large-scale video data have highlighted the growing need for efficient data compression techniques to enhance video processing performance. In this paper, we propose an afterimage-based video compression method that significantly reduces video data volume while maintaining analytical performance. The proposed approach utilizes optical flow to adaptively select the number of keyframes based on scene complexity, optimizing compression efficiency. Additionally, object movement masks extracted from keyframes are accumulated over time using alpha blending to generate the final afterimage. Experiments on the UCF-Crime dataset demonstrated that the proposed method achieved a 95.97% compression ratio. In binary classification experiments on normal/abnormal behaviors, the compressed videos maintained performance comparable to the original videos, while in multi-class classification, they outperformed the originals. Notably, classification experiments focused exclusively on abnormal behaviors exhibited a significant 4.25% improvement in performance. Moreover, further experiments showed that large language models (LLMs) can interpret the temporal context of original videos from single afterimages. These findings confirm that the proposed afterimage-based compression technique effectively preserves spatiotemporal information while significantly reducing data size. Full article

(This article belongs to the Topic Applied Computer Vision and Pattern Recognition: 2nd Edition)

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