Sensors

19 pages, 6032 KiB

Open AccessArticle

Detection of Debonding Defects in Concrete-Filled Steel Tubes Using Fluctuation Analysis Method

by Chenfei Wang, Yixin Yang, Guangming Fan, Junyin Lian and Fangjian Chen

Sensors 2024, 24(24), 8222; https://doi.org/10.3390/s24248222 - 23 Dec 2024

Viewed by 390

This study presents a comprehensive method for detecting debonding defects in concrete-filled steel tube (CFST) structures using wave propagation analysis with externally attached piezoelectric ceramic sensors. Experimental tests and numerical simulations were conducted to evaluate the sensitivity and accuracy of two measurement techniques—the [...] Read more.

This study presents a comprehensive method for detecting debonding defects in concrete-filled steel tube (CFST) structures using wave propagation analysis with externally attached piezoelectric ceramic sensors. Experimental tests and numerical simulations were conducted to evaluate the sensitivity and accuracy of two measurement techniques—the flat and oblique measurement methods—in detecting debonding defects of varying lengths and heights. The results demonstrate that the flat measurement method excels in detecting debonding height, while the oblique method is more effective for detecting debonding length. A normalized judgment index (DI) was introduced to quantify the correlation between debonding characteristics and the detected signal amplitude, revealing the significant influence of sensor spacing on detection accuracy. Furthermore, a mathematical model based on wavelet packet energy analysis was developed to establish a linear relationship between wavelet packet energy and debonding size. This model offers a scientific foundation for the quantitative detection of debonding defects and provides a new approach to the health monitoring of CFST structures. The integrated use of both measurement techniques enhances detection precision, enabling both qualitative and quantitative defect analysis, which can significantly guide the maintenance and repair of CFST structures. Full article

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

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11 pages, 2649 KiB

Open AccessCommunication

Applications of Isosceles Triangular Coupling Structure in Optical Switching and Sensing

by Lili Zeng, Xingjiao Zhang, Qinghua Guo, Yang Fan, Yuanwen Deng, Zhengchao Ma and Boxun Li

Sensors 2024, 24(24), 8221; https://doi.org/10.3390/s24248221 - 23 Dec 2024

Viewed by 331

Abstract

In the case of waveguide-based devices, once they are fabricated, their optical properties are already determined and cannot be dynamically controlled, which limits their applications in practice. In this paper, an isosceles triangular-coupling structure which consists of an isosceles triangle coupled with a [...] Read more.

In the case of waveguide-based devices, once they are fabricated, their optical properties are already determined and cannot be dynamically controlled, which limits their applications in practice. In this paper, an isosceles triangular-coupling structure which consists of an isosceles triangle coupled with a two-bus waveguide is proposed and researched numerically and theoretically. The coupled mode theory (CMT) is introduced to verify the correctness of the simulation results, which are based on the finite difference time domain (FDTD). Due to the existence of the side mode and angular mode, the transmission spectrum presents two high transmittance peaks and two low transmittance peaks. In addition, the four transmission peaks exhibit different variation trends when the dimensions of the isosceles triangle are changed. The liquid crystal (LC) materials comprise anisotropic uniaxial crystal and exhibit a remarkable birefringence effect under the action of the external field. When the isosceles triangle coupling structure is filled with LC, the refractive index of the liquid crystal can be changed by changing the applied voltage, thereby achieving the function of an optical switch. Within a certain range, a linear relationship between refractive index and applied voltage can be obtained. Moreover, the proposed structure can be applied to biochemical sensing to detect glucose concentrations, and the sensitivity reaches as high as 0.283 nm·L/g, which is significantly higher than other values reported in the literature. The triangular coupling structure has advantages such as simple structure and ease of manufacturing, making it an ideal choice for the design of high-performance integrated plasmonic devices. Full article

(This article belongs to the Special Issue Advances in Sensing Technology: From Photon, Electron to Signal Processing)

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22 pages, 8365 KiB

Open AccessArticle

FP-YOLOv8: Surface Defect Detection Algorithm for Brake Pipe Ends Based on Improved YOLOv8n

by Ke Rao, Fengxia Zhao and Tianyu Shi

Sensors 2024, 24(24), 8220; https://doi.org/10.3390/s24248220 - 23 Dec 2024

Viewed by 350

Abstract

To address the limitations of existing deep learning-based algorithms in detecting surface defects on brake pipe ends, a novel lightweight detection algorithm, FP-YOLOv8, is proposed. This algorithm is developed based on the YOLOv8n framework with the aim of improving accuracy and model lightweight [...] Read more.

To address the limitations of existing deep learning-based algorithms in detecting surface defects on brake pipe ends, a novel lightweight detection algorithm, FP-YOLOv8, is proposed. This algorithm is developed based on the YOLOv8n framework with the aim of improving accuracy and model lightweight design. First, the C2f_GhostV2 module has been designed to replace the original C2f module. It reduces the model’s parameter count through its unique design. It achieves improved feature representation by adopting specific technique within its structure. Additionally, it incorporates the decoupled fully connected (DFC) attention mechanism, which minimizes information loss during long-range feature transmission by separately capturing pixel information along horizontal and vertical axes via convolution. Second, the Dynamic ATSS label allocation strategy is applied, which dynamically adjusts label assignments by integrating Anchor IoUs and predicted IoUs, effectively reducing the misclassification of high-quality prediction samples as negative samples. Thus, it improves the detection accuracy of the model. Lastly, an asymmetric small-target detection head, FADH, is proposed to utilize depth-separable convolution to accomplish classification and regression tasks, enabling more precise capture of detailed information across scales and improving the detection of small-target defects. The experimental results show that FP-YOLOv8 achieves a mAP50 of 89.5% and an F1-score of 87% on the ends surface defects dataset, representing improvements of 3.3% and 6.0%, respectively, over the YOLOv8n algorithm, Meanwhile, it reduces model parameters and computational costs by 14.3% and 21.0%. Additionally, compared to the baseline model, the AP50 values for cracks, scratches, and flash defects rise by 5.5%, 5.6%, and 2.3%, respectively. These results validate the efficacy of FP-YOLOv8 in enhancing defect detection accuracy, reducing missed detection rates, and decreasing model parameter counts and computational demands, thus meeting the requirements of online defect detection for brake pipe ends surfaces. Full article

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

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28 pages, 3107 KiB

Open AccessArticle

Application of Machine Learning to Predict CO₂ Emissions in Light-Duty Vehicles

by Jeffrey Udoh, Joan Lu and Qiang Xu

Sensors 2024, 24(24), 8219; https://doi.org/10.3390/s24248219 - 23 Dec 2024

Viewed by 293

Abstract

Climate change caused by greenhouse gas (GHG) emissions is an escalating global issue, with the transportation sector being a significant contributor, accounting for approximately a quarter of all energy-related GHG emissions. In the transportation sector, vehicle emissions testing is a key part of [...] Read more.

Climate change caused by greenhouse gas (GHG) emissions is an escalating global issue, with the transportation sector being a significant contributor, accounting for approximately a quarter of all energy-related GHG emissions. In the transportation sector, vehicle emissions testing is a key part of ensuring compliance with environmental regulations. The Vehicle Certification Agency (VCA) of the UK plays a pivotal role in certifying vehicles for compliance with emissions and safety standards. One of the primary methods employed by the VCA to measure vehicle emissions for light-duty vehicles is the Worldwide Harmonized Light Vehicles Test Procedure (WLTP). The WLTP is a global standard for testing vehicle emissions and fuel consumption, and sensors are crucial in ensuring accurate, real-time data collection in laboratories. Using the data collected by the VCA, regression machine learning models were trained to predict CO₂ emissions in light-duty vehicles. Among six regression models tested, the Decision Tree Regression model achieved the highest accuracy, with a Mean Absolute Error (MAE) of 2.20 and a Mean Absolute Percentage Error (MAPE) of 1.69%. It was then deployed as a web application that provides users with accurate CO₂ emission estimates for vehicles, enabling informed decisions to reduce GHG emissions. This research demonstrates the efficacy of machine learning and AI-driven approaches in fostering sustainability within the transportation sector. Full article

(This article belongs to the Special Issue Intelligent Sensors in Smart Home and Cities)

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30 pages, 1914 KiB

Open AccessReview

Securing the Future of Railway Systems: A Comprehensive Cybersecurity Strategy for Critical On-Board and Track-Side Infrastructure

by Nisrine Ibadah, César Benavente-Peces and Marc-Oliver Pahl

Sensors 2024, 24(24), 8218; https://doi.org/10.3390/s24248218 - 23 Dec 2024

Viewed by 396

Abstract

The growing prevalence of cybersecurity threats is a significant concern for railway systems, which rely on an extensive network of onboard and trackside sensors. These threats have the potential to compromise the safety of railway operations and the integrity of the railway infrastructure [...] Read more.

The growing prevalence of cybersecurity threats is a significant concern for railway systems, which rely on an extensive network of onboard and trackside sensors. These threats have the potential to compromise the safety of railway operations and the integrity of the railway infrastructure itself. This paper aims to examine the current cybersecurity measures in use, identify the key vulnerabilities that they address, and propose solutions for enhancing the security of railway infrastructures. The report evaluates the effectiveness of existing security protocols by reviewing current standards, including IEC62443 and NIST, as well as case histories of recent rail cyberattacks. Significant gaps have been identified, especially where modern and legacy systems need to be integrated. Weaknesses in communication protocols such as MVB, CAN and TCP/IP are identified. To address these challenges, the paper proposes a layered security framework specific to railways that incorporate continuous monitoring, risk-based cybersecurity modeling, AI-assisted threat detection, and stronger authentication methodologies. The aim of these recommendations is to improve the resilience of railway networks and ensure a safer, more secure infrastructure for future operations. Full article

(This article belongs to the Section Internet of Things)

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28 pages, 70926 KiB

Open AccessArticle

Fusion of Visible and Infrared Aerial Images from Uncalibrated Sensors Using Wavelet Decomposition and Deep Learning

by Chandrakanth Vipparla, Timothy Krock, Koundinya Nouduri, Joshua Fraser, Hadi AliAkbarpour, Vasit Sagan, Jing-Ru C. Cheng and Palaniappan Kannappan

Sensors 2024, 24(24), 8217; https://doi.org/10.3390/s24248217 - 23 Dec 2024

Viewed by 424

Abstract

Multi-modal systems extract information about the environment using specialized sensors that are optimized based on the wavelength of the phenomenology and material interactions. To maximize the entropy, complementary systems operating in regions of non-overlapping wavelengths are optimal. VIS-IR (Visible-Infrared) systems have been at [...] Read more.

Multi-modal systems extract information about the environment using specialized sensors that are optimized based on the wavelength of the phenomenology and material interactions. To maximize the entropy, complementary systems operating in regions of non-overlapping wavelengths are optimal. VIS-IR (Visible-Infrared) systems have been at the forefront of multi-modal fusion research and are used extensively to represent information in all-day all-weather applications. Prior to image fusion, the image pairs have to be properly registered and mapped to a common resolution palette. However, due to differences in the device physics of image capture, information from VIS-IR sensors cannot be directly correlated, which is a major bottleneck for this area of research. In the absence of camera metadata, image registration is performed manually, which is not practical for large datasets. Most of the work published in this area assumes calibrated sensors and the availability of camera metadata providing registered image pairs, which limits the generalization capability of these systems. In this work, we propose a novel end-to-end pipeline termed DeepFusion for image registration and fusion. Firstly, we design a recursive crop and scale wavelet spectral decomposition (WSD) algorithm for automatically extracting the patch of visible data representing the thermal information. After data extraction, both the images are registered to a common resolution palette and forwarded to the DNN for image fusion. The fusion performance of the proposed pipeline is compared and quantified with state-of-the-art classical and DNN architectures for open-source and custom datasets demonstrating the efficacy of the pipeline. Furthermore, we also propose a novel keypoint-based metric for quantifying the quality of fused output. Full article

(This article belongs to the Section Physical Sensors)

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17 pages, 3169 KiB

Open AccessArticle

Knowledge Reasoning- and Progressive Distillation-Integrated Detection of Electrical Construction Violations

by Bin Ma, Gang Liang, Yufei Rao, Wei Guo, Wenjie Zheng and Qianming Wang

Sensors 2024, 24(24), 8216; https://doi.org/10.3390/s24248216 - 23 Dec 2024

Viewed by 300

Abstract

To address the difficulty in detecting workers’ violation behaviors in electric power construction scenarios, this paper proposes an innovative method that integrates knowledge reasoning and progressive multi-level distillation techniques. First, standards, norms, and guidelines in the field of electric power construction are collected [...] Read more.

To address the difficulty in detecting workers’ violation behaviors in electric power construction scenarios, this paper proposes an innovative method that integrates knowledge reasoning and progressive multi-level distillation techniques. First, standards, norms, and guidelines in the field of electric power construction are collected to build a comprehensive knowledge graph, aiming to provide accurate knowledge representation and normative analysis. Then, the knowledge graph is combined with the object-detection model in the form of triplets, where detected objects and their interactions are represented as subject–predicate–object relationship. These triplets are embedded into the model using an adaptive connection network, which dynamically weights the relevance of external knowledge to enhance detection accuracy. Furthermore, to enhance the model’s performance, the paper designs a progressive multi-level distillation strategy. On one hand, knowledge transfer is conducted at the object level, region level, and global level, significantly reducing the loss of contextual information during distillation. On the other hand, two teacher models of different scales are introduced, employing a two-stage distillation strategy where the advanced teacher guides the primary teacher in the first stage, and the primary teacher subsequently distills this knowledge to the student model in the second stage, effectively bridging the scale differences between the teacher and student models. Experimental results demonstrate that under the proposed method, the model size is reduced from 14.5 MB to 3.8 MB, and the floating-point operations (FLOPs) are reduced from 15.8 GFLOPs to 5.9 GFLOPs. Despite these optimizations, the AP50 reaches 92.4%, showing a 1.8% improvement compared to the original model. These results highlight the method’s effectiveness in accurately detecting workers’ violation behaviors, providing a quantitative basis for its superiority and offering a novel approach for safety management and monitoring at construction sites. Full article

(This article belongs to the Special Issue Deep Power Vision Technology and Intelligent Vision Sensors: 2nd Edition)

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32 pages, 46700 KiB

Open AccessArticle

Material Visual Perception and Discharging Robot Control for Baijiu Fermented Grains in Underground Tank

by Yan Zhao, Zhongxun Wang, Hui Li, Chang Wang, Jianhua Zhang, Jingyuan Zhu and Xuan Liu

Sensors 2024, 24(24), 8215; https://doi.org/10.3390/s24248215 - 23 Dec 2024

Viewed by 438

Abstract

Addressing the issue of excessive manual intervention in discharging fermented grains from underground tanks in traditional brewing technology, this paper proposes an intelligent grains-out strategy based on a multi-degree-of-freedom hybrid robot. The robot’s structure and control system are introduced, along with analyses of [...] Read more.

Addressing the issue of excessive manual intervention in discharging fermented grains from underground tanks in traditional brewing technology, this paper proposes an intelligent grains-out strategy based on a multi-degree-of-freedom hybrid robot. The robot’s structure and control system are introduced, along with analyses of kinematics solutions for its parallel components and end-effector speeds. According to its structural characteristics and working conditions, a visual-perception-based motion control method of discharging fermented grains is determined. The enhanced perception of underground tanks’ positions is achieved through improved Canny edge detection algorithms, and a YOLO-v7 neural network is employed to train an image segmentation model for fermented grains’ surface, integrating depth information to synthesize point clouds. We then carry out the downsampling and three-dimensional reconstruction of these point clouds, then match the underground tank model with the fermented grain surface model to replicate the tank’s interior space. Finally, a digging motion control method is proposed and experimentally validated for feasibility and operational efficiency. Full article

(This article belongs to the Section Sensors and Robotics)

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1 pages, 134 KiB

Open AccessCorrection

Correction: Oluwasanmi et al. Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction. Sensors 2023, 23, 3836

by Ariyo Oluwasanmi, Muhammad Umar Aftab, Zhiguang Qin, Muhammad Shahzad Sarfraz, Yang Yu and Hafiz Tayyab Rauf

Sensors 2024, 24(24), 8214; https://doi.org/10.3390/s24248214 - 23 Dec 2024

Viewed by 191

Abstract

In the original publication [...] Full article

(This article belongs to the Section Sensor Networks)

18 pages, 13770 KiB

Open AccessArticle

Optimizing Correction Factors on Color Differences for Automotive Painting Services

by Emilia Corina Corbu, Anne-Marie Nitescu and Eduard Edelhauser

Sensors 2024, 24(24), 8213; https://doi.org/10.3390/s24248213 - 23 Dec 2024

Viewed by 583

Abstract

Currently, the automotive sector is showing increased demands regarding the color of cars in general, but especially the quality and the time of painting, in particular. Companies working in this industry, especially in specialized painting services, must perform work of impeccable quality in [...] Read more.

Currently, the automotive sector is showing increased demands regarding the color of cars in general, but especially the quality and the time of painting, in particular. Companies working in this industry, especially in specialized painting services, must perform work of impeccable quality in the shortest possible time in order to be efficient. Color differences that appear in different areas of the car result from the use of different formulas for obtaining color. These differences can be reduced by using correction factors that are established for the colors in the partial or total painting process of cars. There are several factors that lead to settings that are not verified by the real color and, therefore, contribute to incorrect color results and also to high and unnecessary repair costs. In this study, the authors aimed to optimize the values of the correction factors applicable in the automotive industry, based on a set of 135 measurements performed with a BYK Gardner spectrophotometer, in order to minimize color differences. Through this study, authors have also aimed to find out how the color-identification process can be streamlined with the smallest possible tolerances by optimally adjusting the correction factors and by identifying the factors that influence the color-reading and identification process. A total of 85 pairs of samples were used for the DS1 (data set) and 53 pairs of samples for the DS2 (data set); these samples were used in the visual experiments for testing the performance of two color-differentiation formulas. The first part of the research aimed to investigate the visual perception of the painted cars in terms of differences in brightness, chroma and hue, data that were used to optimize the formulas used for color differences. Finally, authors have estimated the closest color variant to the objective color by optimizing the correction factors and thus achieving the efficiency of the color-identification process and the whole painting-identification process. Full article

(This article belongs to the Special Issue Women’s Special Issue Series: Sensors)

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20 pages, 2223 KiB

Open AccessArticle

Disturbance Robust Attitude Stabilization of Multirotors with Control Moment Gyros

by Youyoung Yang, Sungsu Kim, Kwanghyun Lee and Henzeh Leeghim

Sensors 2024, 24(24), 8212; https://doi.org/10.3390/s24248212 - 23 Dec 2024

Viewed by 232

Abstract

This paper presents a novel control framework for enhancing the attitude stabilization of multirotor UAVs using Control Moment Gyros (CMGs) and a Disturbance Robust Drive Law (DRDL). Due to their lightweight and compact structure, multirotor UAVs are highly susceptible to disturbances such as [...] Read more.

This paper presents a novel control framework for enhancing the attitude stabilization of multirotor UAVs using Control Moment Gyros (CMGs) and a Disturbance Robust Drive Law (DRDL). Due to their lightweight and compact structure, multirotor UAVs are highly susceptible to disturbances such as wind, making it challenging to achieve stable attitude control using rotor thrust alone. To address this issue, we employ CMGs to provide robust attitude control and apply Fast Terminal Sliding Mode Control (FTSMC) to ensure fast and accurate convergence within a finite time. The combination of CMGs’ torque amplification capability with the DRDL enables the system to effectively avoid singularities and maintain stable control performance in the presence of disturbances. Simulation results demonstrate that the CMG-equipped hexarotor utilizing the DRDL rapidly converges to the target attitude despite external disturbances, while minimizing oscillations in both motor speed and gimbal movement. Additionally, compared to the pseudo-inverse control method, the proposed approach significantly improves singularity avoidance and disturbance mitigation. The proposed control framework enhances the stability and reliability of UAV operations and demonstrates its potential for high-performance control in challenging disturbance environments. Full article

(This article belongs to the Section Remote Sensors)

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14 pages, 1824 KiB

Open AccessArticle

Real-Time Freezing of Gait Prediction and Detection in Parkinson’s Disease

by Scott Pardoel, Ayham AlAkhras, Ensieh Jafari, Jonathan Kofman, Edward D. Lemaire and Julie Nantel

Sensors 2024, 24(24), 8211; https://doi.org/10.3390/s24248211 - 23 Dec 2024

Viewed by 328

Abstract

Freezing of gait (FOG) is a walking disturbance that can lead to postural instability, falling, and decreased mobility in people with Parkinson’s disease. This research used machine learning to predict and detect FOG episodes from plantar-pressure data and compared the performance of decision [...] Read more.

Freezing of gait (FOG) is a walking disturbance that can lead to postural instability, falling, and decreased mobility in people with Parkinson’s disease. This research used machine learning to predict and detect FOG episodes from plantar-pressure data and compared the performance of decision tree ensemble classifiers when trained on three different datasets. Dataset 1 (n = 11) was collected in a previous study. Dataset 2 (n = 10) included six new participants and four participants from Dataset 1 who were re-tested (approximately 2 years later), and Dataset 3 (n = 21) combined Datasets 1 and 2. The prediction model trained on Dataset 3 had a 2.28% higher sensitivity and 3.09% lower specificity compared to the models trained on Dataset 1. The model trained on Dataset 3 identified 86.84% of the total FOG episodes compared to 74.31% from the model trained on Dataset 1. Also, the model using Dataset 3 identified the FOG episodes 0.3 s earlier than the model developed with Dataset 1. The model trained using Dataset 3 showed improved performance in sensitivity, identification time, and FOG identification. The improvements using the expanded dataset (Dataset 3) in this study compared to the previous model reinforce the validity and generalizability of the original model. The model was able to predict and detect FOG well and is, therefore, ready to be implemented in a FOG prevention device. Full article

(This article belongs to the Section Wearables)

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1 pages, 136 KiB

Open AccessCorrection

Correction: Almadhor et al. AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery. Sensors 2021, 21, 3830

by Ahmad Almadhor, Hafiz Tayyab Rauf, Muhammad Ikram Ullah Lali, Robertas Damaševičius, Bader Alouffi and Abdullah Alharbi

Sensors 2024, 24(24), 8210; https://doi.org/10.3390/s24248210 - 23 Dec 2024

Viewed by 179

Abstract

Affiliation Correction [...] Full article

(This article belongs to the Section Intelligent Sensors)

26 pages, 3992 KiB

Open AccessArticle

Evaluating Communication Performance in Rotating Electrical Machines Using RSSI Measurements and Artificial Intelligence

by Sonia Ben Brahim, Samia Dardouri, Hanen Lajnef, Amel Ben Slimane, Ridha Bouallegue and Tan-Hoa Vuong

Sensors 2024, 24(24), 8209; https://doi.org/10.3390/s24248209 - 23 Dec 2024

Viewed by 302

Abstract

This paper introduces a novel methodology for evaluating communication performance in rotating electric machines using Received Signal Strength Indication (RSSI) measurements coupled with artificial intelligence. The proposed approach focuses on assessing the quality of wireless signals in the complex, dynamic environment inside these [...] Read more.

This paper introduces a novel methodology for evaluating communication performance in rotating electric machines using Received Signal Strength Indication (RSSI) measurements coupled with artificial intelligence. The proposed approach focuses on assessing the quality of wireless signals in the complex, dynamic environment inside these machines, where factors like reflections, metallic surfaces, and rotational movements can significantly impact communication. RSSI is used as a key parameter to monitor real-time signal behavior, enabling a detailed analysis of communication reliability. The methodology comprises several stages, including data collection, preprocessing, feature extraction, and model training. Various machine learning models are implemented and evaluated. Among these, the SVM model with a Radial Basis Function (RBF) kernel outperforms others, achieving an accuracy of 97%, with high precision and recall scores, confirming its robustness in classifying RSSI data and handling complex signal behavior. The confusion matrix further supports the SVM model’s accuracy, showing minimal misclassification. Full article

(This article belongs to the Special Issue Sensors for Severe Environments)

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10 pages, 1474 KiB

Open AccessCommunication

Comparative Analysis of Low-Cost Portable Spectrophotometers for Colorimetric Accuracy on the RAL Design System Plus Color Calibration Target

by Jaša Samec, Eva Štruc, Inese Berzina, Peter Naglič and Blaž Cugmas

Sensors 2024, 24(24), 8208; https://doi.org/10.3390/s24248208 - 23 Dec 2024

Viewed by 280

Abstract

Novel low-cost portable spectrophotometers could be an alternative to traditional spectrophotometers and calibrated RGB cameras by offering lower prices and convenient measurements but retaining high colorimetric accuracy. This study evaluated the colorimetric accuracy of low-cost, portable spectrophotometers on the established color calibration target—RAL [...] Read more.

Novel low-cost portable spectrophotometers could be an alternative to traditional spectrophotometers and calibrated RGB cameras by offering lower prices and convenient measurements but retaining high colorimetric accuracy. This study evaluated the colorimetric accuracy of low-cost, portable spectrophotometers on the established color calibration target—RAL Design System Plus (RAL+). Four spectrophotometers with a listed price between USD 100–1200 (Nix Spectro 2, Spectro 1 Pro, ColorReader, and Pico) and a smartphone RGB camera were tested on a representative subset of 183 RAL+ colors. Key performance metrics included the devices’ ability to match and measure RAL+ colors in the CIELAB color space using the color difference CIEDE2000 ΔE. The results showed that Nix Spectro 2 had the best performance, matching 99% of RAL+ colors with an estimated ΔE of 0.5–1.05. Spectro 1 Pro and ColorReader matched approximately 85% of colors with ΔE values between 1.07 and 1.39, while Pico and the Asus 8 smartphone matched 54–77% of colors, with ΔE of around 1.85. Our findings showed that low-cost, portable spectrophotometers offered excellent colorimetric measurements. They mostly outperformed existing RGB camera-based colorimetric systems, making them valuable tools in science and industry. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Color and Spectral Sensors: 2nd Edition)

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19 pages, 10695 KiB

Open AccessArticle

A Scene Knowledge Integrating Network for Transmission Line Multi-Fitting Detection

by Xinhang Chen, Xinsheng Xu, Jing Xu, Wenjie Zheng and Qianming Wang

Sensors 2024, 24(24), 8207; https://doi.org/10.3390/s24248207 - 23 Dec 2024

Viewed by 310

Abstract

Aiming at the severe occlusion problem and the tiny-scale object problem in the multi-fitting detection task, the Scene Knowledge Integrating Network (SKIN), including the scene filter module (SFM) and scene structure information module (SSIM) is proposed. Firstly, the particularity of the scene in [...] Read more.

Aiming at the severe occlusion problem and the tiny-scale object problem in the multi-fitting detection task, the Scene Knowledge Integrating Network (SKIN), including the scene filter module (SFM) and scene structure information module (SSIM) is proposed. Firstly, the particularity of the scene in the multi-fitting detection task is analyzed. Hence, the aggregation of the fittings is defined as the scene according to the professional knowledge of the power field and the habit of the operators in identifying the fittings. So, the scene knowledge will include global context information, fitting fine-grained visual information and scene structure information. Then, a scene filter module is designed to learn the global context information and fitting fine-grained visual information, and a scene structure module is designed to learn the scene structure information. Finally, the scene semantic features are used as the carrier to integrate three categories of information into the relative scene features, which can assist in the recognition of the occluded fittings and the tiny-scale fittings after feature mining and feature integration. The experiments show that the proposed network can effectively improve the performance of the multi-fitting detection task compared with the Faster R-CNN and other state-of-the-art models. In particular, the detection performances of the occluded and tiny-scale fittings are significantly improved. Full article

(This article belongs to the Special Issue Deep Power Vision Technology and Intelligent Vision Sensors: 2nd Edition)

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19 pages, 5224 KiB

Open AccessArticle

A Spatiotemporal Feature-Based Approach for the Detection of Unlicensed Taxis in Urban Areas

by Yun Xiao, Rongqiao Li and Jinyan Li

Sensors 2024, 24(24), 8206; https://doi.org/10.3390/s24248206 - 23 Dec 2024

Viewed by 268

Abstract

Unlicensed taxis seriously disrupt the transportation market order, and threaten passenger safety. Therefore, this paper proposes a method for identifying unlicensed taxis based on travel characteristics. First, the vehicle mileage and operation time are calculated using traffic surveillance bayonet data, and variance analysis [...] Read more.

Unlicensed taxis seriously disrupt the transportation market order, and threaten passenger safety. Therefore, this paper proposes a method for identifying unlicensed taxis based on travel characteristics. First, the vehicle mileage and operation time are calculated using traffic surveillance bayonet data, and variance analysis is applied to identification indicators for unlicensed taxis. Secondly, the mathematical model for identifying unlicensed taxis is established. The model is validated using the Hosmer–Lemeshow test, confusion matrix and ROC curve analysis. Finally, by applying methods such as geographic information matching, the spatiotemporal distribution characteristics of suspected unlicensed taxis in a city in Anhui Province are identified. The results show that the model effectively identifies suspected unlicensed taxis (ACC = 99.10%). The daily average mileage, daily average operating time, and number of operating days for suspected unlicensed taxis are significantly higher than those for private cars. Additionally, the suspected unlicensed taxis exhibit regular patterns in their travel origin–destination points and temporal distribution, enabling traffic management authorities to implement targeted regulatory measures. Full article

(This article belongs to the Special Issue Data and Network Analytics in Transportation Systems)

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10 pages, 2211 KiB

Open AccessCommunication

Track Deflection Monitoring for Railway Construction Based on Dynamic Brillouin Optical Time-Domain Reflectometry

by Tianfang Zhang, Liming Zhou, Weimin Liu and Linghao Cheng

Sensors 2024, 24(24), 8205; https://doi.org/10.3390/s24248205 - 23 Dec 2024

Viewed by 499

Abstract

Real-time online monitoring of track deformation during railway construction is crucial for ensuring the safe operation of trains. However, existing monitoring technologies struggle to effectively monitor both static and dynamic events, often resulting in high false alarm rates. This paper presents a monitoring [...] Read more.

Real-time online monitoring of track deformation during railway construction is crucial for ensuring the safe operation of trains. However, existing monitoring technologies struggle to effectively monitor both static and dynamic events, often resulting in high false alarm rates. This paper presents a monitoring technology for track deformation during railway construction based on dynamic Brillouin optical time-domain reflectometry (Dy-BOTDR), which effectively meets requirements in the monitoring of both static and dynamic events of track deformation. Dy-BOTDR can provide a two-dimensional spatial–temporal distribution map of track strain changes to characterize various events for better monitoring accuracy and lower false alarm rates. Full article

(This article belongs to the Special Issue Research Progress and Applications of Distributed Optical Fiber Sensing)

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28 pages, 2692 KiB

Open AccessReview

Overview of Wearable Healthcare Devices for Clinical Decision Support in the Prehospital Setting

by Rachel Gathright, Isiah Mejia, Jose M. Gonzalez, Sofia I. Hernandez Torres, David Berard and Eric J. Snider

Sensors 2024, 24(24), 8204; https://doi.org/10.3390/s24248204 - 22 Dec 2024

Viewed by 584

Abstract

Prehospital medical care is a major challenge for both civilian and military situations as resources are limited, yet critical triage and treatment decisions must be rapidly made. Prehospital medicine is further complicated during mass casualty situations or remote applications that require more extensive [...] Read more.

Prehospital medical care is a major challenge for both civilian and military situations as resources are limited, yet critical triage and treatment decisions must be rapidly made. Prehospital medicine is further complicated during mass casualty situations or remote applications that require more extensive medical treatments to be monitored. It is anticipated on the future battlefield where air superiority will be contested that prolonged field care will extend to as much 72 h in a prehospital environment. Traditional medical monitoring is not practical in these situations and, as such, wearable sensor technology may help support prehospital medicine. However, sensors alone are not sufficient in the prehospital setting where limited personnel without specialized medical training must make critical decisions based on physiological signals. Machine learning-based clinical decision support systems can instead be utilized to interpret these signals for diagnosing injuries, making triage decisions, or driving treatments. Here, we summarize the challenges of the prehospital medical setting and review wearable sensor technology suitability for this environment, including their use with medical decision support triage or treatment guidance options. Further, we discuss recommendations for wearable healthcare device development and medical decision support technology to better support the prehospital medical setting. With further design improvement and integration with decision support tools, wearable healthcare devices have the potential to simplify and improve medical care in the challenging prehospital environment. Full article

(This article belongs to the Section Wearables)

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14 pages, 2905 KiB

Open AccessArticle

On Security Performance of SWIPT Multi-User Jamming Based on Mixed RF/FSO Systems with Untrusted Relay

by Xingyue Guo, Shan Tu, Dexian Yan and Yi Wang

Sensors 2024, 24(24), 8203; https://doi.org/10.3390/s24248203 - 22 Dec 2024

Viewed by 484

Abstract

This paper presents research on the security performance of a multi-user interference-based mixed RF/FSO system based on SWIPT untrusted relay. In this work, the RF and FSO channels experience Nakagami-m fading distribution and Málaga (M) turbulence, respectively. Multiple users transmit messages to the [...] Read more.

This paper presents research on the security performance of a multi-user interference-based mixed RF/FSO system based on SWIPT untrusted relay. In this work, the RF and FSO channels experience Nakagami-m fading distribution and Málaga (M) turbulence, respectively. Multiple users transmit messages to the destination with the help of multiple cooperating relays, one of which may become an untrusted relay as an insider attacker. In a multi-user network, SWIPT acts as a charging device for each user node. In order to prevent the untrusted relays from eavesdropping on the information, some users are randomly assigned to transmit artificial noise in order to interfere with untrusted relays, and the remaining users send information to relay nodes. Based on the above system model, the closed-form expressions of secrecy outage probability (SOP) and average secrecy capacity (ASC) for the mixed RF/FSO system are derived. The correctness of these expressions is verified by the Monte Carlo method. The influences of various key factors on the safety performance of the system are analyzed by simulations. The results show that the security performance of the system is considerably improved by increasing the signal–interference noise ratio, the number of interfering users, the time distribution factor and the energy conversion efficiency when the instantaneous signal-to-noise ratio (SNR) of the RF link instantaneous SNR is low. Full article

(This article belongs to the Section Communications)

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17 pages, 11667 KiB

Open AccessArticle

Silicon Drift Detectors for the Measurement and Reconstruction of Beta Spectra

by Andrea Nava, Leonardo Bernardini, Matteo Biassoni, Tommaso Bradanini, Marco Carminati, Giovanni De Gregorio, Carlo Fiorini, Giulio Gagliardi, Peter Lechner, Riccardo Mancino and Chiara Brofferio

Sensors 2024, 24(24), 8202; https://doi.org/10.3390/s24248202 - 22 Dec 2024

Viewed by 481

Abstract

The ASPECT-BET project, or An sdd-SPECTrometer for BETa decay studies, aims to develop a novel technique for the precise measurement of forbidden beta spectra in the 10 keV–1 MeV range. This technique employs a Silicon Drift Detector (SDD) as the main spectrometer with [...] Read more.

The ASPECT-BET project, or An sdd-SPECTrometer for BETa decay studies, aims to develop a novel technique for the precise measurement of forbidden beta spectra in the 10 keV–1 MeV range. This technique employs a Silicon Drift Detector (SDD) as the main spectrometer with the option of a veto system to reject events exhibiting only partial energy deposition in the SDD. A precise understanding of the spectrometer’s response to electrons is crucial for accurately reconstructing the theoretical shape of the beta spectrum. To compute this response, GEANT4 simulations optimized for low-energy electron interactions are used and validated with a custom-made electron gun. In this article we present the performance of these simulations in reconstructing the electron spectra measured with SDDs of a ¹⁰⁹Cd monochromatic source, both in vacuum and in air. The allowed beta spectrum of a ¹⁴C source was also measured and analyzed, proving that this system is suitable for the application in ASPECT-BET. Full article

(This article belongs to the Special Issue Semiconductor Radiation Detectors: Sensors, Readout Electronics and Applications)

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16 pages, 2388 KiB

Open AccessArticle

Mitigating Data Leakage in a WiFi CSI Benchmark for Human Action Recognition

by Domonkos Varga

Sensors 2024, 24(24), 8201; https://doi.org/10.3390/s24248201 - 22 Dec 2024

Viewed by 556

Abstract

Human action recognition using WiFi channel state information (CSI) has gained attention due to its non-intrusive nature and potential applications in healthcare, smart environments, and security. However, the reliability of methods developed for CSI-based action recognition is often contingent on the quality of [...] Read more.

Human action recognition using WiFi channel state information (CSI) has gained attention due to its non-intrusive nature and potential applications in healthcare, smart environments, and security. However, the reliability of methods developed for CSI-based action recognition is often contingent on the quality of the datasets and evaluation protocols used. In this paper, we uncovered a critical data leakage issue, which arises from improper data partitioning, in a widely used WiFi CSI benchmark dataset. Specifically, the benchmark fails to separate individuals between the training and test sets, leading to inflated performance metrics as models inadvertently learn individual-specific features rather than generalizable action patterns. We analyzed this issue in depth, retrained several benchmarked models using corrected data partitioning methods, and demonstrated a significant drop in accuracy when individuals were properly separated across training and testing. Our findings highlight the importance of rigorous data partitioning in CSI-based action recognition and provide recommendations for mitigating data leakage in future research. This work contributes to the development of more robust and reliable human action recognition systems using WiFi CSI. Full article

(This article belongs to the Special Issue Deep Learning Applications for Pose Estimation and Human Action Recognition—2nd Edition)

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19 pages, 11955 KiB

Open AccessArticle

Structural Design and Electromagnetic Performance Analysis of Octupole Active Radial Magnetic Bearing

by Qixuan Zhu, Yujun Lu and Zhongkui Shao

Sensors 2024, 24(24), 8200; https://doi.org/10.3390/s24248200 - 22 Dec 2024

Viewed by 503

Abstract

This study addresses the challenges of magnetic circuit coupling and control complexity in active radial magnetic bearings (ARMBs) by systematically investigating the electromagnetic performance of four magnetic pole configurations (NNSS, NSNS, NNNN, and SSSS). Initially, equivalent magnetic circuit modeling and finite element analysis [...] Read more.

This study addresses the challenges of magnetic circuit coupling and control complexity in active radial magnetic bearings (ARMBs) by systematically investigating the electromagnetic performance of four magnetic pole configurations (NNSS, NSNS, NNNN, and SSSS). Initially, equivalent magnetic circuit modeling and finite element analysis (FEA) were employed to analyze the magnetic circuit coupling phenomena and their effects on the magnetic flux density distribution for each configuration. Subsequently, the air gap flux density and electromagnetic force were quantified under rotor eccentricity caused by unbalanced disturbances, and the dynamic performances of the ARMBs were evaluated for eccentricity along the x-axis and at 45°. Finally, experiments measured the electromagnetic forces acting on the rotor under the NNSS and NSNS configurations during eccentric conditions. The results indicate that the NNSS configuration significantly reduces magnetic circuit coupling, improves the uniformity of electromagnetic force distribution, and offers superior stability and control efficiency under asymmetric conditions. Experimental results deviated by less than 10% from the simulations, confirming the reliability and practicality of the proposed design. These findings provide valuable insights for optimizing ARMB pole configurations and promote their application in high-speed, high-precision industrial fields such as aerospace and power engineering. Full article

(This article belongs to the Section Electronic Sensors)

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20 pages, 6270 KiB

Open AccessArticle

Initial Pose Estimation Method for Robust LiDAR-Inertial Calibration and Mapping

by Eun-Seok Park , Saba Arshad and Tae-Hyoung Park

Sensors 2024, 24(24), 8199; https://doi.org/10.3390/s24248199 - 22 Dec 2024

Viewed by 390

Abstract

Handheld LiDAR scanners, which typically consist of a LiDAR sensor, Inertial Measurement Unit, and processor, enable data capture while moving, offering flexibility for various applications, including indoor and outdoor 3D mapping in fields such as architecture and civil engineering. Unlike fixed LiDAR systems, [...] Read more.

Handheld LiDAR scanners, which typically consist of a LiDAR sensor, Inertial Measurement Unit, and processor, enable data capture while moving, offering flexibility for various applications, including indoor and outdoor 3D mapping in fields such as architecture and civil engineering. Unlike fixed LiDAR systems, handheld devices allow data collection from different angles, but this mobility introduces challenges in data quality, particularly when initial calibration between sensors is not precise. Accurate LiDAR-IMU calibration, essential for mapping accuracy in Simultaneous Localization and Mapping applications, involves precise alignment of the sensors’ extrinsic parameters. This research presents a robust initial pose calibration method for LiDAR-IMU systems in handheld devices, specifically designed for indoor environments. The research contributions are twofold. Firstly, we present a robust plane detection method for LiDAR data. This plane detection method removes the noise caused by mobility of scanning device and provides accurate planes for precise LiDAR initial pose estimation. Secondly, we present a robust planes-aided LiDAR calibration method that estimates the initial pose. By employing this LiDAR calibration method, an efficient LiDAR-IMU calibration is achieved for accurate mapping. Experimental results demonstrate that the proposed method achieves lower calibration errors and improved computational efficiency compared to existing methods. Full article

(This article belongs to the Section Sensors and Robotics)

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12 pages, 1814 KiB

Open AccessArticle

Comparative Analysis of Physiological Vergence Angle Calculations from Objective Measurements of Gaze Position

by Linda Krauze, Karola Panke, Gunta Krumina and Tatjana Pladere

Sensors 2024, 24(24), 8198; https://doi.org/10.3390/s24248198 - 22 Dec 2024

Viewed by 491

Abstract

Eccentric photorefractometry is widely used to measure eye refraction, accommodation, gaze position, and pupil size. While the individual calibration of refraction and accommodation data has been extensively studied, gaze measurements have received less attention. PowerRef 3 does not incorporate individual calibration for gaze [...] Read more.

Eccentric photorefractometry is widely used to measure eye refraction, accommodation, gaze position, and pupil size. While the individual calibration of refraction and accommodation data has been extensively studied, gaze measurements have received less attention. PowerRef 3 does not incorporate individual calibration for gaze measurements, resulting in a divergent offset between the measured and expected gaze positions. To address this, we proposed two methods to calculate the physiological vergence angle based on the visual vergence data obtained from PowerRef 3. Twenty-three participants aged 25 ± 4 years viewed Maltese cross stimuli at distances of 25, 30, 50, 70, and 600 cm. The expected vergence angles were calculated considering the individual interpupillary distance at far. Our results demonstrate that the PowerRef 3 gaze data deviated from the expected vergence angles by 9.64 ± 2.73° at 25 cm and 9.25 ± 3.52° at 6 m. The kappa angle calibration method reduced the discrepancy to 3.93 ± 1.19° at 25 cm and 3.70 ± 0.36° at 600 cm, whereas the linear regression method further improved the accuracy to 3.30 ± 0.86° at 25 cm and 0.26 ± 0.01° at 600 cm. Both methods improved the gaze results, with the linear regression calibration method showing greater overall accuracy. Full article

(This article belongs to the Special Issue Advanced Optics and Photonics Technologies for Sensing Applications)

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20 pages, 4849 KiB

Open AccessArticle

p-CuO/n-ZnO Heterojunction Pyro-Phototronic Photodetector Controlled by CuO Preparation Parameters

by Zhen Zhang, Fangpei Li, Wenbo Peng, Quanzhe Zhu and Yongning He

Sensors 2024, 24(24), 8197; https://doi.org/10.3390/s24248197 - 22 Dec 2024

Viewed by 384

Abstract

The combination of ZnO with narrow bandgap materials such as CuO is now a common method to synthesize high-performance optoelectronic devices. This study focuses on optimizing the performance of p-CuO/n-ZnO heterojunction pyroelectric photodetectors, fabricated through magnetron sputtering, by leveraging the pyro-phototronic effect. The [...] Read more.

The combination of ZnO with narrow bandgap materials such as CuO is now a common method to synthesize high-performance optoelectronic devices. This study focuses on optimizing the performance of p-CuO/n-ZnO heterojunction pyroelectric photodetectors, fabricated through magnetron sputtering, by leveraging the pyro-phototronic effect. The devices’ photoresponse to UV (365 nm) and visible (405 nm) lasers is thoroughly examined. The results show that when the device performance is regulated by adjusting the three parameters—sputtering power, sputtering time, and sputtering oxygen–argon ratio—the optimal sputtering parameters should be as follows: sputtering power of 120 W, sputtering time of 15 min, and sputtering oxygen–argon ratio of 1:3. With the optimal sputtering parameters, the maximum responsivity of the pyroelectric effect and the traditional photovoltaic effect

R_{pyro + photo}

of the detector is 4.7 times that under the basic parameters, and the maximum responsivity of the traditional photovoltaic effect

R_{photo}

is also 5.9 times that under the basic parameters. This study not only showcases the extensive potential of the pyro-phototronic effect in enhancing heterojunction photodetectors for high-performance photodetection but also provides some ideas for fabricating high-performance photodetectors. Full article

(This article belongs to the Special Issue The Advanced Flexible Electronic Devices: 2nd Edition)

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18 pages, 5533 KiB

Open AccessArticle

EGNet: 3D Semantic Segmentation Through Point–Voxel–Mesh Data for Euclidean–Geodesic Feature Fusion

by Qi Li, Yu Song, Xiaoqian Jin, Yan Wu, Hang Zhang and Di Zhao

Sensors 2024, 24(24), 8196; https://doi.org/10.3390/s24248196 - 22 Dec 2024

Viewed by 286

Abstract

With the advancement of service robot technology, the demand for higher boundary precision in indoor semantic segmentation has increased. Traditional methods of extracting Euclidean features using point cloud and voxel data often neglect geodesic information, reducing boundary accuracy for adjacent objects and consuming [...] Read more.

With the advancement of service robot technology, the demand for higher boundary precision in indoor semantic segmentation has increased. Traditional methods of extracting Euclidean features using point cloud and voxel data often neglect geodesic information, reducing boundary accuracy for adjacent objects and consuming significant computational resources. This study proposes a novel network, the Euclidean–geodesic network (EGNet), which uses point cloud–voxel–mesh data to characterize detail, contour, and geodesic features, respectively. The EGNet performs feature fusion through Euclidean and geodesic branches. In the Euclidean branch, the features extracted from point cloud data compensate for the detail features lost by voxel data. In the geodesic branch, geodesic features from mesh data are extracted using inter-domain fusion and aggregation modules. These geodesic features are then combined with contextual features from the Euclidean branch, and the simplified trajectory map of the grid is used for up-sampling to produce the final semantic segmentation results. The Scannet and Matterport datasets were used to demonstrate the effectiveness of the EGNet through visual comparisons with other models. The results demonstrate the effectiveness of integrating Euclidean and geodesic features for improved semantic segmentation. This approach can inspire further research combining these feature types for enhanced segmentation accuracy. Full article

(This article belongs to the Section Sensor Networks)

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18 pages, 488 KiB

Open AccessArticle

A User Association and Resource Allocation Algorithm for UAV-Assisted Smart Grid

by Jianwei Wei, Yuzhu Lei, Zhiyi Wen, Yongqing Xiao, Pengcheng Ma, Lingtao Sun and Lin Su

Sensors 2024, 24(24), 8195; https://doi.org/10.3390/s24248195 - 22 Dec 2024

Viewed by 309

Abstract

Recently, massive intelligent applications have emerged for the smart grid (SG), such as inspection and sensing. To support these applications, there have been high requirements on wireless communication for the SG, especially in remote areas. To tackle these challenges, a UAV-assisted heterogeneous wireless [...] Read more.

Recently, massive intelligent applications have emerged for the smart grid (SG), such as inspection and sensing. To support these applications, there have been high requirements on wireless communication for the SG, especially in remote areas. To tackle these challenges, a UAV-assisted heterogeneous wireless network is proposed in this paper for the SG, where multiple UAVs and a macro base station collaboratively provide a wide range of communication services. To further improve the communication capacity of this system, a joint user association and resource allocation algorithm is developed to maximize the total system throughput. To solve this problem, a matching algorithm is first proposed to solve the user association and subchannel assignment optimization problem. Then, the Lagrangian dual method is utilized to solve the power allocation problem. Finally, extensive simulations show that the proposed algorithm can effectively increase the user communication rate and enhance the capacity of the heterogeneous network for the SG. Full article

(This article belongs to the Special Issue Advanced Technologies in 6G Heterogeneous Networks)

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23 pages, 4503 KiB

Open AccessArticle

Design and Evaluation of a Cloud Computing System for Real-Time Measurements in Polarization-Independent Long-Range DAS Based on Coherent Detection

by Abdusomad Nur, Almaz Demise and Yonas Muanenda

Sensors 2024, 24(24), 8194; https://doi.org/10.3390/s24248194 - 22 Dec 2024

Viewed by 262

Abstract

CloudSim is a versatile simulation framework for modeling cloud infrastructure components that supports customizable and extensible application provisioning strategies, allowing for the simulation of cloud services. On the other hand, Distributed Acoustic Sensing (DAS) is a ubiquitous technique used for measuring vibrations over [...] Read more.

CloudSim is a versatile simulation framework for modeling cloud infrastructure components that supports customizable and extensible application provisioning strategies, allowing for the simulation of cloud services. On the other hand, Distributed Acoustic Sensing (DAS) is a ubiquitous technique used for measuring vibrations over an extended region. Data handling in DAS remains an open issue, as many applications need continuous monitoring of a volume of samples whose storage and processing in real time require high-capacity memory and computing resources. We employ the CloudSim tool to design and evaluate a cloud computing scheme for long-range, polarization-independent DAS using coherent detection of Rayleigh backscattering signals and uncover valuable insights on the evolution of the processing times for a diverse range of Virtual Machine (VM) capacities as well as sizes of blocks of processed data. Our analysis demonstrates that the choice of VM significantly impacts computational times in real-time measurements in long-range DAS and that achieving polarization independence introduces minimal processing overheads in the system. Additionally, the increase in the block size of processed samples per cycle results in diminishing increments in overall processing times per batch of new samples added, demonstrating the scalability of cloud computing schemes in long-range DAS and its capability to manage larger datasets efficiently. Full article

(This article belongs to the Special Issue Optical Sensors for Industrial Applications)

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22 pages, 8896 KiB

Open AccessArticle

Additive Manufacturing of a Frost-Detection Resistive Sensor for Optimizing Demand Defrost in Refrigeration Systems

by Martim Lima de Aguiar, Pedro Dinis Gaspar and Pedro Dinho da Silva

Sensors 2024, 24(24), 8193; https://doi.org/10.3390/s24248193 - 22 Dec 2024

Viewed by 301

Abstract

This article presents the development of a resistive frost-detection sensor fabricated using Fused Filament Fabrication (FFF) with a conductive filament. This sensor was designed to enhance demand-defrost control in industrial refrigeration systems. Frost accumulation on evaporator surfaces blocks airflow and creates a thermal [...] Read more.

This article presents the development of a resistive frost-detection sensor fabricated using Fused Filament Fabrication (FFF) with a conductive filament. This sensor was designed to enhance demand-defrost control in industrial refrigeration systems. Frost accumulation on evaporator surfaces blocks airflow and creates a thermal insulating barrier that reduces heat exchange efficiency, increasing energy consumption and operational costs. Traditional timed defrosting control methods can mitigate these effects but often lead to inefficiencies due to their inability to align with actual frost accumulation, which can vary according to system and environmental conditions. Frost-detection sensors aim to solve this problem by acting as a tool to support defrosting control. A series of tests were conducted to evaluate the sensor’s performance in detecting frost under controlled conditions on a heat exchanger (HX). The sensor reliably detected frost in all cases, demonstrating its effectiveness in real-time frost detection. The sensor measurements were validated by comparison with results obtained through a computer vision method, confirming its reliability. It was also found that the sensor can detect temperature changes. This advancement in sensor technology highlights the potential of additive manufacturing to provide cost-effective, customizable, replicable, and compact sensor designs, contributing to improved system performance and energy efficiency in refrigeration systems. Full article

(This article belongs to the Section Physical Sensors)

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