Sensors

15 pages, 2824 KiB

Open AccessArticle

Spatial–Temporal Transformer Networks for Traffic Flow Forecasting Using a Pre-Trained Language Model

by Ju Ma, Juan Zhao and Yao Hou

Sensors 2024, 24(17), 5502; https://doi.org/10.3390/s24175502 (registering DOI) - 25 Aug 2024

Most current methods use spatial–temporal graph neural networks (STGNNs) to analyze complex spatial–temporal information from traffic data collected from hundreds of sensors. STGNNs combine graph neural networks (GNNs) and sequence models to create hybrid structures that allow for the two networks to collaborate. [...] Read more.

Most current methods use spatial–temporal graph neural networks (STGNNs) to analyze complex spatial–temporal information from traffic data collected from hundreds of sensors. STGNNs combine graph neural networks (GNNs) and sequence models to create hybrid structures that allow for the two networks to collaborate. However, this collaboration has made the model increasingly complex. This study proposes a framework that relies solely on original Transformer architecture and carefully designs embeddings to efficiently extract spatial–temporal dependencies in traffic flow. Additionally, we used pre-trained language models to enhance forecasting performance. We compared our new framework with current state-of-the-art STGNNs and Transformer-based models using four real-world traffic datasets: PEMS04, PEMS08, METR-LA, and PEMS-BAY. The experimental results demonstrate that our framework outperforms the other models in most metrics. Full article

(This article belongs to the Section Intelligent Sensors)

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21 pages, 11659 KiB

Open AccessArticle

Short-Term Wind Power Prediction Based on Encoder–Decoder Network and Multi-Point Focused Linear Attention Mechanism

by Jinlong Mei, Chengqun Wang, Shuyun Luo, Weiqiang Xu and Zhijiang Deng

Sensors 2024, 24(17), 5501; https://doi.org/10.3390/s24175501 (registering DOI) - 25 Aug 2024

Abstract

Wind energy is a clean energy source that is characterised by significant uncertainty. The electricity generated from wind power also exhibits strong unpredictability, which when integrated can have a substantial impact on the security of the power grid. In the context of integrating [...] Read more.

Wind energy is a clean energy source that is characterised by significant uncertainty. The electricity generated from wind power also exhibits strong unpredictability, which when integrated can have a substantial impact on the security of the power grid. In the context of integrating wind power into the grid, accurate prediction of wind power generation is crucial in order to minimise damage to the grid system. This paper proposes a novel composite model (MLL-MPFLA) that combines a multilayer perceptron (MLP) and an LSTM-based encoder–decoder network for short-term prediction of wind power generation. In this model, the MLP first extracts multidimensional features from wind power data. Subsequently, an LSTM-based encoder-decoder network explores the temporal characteristics of the data in depth, combining multidimensional features and temporal features for effective prediction. During decoding, an improved focused linear attention mechanism called multi-point focused linear attention is employed. This mechanism enhances prediction accuracy by weighting predictions from different subspaces. A comparative analysis against the MLP, LSTM, LSTM–Attention–LSTM, LSTM–Self_Attention–LSTM, and CNN–LSTM–Attention models demonstrates that the proposed MLL-MPFLA model outperforms the others in terms of MAE, RMSE, MAPE, and

R^{2}

, thereby validating its predictive performance. Full article

(This article belongs to the Section Electronic Sensors)

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16 pages, 1023 KiB

Open AccessArticle

MFCF-Gait: Small Silhouette-Sensitive Gait Recognition Algorithm Based on Multi-Scale Feature Cross-Fusion

by Chenyang Song, Lijun Yun and Ruoyu Li

Sensors 2024, 24(17), 5500; https://doi.org/10.3390/s24175500 (registering DOI) - 24 Aug 2024

Abstract

Gait recognition based on gait silhouette profiles is currently a major approach in the field of gait recognition. In previous studies, models typically used gait silhouette images sized at 64 × 64 pixels as input data. However, in practical applications, cases may arise [...] Read more.

Gait recognition based on gait silhouette profiles is currently a major approach in the field of gait recognition. In previous studies, models typically used gait silhouette images sized at 64 × 64 pixels as input data. However, in practical applications, cases may arise where silhouette images are smaller than 64 × 64, leading to a loss in detail information and significantly affecting model accuracy. To address these challenges, we propose a gait recognition system named Multi-scale Feature Cross-Fusion Gait (MFCF-Gait). At the input stage of the model, we employ super-resolution algorithms to preprocess the data. During this process, we observed that different super-resolution algorithms applied to larger silhouette images also affect training outcomes. Improved super-resolution algorithms contribute to enhancing model performance. In terms of model architecture, we introduce a multi-scale feature cross-fusion network model. By integrating low-level feature information from higher-resolution images with high-level feature information from lower-resolution images, the model emphasizes smaller-scale details, thereby improving recognition accuracy for smaller silhouette images. The experimental results on the CASIA-B dataset demonstrate significant improvements. On 64 × 64 silhouette images, the accuracies for NM, BG, and CL states reached 96.49%, 91.42%, and 78.24%, respectively. On 32 × 32 silhouette images, the accuracies were 94.23%, 87.68%, and 71.57%, respectively, showing notable enhancements. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sensor-Based Gait Recognition)

17 pages, 4179 KiB

Open AccessCommunication

Clique-like Point Cloud Registration: A Flexible Sampling Registration Method Based on Clique-like for Low-Overlapping Point Cloud

by Xinrui Huang, Xiaorong Gao, Jinlong Li and Lin Luo

Sensors 2024, 24(17), 5499; https://doi.org/10.3390/s24175499 (registering DOI) - 24 Aug 2024

Abstract

Three-dimensional point cloud registration is a critical task in 3D perception for sensors that aims to determine the optimal alignment between two point clouds by finding the best transformation. Existing methods like RANSAC and its variants often face challenges, such as sensitivity to [...] Read more.

Three-dimensional point cloud registration is a critical task in 3D perception for sensors that aims to determine the optimal alignment between two point clouds by finding the best transformation. Existing methods like RANSAC and its variants often face challenges, such as sensitivity to low overlap rates, high computational costs, and susceptibility to outliers, leading to inaccurate results, especially in complex or noisy environments. In this paper, we introduce a novel 3D registration method, CL-PCR, inspired by the concept of maximal cliques and built upon the SC²-PCR framework. Our approach allows for the flexible use of smaller sampling subsets to extract more local consensus information, thereby generating accurate pose hypotheses even in scenarios with low overlap between point clouds. This method enhances robustness against low overlap and reduces the influence of outliers, addressing the limitations of traditional techniques. First, we construct a graph matrix to represent the compatibility relationships among the initial correspondences. Next, we build clique-likes subsets of various sizes within the graph matrix, each representing a consensus set. Then, we compute the transformation hypotheses for the subsets using the SVD algorithm and select the best hypothesis for registration based on evaluation metrics. Extensive experiments demonstrate the effectiveness of CL-PCR. In comparison experiments on the 3DMatch/3DLoMatch datasets using both FPFH and FCGF descriptors, our Fast-CL-PCRv1 outperforms state-of-the-art algorithms, achieving superior registration performance. Additionally, we validate the practicality and robustness of our method with real-world data. Full article

(This article belongs to the Section Sensing and Imaging)

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16 pages, 765 KiB

Open AccessArticle

Energy Minimization for IRS-Assisted SWIPT-MEC System

by Shuai Zhang, Yujun Zhu, Meng Mei, Xin He and Yong Xu

Sensors 2024, 24(17), 5498; https://doi.org/10.3390/s24175498 (registering DOI) - 24 Aug 2024

Abstract

With the rapid development of the internet of things (IoT) era, IoT devices may face limitations in battery capacity and computational capability. Simultaneous wireless information and power transfer (SWIPT) and mobile edge computing (MEC) have emerged as promising technologies to address these challenges. [...] Read more.

With the rapid development of the internet of things (IoT) era, IoT devices may face limitations in battery capacity and computational capability. Simultaneous wireless information and power transfer (SWIPT) and mobile edge computing (MEC) have emerged as promising technologies to address these challenges. Due to wireless channel fading and susceptibility to obstacles, this paper introduces intelligent reflecting surfaces (IRS) to enhance the spectral and energy efficiency of wireless networks. We propose a system model for IRS-assisted uplink offloading computation, downlink offloading computation results, and simultaneous energy transfer. Considering constraints such as IRS phase shifts, latency, energy harvesting, and offloading transmit power, we jointly optimize the CPU frequency of IoT devices, offloading transmit power, local computation workload, power splitting (PS) ratio, and IRS phase shifts. This establishes a multi-variate coupled nonlinear problem aimed at minimizing IoT devices energy consumption. We design an effective alternating optimization (AO) iterative algorithm based on block coordinate descent, and utilize closed-form solutions, Dinkelbach-based Lagrange dual method, and semidefinite relaxation (SDR) method to minimize IoT devices energy consumption. Simulation results demonstrate that the proposed scheme achieves lower energy consumption compared to other resource allocation strategies. Full article

(This article belongs to the Section Internet of Things)

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14 pages, 2577 KiB

Open AccessArticle

A Deep Learning Approach to Distance Map Generation Applied to Automatic Fiber Diameter Computation from Digital Micrographs

by Alain M. Alejo Huarachi and César A. Beltrán Castañón

Sensors 2024, 24(17), 5497; https://doi.org/10.3390/s24175497 (registering DOI) - 24 Aug 2024

Abstract

Precise measurement of fiber diameter in animal and synthetic textiles is crucial for quality assessment and pricing; however, traditional methods often struggle with accuracy, particularly when fibers are densely packed or overlapping. Current computer vision techniques, while useful, have limitations in addressing these [...] Read more.

Precise measurement of fiber diameter in animal and synthetic textiles is crucial for quality assessment and pricing; however, traditional methods often struggle with accuracy, particularly when fibers are densely packed or overlapping. Current computer vision techniques, while useful, have limitations in addressing these challenges. This paper introduces a novel deep-learning-based method to automatically generate distance maps of fiber micrographs, enabling more accurate fiber segmentation and diameter calculation. Our approach utilizes a modified U-Net architecture, trained on both real and simulated micrographs, to regress distance maps. This allows for the effective separation of individual fibers, even in complex scenarios. The model achieves a mean absolute error (MAE) of

0.1094

and a mean square error (MSE) of

0.0711

, demonstrating its effectiveness in accurately measuring fiber diameters. This research highlights the potential of deep learning to revolutionize fiber analysis in the textile industry, offering a more precise and automated solution for quality control and pricing. Full article

(This article belongs to the Section Sensing and Imaging)

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18 pages, 9356 KiB

Open AccessArticle

Drone-DETR: Efficient Small Object Detection for Remote Sensing Image Using Enhanced RT-DETR Model

by Yaning Kong, Xiangfeng Shang and Shijie Jia

Sensors 2024, 24(17), 5496; https://doi.org/10.3390/s24175496 (registering DOI) - 24 Aug 2024

Abstract

Performing low-latency, high-precision object detection on unmanned aerial vehicles (UAVs) equipped with vision sensors holds significant importance. However, the current limitations of embedded UAV devices present challenges in balancing accuracy and speed, particularly in the analysis of high-precision remote sensing images. This challenge [...] Read more.

Performing low-latency, high-precision object detection on unmanned aerial vehicles (UAVs) equipped with vision sensors holds significant importance. However, the current limitations of embedded UAV devices present challenges in balancing accuracy and speed, particularly in the analysis of high-precision remote sensing images. This challenge is particularly pronounced in scenarios involving numerous small objects, intricate backgrounds, and occluded overlaps. To address these issues, we introduce the Drone-DETR model, which is based on RT-DETR. To overcome the difficulties associated with detecting small objects and reducing redundant computations arising from complex backgrounds in ultra-wide-angle images, we propose the Effective Small Object Detection Network (ESDNet). This network preserves detailed information about small objects, reduces redundant computations, and adopts a lightweight architecture. Furthermore, we introduce the Enhanced Dual-Path Feature Fusion Attention Module (EDF-FAM) within the neck network. This module is specifically designed to enhance the network’s ability to handle multi-scale objects. We employ a dynamic competitive learning strategy to enhance the model’s capability to efficiently fuse multi-scale features. Additionally, we incorporate the P2 shallow feature layer from the ESDNet into the neck network to enhance the model’s ability to fuse small-object features, thereby enhancing the accuracy of small object detection. Experimental results indicate that the Drone-DETR model achieves an mAP⁵⁰ of 53.9% with only 28.7 million parameters on the VisDrone2019 dataset, representing an 8.1% enhancement over RT-DETR-R18. Full article

(This article belongs to the Special Issue AI-Based Object Detection and Tracking in UAVs: Challenges and Research Directions)

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16 pages, 12812 KiB

Open AccessArticle

Design of a Compact Multiband Monopole Antenna with MIMO Mutual Coupling Reduction

by Chang-Keng Lin, Ding-Bing Lin, Han-Chang Lin and Chang-Ching Lin

Sensors 2024, 24(17), 5495; https://doi.org/10.3390/s24175495 (registering DOI) - 24 Aug 2024

Abstract

In this article, the authors present the design of a compact multiband monopole antenna measuring 30 × 10 × 1.6 mm³, which is aimed at optimizing performance across various communication bands, with a particular focus on Wi-Fi and sub-6G bands. These [...] Read more.

In this article, the authors present the design of a compact multiband monopole antenna measuring 30 × 10 × 1.6 mm³, which is aimed at optimizing performance across various communication bands, with a particular focus on Wi-Fi and sub-6G bands. These bands include the 2.4 GHz band, the 3.5 GHz band, and the 5–6 GHz band, ensuring versatility in practical applications. Another important point is that this paper demonstrates effective methods for reducing mutual coupling through two meander slits on the common ground, resembling a defected ground structure (DGS) between two antenna elements. This approach achieves mutual coupling suppression from −6.5 dB and −9 dB to −26 dB and −13 dB at 2.46 GHz and 3.47 GHz, respectively. Simulated and measured results are in good agreement, demonstrating significant improvements in isolation and overall multiple-input multiple-output (MIMO) antenna system performance. This research proposes a compact multiband monopole antenna and demonstrates a method to suppress coupling in multiband antennas, making them suitable for internet of things (IoT) sensor devices and Wi-Fi infrastructure systems. Full article

(This article belongs to the Section Internet of Things)

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19 pages, 3781 KiB

Open AccessArticle

Efficiency Analysis of Powertrain for Internal Combustion Engine and Hydrogen Fuel Cell Tractor According to Agricultural Operations

by Hyeon-Ho Jeon, Seung-Yun Baek, Seung-Min Baek, Jang-Young Choi, Yeon-Soo Kim, Wan-Soo Kim and Yong-Joo Kim

Sensors 2024, 24(17), 5494; https://doi.org/10.3390/s24175494 (registering DOI) - 24 Aug 2024

Abstract

As interest in eco-friendly work vehicles grows, research on the powertrains of eco-friendly tractors has increased, including research on the development of eco-friendly vehicles (tractors) using hydrogen fuel cell power packs and batteries. However, batteries require a long time to charge and have [...] Read more.

As interest in eco-friendly work vehicles grows, research on the powertrains of eco-friendly tractors has increased, including research on the development of eco-friendly vehicles (tractors) using hydrogen fuel cell power packs and batteries. However, batteries require a long time to charge and have a short operating time due to their low energy efficiency compared with hydrogen fuel cell power packs. Therefore, recent studies have focused on the development of tractors using hydrogen fuel cell power packs; however, there is a lack of research on powertrain performance analysis considering actual working conditions. To evaluate vehicle performance, an actual load measurement during agricultural operation must be conducted. The objective of this study was to conduct an efficiency analysis of powertrains according to their power source using data measured during agricultural operations. A performance evaluation with respect to efficiency was performed through comparison and an analysis with internal combustion engine tractors of the same level. The specifications of the transmission for hydrogen fuel cell and engine tractors were used in this study. The power loss and efficiency of the transmission were calculated using ISO 14179-1 equations, as shown below. Plow tillage and rotary tillage operations were conducted for data measurement. The measurement system consists of four components. The engine data load measurement was calculated using the vehicle’s controller area network (CAN) data, the axle load was measured using an axle torque meter and proximity sensors, and fuel consumption was measured using the sensor installed on the fuel line. The calculated capacities, considering the engine’s fuel efficiency for plow and rotary tillage operations, were 131.2 and 175.1 kWh, respectively. The capacity of the required power, considering the powertrain’s efficiency for hydrogen fuel cell tractors with respect to plow and rotary tillage operations, was calculated using the efficiency of the motor, inverter, and power pack, and 51.3 and 62.9 kWh were the values obtained, respectively. Considering these factors, the engine exhibited an efficiency of about 47.9% compared with the power pack in the case of plow tillage operations, and the engine exhibited an efficiency of about 29.3% in the case of rotary tillage operations. A hydrogen fuel cell tractor is considered suitable for high-efficiency and eco-friendly vehicles because it can operate on eco-friendly power sources while providing the advantages of a motor. Full article

(This article belongs to the Section Industrial Sensors)

13 pages, 1786 KiB

Open AccessArticle

Thermal Reading of Texts Buried in Historical Bookbindings

by Stefano Paoloni, Giovanni Caruso, Noemi Orazi, Ugo Zammit and Fulvio Mercuri

Sensors 2024, 24(17), 5493; https://doi.org/10.3390/s24175493 (registering DOI) - 24 Aug 2024

Abstract

In the manufacture of ancient books, it was quite common to insert written scraps belonging to earlier library material into bookbindings. For scholars like codicologists and paleographers, it is extremely important to have the possibility of reading the text lying on such scraps [...] Read more.

In the manufacture of ancient books, it was quite common to insert written scraps belonging to earlier library material into bookbindings. For scholars like codicologists and paleographers, it is extremely important to have the possibility of reading the text lying on such scraps without dismantling the book. In this regard, in this paper, we report on the detection of these texts by means of infrared (IR) pulsed thermography (PT), which, in recent years, has been specifically proven to be an effective tool for the investigation of Cultural Heritage. In particular, we present a quantitative analysis based, for the first time, on PT images obtained from books of historical relevance preserved at the Biblioteca Angelica in Rome. The analysis has been carried out by means of a theoretical model for the PT signal, which makes use of two image parameters, namely, the distortion and the contrast, related to the IR readability of buried texts. As shown in this paper, the good agreement between the experimental data obtained with historical books and the theoretical analysis proved the capability of the adopted PT method to be fruitfully applied, in a real case study, to the detection of buried texts and to the quantitative characterization of the parameters affecting their thermal readability. Full article

(This article belongs to the Section Remote Sensors)

25 pages, 2460 KiB

Open AccessArticle

FedAvg-P: Performance-Based Hierarchical Federated Learning-Based Anomaly Detection System Aggregation Strategy for Advanced Metering Infrastructure

by Hend Alshede, Kamal Jambi, Laila Nassef, Nahed Alowidi and Etimad Fadel

Sensors 2024, 24(17), 5492; https://doi.org/10.3390/s24175492 (registering DOI) - 24 Aug 2024

Abstract

Advanced metering infrastructures (AMIs) aim to enhance the efficiency, reliability, and stability of electrical systems while offering advanced functionality. However, an AMI collects copious volumes of data and information, making the entire system sensitive and vulnerable to malicious attacks that may cause substantial [...] Read more.

Advanced metering infrastructures (AMIs) aim to enhance the efficiency, reliability, and stability of electrical systems while offering advanced functionality. However, an AMI collects copious volumes of data and information, making the entire system sensitive and vulnerable to malicious attacks that may cause substantial damage, such as a deficit in national security, a disturbance of public order, or significant economic harm. As a result, it is critical to guarantee a steady and dependable supply of information and electricity. Furthermore, storing massive quantities of data in one central entity leads to compromised data privacy. As such, it is imperative to engineer decentralized, federated learning (FL) solutions. In this context, the performance of participating clients has a significant impact on global performance. Moreover, FL models have the potential for a Single Point of Failure (SPoF). These limitations contribute to system failure and performance degradation. This work aims to develop a performance-based hierarchical federated learning (HFL) anomaly detection system for an AMI through (1) developing a deep learning model that detects attacks against this critical infrastructure; (2) developing a novel aggregation strategy, FedAvg-P, to enhance global performance; and (3) proposing a peer-to-peer architecture guarding against a SPoF. The proposed system was employed in experiments on the CIC-IDS2017 dataset. The experimental results demonstrate that the proposed system can be used to develop a reliable anomaly detection system for AMI networks. Full article

(This article belongs to the Section Industrial Sensors)

20 pages, 10124 KiB

Open AccessArticle

3D Positioning of Drones through Images

by Jianxing Yang, Enhui Zheng, Jiqi Fan and Yuwen Yao

Sensors 2024, 24(17), 5491; https://doi.org/10.3390/s24175491 (registering DOI) - 24 Aug 2024

Abstract

Drones traditionally rely on satellite signals for positioning and altitude. However, when in a special denial environment, satellite communication is interrupted, and the traditional positioning and height determination methods face challenges. We made a dataset at the height of 80–200 m and proposed [...] Read more.

Drones traditionally rely on satellite signals for positioning and altitude. However, when in a special denial environment, satellite communication is interrupted, and the traditional positioning and height determination methods face challenges. We made a dataset at the height of 80–200 m and proposed a multi-scale input network. The positioning index RDS achieved 76.3 points, and the positioning accuracy within 20 m was 81.7%. This paper proposes a method to judge the height by image alone, without the support of other sensor data. One height judgment can be made per single image. Based on the UAV image–satellite image matching positioning technology, by calculating the actual area represented by the UAV image in real space, combined with the fixed parameters of the optical camera, the actual height of the UAV flight is calculated, which is 80–200 m, and the relative error rate of height is 18.1%. Full article

(This article belongs to the Section Electronic Sensors)

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14 pages, 3592 KiB

Open AccessArticle

Miniaturized Antenna Design for Wireless and Powerless Surface Acoustic Wave Temperature Sensors

by Naranut Sreang and Jae-Young Chung

Sensors 2024, 24(17), 5490; https://doi.org/10.3390/s24175490 (registering DOI) - 24 Aug 2024

Abstract

This paper presents the introduction, design, and experimental validation of two small helical antennae. These antennae are a component of the surface acoustic wave (SAW) sensor interrogation system, which has been miniaturized to operate at 915 MHz and aims to improve the performance [...] Read more.

This paper presents the introduction, design, and experimental validation of two small helical antennae. These antennae are a component of the surface acoustic wave (SAW) sensor interrogation system, which has been miniaturized to operate at 915 MHz and aims to improve the performance of wireless passive SAW temperature-sensing applications. The proposed antenna designs are the normal-mode cylindrical helical antenna (CHA) and the hemispherical helical antenna (HSHA); both designed structures are developed for the ISM band, which ranges from 902 MHz to 928 MHz. The antennae exhibit resonance at 915 MHz with an operational bandwidth of 30 MHz for the CHA and 22 MHz for the HSHA. A notch occurs in the operating band, caused by the characteristics of the SAW sensor. The presence of this notch is crucial for the temperature measurement by aiding in calculating the frequency shifting of that notch. The decrement in the resonance frequency of the SAW sensor is about 66.67 kHz for every 10

^{\circ} C

, which is obtained by conducting the temperature measurement of the system model across temperature environments ranging from 30

^{\circ} C

to 90

^{\circ} C

to validate the variation in system performance. Full article

(This article belongs to the Special Issue Applications of Antenna Technology in Sensors II)

20 pages, 1879 KiB

Open AccessArticle

A Dynamic Topology Optimization Method for Tactical Edge Networks Based on Virtual Backbone Networks

by Zhixiang Kong, Zilong Jin and Chengsheng Pan

Sensors 2024, 24(17), 5489; https://doi.org/10.3390/s24175489 (registering DOI) - 24 Aug 2024

Abstract

To address the issues of low survivability and communication efficiency in wireless sensor networks caused by frequent node movement or damage in highly dynamic and high-mobility battlefield environments, we propose a dynamic topology optimization method based on a virtual backbone network. This method [...] Read more.

To address the issues of low survivability and communication efficiency in wireless sensor networks caused by frequent node movement or damage in highly dynamic and high-mobility battlefield environments, we propose a dynamic topology optimization method based on a virtual backbone network. This method involves two phases: topology reconstruction and topology maintenance, determined by a network coverage threshold. When the coverage falls below the threshold, a virtual backbone network is established using a connected dominating set (CDS) and non-backbone node optimization strategies to reconstruct the network topology, quickly restore network connectivity, effectively improve network coverage, and optimize the network structure. When the coverage is above the threshold, a multi-CDS scheduling algorithm and slight position adjustments of non-backbone nodes are employed to maintain the network topology, further enhancing network coverage with minimal node movement. Simulations demonstrate that this method can improve coverage and optimize network structure under different scales of network failures. Under three large-scale failure operational scenarios where the network coverage threshold was set to 80%, the coverage was enhanced by 26.12%, 15.88%, and 13.36%, and in small-scale failures, the coverage was enhanced by 7.55%, 4.90% and 7.84%. Full article

(This article belongs to the Special Issue Internet of Mobile Things and Wireless Sensor Networks)

8 pages, 1118 KiB

Open AccessCommunication

Electrochemical Aptasensor with Antifouling Properties for Label-Free Detection of Oxytetracycline

by Dimitra Kourti, Georgia Geka, Lidia Nemtsov, Soha Ahmadi, Anastasios Economou and Michael Thompson

Sensors 2024, 24(17), 5488; https://doi.org/10.3390/s24175488 (registering DOI) - 24 Aug 2024

Abstract

Oxytetracycline (OTC) is a widely employed antibiotic in veterinary treatment and in the prevention of infections, potentially leaving residues in animal-derived food products, such as milk, that are consumed by humans. Given the detrimental effects of prolonged human exposure to antibiotics, it has [...] Read more.

Oxytetracycline (OTC) is a widely employed antibiotic in veterinary treatment and in the prevention of infections, potentially leaving residues in animal-derived food products, such as milk, that are consumed by humans. Given the detrimental effects of prolonged human exposure to antibiotics, it has become imperative to develop precise and sensitive methods for monitoring the presence of OTC in food. Herein, we describe the development and results of a preliminary label-free electrochemical aptasensor with antifouling properties designed to detect OTC in milk samples. The sensor was realized by modifying a gold screen-printed electrode with α-lipoic acid–NHS and an amine-terminated aptamer. Different electrochemical techniques were used to study the steps of the fabrication process and to quantify OTC in the presence of the Fe(CN)₆⁴⁻/Fe(CN)₆³⁻ redox couple The detectable range of concentrations satisfy the maximum residue limits set by the European Union, with an limit of detection (LOD) of 14 ng/mL in phosphate buffer (BP) and 10 ng/mL in the milk matrix, and a dynamic range of up to 500 ng/mL This study is a steppingstone towards the implementation of a sensitive monitoring method for OTC in dairy products. Full article

(This article belongs to the Special Issue Electrochemical DNA- and Aptasensors for the Detection of Low-Molecular Compounds)

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33 pages, 1415 KiB

Open AccessReview

Smart Classrooms: How Sensors and AI Are Shaping Educational Paradigms

by Xiaochen Zhang, Yiran Ding, Xiaoyu Huang, Wujing Li, Liumei Long and Shiyao Ding

Sensors 2024, 24(17), 5487; https://doi.org/10.3390/s24175487 (registering DOI) - 24 Aug 2024

Abstract

The integration of advanced technologies is revolutionizing classrooms, significantly enhancing their intelligence, interactivity, and personalization. Central to this transformation are sensor technologies, which play pivotal roles. While numerous surveys summarize research progress in classrooms, few studies focus on the integration of sensor and [...] Read more.

The integration of advanced technologies is revolutionizing classrooms, significantly enhancing their intelligence, interactivity, and personalization. Central to this transformation are sensor technologies, which play pivotal roles. While numerous surveys summarize research progress in classrooms, few studies focus on the integration of sensor and AI technologies in developing smart classrooms. This systematic review classifies sensors used in smart classrooms and explores their current applications from both hardware and software perspectives. It delineates how different sensors enhance educational outcomes and the crucial role AI technologies play. The review highlights how sensor technology improves the physical classroom environment, monitors physiological and behavioral data, and is widely used to boost student engagements, manage attendance, and provide personalized learning experiences. Additionally, it shows that combining sensor software algorithms with AI technology not only enhances the data processing and analysis efficiency but also expands sensor capabilities, enriching their role in smart classrooms. The article also addresses challenges such as data privacy protection, cost, and algorithm optimization associated with emerging sensor technologies, proposing future research directions to advance educational sensor technologies. Full article

(This article belongs to the Special Issue Smart Educational Systems: Hardware and Software Aspects)

14 pages, 1291 KiB

Open AccessArticle

Innovative Detection and Segmentation of Mobility Activities in Patients Living with Parkinson’s Disease Using a Single Ankle-Positioned Smartwatch

by Etienne Goubault, Christian Duval, Camille Martin and Karina Lebel

Sensors 2024, 24(17), 5486; https://doi.org/10.3390/s24175486 (registering DOI) - 24 Aug 2024

Abstract

Background: The automatic detection of activities of daily living (ADL) is necessary to improve long-term home-based monitoring of Parkinson’s disease (PD) symptoms. While most body-worn sensor algorithms for ADL detection were developed using laboratory research systems covering full-body kinematics, it is now crucial [...] Read more.

Background: The automatic detection of activities of daily living (ADL) is necessary to improve long-term home-based monitoring of Parkinson’s disease (PD) symptoms. While most body-worn sensor algorithms for ADL detection were developed using laboratory research systems covering full-body kinematics, it is now crucial to achieve ADL detection using a single body-worn sensor that remains commercially available and affordable for ecological use. Aim: to detect and segment Walking, Turning, Sitting-down, and Standing-up activities of patients with PD using a Smartwatch positioned at the ankle. Method: Twenty-two patients living with PD performed a Timed Up and Go (TUG) task three times before engaging in cleaning ADL in a simulated free-living environment during a 3 min trial. Accelerations and angular velocities of the right or left ankle were recorded in three dimensions using a Smartwatch. The TUG task was used to develop detection algorithms for Walking, Turning, Sitting-down, and Standing-up, while the 3 min trial in the free-living environment was used to test and validate these algorithms. Sensitivity, specificity, and F-scores were calculated based on a manual segmentation of ADL. Results: Sensitivity, specificity, and F-scores were 96.5%, 94.7%, and 96.0% for Walking; 90.0%, 93.6%, and 91.7% for Turning; 57.5%, 70.5%, and 52.3% for Sitting-down; and 57.5%, 72.9%, and 54.1% for Standing-up. The median of time difference between the manual and automatic segmentation was 1.31 s for Walking, 0.71 s for Turning, 2.75 s for Sitting-down, and 2.35 s for Standing-up. Conclusion: The results of this study demonstrate that segmenting ADL to characterize the mobility of people with PD based on a single Smartwatch can be comparable to manual segmentation while requiring significantly less time. While Walking and Turning were well detected, Sitting-down and Standing-up will require further investigation to develop better algorithms. Nonetheless, these achievements increase the odds of success in implementing wearable technologies for PD monitoring in ecological environments. Full article

(This article belongs to the Collection Sensors for Gait, Human Movement Analysis, and Health Monitoring)

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14 pages, 1715 KiB

Open AccessArticle

Gait and Balance Assessments with Augmented Reality Glasses in People with Parkinson’s Disease: Concurrent Validity and Test–Retest Reliability

by Jara S. van Bergem, Pieter F. van Doorn, Eva M. Hoogendoorn, Daphne J. Geerse and Melvyn Roerdink

Sensors 2024, 24(17), 5485; https://doi.org/10.3390/s24175485 (registering DOI) - 24 Aug 2024

Abstract

State-of-the-art augmented reality (AR) glasses record their 3D pose in space, enabling measurements and analyses of clinical gait and balance tests. This study’s objective was to evaluate concurrent validity and test–retest reliability for common clinical gait and balance tests in people with Parkinson’s [...] Read more.

State-of-the-art augmented reality (AR) glasses record their 3D pose in space, enabling measurements and analyses of clinical gait and balance tests. This study’s objective was to evaluate concurrent validity and test–retest reliability for common clinical gait and balance tests in people with Parkinson’s disease: Five Times Sit To Stand (FTSTS) and Timed Up and Go (TUG) tests. Position and orientation data were collected in 22 participants with Parkinson’s disease using HoloLens 2 and Magic Leap 2 AR glasses, from which test completion durations and durations of distinct sub-parts (e.g., sit to stand, turning) were derived and compared to reference systems and over test repetitions. Regarding concurrent validity, for both tests, an excellent between-systems agreement was found for position and orientation time series (ICC_(C,1) > 0.933) and test completion durations (ICC_(A,1) > 0.984). Between-systems agreement for FTSTS (sub-)durations were all excellent (ICC_(A,1) > 0.921). TUG turning sub-durations were excellent (turn 1, ICC_(A,1) = 0.913) and moderate (turn 2, ICC_(A,1) = 0.589). Regarding test–retest reliability, the within-system test–retest variation in test completion times and sub-durations was always much greater than the between-systems variation, implying that (sub-)durations may be derived interchangeably from AR and reference system data. In conclusion, AR data are of sufficient quality to evaluate gait and balance aspects in people with Parkinson’s disease, with valid quantification of test completion durations and sub-durations of distinct FTSTS and TUG sub-parts. Full article

(This article belongs to the Collection Sensors for Gait, Human Movement Analysis, and Health Monitoring)

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16 pages, 4528 KiB

Open AccessArticle

Data-Driven Strain Sensor Design Based on a Knowledge Graph Framework

by Junmin Ke, Furong Liu, Guofeng Xu and Ming Liu

Sensors 2024, 24(17), 5484; https://doi.org/10.3390/s24175484 (registering DOI) - 24 Aug 2024

Abstract

Wearable flexible strain sensors require different performance depending on the application scenario. However, developing strain sensors based solely on experiments is time-consuming and often produces suboptimal results. This study utilized sensor knowledge to reduce knowledge redundancy and explore designs. A framework combining knowledge [...] Read more.

Wearable flexible strain sensors require different performance depending on the application scenario. However, developing strain sensors based solely on experiments is time-consuming and often produces suboptimal results. This study utilized sensor knowledge to reduce knowledge redundancy and explore designs. A framework combining knowledge graphs and graph representational learning methods was proposed to identify targeted performance, decipher hidden information, and discover new designs. Unlike process-parameter-based machine learning methods, it used the relationship as semantic features to improve prediction precision (up to 0.81). Based on the proposed framework, a strain sensor was designed and tested, demonstrating a wide strain range (300%) and closely matching predicted performance. This predicted sensor performance outperforms similar materials. Overall, the present work is favorable to design constraints and paves the way for the long-awaited implementation of text-mining-based knowledge management for sensor systems, which will facilitate the intelligent sensor design process. Full article

(This article belongs to the Special Issue Flexible and Wearable Sensors: Design, Fabrication Methods, and Applications)

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23 pages, 11785 KiB

Open AccessArticle

A Study on the Motion Behavior of Metallic Contaminant Particles in Transformer Insulation Oil under Multiphysical Fields

by Binbin Wei, Zhijuan Wang, Runze Qi, Xiaolong Wang and Tong Zhao

Sensors 2024, 24(17), 5483; https://doi.org/10.3390/s24175483 (registering DOI) - 24 Aug 2024

Abstract

When using transformer insulation oil as a liquid dielectric, the oil is easily polluted by the solid particles generated in the operation of the transformer, and these metallic impurity particles have a significant impact on the insulation performance inside the power transformer. The [...] Read more.

When using transformer insulation oil as a liquid dielectric, the oil is easily polluted by the solid particles generated in the operation of the transformer, and these metallic impurity particles have a significant impact on the insulation performance inside the power transformer. The force of the metal particles suspended in the flow insulation oil is multidimensional, which will lead to a change in the movement characteristics of the metal particles. Based on this, this study explored the motion rules of suspended metallic impurity particles in mobile insulating oil in different electric field environments and the influencing factors. A multiphysical field model of the solid–liquid two-phase flow of single-particle metallic impurity particles in mobile insulating oil was constructed using the dynamic analysis method, and the particles’ motion characteristics in the oil in different electric field environments were simulated. The motion characteristics of metallic impurity particles under conditions of different particle sizes, oil flow velocities, and insulation oil qualities and influencing factors were analyzed to provide theoretical support for the detection of impurity particles in transformer insulation oil and enable accurate estimations of the location of equipment faults. Our results show that there are obvious differences in the trajectory of metallic impurity particles under different electric field distributions. The particles will move towards the region of high field intensity under an electric field, and the metallic impurity particles will not collide with the electrode under an AC field. When the electric field intensity and particle size increase, the trajectory of the metallic impurity particles between electrodes becomes denser, and the number of collisions between particles and electrodes and the motion speed both increase. Under the condition of a higher oil flow velocity, the number of collisions between metal particles and electrodes is reduced, which reduces the possibility of particle agglomeration. When the temperature of the insulation oil changes and the quality deteriorates, its dynamic viscosity changes. With a decrease in the dynamic viscosity of the insulation oil, the movement of the metallic impurity particles between the electrodes becomes denser, the collision times between the particles and electrodes increase, and the maximum motion speed of the particles increases. Full article

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23 pages, 324 KiB

Open AccessArticle

Framework for a Research-Based and Interdisciplinary Use of Sensors in Elementary Teacher Education

by Maria João Silva, Margarida Rodrigues and Tiago Tempera

Sensors 2024, 24(17), 5482; https://doi.org/10.3390/s24175482 (registering DOI) - 24 Aug 2024

Abstract

Sensors should be integrated into teacher education, as they are essential tools in the digital practices needed for full participation in society. Electronic sensors can be used as laboratory/scientific tools, as everyday mobile learning tools, and as epistemic mediators in several scientific fields, [...] Read more.

Sensors should be integrated into teacher education, as they are essential tools in the digital practices needed for full participation in society. Electronic sensors can be used as laboratory/scientific tools, as everyday mobile learning tools, and as epistemic mediators in several scientific fields, as well as in interdisciplinary approaches. In this way, electronic sensors can play multiple roles in the main dimensions of teacher education. The aim of the research presented in this paper was to create a framework for the research-based and interdisciplinary use of sensors in elementary teacher education, based on the thematic analysis of seven case studies implemented in Portugal. The thematic categories used in the cross-case analysis were fundamental in revealing the different roles played by sensors in the different phases of the didactic sequences of the cases. Subsequently, the thematic analysis made it possible to identify patterns of affordances of sensors and to relate the multiple roles of electronic sensors to different areas of the Portuguese elementary teacher education model. The research synthesis made it possible to outline the framework perspectives. The resulting framework systematized and highlighted the affordances of sensors in pre-service and in-service elementary teacher education as scientific, epistemic, interdisciplinary, and didactic mediators. These affordances were revealed to be particularly important in data-driven inquiry problem-solving, pedagogical content knowledge, and professional knowledge development. The framework created can be expanded in future related research. Full article

(This article belongs to the Section Electronic Sensors)

16 pages, 4020 KiB

Open AccessArticle

SPRi Biosensor for Simultaneous Determination of HIF-1α, Angiopoietin-2, and Interleukin-1β in Blood Plasma

by Zuzanna Zielinska, Lukasz Oldak, Tomasz Guszcz, Adam Hermanowicz and Ewa Gorodkiewicz

Sensors 2024, 24(17), 5481; https://doi.org/10.3390/s24175481 (registering DOI) - 24 Aug 2024

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Abstract

A new analytical method, based on SPRi biosensors, has been developed for the simultaneous determination of the pro-angiogenic factors HIF-1α, angiopoietin-2 (ANG-2), and interleukin-1β (IL-1β) in biological fluids. These proteins take part in the process of angiogenesis, i.e., the creation of new blood [...] Read more.

A new analytical method, based on SPRi biosensors, has been developed for the simultaneous determination of the pro-angiogenic factors HIF-1α, angiopoietin-2 (ANG-2), and interleukin-1β (IL-1β) in biological fluids. These proteins take part in the process of angiogenesis, i.e., the creation of new blood vessels, which is a key stage of cancer development and metastasis. A separate validation process was carried out for each individual compound, indicating that the method can also be used to study one selected protein. Low values of the limit of detection (LOD) and quantification (LOQ) indicate that the developed method enables the determination of very low concentrations, in the order of pg/mL. The LOD values obtained for HIF-1α, ANG-2, and IL-1β were 0.09, 0.01, and 0.01 pg/mL, respectively. The LOQ values were 0.27, 0.039, and 0.02 pg/mL, and the response ranges of the biosensor were 5.00–100.00, 1.00–20.00, and 1.00–15.00 pg/mL. Moreover, determining the appropriate validation parameters confirmed that the design offers high precision, accuracy, and sensitivity. To prove the usefulness of the biosensor in practice, determinations were made in plasma samples from a control group and from a study group consisting of patients with diagnosed bladder cancer. The preliminary results obtained indicate that this biosensor can be used for broader analyses of bladder cancer. Each of the potential biomarkers, HIF-1α, ANG-2, and IL-1β, produced higher concentrations in the study group than in the control group. These are preliminary studies that serve to develop hypotheses, and their confirmation requires the analysis of a larger number of samples. However, the constructed biosensor is characterized by its ease and speed of measurement, and the method does not require special preparation of samples. SPRi biosensors can be used as a sensitive and highly selective method for determining potential blood biomarkers, which in the future may become part of the routine diagnosis of cancers. Full article

(This article belongs to the Section Biosensors)

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17 pages, 2684 KiB

Open AccessArticle

Ergodic Rate Analysis of Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surface-Assisted Rate-Splitting Multiple Access Systems Based on Discrete Phase Shifts

by Tao Liu and Yue Zhou

Sensors 2024, 24(17), 5480; https://doi.org/10.3390/s24175480 (registering DOI) - 23 Aug 2024

Viewed by 243

Abstract

In this paper, we combine simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) with rate-splitting multiple access (RSMA) technology and investigate the ergodic rate performance of an STAR-assisted RSMA system. Considering the discrete phase shifts of the STAR-RIS in practice, the downlink performance [...] Read more.

In this paper, we combine simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) with rate-splitting multiple access (RSMA) technology and investigate the ergodic rate performance of an STAR-assisted RSMA system. Considering the discrete phase shifts of the STAR-RIS in practice, the downlink performance of STAR-RIS-assisted RSMA with discrete phase shifts is compared to that with continuous phase shifts. Firstly, the cumulative distribution function of signal-to-interference-plus-noise ratio (SINR) of users is analyzed. Then, the total ergodic rate of the system and its approximate closed-form solution are, respectively, derived based on the cumulative distribution function of users. The simulation results validate the effectiveness of the theoretical analysis, showing good agreement between the derived theoretical ergodic rate and the corresponding simulations. Although the system performance with discrete phase shifts is inferior to that with continuous phase shifts due to quantization errors, the performance of the continuous phase shift system is well approximated when the quantization bit of the phase shift system reaches 3 in the simulations. Additionally, the impact of the number of STAR-RIS elements on the system’s performance is analyzed. Full article

(This article belongs to the Special Issue Energy-Efficient Communication Networks and Systems: 2nd Edition)

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20 pages, 15560 KiB

Open AccessArticle

IoT-Based Assessment of a Driver’s Stress Level

by Veronica Mattioli, Luca Davoli, Laura Belli, Sara Gambetta, Luca Carnevali, Andrea Sgoifo, Riccardo Raheli and Gianluigi Ferrari

Sensors 2024, 24(17), 5479; https://doi.org/10.3390/s24175479 (registering DOI) - 23 Aug 2024

Viewed by 195

Abstract

Driver Monitoring Systems (DMSs) play a key role in preventing hazardous events (e.g., road accidents) by providing prompt assistance when anomalies are detected while driving. Different factors, such as traffic and road conditions, might alter the psycho-physiological status of a driver by increasing [...] Read more.

Driver Monitoring Systems (DMSs) play a key role in preventing hazardous events (e.g., road accidents) by providing prompt assistance when anomalies are detected while driving. Different factors, such as traffic and road conditions, might alter the psycho-physiological status of a driver by increasing stress and workload levels. This motivates the development of advanced monitoring architectures taking into account psycho-physiological aspects. In this work, we propose a novel in-vehicle Internet of Things (IoT)-oriented monitoring system to assess the stress status of the driver. In detail, the system leverages heterogeneous components and techniques to collect driver (and, possibly, vehicle) data, aiming at estimating the driver’s arousal level, i.e., their psycho-physiological response to driving tasks. In particular, a wearable sensorized bodice and a thermal camera are employed to extract physiological parameters of interest (namely, the heart rate and skin temperature of the subject), which are processed and analyzed with innovative algorithms. Finally, experimental results are obtained both in simulated and real driving scenarios, demonstrating the adaptability and efficacy of the proposed system. Full article

(This article belongs to the Special Issue Robust Multimodal Sensing for Automated Driving Systems)

7 pages, 165 KiB

Open AccessEditorial

Advanced Sensing and Control Technologies for Autonomous Robots

by Yuanlong Xie, Shuting Wang, Shiqi Zheng and Zhaozheng Hu

Sensors 2024, 24(17), 5478; https://doi.org/10.3390/s24175478 - 23 Aug 2024

Viewed by 135

Abstract

The development of advanced sensing and control technologies provides increased intelligence and autonomy for robots and enhances the robots’ agility, maneuverability, and efficiency, which has attracted growing attention in various industries and domains [...] Full article

(This article belongs to the Special Issue Advanced Sensing and Control Technologies for Autonomous Robots)

20 pages, 1287 KiB

Open AccessArticle

Innovative Air-Preconditioning Method for Accurate Particulate Matter Sensing in Humid Environments

by Zdravko Kunić, Leo Mršić, Goran Đambić and Tomislav Ražov

Sensors 2024, 24(17), 5477; https://doi.org/10.3390/s24175477 - 23 Aug 2024

Viewed by 186

Abstract

Smart cities rely on a network of sensors to gather real-time data on various environmental factors, including air quality. This paper addresses the challenges of improving the accuracy of low-cost particulate matter sensors (LCPMSs) which can be compromised by environmental conditions, such as [...] Read more.

Smart cities rely on a network of sensors to gather real-time data on various environmental factors, including air quality. This paper addresses the challenges of improving the accuracy of low-cost particulate matter sensors (LCPMSs) which can be compromised by environmental conditions, such as high humidity, which is common in many urban areas. Such weather conditions often lead to the overestimation of particle counts due to hygroscopic particle growth, resulting in a potential public concern, although most of the detected particles consist of just water. The paper presents an innovative design for an indicative air-quality measuring station that integrates the particulate matter sensor with a preconditioning subsystem designed to mitigate the impact of humidity. The preconditioning subsystem works by heating the incoming air, effectively reducing the relative humidity and preventing the hygroscopic growth of particles before they reach the sensor. To validate the effectiveness of this approach, parallel measurements were conducted using both preconditioned and non-preconditioned sensors over a period of 19 weeks. The data were analyzed to compare the performance of the sensors in terms of accuracy for PM₁, PM_2.5, and PM₁₀ particles. The results demonstrated a significant improvement in measurement accuracy for the preconditioned sensor, especially in environments with high relative humidity. When the conditions were too severe and both sensors started measuring incorrect values, the preconditioned sensor-measured values were closer to the actual values. Also, the period of measuring incorrect values was shorter with the preconditioned sensor. The results suggest that the implementation of air preconditioning subsystems in LCPMSs deployed in smart cities can provide a cost-effective solution to overcome humidity-related inaccuracies, thereby improving the overall quality of measured air pollution data. Full article

(This article belongs to the Special Issue Sensing Technology for Smart Cities: Data, Analytics, and Visualizations)

21 pages, 9586 KiB

Open AccessArticle

Improved YOLOv5 Network for High-Precision Three-Dimensional Positioning and Attitude Measurement of Container Spreaders in Automated Quayside Cranes

by Yujie Zhang, Yangchen Song, Luocheng Zheng, Octavian Postolache, Chao Mi and Yang Shen

Sensors 2024, 24(17), 5476; https://doi.org/10.3390/s24175476 - 23 Aug 2024

Viewed by 169

Abstract

For automated quayside container cranes, accurate measurement of the three-dimensional positioning and attitude of the container spreader is crucial for the safe and efficient transfer of containers. This paper proposes a high-precision measurement method for the spreader’s three-dimensional position and rotational angles based [...] Read more.

For automated quayside container cranes, accurate measurement of the three-dimensional positioning and attitude of the container spreader is crucial for the safe and efficient transfer of containers. This paper proposes a high-precision measurement method for the spreader’s three-dimensional position and rotational angles based on a single vertically mounted fixed-focus visual camera. Firstly, an image preprocessing method is proposed for complex port environments. The improved YOLOv5 network, enhanced with an attention mechanism, increases the detection accuracy of the spreader’s keypoints and the container lock holes. Combined with image morphological processing methods, the three-dimensional position and rotational angle changes of the spreader are measured. Compared to traditional detection methods, the single-camera-based method for three-dimensional positioning and attitude measurement of the spreader employed in this paper achieves higher detection accuracy for spreader keypoints and lock holes in experiments and improves the operational speed of single operations in actual tests, making it a feasible measurement approach. Full article

(This article belongs to the Special Issue Dynamics and Control System Design for Robot Manipulation)

15 pages, 2599 KiB

Open AccessArticle

A Self-Attention Legendre Graph Convolution Network for Rotating Machinery Fault Diagnosis

by Jiancheng Ma, Jinying Huang, Siyuan Liu, Jia Luo and Licheng Jing

Sensors 2024, 24(17), 5475; https://doi.org/10.3390/s24175475 - 23 Aug 2024

Viewed by 164

Abstract

Rotating machinery is widely used in modern industrial systems, and its health status can directly impact the operation of the entire system. Timely and accurate diagnosis of rotating machinery faults is crucial for ensuring production safety, reducing economic losses, and improving efficiency. Traditional [...] Read more.

Rotating machinery is widely used in modern industrial systems, and its health status can directly impact the operation of the entire system. Timely and accurate diagnosis of rotating machinery faults is crucial for ensuring production safety, reducing economic losses, and improving efficiency. Traditional deep learning methods can only extract features from the vertices of the input data, thereby overlooking the information contained in the relationships between vertices. This paper proposes a Legendre graph convolutional network (LGCN) integrated with a self-attention graph pooling method, which is applied to fault diagnosis of rotating machinery. The SA-LGCN model converts vibration signals from Euclidean space into graph signals in non-Euclidean space, employing a fast local spectral filter based on Legendre polynomials and a self-attention graph pooling method, significantly improving the model’s stability and computational efficiency. By applying the proposed method to 10 different planetary gearbox fault tasks, we verify that it offers significant advantages in fault diagnosis accuracy and load adaptability under various working conditions. Full article

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

25 pages, 19272 KiB

Open AccessArticle

6DoF Object Pose and Focal Length Estimation from Single RGB Images in Uncontrolled Environments

by Mayura Manawadu and Soon-Yong Park

Sensors 2024, 24(17), 5474; https://doi.org/10.3390/s24175474 - 23 Aug 2024

Viewed by 271

Abstract

Accurate 6DoF (degrees of freedom) pose and focal length estimation are important in extended reality (XR) applications, enabling precise object alignment and projection scaling, thereby enhancing user experiences. This study focuses on improving 6DoF pose estimation using single RGB images of unknown camera [...] Read more.

Accurate 6DoF (degrees of freedom) pose and focal length estimation are important in extended reality (XR) applications, enabling precise object alignment and projection scaling, thereby enhancing user experiences. This study focuses on improving 6DoF pose estimation using single RGB images of unknown camera metadata. Estimating the 6DoF pose and focal length from an uncontrolled RGB image, obtained from the internet, is challenging because it often lacks crucial metadata. Existing methods such as FocalPose and Focalpose++ have made progress in this domain but still face challenges due to the projection scale ambiguity between the translation of an object along the z-axis (

t_{z}

) and the camera’s focal length. To overcome this, we propose a two-stage strategy that decouples the projection scaling ambiguity in the estimation of z-axis translation and focal length. In the first stage,

t_{z}

is set arbitrarily, and we predict all the other pose parameters and focal length relative to the fixed

t_{z}

. In the second stage, we predict the true value of

t_{z}

while scaling the focal length based on the

t_{z}

update. The proposed two-stage method reduces projection scale ambiguity in RGB images and improves pose estimation accuracy. The iterative update rules constrained to the first stage and tailored loss functions including Huber loss in the second stage enhance the accuracy in both 6DoF pose and focal length estimation. Experimental results using benchmark datasets show significant improvements in terms of median rotation and translation errors, as well as better projection accuracy compared to the existing state-of-the-art methods. In an evaluation across the Pix3D datasets (chair, sofa, table, and bed), the proposed two-stage method improves projection accuracy by approximately 7.19%. Additionally, the incorporation of Huber loss resulted in a significant reduction in translation and focal length errors by 20.27% and 6.65%, respectively, in comparison to the Focalpose++ method. Full article

(This article belongs to the Special Issue Computer Vision and Virtual Reality: Technologies and Applications)

47 pages, 818 KiB

Open AccessSystematic Review

Workplace Well-Being in Industry 5.0: A Worker-Centered Systematic Review

by Francesca Giada Antonaci, Elena Carlotta Olivetti, Federica Marcolin, Ivonne Angelica Castiblanco Jimenez, Benoît Eynard, Enrico Vezzetti and Sandro Moos

Sensors 2024, 24(17), 5473; https://doi.org/10.3390/s24175473 - 23 Aug 2024

Viewed by 216

Abstract

The paradigm of Industry 5.0 pushes the transition from the traditional to a novel, smart, digital, and connected industry, where well-being is key to enhance productivity, optimize man–machine interaction and guarantee workers’ safety. This work aims to conduct a systematic review of current [...] Read more.

The paradigm of Industry 5.0 pushes the transition from the traditional to a novel, smart, digital, and connected industry, where well-being is key to enhance productivity, optimize man–machine interaction and guarantee workers’ safety. This work aims to conduct a systematic review of current methodologies for monitoring and analyzing physical and cognitive ergonomics. Three research questions are addressed: (1) which technologies are used to assess the physical and cognitive well-being of workers in the workplace, (2) how the acquired data are processed, and (3) what purpose this well-being is evaluated for. This way, individual factors within the holistic assessment of worker well-being are highlighted, and information is provided synthetically. The analysis was conducted following the PRISMA 2020 statement guidelines. From the sixty-five articles collected, the most adopted (1) technological solutions, (2) parameters, and (3) data analysis and processing were identified. Wearable inertial measurement units and RGB-D cameras are the most prevalent devices used for physical monitoring; in the cognitive ergonomics, and cardiac activity is the most adopted physiological parameter. Furthermore, insights on practical issues and future developments are provided. Future research should focus on developing multi-modal systems that combine these aspects with particular emphasis on their practical application in real industrial settings. Full article

(This article belongs to the Section Industrial Sensors)

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