Sensors

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 (registering DOI) - 23 Aug 2024

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 (registering DOI) - 23 Aug 2024

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 (registering DOI) - 23 Aug 2024

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 (registering DOI) - 23 Aug 2024

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 (registering DOI) - 23 Aug 2024

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 (registering DOI) - 23 Aug 2024

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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24 pages, 4119 KiB

Open AccessArticle

Exploring Sustainable Approaches for Electronic Textile Products and Prototypes

by Nishadi Perera, Arash M. Shahidi, Kalana Marasinghe, Jake Kaner, Carlos Oliveira, Rachael Wickenden, Tilak Dias and Theo Hughes-Riley

Sensors 2024, 24(17), 5472; https://doi.org/10.3390/s24175472 (registering DOI) - 23 Aug 2024

Abstract

This research investigated the sustainability of textile garments with integrated electronics and their potential impact on the environment. The electronic textiles (E-textiles) sector is booming, with many advancements in E-textile product designs and construction methods having been made in recent years. Although there [...] Read more.

This research investigated the sustainability of textile garments with integrated electronics and their potential impact on the environment. The electronic textiles (E-textiles) sector is booming, with many advancements in E-textile product designs and construction methods having been made in recent years. Although there is a rapidly increasing interest in the reusability and sustainability of textiles, work towards E-textile sustainability requires further attention. Vastly different components are combined when constructing an electronic textile product, which makes it challenging at the end of the life of these products to dispose of them in a responsible way. In this study, a teardown analysis was conducted using a structured method, which first mapped out the interactions between each component of the product with the environment, followed by using Kuusk’s sustainable framework to analyze sustainable strategies. The research provides a unique contribution to transitioning sustainability theories into practical applications in the area of E-textiles, and the method proposed in this work can be employed in modifying electronics-embedded textiles to improve longevity and reduce the negative environmental impact. The work has highlighted key points of improvement that could be applied to a series of commercial E-textile garments, as well as a prototype E-textile device. Beyond this, the work provides a systematic approach for implementing new E-textile product designs that can evaluate overall product sustainability from the design stage to material selection, construction, and the planning of the commercial approaches of a product Full article

(This article belongs to the Special Issue Challenges, Opportunities, Innovations and Applications in Electronic Textiles: E-Textiles 2023)

15 pages, 539 KiB

Open AccessArticle

A Novel Waveform Optimization Method for Orthogonal-Frequency Multiple-Input Multiple-Output Radar Based on Dual-Channel Neural Networks

by Meng Xia, Wenrong Gong and Lichao Yang

Sensors 2024, 24(17), 5471; https://doi.org/10.3390/s24175471 (registering DOI) - 23 Aug 2024

Abstract

The orthogonal frequency-division multiplexing (OFDM) mode with a linear frequency modulation (LFM) signal as the baseband waveform has been widely studied and applied in multiple-input multiple-output (MIMO) radar systems. However, its high sidelobe levels after pulse compression affect the target detection of radar [...] Read more.

The orthogonal frequency-division multiplexing (OFDM) mode with a linear frequency modulation (LFM) signal as the baseband waveform has been widely studied and applied in multiple-input multiple-output (MIMO) radar systems. However, its high sidelobe levels after pulse compression affect the target detection of radar systems. For this paper, theoretical analysis was performed, to investigate the causes of high sidelobe levels in OFDM-LFM waveforms, and a novel waveform optimization design method based on deep neural networks is proposed. This method utilizes the classic ResNeXt network to construct dual-channel neural networks, and a new loss function is employed to design the phase and bandwidth of the OFDM-LFM waveforms. Meanwhile, the optimization factor is exploited, to address the optimization problem of the peak sidelobe levels (PSLs) and integral sidelobe levels (ISLs). Our numerical results verified the correctness of the theoretical analysis and the effectiveness of the proposed method. The designed OFDM-LFM waveforms exhibited outstanding performance in pulse compression and improved the detection performance of the radar. Full article

(This article belongs to the Section Radar Sensors)

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13 pages, 11152 KiB

Open AccessArticle

Direct Multi-Target Teaching Interface for Autonomous Handling of Multi-Stack Logistics in a Warehouse

by Haegyeom Choi, Jaehyun Jeong, Taezoon Park and Donghun Lee

Sensors 2024, 24(17), 5470; https://doi.org/10.3390/s24175470 (registering DOI) - 23 Aug 2024

Abstract

This study presents a framework for enabling autonomous pick–place operations, addressing the need for efficiency in complex logistics environments using a direct multi-target teaching interface. First, tag and segmentation information were combined to recognize products in a complex warehouse, and a camera was [...] Read more.

This study presents a framework for enabling autonomous pick–place operations, addressing the need for efficiency in complex logistics environments using a direct multi-target teaching interface. First, tag and segmentation information were combined to recognize products in a complex warehouse, and a camera was installed on the rack to allow workers to remotely see the work environment, allowing workers to view the work environment in real time through a tablet. Workers can access the camera view showing the rack containing the target product through a swiping action and select the target product through direct teaching action. When the target product is finally selected, an optimal path is created through task planning, and an autonomous pick–place operation is performed based on the generated path. As a result of conducting a usability evaluation using the SUS (System Usability Scale) with six users on the interface that enables these tasks, it was confirmed that high user satisfaction was achieved with an average of 77.5 points. In conclusion, the proposed interface enhances operational efficiency and provides a user-friendly solution for complex warehouse tasks. Full article

(This article belongs to the Section Intelligent Sensors)

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35 pages, 1857 KiB

Open AccessReview

A Review of Gas Sensors for CO₂ Based on Copper Oxides and Their Derivatives

by Christian Maier, Larissa Egger, Anton Köck and Klaus Reichmann

Sensors 2024, 24(17), 5469; https://doi.org/10.3390/s24175469 (registering DOI) - 23 Aug 2024

Abstract

Buildings worldwide are becoming more thermally insulated, and air circulation is being reduced to a minimum. As a result, measuring indoor air quality is important to prevent harmful concentrations of various gases that can lead to safety risks and health problems. To measure [...] Read more.

Buildings worldwide are becoming more thermally insulated, and air circulation is being reduced to a minimum. As a result, measuring indoor air quality is important to prevent harmful concentrations of various gases that can lead to safety risks and health problems. To measure such gases, it is necessary to produce low-cost and low-power-consuming sensors. Researchers have been focusing on semiconducting metal oxide (SMOx) gas sensors that can be combined with intelligent technologies such as smart homes, smart phones or smart watches to enable gas sensing anywhere and at any time. As a type of SMOx, p-type gas sensors are promising candidates and have attracted more interest in recent years due to their excellent electrical properties and stability. This review paper gives a short overview of the main development of sensors based on copper oxides and their composites, highlighting their potential for detecting CO₂ and the factors influencing their performance. Full article

(This article belongs to the Special Issue Gas Sensors: Materials, Mechanism and Applications)

14 pages, 2248 KiB

Open AccessArticle

On the Reliability of Wireless Sensor Networks with Multiple Sinks

by Vladimir Shakhov and Denis Migov

Sensors 2024, 24(17), 5468; https://doi.org/10.3390/s24175468 (registering DOI) - 23 Aug 2024

Abstract

The convergence of heterogeneous wireless sensor networks provides many benefits, including increased coverage, flexible load balancing capabilities, more efficient use of network resources, and the provision of additional data by different types of sensors, thus leading to improved customer service based on more [...] Read more.

The convergence of heterogeneous wireless sensor networks provides many benefits, including increased coverage, flexible load balancing capabilities, more efficient use of network resources, and the provision of additional data by different types of sensors, thus leading to improved customer service based on more complete information. However, despite these advances, the challenge of ensuring reliability and survivability remains due to low-cost sensor requirements and the inherent unreliability of the wireless environment. Integrating different sensor networks and unifying protocols naturally leads to the creation of a network with multiple sinks, necessitating the exploration of new approaches to rational reliability assurance. The failure of some sensors does not necessarily lead to a shutdown of the network, since other sensors can duplicate information and deliver data to sinks via an increased number of alternative routes. In this paper, the reliability indicator is defined as the probability that sinks can collect data from a given number of sensors. In this context, a dedicated reliability metric is introduced and examined for its effectiveness. This metric is computed using an algorithm rooted in the modified factoring method. Furthermore, we introduce a heuristic algorithm designed for optimal sink placement in wireless sensor networks to achieve the highest level of network reliability. Full article

(This article belongs to the Special Issue Convergence of Heterogeneous Wireless Sensor Networks: Opportunities and Solutions)

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16 pages, 3219 KiB

Open AccessArticle

Balance Assessment Using a Handheld Smartphone with Principal Component Analysis for Anatomical Calibration

by Evan C. Anthony, Olivia K. Kam, Stephen M. Klisch, Scott J. Hazelwood and Britta Berg-Johansen

Sensors 2024, 24(17), 5467; https://doi.org/10.3390/s24175467 (registering DOI) - 23 Aug 2024

Abstract

Most balance assessment studies using inertial measurement units (IMUs) in smartphones use a body strap and assume the alignment of the smartphone with the anatomical axes. To replace the need for a body strap, we have used an anatomical alignment method that employs [...] Read more.

Most balance assessment studies using inertial measurement units (IMUs) in smartphones use a body strap and assume the alignment of the smartphone with the anatomical axes. To replace the need for a body strap, we have used an anatomical alignment method that employs a calibration maneuver and Principal Component Analysis (PCA) so that the smartphone can be held by the user in a comfortable position. The objectives of this study were to determine if correlations existed between angular velocity scores derived from a handheld smartphone with PCA functional alignment vs. a smartphone placed in a strap with assumed alignment, and to analyze acceleration score differences across balance poses of increasing difficulty. The handheld and body strap smartphones exhibited moderately to strongly correlated angular velocity scores in the calibration maneuver (r = 0.487–0.983, p < 0.001). Additionally, the handheld smartphone with PCA functional calibration successfully detected significant variance between pose type scores for anteroposterior, mediolateral, and superoinferior acceleration data (p < 0.001). Full article

(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis)

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10 pages, 5546 KiB

Open AccessCommunication

Enhancing the Longevity and Structural Stability of Humidity Sensors: Iron Thin Films with Nitride Bonding Synthesized via Magnetic Field-Assisted Sparking Discharge

by Stefan Ručman, Posak Tippo, Arisara Panthawan, Niwat Jhuntama, Nidchamon Jumrus and Pisith Singjai

Sensors 2024, 24(17), 5466; https://doi.org/10.3390/s24175466 (registering DOI) - 23 Aug 2024

Abstract

Developing long-lasting humidity sensors is essential for sustainable advancements in nanotechnology. Prolonged exposure to high humidity can cause sensors to drift from their calibration points, leading to long-term accuracy issues. Our research aims to develop a fabrication method that produces stable sensors capable [...] Read more.

Developing long-lasting humidity sensors is essential for sustainable advancements in nanotechnology. Prolonged exposure to high humidity can cause sensors to drift from their calibration points, leading to long-term accuracy issues. Our research aims to develop a fabrication method that produces stable sensors capable of withstanding the environmental challenges faced by humidity sensors. Traditional iron-based nanoparticles often require complex treatments, such as chemical modification or thermal annealing, to maintain their properties. This study introduces a novel, one-step synthesis method for iron-based thin films with exceptional stability. The synthesized films were thoroughly characterized using X-ray photoelectron spectroscopy (XPS) to evaluate their phase stability and nitride formation. The method proposed in this study employs an electrical sparking discharge process within a pure nitrogen atmosphere under a 0.2 T magnetic field, producing thin films composed of nanoparticles approximately 20 nm in size. The resulting films demonstrate superior performance in humidity sensing applications compared to conventional methods. This straightforward and efficient approach offers a promising path toward robust and sustainable humidity sensors. Full article

(This article belongs to the Special Issue Thin Film Materials and Nanostructure Devices for Sensing Applications)

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19 pages, 283 KiB

Open AccessArticle

Security Evaluation of Companion Android Applications in IoT: The Case of Smart Security Devices

by Ashley Allen, Alexios Mylonas, Stilianos Vidalis and Dimitris Gritzalis

Sensors 2024, 24(17), 5465; https://doi.org/10.3390/s24175465 (registering DOI) - 23 Aug 2024

Abstract

Smart security devices, such as smart locks, smart cameras, and smart intruder alarms are increasingly popular with users due to the enhanced convenience and new features that they offer. A significant part of this convenience is provided by the device’s companion smartphone app. [...] Read more.

Smart security devices, such as smart locks, smart cameras, and smart intruder alarms are increasingly popular with users due to the enhanced convenience and new features that they offer. A significant part of this convenience is provided by the device’s companion smartphone app. Information on whether secure and ethical development practices have been used in the creation of these applications is unavailable to the end user. As this work shows, this means that users are impacted both by potential third-party attackers that aim to compromise their device, and more subtle threats introduced by developers, who may track their use of their devices and illegally collect data that violate users’ privacy. Our results suggest that users of every application tested are susceptible to at least one potential commonly found vulnerability regardless of whether their device is offered by a known brand name or a lesser-known manufacturer. We present an overview of the most common vulnerabilities found in the scanned code and discuss the shortcomings of state-of-the-art automated scanners when looking at less structured programming languages such as C and C++. Finally, we also discuss potential methods for mitigation, and provide recommendations for developers to follow with respect to secure coding practices. Full article

(This article belongs to the Section Internet of Things)

21 pages, 4782 KiB

Open AccessArticle

A New Method Based on Locally Optimal Step Length in Accelerated Gradient Descent for Quantum State Tomography

by Mohammad Dolatabadi, Vincenzo Loia and Pierluigi Siano

Sensors 2024, 24(17), 5464; https://doi.org/10.3390/s24175464 (registering DOI) - 23 Aug 2024

Abstract

Quantum state tomography (QST) is one of the key steps in determining the state of the quantum system, which is essential for understanding and controlling it. With statistical data from measurements and Positive Operator-Valued Measures (POVMs), the goal of QST is to find [...] Read more.

Quantum state tomography (QST) is one of the key steps in determining the state of the quantum system, which is essential for understanding and controlling it. With statistical data from measurements and Positive Operator-Valued Measures (POVMs), the goal of QST is to find a density operator that best fits the measurement data. Several optimization-based methods have been proposed for QST, and one of the most successful approaches is based on Accelerated Gradient Descent (AGD) with fixed step length. While AGD with fixed step size is easy to implement, it is computationally inefficient when the computational time required to calculate the gradient is high. In this paper, we propose a new optimal method for step-length adaptation, which results in a much faster version of AGD for QST. Numerical results confirm that the proposed method is much more time-efficient than other similar methods due to the optimized step size. Full article

(This article belongs to the Special Issue Quantum IoT (QIoT): Revolutionizing Connectivity, Innovation, Challenges, and Applications)

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19 pages, 4618 KiB

Open AccessReview

A Survey on Multi-Sensor Fusion Perimeter Intrusion Detection in High-Speed Railways

by Tianyun Shi, Pengyue Guo, Rui Wang, Zhen Ma, Wanpeng Zhang, Wentao Li, Huijin Fu and Hao Hu

Sensors 2024, 24(17), 5463; https://doi.org/10.3390/s24175463 (registering DOI) - 23 Aug 2024

Abstract

In recent years, the safety issues of high-speed railways have remained severe. The intrusion of personnel or obstacles into the perimeter has often occurred in the past, causing derailment or parking, especially in the case of bad weather such as fog, haze, rain, [...] Read more.

In recent years, the safety issues of high-speed railways have remained severe. The intrusion of personnel or obstacles into the perimeter has often occurred in the past, causing derailment or parking, especially in the case of bad weather such as fog, haze, rain, etc. According to previous research, it is difficult for a single sensor to meet the application needs of all scenario, all weather, and all time domains. Due to the complementary advantages of multi-sensor data such as images and point clouds, multi-sensor fusion detection technology for high-speed railway perimeter intrusion is becoming a research hotspot. To the best of our knowledge, there has been no review of research on multi-sensor fusion detection technology for high-speed railway perimeter intrusion. To make up for this deficiency and stimulate future research, this article first analyzes the situation of high-speed railway technical defense measures and summarizes the research status of single sensor detection. Secondly, based on the analysis of typical intrusion scenarios in high-speed railways, we introduce the research status of multi-sensor data fusion detection algorithms and data. Then, we discuss risk assessment of railway safety. Finally, the trends and challenges of multi-sensor fusion detection algorithms in the railway field are discussed. This provides effective theoretical support and technical guidance for high-speed rail perimeter intrusion monitoring. Full article

(This article belongs to the Section Intelligent Sensors)

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20 pages, 1623 KiB

Open AccessArticle

Adaptive Finite-Time-Based Neural Optimal Control of Time-Delayed Wheeled Mobile Robotics Systems

by Shu Li, Tao Ren, Liang Ding and Lei Liu

Sensors 2024, 24(17), 5462; https://doi.org/10.3390/s24175462 (registering DOI) - 23 Aug 2024

Abstract

For nonlinear systems with uncertain state time delays, an adaptive neural optimal tracking control method based on finite time is designed. With the help of the appropriate LKFs, the time-delay problem is handled. A novel nonquadratic Hamilton–Jacobi–Bellman (HJB) function is defined, where finite [...] Read more.

For nonlinear systems with uncertain state time delays, an adaptive neural optimal tracking control method based on finite time is designed. With the help of the appropriate LKFs, the time-delay problem is handled. A novel nonquadratic Hamilton–Jacobi–Bellman (HJB) function is defined, where finite time is selected as the upper limit of integration. This function contains information on the state time delay, while also maintaining the basic information. To meet specific requirements, the integral reinforcement learning method is employed to solve the ideal HJB function. Then, a tracking controller is designed to ensure finite-time convergence and optimization of the controlled system. This involves the evaluation and execution of gradient descent updates of neural network weights based on a reinforcement learning architecture. The semi-global practical finite-time stability of the controlled system and the finite-time convergence of the tracking error are guaranteed. Full article

(This article belongs to the Special Issue Advances in Mobile Robot Perceptions, Planning, Control and Learning: 2nd Edition)

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14 pages, 2258 KiB

Open AccessArticle

Study on Data Preprocessing for Machine Learning Based on Semiconductor Manufacturing Processes

by Ha-Je Park, Yun-Su Koo, Hee-Yeong Yang, Young-Shin Han and Choon-Sung Nam

Sensors 2024, 24(17), 5461; https://doi.org/10.3390/s24175461 (registering DOI) - 23 Aug 2024

Abstract

Various data types generated in the semiconductor manufacturing process can be used to increase product yield and reduce manufacturing costs. On the other hand, the data generated during the process are collected from various sensors, resulting in diverse units and an imbalanced dataset [...] Read more.

Various data types generated in the semiconductor manufacturing process can be used to increase product yield and reduce manufacturing costs. On the other hand, the data generated during the process are collected from various sensors, resulting in diverse units and an imbalanced dataset with a bias towards the majority class. This study evaluated analysis and preprocessing methods for predicting good and defective products using machine learning to increase yield and reduce costs in semiconductor manufacturing processes. The SECOM dataset is used to achieve this, and preprocessing steps are performed, such as missing value handling, dimensionality reduction, resampling to address class imbalances, and scaling. Finally, six machine learning models were evaluated and compared using the geometric mean (GM) and other metrics to assess the combinations of preprocessing methods on imbalanced data. Unlike previous studies, this research proposes methods to reduce the number of features used in machine learning to shorten the training and prediction times. Furthermore, this study prevents data leakage during preprocessing by separating the training and test datasets before analysis and preprocessing. The results showed that applying oversampling methods, excluding KM SMOTE, achieves a more balanced class classification. The combination of SVM, ADASYN, and MaxAbs scaling showed the best performance with an accuracy and GM of 85.14% and 72.95%, respectively, outperforming all other combinations. Full article

(This article belongs to the Section Industrial Sensors)

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18 pages, 3626 KiB

Open AccessArticle

Detection of Oil Spill in SAR Image Using an Improved DeepLabV3+

by Jiahao Zhang, Pengju Yang and Xincheng Ren

Sensors 2024, 24(17), 5460; https://doi.org/10.3390/s24175460 (registering DOI) - 23 Aug 2024

Abstract

Oil spill SAR images are characterized by high noise, low contrast, and irregular boundaries, which lead to the problems of overfitting and insufficient capturing of detailed features of the oil spill region in the current method when processing oil spill SAR images. An [...] Read more.

Oil spill SAR images are characterized by high noise, low contrast, and irregular boundaries, which lead to the problems of overfitting and insufficient capturing of detailed features of the oil spill region in the current method when processing oil spill SAR images. An improved DeepLabV3+ model is proposed to address the above problems. First, the original backbone network Xception is replaced by the lightweight MobileNetV2, which significantly improves the generalization ability of the model while drastically reducing the number of model parameters and effectively addresses the overfitting problem. Further, the spatial and channel Squeeze and Excitation module (scSE) is introduced and the joint loss function of Bce + Dice is adopted to enhance the sensitivity of the model to the detailed parts of the oil spill area, which effectively solves the problem of insufficient capture of the detailed features of the oil spill area. The experimental results show that the mIOU and F1-score of the improved model in an oil spill region in the Gulf of Mexico reach 80.26% and 88.66%, respectively. In an oil spill region in the Persian Gulf, the mIOU and F1-score reach 81.34% and 89.62%, respectively, which are better than the metrics of the control model. Full article

(This article belongs to the Special Issue Applications of Synthetic-Aperture Radar (SAR) Imaging and Sensing)

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27 pages, 11381 KiB

Open AccessArticle

Green Wearable Sensors and Antennas for Bio-Medicine, Green Internet of Things, Energy Harvesting, and Communication Systems

by Albert Sabban

Sensors 2024, 24(17), 5459; https://doi.org/10.3390/s24175459 (registering DOI) - 23 Aug 2024

Abstract

This paper presents innovations in green electronic and computing technologies. The importance and the status of the main subjects in green electronic and computing technologies are presented in this paper. In the last semicentennial, the planet suffered from rapid changes in climate. The [...] Read more.

This paper presents innovations in green electronic and computing technologies. The importance and the status of the main subjects in green electronic and computing technologies are presented in this paper. In the last semicentennial, the planet suffered from rapid changes in climate. The planet is suffering from increasingly wild storms, hurricanes, typhoons, hard droughts, increases in seawater height, floods, seawater acidification, decreases in groundwater reserves, and increases in global temperatures. These climate changes may be irreversible if companies, organizations, governments, and individuals do not act daily and rapidly to save the planet. Unfortunately, the continuous growth in the number of computing devices, cellular devices, smartphones, and other smart devices over the last fifty years has resulted in a rapid increase in climate change. It is severely crucial to design energy-efficient “green” technologies and devices. Toxic waste from computing and cellular devices is rapidly filling up landfills and increasing air and water pollution. This electronic waste contains hazardous and toxic materials that pollute the environment and affect our health. Green computing and electronic engineering are employed to address this climate disaster. The development of green materials, green energy, waste, and recycling are the major objectives in innovation and research in green computing and electronics technologies. Energy-harvesting technologies can be used to produce and store green energy. Wearable active sensors and metamaterial antennas with circular split ring resonators (CSSRs) containing energy-harvesting units are presented in this paper. The measured bandwidth of the matched sensor is around 65% for VSWR, which is better than 3:1. The sensor gain is 14.1 dB at 2.62 GHz. A wideband 0.4 GHz to 6.4 GHz slot antenna with an RF energy-harvesting unit is presented in this paper. The Skyworks Schottky diode, SMS-7630, was used as the rectifier diode in the harvesting unit. If we transmit 20 dBm of RF power from a transmitting antenna that is located 0.2 m from the harvesting slot antenna at 2.4 GHz, the output voltage at the output port of the harvesting unit will be around 1 V. The power conversion efficiency of the metamaterial antenna dipole with metallic strips is around 75%. Wearable sensors with energy-harvesting units provide efficient, low-cost healthcare services that contribute to a green environment and minimize energy consumption. The measurement process and setups of wearable sensors are presented in this paper. Full article

(This article belongs to the Section Biomedical Sensors)

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14 pages, 598 KiB

Open AccessArticle

A Gradient Dynamics-Based Singularity Avoidance Method for Backstepping Control of Underactuated TORA Systems

by Changzhong Pan, Hongsen Pu, Zhijing Li and Jinsen Xiao

Sensors 2024, 24(17), 5458; https://doi.org/10.3390/s24175458 (registering DOI) - 23 Aug 2024

Abstract

In this paper, a gradient dynamics-based control method is proposed to directly tackle the singularity problem in the backstepping control design of the TORA system. This method is founded upon the construction of an energy-like positive function, which includes an auxiliary variable in [...] Read more.

In this paper, a gradient dynamics-based control method is proposed to directly tackle the singularity problem in the backstepping control design of the TORA system. This method is founded upon the construction of an energy-like positive function, which includes an auxiliary variable in terms of the intermediate virtual control law. On this basis, a gradient dynamics is created to obtain a new virtual control command, which is capable of making the auxiliary variable gradually approach zero, thereby mitigating the issue of division by zero. The core innovation is the integration of the gradient dynamics into the recursive backstepping design to overcome the singularity problem and stabilize the system at the equilibrium quickly. In addition, it rigorously proves that all the signals in the closed-loop control system are uniformly ultimately bounded, and the tracking errors converge to a small neighborhood around zero through a Lyapunov-based stability analysis. Comparative simulations demonstrate that the proposed approach not only avoids the singularity issue, but also achieves a better transient performance over other methods. Full article

(This article belongs to the Special Issue Advanced Precision Motion Control for Actuator Systems)

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21 pages, 2501 KiB

Open AccessArticle

RetinaViT: Efficient Visual Backbone for Online Video Streams

by Tomoyuki Suzuki and Yoshimitsu Aoki

Sensors 2024, 24(17), 5457; https://doi.org/10.3390/s24175457 (registering DOI) - 23 Aug 2024

Abstract

In online video understanding, which has a wide range of real-world applications, inference speed is crucial. Many approaches involve frame-level visual feature extraction, which often represents the biggest bottleneck. We propose RetinaViT, an efficient method for extracting frame-level visual features in an online [...] Read more.

In online video understanding, which has a wide range of real-world applications, inference speed is crucial. Many approaches involve frame-level visual feature extraction, which often represents the biggest bottleneck. We propose RetinaViT, an efficient method for extracting frame-level visual features in an online video stream, aiming to fundamentally enhance the efficiency of online video understanding tasks. RetinaViT is composed of efficiently approximated Transformer blocks that only take changed tokens (event tokens) as queries and reuse the already processed tokens from the previous timestep for the others. Furthermore, we restrict keys and values to the spatial neighborhoods of event tokens to further improve efficiency. RetinaViT involves tuning multiple parameters, which we determine through a multi-step process. During model training, we randomly vary these parameters and then perform black-box optimization to maximize accuracy and efficiency on the pre-trained model. We conducted extensive experiments on various online video recognition tasks, including action recognition, pose estimation, and object segmentation, validating the effectiveness of each component in RetinaViT and demonstrating improvements in the speed/accuracy trade-off compared to baselines. In particular, for action recognition, RetinaViT built on ViT-B16 reduces inference time by approximately 61.9% on the CPU and 50.8% on the GPU, while achieving slight accuracy improvements rather than degradation. Full article

(This article belongs to the Special Issue Image and Video Processing and Recognition Based on Artificial Intelligence: 3rd Edition)

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10 pages, 678 KiB

Open AccessArticle

PolyMeme: Fine-Grained Internet Meme Sensing

by Vasileios Arailopoulos, Christos Koutlis, Symeon Papadopoulos and Panagiotis C. Petrantonakis

Sensors 2024, 24(17), 5456; https://doi.org/10.3390/s24175456 (registering DOI) - 23 Aug 2024

Abstract

Internet memes are a special type of digital content that is shared through social media. They have recently emerged as a popular new format of media communication. They are often multimodal, combining text with images and aim to express humor, irony, sarcasm, or [...] Read more.

Internet memes are a special type of digital content that is shared through social media. They have recently emerged as a popular new format of media communication. They are often multimodal, combining text with images and aim to express humor, irony, sarcasm, or sometimes convey hatred and misinformation. Automatically detecting memes is important since it enables tracking of social and cultural trends and issues related to the spread of harmful content. While memes can take various forms and belong to different categories, such as image macros, memes with labeled objects, screenshots, memes with text out of the image, and funny images, existing datasets do not account for the diversity of meme formats, styles and content. To bridge this gap, we present the PolyMeme dataset, which comprises approximately 27 K memes from four categories. This was collected from Reddit and a part of it was manually labelled into these categories. Using the manual labels, deep learning networks were trained to classify the unlabelled images with an estimated error rate of 7.35%. The introduced meme dataset in combination with existing datasets of regular images were used to train deep learning networks (ResNet, ViT) on meme detection, exhibiting very high accuracy levels (98% on the test set). In addition, no significant gains were identified from the use of regular images containing text. Full article

(This article belongs to the Special Issue Multimodal Sensing Technologies for IoT and AI-Enabled Systems)

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17 pages, 3025 KiB

Open AccessArticle

A Deep Learning Framework for Real-Time Bird Detection and Its Implications for Reducing Bird Strike Incidents

by Najiba Said Hamed Alzadjail, Sundaravadivazhagan Balasubaramainan, Charles Savarimuthu and Emanuel O. Rances

Sensors 2024, 24(17), 5455; https://doi.org/10.3390/s24175455 (registering DOI) - 23 Aug 2024

Viewed by 89

Abstract

Bird strikes are a substantial aviation safety issue that can result in serious harm to aircraft components and even passenger deaths. In response to this increased tendency, the implementation of new and more efficient detection and prevention technologies becomes urgent. The paper presents [...] Read more.

Bird strikes are a substantial aviation safety issue that can result in serious harm to aircraft components and even passenger deaths. In response to this increased tendency, the implementation of new and more efficient detection and prevention technologies becomes urgent. The paper presents a novel deep learning model which is developed to detect and alleviate bird strike issues in airport conditions boosting aircraft safety. Based on an extensive database of bird images having different species and flight patterns, the research adopts sophisticated image augmentation techniques which generate multiple scenarios of aircraft operation ensuring that the model is robust under different conditions. The methodology evolved around the building of a spatiotemporal convolutional neural network which employs spatial attention structures together with dynamic temporal processing to precisely recognize flying birds. One of the most important features of this research is the architecture of its dual-focus model which consists of two components, the attention-based temporal analysis network and the convolutional neural network with spatial awareness. The model’s architecture can identify specific features nested in a crowded and shifting backdrop, thereby lowering false positives and improving detection accuracy. The mechanisms of attention of this model itself enhance the model’s focus by identifying vital features of bird flight patterns that are crucial. The results are that the proposed model achieves better performance in terms of accuracy and real time responses than the existing bird detection systems. The ablation study demonstrates the indispensable roles of each component, confirming their synergistic effect on improving detection performance. The research substantiates the model’s applicability as a part of airport bird strike surveillance system, providing an alternative to the prevention strategy. This work benefits from the unique deep learning feature application, which leads to a large-scale and reliable tool for dealing with the bird strike problem. Full article

(This article belongs to the Topic Applications in Image Analysis and Pattern Recognition)

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14 pages, 527 KiB

Open AccessArticle

CAWE-ACNN Algorithm for Coprime Sensor Array Adaptive Beamforming

by Fulai Liu, Wu Zhou, Dongbao Qin, Zhixin Liu, Huifang Wang and Ruiyan Du

Sensors 2024, 24(17), 5454; https://doi.org/10.3390/s24175454 (registering DOI) - 23 Aug 2024

Viewed by 131

Abstract

This paper presents a robust adaptive beamforming algorithm based on an attention convolutional neural network (ACNN) for coprime sensor arrays, named the CAWE-ACNN algorithm. In the proposed algorithm, via a spatial and channel attention unit, an ACNN model is constructed to enhance the [...] Read more.

This paper presents a robust adaptive beamforming algorithm based on an attention convolutional neural network (ACNN) for coprime sensor arrays, named the CAWE-ACNN algorithm. In the proposed algorithm, via a spatial and channel attention unit, an ACNN model is constructed to enhance the features contributing to beamforming weight vector estimation and to improve the signal-to-interference-plus-noise ratio (SINR) performance, respectively. Then, an interference-plus-noise covariance matrix reconstruction algorithm is used to obtain an appropriate label for the proposed ACNN model. By the calculated label and the sample signals received from the coprime sensor arrays, the ACNN is well-trained and capable of accurately and efficiently outputting the beamforming weight vector. The simulation results verify that the proposed algorithm achieves excellent SINR performance and high computation efficiency. Full article

(This article belongs to the Special Issue Signal Detection and Processing of Sensor Arrays)

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20 pages, 10856 KiB

Open AccessArticle

ASCEND-UNet: An Improved UNet Configuration Optimized for Rural Settlements Mapping

by Xinyu Zheng, Shengwei Pu and Xingyu Xue

Sensors 2024, 24(17), 5453; https://doi.org/10.3390/s24175453 (registering DOI) - 23 Aug 2024

Viewed by 155

Abstract

Different types of rural settlement agglomerations have been formed and mixed in space during the rural revitalization strategy implementation in China. Discriminating them from remote sensing images is of great significance for rural land planning and living environment improvement. Currently, there is a [...] Read more.

Different types of rural settlement agglomerations have been formed and mixed in space during the rural revitalization strategy implementation in China. Discriminating them from remote sensing images is of great significance for rural land planning and living environment improvement. Currently, there is a lack of automatic methods for obtaining information on rural settlement differentiation. In this paper, an improved encoder–decoder network structure, ASCEND-UNet, was designed based on the original UNet. It was implemented to segment and classify dispersed and clustered rural settlement buildings from high-resolution satellite images. The ASCEND-UNet model incorporated three components: firstly, the atrous spatial pyramid pooling (ASPP) multi-scale feature fusion module was added into the encoder, then the spatial and channel squeeze and excitation (scSE) block was embedded at the skip connection; thirdly, the hybrid dilated convolution (HDC) block was utilized in the decoder. In our proposed framework, the ASPP and HDC were used as multiple dilated convolution blocks to expand the receptive field by introducing a series of dilated rate convolutions. The scSE is an attention mechanism block focusing on features both in the spatial and channel dimension. A series of model comparisons and accuracy assessments with the original UNet, PSPNet, DeepLabV3+, and SegNet verified the effectiveness of our proposed model. Compared with the original UNet model, ASCEND-UNet achieved improvements of 4.67%, 2.80%, 3.73%, and 6.28% in precision, recall, F1-score and MIoU, respectively. The contributions of HDC, ASPP, and scSE modules were discussed in ablation experiments. Our proposed model obtained more accurate and stable results by integrating multiple dilated convolution blocks with an attention mechanism. This novel model enriches the automatic methods for semantic segmentation of different rural settlements from remote sensing images. Full article

(This article belongs to the Special Issue Application of Satellite Remote Sensing in Geospatial Monitoring)

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19 pages, 5154 KiB

Open AccessArticle

DAEiS-Net: Deep Aggregation Network with Edge Information Supplement for Tunnel Water Stain Segmentation

by Yuliang Wang, Kai Huang, Kai Zheng and Shuliang Liu

Sensors 2024, 24(17), 5452; https://doi.org/10.3390/s24175452 (registering DOI) - 23 Aug 2024

Viewed by 180

Abstract

Tunnel disease detection and maintenance are critical tasks in urban engineering, and are essential for the safety and stability of urban transportation systems. Water stain detection presents unique challenges due to its variable morphology and scale, which leads to insufficient multiscale contextual information [...] Read more.

Tunnel disease detection and maintenance are critical tasks in urban engineering, and are essential for the safety and stability of urban transportation systems. Water stain detection presents unique challenges due to its variable morphology and scale, which leads to insufficient multiscale contextual information extraction and boundary information loss in complex environments. To address these challenges, this paper proposes a method called Deep Aggregation Network with Edge Information Supplement (DAEiS-Net) for detecting tunnel water stains. The proposed method employs a classic encoder–decoder architecture. Specifically, in the encoder part, a Deep Aggregation Module (DAM) is introduced to enhance feature representation capabilities. Additionally, a Multiscale Cross-Attention Module (MCAM) is proposed to suppress noise in the shallow features and enhance the texture information of the high-level features. Moreover, an Edge Information Supplement Module (EISM) is designed to mitigate semantic gaps across different stages of feature extraction, improving the extraction of water stain edge information. Furthermore, a Sub-Pixel Module (SPM) is proposed to fuse features at various scales, enhancing edge feature representation. Finally, we introduce the Tunnel Water Stain Dataset (TWS), specifically designed for tunnel water stain segmentation. Experimental results on the TWS dataset demonstrate that DAEiS-Net achieves state-of-the-art performance in tunnel water stain segmentation. Full article

(This article belongs to the Special Issue AI-Assisted Condition Monitoring and Fault Diagnosis)

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Sensors, Volume 24, Issue 17 (September-1 2024) – 27 articles

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