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Wearable Fiber Bragg Grating Sensors for Physiological Signal and Body Motion Monitoring: A Review

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Zhang, Feng Hao, Yupeng Tian, Aofei Yang, Zhengbao Zhang, Bo Fukuda, Toshio Shi, Chaoyang Tianjin University Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education School of Mechanical Engineering Tianjin300072 China The Hong Kong University of Science and Technology Department of Mechanical and Aerospace Engineering Hong Kong Future Robotics Organization Waseda University Tokyo162-0044 Japan Nagoya University Department of Micro-Nano Systems Engineering Aichi Nagoya464-8601 Japan

Wearable sensors play a crucial role in real-time physiological parameters and body motion monitoring, enabling early detection and prevention of potential health issues. However, conventional electrical sensors are susceptible to electromagnetic interference (EMI), limiting their application in magnetic resonance imaging (MRI), electrosurgery, and humid environments. Optical wearable devices based on fiber Bragg gratings (FBGs) offer distinct advantages such as miniaturization, immunity to EMI, high sensitivity, and multiplexing capabilities, making them promising for healthcare applications. Nevertheless, FBG sensors face challenges such as susceptibility to breakage and cross-sensitivity to temperature and multiple strain signals, creating obstacles in expanding strain monitoring range, reducing monitoring errors, and improving wear resistance. This review comprehensively introduces the latest advancements, technical challenges, and potential solutions associated with wearable FBG sensing devices for detecting physiological signals and body motion, particularly emphasizing their applications in monitoring small- and large-strain body movements. This article focuses on the sensing characteristics of FBG units in both soft and rigid substrate materials and tracks the advancement in sensing structure optimization for enhanced measurement capabilities. Future directions and opportunities for technological improvement in this field are also discussed. © 1963-2012 IEEE.

关键词： Fiber Bragg gratings

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Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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Safe and Reliable Distributed Fault-Tolerant Formation Control for Quadrotor Swarms

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Hu, Yang Miao, Zhiqiang Wang, Yaonan Zhang, Hui Wang, Xiangke He, Wei Hunan University College of Electrical and Information Engineering Changsha410082 China Hunan University School of Robotics Changsha410082 China National University of Defense Technology College of Intelligence Science and Technology Changsha410073 China Beijing Information Science and Technology University School of Automation Institute of Artificial Intelligence China University of Science and Technology Beijing Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education Beijing100083 China

This paper addresses formation control of quadrotor swarms subject to external time-varying disturbances and actuator failures, while ensuring collision avoidance. Firstly, a complete dynamics model incorporating translational movement, rotation, motor, and actuator failures was developed for each quadrotor. Then, nominal controllers for the position and attitude subsystems are designed separately to achieve formation control under ideal conditions. A higher-order sliding mode (HOSM) observer is implemented to effectively estimate and compensate for external disturbances and actuator bias faults, thereby improving the stability of the system. Next, a potential energy function-based collision avoidance mechanism is implemented to ensure inter-quadrotor collision avoidance. Furthermore, a fault detection and fault-tolerant control(FTC) method is designed to estimate the lift coefficient of actuator partial failures online in real time by employing Newtons correction method, which ensures the safe and reliable flight of the system. The stability of the closed-loop system is analyzed using Lyapunov stability theory. Finally, numerical simulations and hardware-in-the-loop (HIL) experiment results are performed to validate the approach's effectiveness and performance in quadrotor swarm formation control. © 2025 IEEE. All rights reserved.

关键词： Potential energy functions

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Towards AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年

作者： Ahmed, Imran Ahmad, Misbah Siddiqi, Muftooh Ur Rehman Chehri, Abdellah Jeon, Gwangill Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom University of West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Hartpury University Department of Animal and Agriculture Gloucester United Kingdom Aston University College of Engineering and Physical Sciences Birmingham United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

In the rapidly advancing field of intelligent transportation systems, integrating Artificial Intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model's effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model's capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision-making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step towards recognising the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology. © 2025 IEEE.

关键词： Artificial Intelligence Autonomous Vehicles Deep leaning Edge Computing Internet of Vehicles

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Towards Fast Face Image Quality Assessment via Latent Diffusion Model 18th

Towards Fast Face Image Quality Assessment via Latent Diffu...

引用

18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Yan, Zheyu Zhao, Weisong Zhu, Xiangyu Gao, Li Zhang, Xiao-Yu Lei, Zhen State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China China Mobile Financial Technology Co. Ltd. Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Hong Kong Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819610679

Face recognition technology has gradually come into wide application in real life. However, in real-life unconstrained scenarios, low-quality images could be matched to unpredictable individuals, thereby affecting the stability of recognition and even causing security problems. Conducting quality assessment on face images before facial recognition can help alleviate these problems by rejecting low-quality samples. In this paper, we propose a Fast Diffusion-model-based Face Image Quality Assessment method (FDif-FIQA). It uses a diffusion model to perturb face images and measures image quality based on the similarity of features before and after the perturbation. The denoising process is transferred from the pixel space to the latent space, thereby reducing the computational cost. We evaluated the performance of this method with multiple mainstream face recognition models on various public datasets. Compared to state-of-the-art methods, our approach demonstrates highly competitive performance and significant improvements in computational efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Face recognition

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Toward AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Imran Ahmed Misbah Ahmad Muftooh Ur Rehman Siddiqi Abdellah Chehri Gwangill Jeon School of Computing and Information Science Anglia Ruskin University Cambridge U.K. Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol U.K. Department of Animal and Agriculture Hartpury University Gloucester U.K. College of Engineering and Physical Sciences Aston University Birmingham U.K. Department of Mathematics and Computer Science Royal Military College of Canada Kingston ON Canada Department of Embedded Systems Engineering Incheon National University Incheon South Korea

In the rapidly advancing field of intelligent transportation systems, integrating artificial intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning-based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios-based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model’s effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model’s capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step toward recognizing the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology.

关键词： Computational modeling Object detection Edge computing Image edge detection Artificial intelligence Computer architecture Data models Accuracy Servers Safety

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Embedding Soft Material Channels for Tactile Sensing of Complex Surfaces—Light Propagation Modeling

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IEEE Sensors Journal 2025年第10期25卷 17230-17238页

作者： Hu, Jian Chen, Jian Back, Junghwan Zhao, Tianrui Dasgupta, Prokar Liu, Hongbin Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Haptron Scientific Ltd. Shenzhen China School of Biomedical Engineering and Imaging Sciences King’s College London LondonSE1 7EH United Kingdom King’s College London MRC Centre for Transplantation NIHR Biomedical Research Centre LondonSE1 7EH United Kingdom Guy’s and St. Thomas’ NHS Foundation Trust LondonSE1 7EH United Kingdom

In previous work, "embedding softness into structure enabling distributed tactile sensing of high-order curved surfaces" (ESSENCE) technology, a general principle for accurate and robust tactile sensing on arbitrary surfaces, has been developed. Each tactel consists of a soft material sensing channel, a light source, and an optical receiver. Force applied on the tactel is transformed through the channel to cause an axial protrusion of the soft material. By measuring the change in light intensity caused by the protrusion, the magnitude of the force can be determined. However, multiple receivers share a single light source, resulting in lighting conditions in the region of interest (ROI) to vary from place to place, which in turn affects the consistency of sensing performance. To overcome this challenge, a freeform reflective funnel of the light source that is coated with a layer of high reflective material is designed to reshape the light distribution. Theoretical analysis and experimental results show that the proposed method improves the sensing consistency. For a 3 × 3 array-type sensor, the standard deviations of the force measuring range, linearity, and sensitivity before and after using the freeform surface are 0.29 and 0.08 N, 4.58% and 4.04%, and 0.016 and 0.007 N-1 , respectively. © 2001-2012 IEEE.

关键词： Light sources

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DDCM: Cracking Anonymized Facial Images Using Denoising Diffusion Cryptanalytic Model

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IEEE Transactions on Consumer Electronics 2025年

作者： Jiang, Donghua Ni, Jiangqun Alasbali, Nada Nafea, Ibtehal Liu, Qingliang Ahmad, Jawad Boulila, Wadii Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Sun Yat-sen University School of Cyber Science and Technology Shenzhen518107 China Peng Cheng Laboratory Department of New Networks Shenzhen518052 China King Khalid University College of Computer Science Department of Informatics and Computing Systems Abha61421 Saudi Arabia Taibah University College of Computer Science and Engineering Medina41477 Saudi Arabia Qingdao University of Science and Technology School of Mathematics and Physics Qingdao266061 China Prince Mohammad Bin Fahd University Cybersecurity Center Alkhobar Saudi Arabia Prince Sultan University Robotics and Internet-of-Things Laboratory Riyadh12435 Saudi Arabia

Currently, in consumer electronics industry, quite a number of E-healthcare products highly depend on consumers' facial biometrics for both disease diagnosis and identity authen-tication. In light of this, various facial privacy-preserving approa-ches oriented towards the internet of medical things have been proposed, including visual cryptography and desensitization tech-nique. Nevertheless, few attentions have been paid to investigating their countermeasure, i.e., facial privacy analysis, owning to significant distribution discrepancy between the original and anony-mized facial images. To this end, a general Denoising Diffusion Cryptanalytic Model (DDCM) is proposed in this paper to recon-struct the original facial images in a coarse-to-fine manner from their encrypted ones with diverse anonymous approaches. Spec-ifically, to accurately reconstruct the original facial image without any prior knowledge, a customized initial prediction module with an encoder-decoder structure is introduced by taking advantage of its leaked visual cues to generate the coarse face. Furthermore, the residual map between the original and coarse facial images is explicitly modeled with the output of the customized prediction module as a conditional prior, thereby obtaining a refined facial image. Finally, extensive experiments demonstrate that the propo-sed DDCM could not only effectively crack multiple handcrafted facial anonymization approaches, such as blurring, pixelation and thumbnail-preserving encryption, but also outperform the prev-ious works (e.g., Pix2Pix) in terms of image fidelity and attack success rate. © 1975-2011 IEEE.

关键词： Anonymity

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Exploring Nanoscale Perovskite Materials for Next‑Generation Photodetectors:A Comprehensive Review and Future Directions

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Nano-Micro Letters 2025年第2期17卷 46-108页

作者： Xin Li Sikandar Aftab Maria Mukhtar Fahmid Kabir Muhammad Farooq Khan Hosameldin Helmy Hegazy Erdi Akman State Key Laboratory of Pulsed Power Laser Technology National University of Defense TechnologyHefei 230037AnhuiPeople’s Republic of China Anhui Laboratory of Advanced Laser Technology Hefei 230037AnhuiPeople’s Republic of China Nanhu Laser Laboratory Changsha 410015HunanPeople’s Republic of China Department of Semiconductor Systems Engineering and Clean Energy Sejong UniversitySeoul 05006Republic of Korea Department of Artificial Intelligence and Robotics Sejong UniversitySeoul 05006Republic of Korea School of Engineering Science Simon Fraser UniversityBurnabyBC V5A 1S6Canada Department of Electrical Engineering Sejong UniversitySeoul 05006South Korea Department of Physics Faculty of ScienceKing Khalid UniversityP.O.Box 9004AbhaSaudi Arabia Central Labs King Khalid UniversityAlQura’aP.O.Box 96061413 AbhaSaudi Arabia Scientific and Technological Research and Application Center Karamanoglu Mehmetbey University70100 KaramanTurkey

The rapid advancement of nanotechnology has sparked much interest in applying nanoscale perovskite materials for photodetection *** materials are promising candidates for next-generation photodetectors(PDs)due to their unique optoelectronic properties and flexible synthesis *** review explores the approaches used in the development and use of optoelectronic devices made of different nanoscale perovskite architectures,including quantum dots,nanosheets,nanorods,nanowires,and *** a thorough analysis of recent literature,the review also addresses common issues like the mechanisms underlying the degradation of perovskite PDs and offers perspectives on potential solutions to improve stability and scalability that impede widespread *** addition,it highlights that photodetection encompasses the detection of light fields in dimensions other than light intensity and suggests potential avenues for future research to overcome these obstacles and fully realize the potential of nanoscale perovskite materials in state-of-the-art photodetection *** review provides a comprehensive overview of nanoscale perovskite PDs and guides future research efforts towards improved performance and wider applicability,making it a valuable resource for researchers.

关键词： Nanoscale perovskites Photodetectors Nanosheets Nanorods Nanowires Quantum dots Nanocrystals

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BAN: A Universal Paradigm for Cross-Scene Classification Under Noisy Annotations From RGB and Hyperspectral Remote Sensing Images

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Chen, Wentang Wen, Yibin Zheng, Juepeng Huang, Jianxi Fu, Haohuan Sun Yat-sen University School of Artificial Intelligence Zhuhai519082 China Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province School of Mechanical Engineering Robotics Institute Hangzhou310027 China Southwest Jiaotong University Faculty of Geosciences and Engineering Chengdu610032 China China Agricultural University College of Land Science and Technology Beijing100107 China Ministry of Agriculture and Rural Affairs Key Laboratory of Remote Sensing for Agri-Hazards Beijing100125 China Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen518071 China Tsinghua University Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Beijing100190 China National Supercomputer Center in Shenzhen Shenzhen518071 China

While domain adaptation (DA) methods have made significant strides in remote sensing community, most current works assume that the source domain labels are accurate. However, limited emphasis has been placed on the scenario where source data are mislabeled with noisy annotations, which is more common in real applications and referred to as noisy DA (NDA). This article formulates remote sensing cross-scene classification on NDA scenarios and proposes a novel network called bilateral adaptation network (BAN), which consists of two parts: 1) forward learning (FL), which utilizes a model learning from the noisy source domain and transfers knowledge to target domain;and 2) backward learning (BL), which utilizes a dual model to acquire knowledge from the target domain and transfer it to source domain. We conduct two parts alternately and adopt a symmetrical Kullback-Leibler (KL) loss to align predictions of the model and its dual model in the same domain. This interactive strategy is able to explore bilateral relationships between domains, implicitly reducing label noise in the source domain. In addition, BAN could serve as a universal paradigm to not only improve the existing NDA methods but also enhance recent DA approaches. Comprehensive evaluations on three publicly available RGB-band remote sensing datasets and two hyperspectral datasets validate the superior effectiveness of our proposed BAN. BAN improves the average accuracy by 6.70%-15.70% on RGB datasets and overall accuracy (OA) by 1.36%-3.14% on hyperspectral datasets with flip-20% noise compared to other state-of-the-art DA and NDA approaches. Promising results indicate the potential of our approach in tackling more general and practical problems with noisy source domain. © 2025 IEEE.

关键词： Contrastive Learning

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