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41st computer Graphics International Conference, CGI 2024

作者： Yu, Hanchen He, Mingwei Yu, Feng Liu, Li Jiang, Minghua School of Electronic and Electrical Engineering Wuhan Textile University Wuhan China School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Engineering Research Center of Hubei Province for Clothing Information Wuhan China

ISBN: (纸本)9783031820236

With the ongoing evolution of intelligent sensors and artificial intelligence, wearable devices have achieved remarkable breakthroughs. Smart clothing, encompassing a diverse array of types, has found widespread applications across various life domains, particularly in the healthcare sector. However, despite these advancements, current smart clothing still faces limitations such as limited interaction capabilities, significant application constraints, and inadequate monitoring functionalities. To overcome these challenges and further enhance the user experience, this paper proposes a new smart clothing system. The system has the capability to monitor multiple physiological parameters of the human body. Furthermore, it incorporates a new algorithm called ARDN specifically designed for initial arrhythmia detection. To validate the effectiveness of the arrhythmia detection algorithm, an experimental analysis was conducted using the MIT-BIH arrhythmia database. Furthermore, to facilitate real-time monitoring and effective human-computer interaction, the system incorporates digital twin (DT) technology. This integration of intelligent sensors and terminal equipment through DT technology enables the system to conduct a comprehensive analysis of the users’ physical condition, thereby providing an initial health diagnosis. The system presents novel opportunities for advancing the development of personalized healthcare, facilitating seamless integration into everyday life, and enhancing users’ well-being. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Diseases

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A Real-Time Semantic Segmentation Network for Robotic Arm Grasp 41st

A Real-Time Semantic Segmentation Network for Robotic Arm G...

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41st computer Graphics International Conference, CGI 2024

作者： Liu, Li Zhou, Xinlei He, Mingwei Yu, Feng Peng, Tao Hu, Xinrong Jiang, Minghua School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Engineering Research Center of Hubei Province for Clothing Information Wuhan China

ISBN: (纸本)9783031820236

A robotic arm equipped with a vision system can significantly improve production efficiency. However, due to the complexity of the robotic arm’s working environment, the useful information about the objects recognized by the visual system is significantly reduced. To solve this problem, we propose a real-time semantic segmentation network to achieve high-precision and efficient robotic arm grasp. First, we propose an enhanced feature extraction module for the encoding stage, which can improve feature extraction capabilities without increasing or even reducing the amount of model calculations and parameters. Then, we propose an enhanced feature reconstruction module for the decoding stage, which can better preserve essential features in the feature map restoration stage. To validate the efficacy of our proposed approach, experiments are conducted on the rigid dataset Jacquard and the flexible dataset Electric Wires. Experimental results show that our method can quickly and accurately identify target objects, achieving the best trade-off between accuracy and inference speed. Our method achieves an mIOU of 94.66% and an inference speed of 99.39 FPS on the Jacquard dataset. On the Electric Wires dataset, our method achieves an mIOU of 96.70% with an inference speed of 98.44 FPS. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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SCB-LEDN: Lightweight and Efficient Object Detection Network for Student Classroom Behavior 41st

SCB-LEDN: Lightweight and Efficient Object Detection Networ...

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41st computer Graphics International Conference, CGI 2024

作者： Jiang, Minghua Zheng, Xingwei Wang, Cheng He, Mingwei Liu, Li Yu, Feng School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Engineering Research Center of Hubei Province for Clothing Information Wuhan China

ISBN: (纸本)9783031818059

Object detection techniques in computer vision are increasingly employed in education to analyze student performance and evaluate teaching effectiveness. However, current classroom behavior recognition methods have problems such as decreased accuracy, high memory usage, and slow detection speed. In order to address these challenges, we design a lightweight object detection network (SCB-LEDN) based on the classroom monitoring perspective to enhance the detection accuracy of objects while reducing the model’s parameters and accelerating real-time inference. First, we create the student classroom behavior dataset (SCB-E), which includes five behaviors: raising hands, reading, sleeping, writing, and using a mobile phone. Secondly, We propose the partial detection head (PHead), effectively reducing parameter count and improving detection speed. We introduce the diverse branch block (DBB) in the feature extraction layer and create the C2DB module, enhancing student classroom behavior recognition accuracy. We introduce the EfficientRepBiPAN structure in the neck layer, enabling practical preservation and fusion of spatial information. The experimental results show that our method achieves an average precision (mAP) of 91.5% on the SCB-E dataset and reduces the parameters by 20% compared to the baseline method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

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MFENet: Multi-scale and Local Frequency Enhancement Network for Skin Lesion Classification 41st

MFENet: Multi-scale and Local Frequency Enhancement Network...

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41st computer Graphics International Conference, CGI 2024

作者： Jin, Yuyu Xiao, Zhiyong Yuan, Jiaqi He, Mingwei Liu, Li Yu, Feng Jiang, Minghua School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Engineering Research Center of Hubei Province for Clothing Information Wuhan China

ISBN: (纸本)9783031820236

Dermatoscopy is a common diagnostic tool used by dermatologists to examine skin lesions. However, existing classification methods often rely on spatial domain feature extraction. Dermatoscopic images are frequently affected by various sources of noise, such as hair and pigmented areas, resulting in non-robust feature extraction. Therefore, relying solely on spatial domain feature extraction is limited and cannot fully differentiate the subtle features of skin lesions. This paper proposes a multi-scale and local frequency enhancement network (MFENet) for skin lesion, which significantly improves the diagnostic accuracy and interpretability of dermatoscopic images. Specifically, we introduce the multi-scale and local of frequency (MLF) module to reduce noise interference and enhance edge, shape, and texture features. The module selects low-frequency information and suppresses high-frequency noise at different scales. It further enhances details and texture information in the image by processing local frequency blocks. This makes important morphological features, such as the shape and edge, more prominent in diagnosis. Additionally, a lesion channel attention (LCA) module is proposed to further optimize the extraction and amplification of key features by learning dependencies between channels. The results show that the algorithm achieves optimal performance on the ISIC 2017 and HAM10000 datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Conditional Disturbance Compensation-Based Fault-Tolerant Group Consensus control for MASs: An Event-Based Switching Method

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IEEE Transactions on Automation science and engineering 2025年 22卷 13828-13841页

作者： Liu, Pei-Ming Guo, Xiang-Gui Wang, Jian-Liang Che, Wei-Wei Wu, Zheng-Guang University of Science and Technology Beijing Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering Beijing100083 China University of Science and Technology Beijing Shunde Innovation School Foshan528000 China Beihang University Hangzhou Innovation Institute Autonomous Intelligent Systems Department Hangzhou310051 China Northeastern University College of Information Science and Engineering Shenyang266071 China Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou310027 China

With respect to the group consensus control for multi-agent systems (MASs) with disturbances and actuator nonidentical and unknown direction faults (NUDFs), this paper proposes a novel fault-tolerant control scheme by incorporating an event-based switching function and a saturation function. The event-based switching function is implemented to directly switch to the desired working mode, thereby reducing invalid switching after a reverse fault. Concurrently, the saturation function, acting at the software level, can not only prevent the instantaneous impact of excessive reverse input caused by a reverse fault, but also eliminate input peaking and state chattering after switching the working mode. It is worth mentioning that the proposed switching-based fault-tolerant control method can avoid the control chocks and excessive control gains as in the existing methods by using Nussbaum functions and thus improve the transient performance. Additionally, for disturbances, the designed conditional disturbance compensation mechanism ensures a smaller steady-state error, and less energy consumption by utilizing beneficial disturbances and compensating for harmful ones. Besides, the conditional compensation mechanism also reduces the saturation error. Finally, the effectiveness of the proposed control scheme are verified through a simulation example of uncrewed aerial vehicles (UAVs). © 2004-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Elastic Spectrum Sensing: An Adaptive Sensing Method for Non-Terrestrial Communication Under Highly Dynamic Channels

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IEEE Transactions on Wireless Communications 2025年第5期24卷 3868-3881页

作者： Xu, Tianheng Xu, Yinjun Zhou, Ting Zhang, Haijun Hu, Honglin Leung, Victor C. M. Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201210 China Shanghai Frontier Innovation Research Institute Shanghai201100 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China Shanghai University School of Microelectronics Shanghai200444 China Institute of Artificial Intelligence Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T1Z4 Canada

With the rapid development of space technology, the role of satellite communications has become progressively significant. Non-terrestrial communication is deemed a critical scenario in the sixth generation (6G) communication systems, which showcases seamless connectivity, minimal geographic constraints and substantial communication capacity. Simultaneously, satellites and terminals spring up, spatial density increases, which further emphasizes the scarcity of spectrum resources. Consequently, to improve spectrum utilization for non-terrestrial communication is a significant concern. Spectrum sensing, which allows dynamic resource reuse, plays an important role in 6G. However, high mobility in non-terrestrial scenarios poses great challenges, such as fast time-varying channels, Doppler effect, etc., which seriously affect sensing accuracy and cannot well support optimal spectrum utilization. Motivated by such circumstances, this paper proposes an elastic sensing method for the downlink non-terrestrial communication scenario. Firstly, we design the system architecture and sensing workflow. To overcome the negative effects raised by high mobility, we propose the elastic sensing criterion and multi-area dividing scheme for the sensing zone. Thresholds affected by the elastic sensing are derived for different areas. Finally, the numerical results show that from -10 dB to -5 dB, the proposed method can improve the total performance by an average of 28.3% while stabilizing the false alarm probability around 0.1 typical level and demonstrating higher constancy compared with traditional technologies from −10 dB to −5 dB. © 2002-2012 IEEE.

关键词： Satellite communication systems

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE Robotics and Automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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IDSDeep-CCD: intelligent decision support system based on deep learning for concrete cracks detection

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Multimedia Tools and Applications 2025年第12期84卷 10013-10026页

作者： Abualigah, Sayel M. Al-Naimi, Ahmad F. Sachdeva, Gagan AlAmri, Omran Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid Jordan Gagan Sachdeva Dubai United Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Computer Science Department Al al-Bayt University Mafraq25113 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Applied Science Research Center Applied Science Private University Amman11931 Jordan College of Engineering Yuan Ze University Taoyuan32003 Taiwan

In an era where Intelligent Decision Support Systems (IDSS) are integral to managing the vast data from Internet of Everything (IoE) systems, this study introduces IDSDeep-CCD, a novel IDSS approach for detecting concrete cracks, a critical issue in civil infrastructure maintenance. Traditional visual inspection methods for crack detection are time-consuming and error-prone. To address this, IDSDeep-CCD employs deep learning, utilizing an open-source dataset of concrete crack images for enhanced accuracy. The system's two-stage approach, featuring feature extraction via pre-trained models and a fully connected network for classification, significantly outperforms existing methods. Our results demonstrate a remarkable 99.9% accuracy rate in crack detection without data augmentation, marking a notable advancement in automatic detection technology. This breakthrough offers practical benefits for the industry by enabling more precise and efficient identification of concrete cracks, paving the way for timely and accurate remedial actions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Policy for Optimal Avoidance of Multiple Engagement Zones

Policy for Optimal Avoidance of Multiple Engagement Zones

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Milutinović, Dejan Von Moll, Alexander Weintraub, Isaac E. Casbeer, David Department of Electrical and Computer Engineering University of California Santa Cruz Santa CruzCA95064 United States Control Science Center Air Force Research Laboratory WPAFB OH45433 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This work formulates the feedback control strategies for vehicles to reach a goal point amongst a field of dynamic risk regions. Whereas previous work has considered deterministic versions of this problem, we consider the scenario in the context of uncertainty. This uncertainty could be due to unknown wind or other external disturbances. The risk regions’ dynamics are tightly coupled to the state of the vehicle and as a result the task of navigating through a field dynamically changes as the vehicle traverses through it. Rather than using a nonlinear program solver, the approach taken here is one of formulating the problem as a stochastic optimal control problem wherein a feedback control law is derived. This feedback control law allows a single or multiple vehicles to reach the desired goal location efficiently and with minimal risk incursion. When inside a risk region, there is a probability rate of the vehicle being neutralized. The stochastic dynamical system is modeled as a hybrid system, i.e., regime switching diffusion. The hybrid system is comprised of continuous spatial dynamics and discrete operational states corresponding to "normal" and "neutralized". The optimal control problem is discretized into a field of vehicle states and control. Using Value Iteration, the control signal at each discrete location is optimized, providing a feedback control strategy over the field. Following a discussion of the methods and approaches, this problem is formulated, simulations are performed, and conclusions and remarks are made © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Stochastic systems

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