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6th Asia Symposium on Image processing, ASIP 2024

作者： Dai, Jianyi Lu, Guangda Qin, Zhuanpin Guo, Xiaoqian Liu, Xinlin Yin, Qianhan Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Hexi District No. 1310 Tianjin300222 China

ISBN: (纸本)9798350376159

(sEMG) Surface EMG signals can quickly and accurately reflect the movement intention of human body, gesture recognition by sEMG has been widely studied in the field of human-robot interaction in rehabilitation robots, in order to improve the accuracy of gesture recognition. A gesture recognition method based on PSO optimised LSTM for surface muscle signals is investigated. Firstly, the S_Dbw method is used to evaluate the clustering validity of the time domain features of the surface EMG signals, and the feature (MAV)combination of absolute mean +(RMS)root mean square (RMS)+ (VAR) variance is selected as the feature space and input to the model of the depth algorithm. Secondly, in order to solve the problem of cumbersome parameter debugging and low accuracy of (LSTM)Long Short-Term Memory neural network (LSTM), Particle Swarm Optimization-Long Short-Term Memory PSO-LSTM algorithm is proposed and combined with convolutional neural network (convolutional neural network, CNN) and Long Short-Term Memory LSTM. The experimental results show that the PSO-LSTM model has an average recognition accuracy of 94.1%, which is 8.3% and 4.7% higher than the CNN and LSTM, and the average training time of the LSTM model is reduced by 35.52s compared to the LSTM. Therefore, it is advantageous to use PSO-LSTM model in gesture sEMG signal recognition and classification. © 2024 IEEE.

关键词： Convolutional neural networks

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Joint state-sharing and truncation BCJR for power-efficient coherent optical interconnections utilizing symbol-rate sampling DSP over a hollow-core fiber

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Optics Letters 2025年第10期50卷 3461-3464页

作者： Wang, Chenchen Li, Zhipei Dong, Ze Gao, Ran Song, Junyuan Mai, Tianle Guo, Dong Chang, Huan Pan, Xiaolong Zhu, Lei Xin, Xiangjun School of Information and Electronics Beijing Institute of Technology Beijing100081 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing100081 China School of Integrated Circuits Beijing University of Posts and Telecommunications Beijing100876 China

Power-efficient digital signal processing (DSP) architectures are indispensable for enabling the sustainable scalability of optical networks in next-generation data center interconnects (DCIs). This paper introduces a minimalist symbol-rate sampling (SRS) DSP framework for coherent optical systems, seamlessly integrated with a hollow-core fiber (HCF) to enable ultra-low-complexity signal processing. The core of this framework is the joint state-sharing and truncation Bahl–Cocke–Jelinek–Raviv (JSST-BCJR) algorithm, which enhances hardware efficiency by eliminating up to 91% of state-space computations, significantly reducing computational complexity while maintaining negligible performance degradation. Experimental validation confirms the feasibility of the proposed minimalist SRS-DSP framework through the successful transmission of a 100-Gbaud 16QAM signal over 20.6 km of HCF. This work underscores the viability of SRS-DSP in power-constrained DCIs and highlights the synergistic integration of low-complexity DSP and HCF, offering a pathway toward power-efficient, high-throughput optical networks. © 2025 Optica Publishing Group. All rights are reserved.

关键词： Network coding

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Joint Secure Transmit Beamforming Designs for Physical Layer Security in MIMO-ISAC System 2

Joint Secure Transmit Beamforming Designs for Physical Layer...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Guo, Sitong Zheng, Hao Zhang, Yinghui Yan, Junkun Inner Mongolia University College of Electronic Information Engineering Hohhot China Inner Mongolia University Inner Mongolia Key Laboratory of Intelligent Communication and Sensing and Signal Processing Hohhot China Xidian University National Key Laboratory of Radar Signal Processing Xian China

ISBN: (纸本)9798331515669

This article concentrates on the physical layer security (PLS) towards an integrated sensing and communication (ISAC) system, which communicates with legitimate communication users (CUs) and detects the single sensing target, which is considered to be eavesdropper because it might intercept the transmitting message from the base station to CUs. The joint transmitting beamforming for secure communication and sensing is designed to maximize the sum secrecy rate (SR) of system under the constraints of quality-of-service of CUs, the Signal-to-Noise Ratio (SNR) of sensing and the power budget of the system. The optimal solution is obtained by the semidefinite relaxation and successive convex approximation algorithms. The availability of the beamforming design in ensuring the SR of the ISAC system is showed by Simulation results. © 2024 IEEE.

关键词： Budget control

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Distributed Group Coordination of Random Communication Constrained Cyber-Physical Systems Using Cloud Edge Computing

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 196-205页

作者： Ren, Hongru Long, Yinren Ma, Hui Li, Hongyi Guangdong University of Technology School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China Guangdong University of Technology School of Mathematics and Statistics Guangzhou510006 China Southwest University College of Electronic and Information Engineering Chongqing Key Laboratory of Generic Technology and System of Service Robots Chongqing400715 China

This paper studied the distributed group coordinated control problem of cyber-physical systems (CPSs) with multi-agent architecture. We build the distributed networked multi-group agent systems (NMGASs) with nonlinear and unknown dynamics via cloud edge computing. The common and challenging situations of random communication constraints in CPSs are considered, including network-induced delay, packet dropout, and packet disorder, which are treated as round-trip time (RTT) delay. To actively compensate for RTT delay and achieve coordination among all agents, a data-driven cloud edge predicted control strategy is designed. This strategy only needs to obtain the I/O measurement data of the systems, and can automatically carry out adaptive learning, which has more extensive application scenarios compared to model-based control methods. Theoretical analysis yields the conditions of simultaneous stability and consensus of the closed-loop systems with the proposed strategy. Finally, the practical examples are provided to illustrate the effectiveness of the proposed strategy. © 2023 IEEE.

关键词： Edge computing

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Research Progress of Human Movement Assessment System 6

Research Progress of Human Movement Assessment System

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6th Asia Symposium on Image processing, ASIP 2024

作者： Guo, Tinghang Yin, Qianhan Li, Xiaohan Ji, Xin Qin, Zhuanping Han, Yu Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Hexi District No. 1310 Tianjin300222 China

ISBN: (纸本)9798350376159

The working principles and methods included in the existing motion estimation techniques are classified, and the research on motion estimation in various fields at home and abroad is collected in the existing literature to investigate and analyse the application of motion estimation in different fields as well as their respective advantages and limitations. Firstly, in terms of obtaining human movement information, two approaches are generally used: wearable sensor-based and vision-based pose estimation techniques. On this basis, the current methods of motion assessment are reviewed in terms of rule-based and model-based approaches, and then, the limitations of rule-based assessment methods are discussed and analysed, and it is pointed out that, with the development of technology and the increase of demand for personalised training, model-based scoring methods are more promising in the field of motion, and they can better solve the problem of evaluating and analyses. Finally, suggestions are made for future research directions. © 2024 IEEE.

关键词： Motion estimation

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A Dual-branch Model for Early Detection of Alzheimer's Disease Using Resting-State fMRI 7

A Dual-branch Model for Early Detection of Alzheimer's Disea...

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7th IEEE Advanced information Technology, Electronic and Automation control Conference, IAEAC 2024

作者： Wang, Yixuan Li, Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

ISBN: (纸本)9798350339161

Alzheimer's disease (AD) is the most prevalent form of dementia, and early diagnosis is crucial for delaying and treating AD. Resting-state functional magnetic resonance imaging (rs-fMRI), a widely used medical imaging technique, offers rich temporal and spatial data, which has led researchers to explore various feature extraction methods based on rs-fMRI images for AD identification. However, the related work still suffers from insufficient utilization of temporal and spatial information which leads to unsatisfactory early diagnosis. In this study, we propose a dual-branch fusion model to extract spatial-temporal features from rs-fMRI images. Our proposed model can extract temporal features at different levels. We developed a Class Activation Sequence (CAS) branch, which is a structure that emphasizes the function of each temporal node throughout the whole time series. Additionally, we created a time-domain local branch for local feature extraction. Further, we designed a fusion module for the model to describe temporal contextual relationships and fuse features at various levels. We tested the performance of the model on the ADNI dataset, and the experimental results show that compared with other algorithms, the dual-branch fusion model achieves higher classification accuracy on several classification tasks including early diagnosis, which proves the advantage of the dual-branch fusion model in temporal and spatial feature extraction for rs-fMRI images, and our work also provides a foundation for the temporal domain characterization of rs-fMRI images. © 2024 IEEE.

关键词： Feature extraction

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ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios 31

ToolEyes: Fine-Grained Evaluation for Tool Learning Capabili...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Ye, Junjie Li, Guanyu Gao, Songyang Huang, Caishuang Wu, Yilong Li, Sixian Fan, Xiaoran Dou, Shihan Ji, Tao Zhang, Qi Gui, Tao Huang, Xuanjing School of Computer Science Fudan University China Institute of Modern Languages and Linguistics Fudan University China Research Institute of Intelligent Complex Systems Fudan University China Shanghai Key Laboratory of Intelligent Information Processing China Pengcheng Laboratory China

ISBN: (纸本)9798891761964

Existing evaluations of tool learning primarily focus on validating the alignment of selected tools (e.g., various APIs) for large language models (LLMs) with expected outcomes. However, these approaches rely on a limited set of scenarios where answers can be pre-determined. Furthermore, a sole emphasis on outcomes disregards the complex capabilities required for LLMs to effectively use tools. To tackle this issue, we propose ToolEyes, a fine-grained system tailored for the evaluation of the LLMs' tool learning capabilities in authentic scenarios. The system meticulously examines seven real-world scenarios, analyzing five dimensions crucial to LLMs in tool learning: format alignment, intent comprehension, behavior planning, tool selection, and answer organization. Additionally, ToolEyes incorporates a tool library boasting approximately 600 tools, serving as an intermediary between LLMs and the physical world. Evaluations involving ten LLMs across three categories reveal a preference for specific scenarios and limited cognitive abilities in tool learning. Intriguingly, expanding the model size even exacerbates the hindrance to tool learning. The code and data are available at https://***/Junjie-Ye/ToolEyes. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Bandwidth-Aware Transmission Scheduling and Event-Triggered Distributed MPC for Vehicle Platoons 41

Bandwidth-Aware Transmission Scheduling and Event-Triggered ...

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第41届中国控制会议

作者： Qiaoni Han Guowen Cheng Hongjiu Yang Zhiqiang Zuo School of Electrical and Information Engineering Tianjin University Key Laboratory of System Control and Information Processing Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper j ointly designs the communication and control schemes for vehicle platoons by taking into consideration of the limited communication and computing resources of *** at achieving the stable tracking of the member vehicles at a desired inter-vehicle distance and the same velocity,while improving the communication and computation efficiency,we propose a bandwidth-aware transmission scheduling(BATS) mechanism and an event-triggered distributed model predictive control(ET-DMPC) scheme for the longitudinal control of a vehicle ***,by introducing bandwidth-aware parameters,the triggering threshold for the event-triggered BATS mechanism is dynamically adjusted according to vehicle and bandwidth ***,an output-based ET-DMPC scheme is designed to ensure that the platoon tracks a reference velocity and maintains the desired inter-vehicle ***,extensive simulations are carried out and the results demonstrate the effectiveness of the proposed BATS mechanism and ET-DMPC scheme in terms of adaptive communication scheduling and low computational burden.

关键词： Vehicle Platoon Longitudinal control Communication Efficiency Computational Burden MPC

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Enhancing defocus blur in three-dimensional scenes via high-speed optical axial scanning and deep learning 2

Enhancing defocus blur in three-dimensional scenes via high-...

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2nd International Academic Conference on Optics and Photonics, IACOP 2024

作者： Li, Zilong Tang, Chenxi Dong, Jiaqing Liu, Xuan Lin, Jiabin Liu, Qiegen Song, Xianlin School of Information Engineering Nanchang University Nanchang330031 China Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications Nanchang University Nanchang330031 China

ISBN: (数字)9781510688827

ISBN: (纸本)9781510688810

Mitigating image defocus blur induced by the limited depth of field in imaging systems and reconstructing the three-dimensional depth of scenes represents a significant hurdle. In this paper, a method for enhancing defocus blur in three-dimensional scenes via high-speed optical axial scanning and deep learning was proposed. The optical axial scanning system functions by synchronizing a liquid electronically tunable lens and a CCD camera to capture multi-focus image sequences. The system efficiently captures real-world images with a fast acquisition time of ~50 ms per image. The multi-focus image sequences are then fused to all-in-focus images. The multi-focus images and all-in-focus images (ground truth) serve as the dataset for the deep learning neural network. Defocused images can be reconstructed using the trained deep learning network. Furthermore, by establishing a direct relationship between the focal depth and the driving current applied to the liquid electronically tunable lens, the three-dimensional depth of the scene can be rapidly determined. Experimental results validate the feasibility and effectiveness of this method. It is expected to expand the applications of image defocus enhancement in fields such as medical imaging, 3D shape measurement, and augmented reality. © 2025 SPIE.

关键词： CCD cameras

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The Application of Generative Artificial Intelligence in the Teaching of Engineering Courses in Chinese Universities 24

The Application of Generative Artificial Intelligence in the...

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7th International Conference on Educational Technology Management, ICETM 2024

作者： Zhao, Dong Zhang, Dan Ma, Xiujuan School of Computer Qinghai Normal University Qinghai Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University Qinghai Xining China

ISBN: (纸本)9798400717468

Generative Artificial Intelligence (GenAI) is revolutionizing the field of higher education by leveraging deep learning models to generate human-like content. However, the use of GenAI in education raises ethical concerns such as the potential impact on critical thinking skills and the unethical or dishonest use by students. This paper analyzed the main problems existing in engineering courses in Chinese universities firstly and then proposed corresponding teaching reform measures based on the comprehensive consideration of the benefits and threats of GenAI. We applied the proposed teaching reform measures in the teaching of engineering courses, which greatly improved the teaching quality. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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