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16th International Conference on Modelling, Identification and control, ICMIC 2024

作者： Luo, Xin Ma, Jun Wang, Xian Na, Jing Xing, Yashan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Kunming650500 China

ISBN: (纸本)9789819617760

In this paper, we present a control strategy for bilateral teleoperators operated in the task space, which is designed to estimate the uncertain dynamics. To implement this proposed strategy, we develop a straightforward yet robust unknown system dynamics estimator (USDE) by incorporating a low-pass filtering operation. By using the USDE, we can eliminate the impact of unmodeled system dynamics without the need to measure the acceleration signal. Furthermore, the proposed USDE offers the advantages of requiring only one tuning parameter and being easily adjustable compared to conventional estimators. The convergence of the estimation error associated with the unmodeled system dynamics and the stability of the closed-loop control system are both rigorously demonstrated through Lyapunov’s theorem. Ultimately, the superior performance of our method, as evidenced by enhancing motion synchronization and stability, is confirmed through simulation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Time delay

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A Gaze-Guided Human Locomotion Intention Recognition and Volitional control Method for Knee-Ankle Prostheses

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Chen, Xinxing Chen, Zhaokai Wang, Yuxuan Yin, Shucong Chen, Chuheng Huang, Jian Fu, Chenglong Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Hubei Key Laboratory of Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Wuhan430074 China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen518055 China Shanghai Jiaotong University Shanghai China

This paper presents a novel gaze-guided volitional control method for knee-ankle prostheses, designed to enhance the precision and intuitiveness of prosthetic control in complex locomotion tasks. The method utilizes an eye-tracking system coupled with an RGBD camera to estimate 3D gaze points, which are then used to construct a gaze attention map. This map serves as the basis for predicting human locomotion intentions, enabling the prosthesis to accurately step on the intended foot placement. Experimental validation was conducted with both a healthy subject and an amputee subject, focusing on level-ground walking and stair ascent tasks. The results demonstrated that the prosthesis could achieve precise foot placement, with a mean error of 1.22 cm ± 3.14 cm for the healthy subject and 3.40 cm ± 0.66 cm for the amputee subject during level-ground walking. In stair ascent tasks, the system successfully adapted to various gait patterns, with the prosthetic joint angles closely matching natural human movements. The proposed method also effectively reduced the root mean square error (RMSE) of 3D gaze estimation to 2.70 cm, 2.63 cm, and 1.97 cm along the x, y, and z axes, respectively. These findings indicate that the gaze-guided volitional control method not only enhances the functionality of knee-ankle prostheses but also offers a more intuitive interface for users, aligning prosthetic actions with the user’s visual focus and intentions. © 2016 IEEE.

关键词： Knee prostheses

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MLP-AMDC: A MLP Architecture for Adaptive-Mask-Based Dual-Camera Snapshot Hyperspectral Imaging 31st

MLP-AMDC: A MLP Architecture for Adaptive-Mask-Based Dual-C...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Cai, Zeyu Zhang, Can Chen, Yuchong Chen, Xunhao Yang, Jiming Shi, Wubin Da, Feipeng Jin, Chengqian School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of CSE Ministry of Education Nanjing210000 China Agricultural Mechanization Development and Intelligent Information Research Center Nanjing Institute of Argicultural Mechanization Ministry of Agriculture and Rural Affairs Nanjing210000 China

ISBN: (纸本)9789819620609

The coded Aperture Snapshot Spectral Imaging (CASSI) system has great advantages in dynamically acquiring Hyper-Spectral Image (HSI) compared to traditional measurement methods, but there are the following problems. 1) Traditional mask relies on random patterns or analytical design, both of which limit the performance improvement of CASSI. 2) Existing high-quality reconstruction algorithms are slow in reconstruction and can only reconstruct scene information offline. To address the above two problems, this paper introduces an RGB camera with CASSI based on Adaptive-Mask as multimodal input to improve the reconstruction quality. The existing SOTA reconstruction schemes are based on the transformer, but the operation of self-attention pulls down the operation efficiency of the network. To improve the inference speed of the reconstruction network, this paper proposes An MLP Architecture for Adaptive-Mask-based Dual-Camera (MLP-AMDC) to replace the transformer structure of the network. Numerous experiments have shown that MLP performs no less well than transformer-based structures for HSI reconstruction, while MLP greatly improves the network inference speed and has less number of parameters and operations, our method has an 8 dB improvement over SOTA and at least a 5-fold improvement in reconstruction speed. (https://***/caizeyu1992/MLP-AMDC.) © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hyperspectral imaging

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Fast Brillouin frequency shift estimation using digital elevation modeling method in BOTDR 4

Fast Brillouin frequency shift estimation using digital elev...

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4th International Computational Imaging Conference, CITA 2024

作者： Zan, Wei Liu, Xinyi Yang, Mingyuan Bai, Qai Wang, Yu Jin, Baoquan Key Laboratory of Advanced Transducers and Intelligent Control Systems Ministry of Education Taiyuan University of Technology Taiyuan030024 China College of Physics and Optoelectronic Engineering Taiyuan University of Technology Taiyuan030024 China College of Electronic Information Engineering Taiyuan University of Technology Taiyuan030024 China

ISBN: (纸本)9781510688834

In the practical application of Brillouin optical time-domain reflectometry (BOTDR) in temperature and strain monitoring, enhancing measurement speed remains a critical challenge. This paper presents a novel method for Brillouin frequency shift (BFS) estimation utilizing level set and digital elevation modeling (DEM). The algorithm demonstrates superior time efficiency. At the same time, the accuracy of the scheme is proved in the comparation experiment and correlation analysis. The method reduces computational time, without spatial resolution or measurement error degradation. © 2025 SPIE.

关键词： Digital elevation model

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Research on Medical Image Classification for Oral Cancer 3rd

Research on Medical Image Classification for Oral Cancer

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3rd International Conference on Machine Learning, Cloud Computing and intelligent Mining, MLCCIM 2024

作者： Wang, Kunkun Shi, Linjun Gu, Chaochen Mo, Fan Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Oral Mucosal Diseases Ninth People’s Hospital Shanghai Jiaotong University School of Medicine Shanghai China Shanghai Key Laboratory of Stomatology and Shanghai Research Institution of Stomotology National Clinical Research Center of Stomatology Shanghai China

ISBN: (纸本)9789819624676

In recent years, deep learning has made significant progress in medical imaging, deepening the crossover between the medical and industrial fields. However, not all medical images are suitable for deep learning neural networks;simultaneously, some medical images are difficult to collect and preprocess. In this paper, we reconstruct the image information collected by dental clinicians over many years, normalizing it with diagnostic results as the gold standard. On this basis, we explore the applicability and potential of deep learning on complex medical images. Further, we compare the performance of multiple models on this dataset and visualize the learning outcomes, demonstrating that deep learning networks exhibit good learning capabilities on certain images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Corrigendum to "Hyperledger sawtooth based supplychain traceability system for counterfeit drugs" [Comput. Ind. Eng. 190 (2024) 110021] (Computers & Industrial Engineering (2024) 190, (S0360835224001426), (10.1016/***.2024.110021))

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Computers and Industrial Engineering 2025年 204卷

作者： Nawaz, Anum Wang, Liguan Irfan, Muhammad Westerlund, Tomi Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Turku Intelligent and Embedded Robotic Systems Lab Faculty of Technology University of Turku Finland

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Securing Mobile Robotic Networks Against Replacement Attack

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IEEE Transactions on Automatic control 2025年

作者： Li, Yushan He, Jianping Chen, Cailian Guan, Xinping Cai, Lin Shanghai Jiao Tong University Dept. of Automation China Ministry of Education of China The Key Laboratory of System Control and Information Processing China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China University of Victoria Dept. of Electrical and Computer Engineering BC Canada

The security of mobile robotic networks (MRNs) has been an active research topic in recent years. This paper aims to secure the ubiquitous formation control of MRNs against the replacement attack, where an external robot can replace a formation robot by compromising the communication and physically interfering with the victim simultaneously. To counter this advanced attack, the novel idea of this work is to leverage the physical proximity of the formation shape and the interaction topology among robots for defense design. First, from the physical proximity perspective, we propose the convex neighbor polygon (CNP) to capture the geometric characteristic of the formation robots, and design a CNP-based security mechanism for the robots during the replacement attack. Then, from the interaction perspective, we introduce the indirect controllability to characterize the possibility that the attacker leverages the interaction between robots to deactivate the CNP mechanism, and establish the conditions regarding the topology structure and the attack input to counter the replacement. Finally, based on the obtained conditions, we demonstrate how to design the initiatory topology among the formation robots to enhance the defense performance. Comparative simulations verify the effectiveness of the proposed method. © 1963-2012 IEEE.

关键词： Topology

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Multi-scale compression artifact attention-driven compressed video quality enhancement

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Digital Signal processing: A Review Journal 2025年 163卷

作者： Lan, Chengdong Lin, Wei Liang, Haolin Kang, Yuanxun College of Physics and Information Engineering Fuzhou University Fujian Fuzhou350116 China Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fujian Fuzhou350116 China Yealink Fujian Xiamen61009 China

Compressed video quality enhancement is crucial for mitigating artifacts introduced by video coding. Video compression often results in unevenly distributed artifacts across different regions of video frames, leading to significant quality variations. Existing algorithms treat all regions uniformly, ignoring these localized differences, limiting their ability to extract high-quality information from reference frames and accurately reconstruct residuals. Additionally, larger temporal gaps between reference and target frames can cause alignment errors, which propagate during fusion and degrade performance. To address these challenges, we propose a Multi-scale Compression Artifact Attention (MSCAA) module that captures artifact distribution, guiding the model to focus on distorted regions. We also introduce a Progressive Fusion Stage that sequentially fuses reference frames based on temporal proximity, reducing error propagation and enhancing temporal coherence. The experimental results show that the proposed method improves average ΔPSNR by 5.15% and ΔSSIM by 3.35% compared to the state-of-the-art method, demonstrating its superior performance in quality enhancement. © 2025 Elsevier Inc.

关键词： Compression artifacts Deep learning Video quality enhancement

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Sketch-based Point Cloud Generation with Diffusion Model and Pre-training Enhancement

Sketch-based Point Cloud Generation with Diffusion Model and...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yangdong Chen Mohan Chen Yuejie Zhang Rui Feng Tao Zhang Shang Gao School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China School of Information Management and Engineering Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics Shanghai China School of Information Technology Deakin University Victoria Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Diffusion models, known for their success in various generative tasks like image generation and super-resolution, are applied in this study for point cloud generation, a field that has not been extensively explored due to the complexity of point clouds. We propose a novel method using a diffusion model to generate high-quality 3D point clouds from 2D sketches. This method employs a self-supervised contrastive learning scheme to align sketch and point cloud modalities. Additionally, it incorporates a specific partition mixing strategy to integrate edge information during pre-training. Evaluated on two benchmark datasets, our method outperforms existing state-of-the-art approaches, showcasing the potential of diffusion models in point cloud generation and setting a new direction for future research.

关键词： Point cloud compression Three-dimensional displays Image synthesis Superresolution Contrastive learning Benchmark testing Signal processing Diffusion models Stability analysis Speech processing

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DiffuSyn: A Diffusion-Driven Framework With Syntactic Dependency for Aspect Sentiment Triplet Extraction

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language processing 2025年 33卷 842-854页

作者： Qiuhua Yi Xiangjie Kong Linan Zhu Chenwei Zhang Guojiang Shen College of Computer Science and Technology Zhejiang University of Technology Hangzhou China Zhejiang Key Laboratory of Visual Information Intelligent Processing Hangzhou China School of Faculty of Education University of Hong Kong Hong Kong

Aspect Sentiment Triplet Extraction (ASTE) is a fine-grained sentiment analysis task that involves identifying aspect and opinion terms and conducting sentiment analysis for each aspect-opinion pair. We categorize existing methods into tagging-based, span-based, and generation methods. However, despite notable achievements, these methods still face certain challenges: span-based methods struggle to distinguish similar spans, and generation methods are prone to slower decoding speeds. To address these challenges, we propose a novel ASTE framework, called DiffuSyn, which leverages diffusion models and syntactic dependency parsing to improve the performance of sentiment analysis. Specifically, we first add Gaussian noise to the indices at the beginning and end of aspect words and opinion words in sentences based on a non-autoregressive diffusion model. We obtain accurate boundaries of aspect and opinion terms through boundary denoising, and thus identify precise spans. Secondly, we introduce a syntactic dependency parser to capture the syntactic dependencies within review sentences, thereby providing effective syntactic information for sentiment analysis. Finally, we complete the matching of aspects and opinions by leveraging the extracted boundaries and syntactic semantic information, facilitating the prediction of sentiment relationships. We conduct experiments on four public datasets (ASTE-Data-V2), and the results indicate the effectiveness of our approach in the ASTE task. Furthermore, our method achieves state-of-the-art performance in both Aspect Term Extraction (ATE) and Opinion Term Extraction (OTE) tasks.

关键词： Diffusion models Syntactics Sentiment analysis Fish Speech processing Data mining Transforms Noise reduction Decoding Tagging

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