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arXiv 2021年

作者： Wu, Dongrui Xu, Jiaxin Fang, Weili Zhang, Yi Yang, Liuqing Xu, Xiaodong Luo, Hanbin Yu, Xiang The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China The School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China The College of Public Administration Huazhong University of Science and Technology Wuhan430074 China The University of Michigan Ann ArborMI48109 United States The School of Management and Sino European Institute for Intellectual Property Huazhong University of Science and Technology Wuhan430074 China

Physiological computing uses human physiological data as system inputs in real time. It includes, or significantly overlaps with, brain-computer interfaces, affective computing, adaptive automation, health informatics, and physiological signal based biometrics. Physiological computing increases the communication bandwidth from the user to the computer, but is also subject to various types of adversarial attacks, in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output, leading to possible user confusion, frustration, injury, or even death. However, the vulnerability of physiological computing systems has not been paid enough attention to, and there does not exist a comprehensive review on adversarial attacks to them. This paper fills this gap, by providing a systematic review on the main research areas of physiological computing, different types of adversarial attacks and their applications to physiological computing, and the corresponding defense strategies. We hope this review will attract more research interests on the vulnerability of physiological computing systems, and more importantly, defense strategies to make them more secure. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Natural grasp intention recognition based on gaze fixation in human-robot interaction

arXiv

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arXiv 2020年

作者： Yang, Bo Huang, Jian Li, Xiaolong Chen, Xinxing Xiong, Caihua Hasegawa, Yasuhisa Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Mechanical Science and Engineering The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Micro-Nano Mechanical Secience and Engineering Nagoya University Furo-cho Chikusa-ku Nagoya464-8603 Japan

Eye movement is closely related to limb actions, so it can be used to infer movement intentions. More importantly, in some cases, eye movement is the only way for paralyzed and impaired patients with severe movement disorders to communicate and interact with the environment. Despite this, eye-tracking technology still has very limited application scenarios as an intention recognition method. The goal of this paper is to achieve a natural fixation-based grasping intention recognition method, with which a user with hand movement disorders can intuitively express what tasks he/she wants to do by directly looking at the object of interest. Toward this goal, we design experiments to study the relationships of fixations in different tasks. We propose some quantitative features from these relationships and analyze them statistically. Then we design a natural method for grasping intention recognition. The experimental results prove that the accuracy of the proposed method for the grasping intention recognition exceeds 89% on the training objects. When this method is extendedly applied to objects not included in the training set, the average accuracy exceeds 85%. The grasping experiment in the actual environment verifies the effectiveness of the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Eye movements

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Mem Brain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

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Nano-Micro Letters 2018年第1期10卷 12-19页

作者： Xi Yin Jing Yang Feng Xiao Yang Yang Hong-Bin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Computer Science Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments,accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called Mem Brain, whose input is the amino acid sequence. Mem Brain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of a-helical membrane proteins. Mem Brain achieves aprediction accuracy of 97.9% of ATMH, 87.1% of AP,3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. Mem BrainContact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction,respectively. And Mem Brain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of13.593. These prediction results provide valuable hints for revealing the structure and function of membrane *** Brain web server is free for academic use and available at ***/bioinf/Mem Brain/.

关键词： Transmembrane a-helices Structure prediction Machine learning Contact map prediction Relative accessible surface area

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Spatial-temporal Transformer For Skeleton-based Action Recognition

Spatial-temporal Transformer For Skeleton-based Action Recog...

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Chinese Automation Congress (CAC)

作者： Qipeng Zhang Tian Wang Mengyi Zhang Kexin Liu Peng Shi Hichem Snoussi School of Automation Science and Electrical Engineering Beihang University Beijing China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing China Institute of Artificial Intelligence Beihang University Beijing China College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China School of Mathematics and Computer Science Fujian Normal University Fujian China

ISBN: (纸本)9781665426480

In the area of skeleton-based human action recognition, GCN has achieved good results in previous research due to its excellent modeling ability on graph data. Recently, transformers have achieved extraordinary results in many computer vision fields. Comparing transformer and GCN, from a certain point of view, we can regard transformer as a kind of dynamic GCN, and the weight of each node is dynamically determined by data. In this work, a three-dimensional position encoding was proposed by us to solve the representation of node spatial information, in order to apply the transformer to the graph data. In addition, similar to Spatial-Temporal Graph Convolutional Networks (ST-GCN), we proposed a Space-Time Transformer (ST-TR), which applies transformers in space and time to extract spatiotemporal feature of skeleton data to complete action recognition.

关键词： Computer vision image recognition Computational modeling Transformers Feature extraction Skeleton Encoding

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Impulsive control with time windows for hyperchaotic exponential synchronization 6

Impulsive control with time windows for hyperchaotic exponen...

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6th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2019

作者： Wu, Hongjuan Hu, Xiang Feng, Yuming Tu, Zhengwen Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Wanzhou Chongqing China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Wanzhou Chongqing China

ISBN: (纸本)9781728158570

In actual situation, the input error of impulsive signal is inevitable and there are always some time delays when different systems communicate with each other. Then, considering the input error and time delays in the real world, this paper provides the criteria for the exponential lag synchronization of one kind of hyperchaotic system based on impulsive control with time windows, matrix inequalities, etc. Finally, the simulation results are displayed to illustrate the theoretical results. © 2019 IEEE.

关键词： Time delay

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Fault Detection of Lithium-Ion Batteries Subject to Probabilistic Parametric Uncertainties

Fault Detection of Lithium-Ion Batteries Subject to Probabil...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Yuhao Liu Yiming Wan Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China

ISBN: (数字)9781728106816

ISBN: (纸本)9781728106823

The fault detection task in lithium-ion battery management system (BMS) is critical to the safety and reliability of rechargeable and hybrid electric vehicles. To explicitly account for inevitable errors of battery model parameters, a robust fault detection method is proposed in this paper. The residual generator is established by exploiting an equivalent circuit model. The effect of uncertainties on the residual is parametrized as polynomial dependence on probabilistic uncertain parameters and noises. Then Gaussian mixtures are adopted to derive the residual distribution. To account for residual uncertainties in the detection decision, the weighted sum of distances from the generated residual to each Gaussian component of the residual distribution is proposed. Simulation results illustrate that our proposed method outperforms the conventional approach that does not consider parametric uncertainties.

关键词： Uncertainty Batteries Integrated circuit modeling Uncertain systems Voltage measurement Electronic countermeasures State of charge

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One-shot distributed algorithm for generalized eigenvalue problem

arXiv

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arXiv 2020年

作者： Lyu, Kexin He, Fan Huang, Xiaolin Yang, Jie Chen, Liming Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing 800 Dongchuan Road Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Ecole Centrale de Lyon France

Nowadays, more and more datasets are stored in a distributed way for the sake of memory storage or data privacy. The generalized eigenvalue problem (GEP) plays a vital role in a large family of high-dimensional statistical models. However, the existing distributed method for eigenvalue decomposition cannot be applied in GEP for the divergence of the empirical covariance matrix. Here we propose a general distributed GEP framework with one-shot communication for GEP. If the symmetric data covariance has repeated eigenvalues, e.g., in canonical component analysis, we further modify the method for better convergence. The theoretical analysis on approximation error is conducted and the relation to the divergence of the data covariance, the eigenvalues of the empirical data covariance, and the number of local servers is analyzed. Numerical experiments also show the effectiveness of the proposed algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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ÆMMamba: An Efficient Medical Segmentation Model With Edge Enhancement

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IEEE journal of biomedical and health informatics 2025年 PP卷 PP页

作者： Xingbo Dong Bowen Zhou Chen Yin Iman Yi Liao Zhe Jin Zhaozhao Xu Bin Pu Anhui University Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging Hefei 230093 China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China Nanchang Hangkong University Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang 330063 China University of Nottingham Malaysia Campus School of Computer Science Semenyih 43500 Malaysia Henan Polytechnic University School of Computer Science and Technology Jiaozuo China Hunan University College of Computer Science and Electronic Engineering Changsha China

Medical image segmentation is critical for disease diagnosis, treatment planning, and prognosis assessment, yet the complexity and diversity of medical images pose significant challenges to accurate segmentation. While Convolutional Neural Networks capture local features and Vision Transformers excel in the global context, both struggle with efficient long-range dependency modeling. Inspired by Mamba's State Space Modeling efficiency, we propose ÆMMamba, a novel multi-scale feature extraction framework built on the Mamba backbone network. AÆMMamba integrates several innovative modules: the Efficient Fusion Bridge (EFB) module, which employs a bidirectional state-space model and attention mechanisms to fuse multi-scale features; the Edge-Aware Module (EAM), which enhances low-level edge representation using Sobel-based edge extraction; and the Boundary Sensitive Decoder (BSD), which leverages inverse attention and residual convolutional layers to handle cross-level complex boundaries. ÆMMamba achieves state-of-the-art performance across 8 medical segmentation datasets. On polyp segmentation datasets (Kvasir, ClinicDB, ColonDB, EndoScene, ETIS), it records the highest mDice and mIoU scores, outperforming methods like MADGNet and Swin-UMamba, with a standout mDice of 72.22 on ETIS, the most challenging dataset in this domain. For lung and breast segmentation, ÆMMamba surpasses competitors such as H2Former and SwinUnet, achieving Dice scores of 84.24 on BUSI and 79.83 on COVID-19 Lung. And on the LGG brain MRI dataset, ÆMMamba attains an mDice of 87.25 and an mIoU of 79.31, outperforming all compared methods. The source code will be released at https://***/xingbod/eMMamba.

关键词： boundary-sensitive edge-aware feature fusion Medical image segmentation state-space model

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Study on the Preparation and Application of Vector Reference Map

Study on the Preparation and Application of Vector Reference...

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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Yaqiang Shi Jingyuan Bao Li Xu Zhenpo Tian Delie Ming National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China The Second Navy Representative Office Wuhan China Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9781728143231

ISBN: (纸本)9781728143248

In scene matching, we use vector data instead of raster data to prepare a reference map. The BSP -algorithm is used to manage the vector data and then it is loaded on the aircraft. During the guidance process, reference maps are real-time prepared based on the track information and camera parameters. As the vector reference map is very different from the real-time map, conditional generation adversarial networks are used to convert the real-time map to a vector-style image and then match it to the vector reference map, which can achieve 86% matching accuracy.

关键词： Real-time systems Aircraft Cameras Buildings Generators Aircraft navigation Roads

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Re-ID driven localization refinement for person search

arXiv

引用

arXiv 2019年

作者： Han, Chuchu Ye, Jiacheng Zhong, Yunshan Tan, Xin Zhang, Chi Gao, Changxin Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Peking University Shanghai Jiao Tong University Megvii Technology

Person search aims at localizing and identifying a query person from a gallery of uncropped scene images. Different from person re-identification (re-ID), its performance also depends on the localization accuracy of a pedestrian detector. The state-of-the-art methods train the detector individually, and the detected bounding boxes may be suboptimal for the following re-ID task. To alleviate this issue, we propose a re-ID driven localization refinement framework for providing the refined detection boxes for person search. Specifically, we develop a differentiable ROI transform layer to effectively transform the bounding boxes from the original images. Thus, the box coordinates can be supervised by the re-ID training other than the original detection task. With this supervision, the detector can generate more reliable bounding boxes, and the downstream re-ID model can produce more discriminative embeddings based on the refined person localizations. Extensive experimental results on the widely used benchmarks demonstrate that our proposed method performs favorably against the state-of-the-art person search methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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