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arXiv 2020年

作者： Wu, Dongrui Xu, Yifan Lu, Bao-Liang 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 The Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Qing Yuan Research Institute Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China

A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common non-invasive BCI modality, electroencephalogram (EEG), is sensitive to noise/artifact and suffers between-subject/within-subject non-stationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time-consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This paper reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications - motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks - are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the paper, which may point to future research directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Brain computer interface

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Quality Assessment of Electrocardiogram Signals Using Contrastive Learning

Quality Assessment of Electrocardiogram Signals Using Contra...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Meiping Zhong Zuoyong Li ChuanSheng Wang Jiayan Huang WanQi Zhao Haoyi Fan College of Computer and Big Data Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona Spain New Engineering Industry College Putian University Putian China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Quality assessment of electrocardiogram (ECG) signals holds significant importance in reducing the high-frequency false alarms in automated arrhythmia diagnosis. Existing quality assessment methods primarily rely on empirical threshold setting or the calculation of relevant statistical values to formulate decision rules. However, the thresholds set in this manner are typically based on experience and knowledge, resulting in decision rules that lack flexibility and struggle to adapt to changes in the dataset. To address this issue, many researchers have turned to deep learning algorithms for ECG signal quality assessment. Nevertheless, most existing deep learning methods usually require large-scale and labeled ECG data for training, whose acquiring is typically challenging. Therefore, this paper proposes an efficient method for ECG signal quality assessment. First, we use a contrast learning framework to construct sample pairs through multilevel data augmentation. Second, a densely connected convolutional network (DenseNet) is used to improve the model's ability of extracting local features in ECG signals, and a Squeeze-and-Excitation attention module is introduced to make the model pay more attention to critical features. Finally, the feature representation is learned by minimizing the contextual contrast loss to bring similar samples closer and push dissimilar samples farther away. In this way, we can utilize the contrastive learning framework to extract temporal representations from unlabeled data and transfer them to downstream ECG signal quality assessment tasks. Extensive experiments on the Physionet/CinC Challenge 2011 dataset demonstrate that our approach outperforms most existing quality assessment methods.

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Visual Servo control of Cable-driven Soft Robotic Manipulator

Visual Servo Control of Cable-driven Soft Robotic Manipulato...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Hesheng Wang Weidong Chen Xiaojin Yu Tao Deng Xiaozhou Wang Rolf Pfeifer Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China hanghai Chest Hospital Shanghai Jiao Tong University Shanghai china. Artificial Intelligence Laboratory Department of Informatics University of Zurich Andreasstrasse 15 (Office 2.32) 8050 Zurich Switzerland

ISBN: (纸本)9781467363563

Aim at enhancing dexterous and safe operation in unstructured environment, a cable-driven soft robotic manipulator is designed in this paper. Due to soft material it made of and nearly infinite degree of freedom it owns, the soft robotic manipulator has higher security and dexterity than traditional rigid-link manipulator, which make it suitable to perform tasks in complex environments that is narrow, confined and unstructured. Though the soft robotic manipulator possesses advantages above, it is not an easy thing for it to achieve precise position control. In order to solve this problem, a kinematic model based on piecewise constant curvature hypothesis is proposed. Through building up three spaces and two mappings, the relationship between the length variables of 4 cables and the position and orientation of the soft robotic manipulator end-effector is obtained. Afterwards, a depth-independent image Jacobian matrix is introduced and an image-based visual servo controller is presented. Applied by adaptive algorithm, the controller could estimate unknown position of the feature point online, and then Lyapunov theory is used to prove the stability of the proposed controller. At last, experiments are conducted to demonstrate rationality and validity of the kinematic model and adaptive visual servo controller.

关键词： Visual Servo control adaptive visual servo controller kinematic model kinematics Visual servo control Degree of freedom Position control Robot Manipulator Visual servoing controllers Servo control FLEXIBLE MATERIALS servo controller

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RLSAC: Reinforcement Learning enhanced Sample Consensus for End-to-End Robust Estimation

arXiv

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arXiv 2023年

作者： Nie, Chang Wang, Guangming Liu, Zhe Cavalli, Luca Pollefeys, Marc Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Department of Computer Science ETH Zürich Switzerland Microsoft Mixed Reality AI Zürich Lab Switzerland

Robust estimation is a crucial and still challenging task, which involves estimating model parameters in noisy environments. Although conventional sampling consensus-based algorithms sample several times to achieve robustness, these algorithms cannot use data features and historical information effectively. In this paper, we propose RLSAC, a novel Reinforcement Learning enhanced SAmple Consensus framework for end-to-end robust estimation. RLSAC employs a graph neural network to utilize both data and memory features to guide exploring directions for sampling the next minimum set. The feedback of downstream tasks serves as the reward for unsupervised training. Therefore, RLSAC can avoid differentiating to learn the features and the feedback of downstream tasks for end-to-end robust estimation. In addition, RLSAC integrates a state transition module that encodes both data and memory features. Our experimental results demonstrate that RLSAC can learn from features to gradually explore a better hypothesis. Through analysis, it is apparent that RLSAC can be easily transferred to other sampling consensus-based robust estimation tasks. To the best of our knowledge, RLSAC is also the first method that uses reinforcement learning to sample consensus for end-to-end robust estimation. We release our codes at https://***/IRMVLab/RLSAC. © 2023, CC BY.

关键词： Reinforcement learning

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Integration of nonlinear disturbance observer within proxy-based sliding mode control for pneumatic muscle actuators

arXiv

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arXiv 2019年

作者： Cao, Yu Huang, Jian Wu, Dongrui Zhang, Mengshi Xiong, Caihua Li, Zhijun Key Laboratory for Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Mechanical Science Engineering State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China College of Automation Science and Engineering South China University of Technology Guangzhou510641 China

This paper presents an integration of nonlinear disturbance observer within proxy-based sliding mode control (IDO-PSMC) approach for Pneumatic Muscle Actuators (PMAs). Due to the nonlinearities, uncertainties, hysteresis, and timevarying characteristics of the PMA, the model parameters are difficult to be identified accurately, which results in unmeasurable uncertainties and disturbances of the system. To solve this problem, a novel design of proxy-based sliding mode controller (PSMC) combined with a nonlinear disturbance observer (DO) is used for the tracking control of the PMA. Our approach combines both the merits of the PSMC and the DO so that it is effective in both reducing the "chattering" phenomeno n and improving the system robustness. A constrained Firefly Algorithm is used to search for the optimal control parameters. Based on the Lyapunov theorem, the states of the PMA are shown to be globally uniformly ultimately bounded. Extensive experiments were conducted to verify the superior performance of our approach, in multiple tracking scenarios. Copyright © 2019, The Authors. All rights reserved.

关键词： Sliding mode control

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COLLABORATIVE AUTO-ENCODING FOR BLIND IMAGE QUALITY ASSESSMENT

arXiv

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arXiv 2023年

作者： Zhou, Zehong Zhou, Fei Qiu, Guoping College of Electronic and Information Engineering Shenzhen University Shenzhen China Peng Cheng Laboratory Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen Institute for Artificial Intelligence and Robotics for Society Shenzhen China School of Computer Science University of Nottingham Nottingham United Kingdom Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen China

Blind image quality assessment (BIQA) is a challenging problem with important real-world applications. Recent efforts attempting to exploit powerful representations by deep neural networks (DNN) are hindered by the lack of subjectively annotated data. This paper presents a novel BIQA method which overcomes this fundamental obstacle. Specifically, we design a pair of collaborative autoencoders (COAE) consisting of a content autoencoder (CAE) and a distortion autoencoder (DAE) that work together to extract content and distortion representations, which are shown to be highly descriptive of image quality. While the CAE follows a standard codec procedure, we introduce the CAE-encoded feature as an extra input to the DAE’s decoder for reconstructing distorted images, thus effectively forcing DAE’s encoder to extract distortion representations. The self-supervised learning framework allows the COAE including two feature extractors to be trained by almost unlimited amount of data, thus leaving limited samples with annotations to finetune a BIQA model. We will show that the proposed BIQA method achieves state-of-the-art performance and has superior generalization capability over other learning based models. The codes are available at: https://***/Macro-Zhou/NRIQA-VISOR/. © 2023, CC BY.

关键词： Supervised learning

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AoI-Reliability Analysis with Block Length over Erasure Channels Using Rateless Codes

AoI-Reliability Analysis with Block Length over Erasure Chan...

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International Conference on Communication Technology (ICCT)

作者： Haobo Huang Yusun Fu Yue Qiao Junpeng Yin Weiwu Yan Ningbo Artificial Intelligence Institute Shanghai Jiao Tong University Ningbo China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

As a metric to measure the information freshness, Age of Information (AoI) is widely used to evaluate low-latency high-reliability communication. Since the communication in Industrial Internet of Things (IIoT) often has the special requirements, the relationship among AoI, reliability and block length in time-critical scenarios with bounded latency and non-time-critical scenarios without bounded latency is explored respectively in this paper, based on erasure channels using rateless codes. We build a model using LT codes over erasure channels and consider two metrics: average AoI and bounded AoI. Theoretical analysis and simulation results show that AoI has a minimum value with different block lengths. Furthermore, we draw a conclusion that the minimum value of AoI under different reliability is monotonically decreasing functioned with reliability. Finally, the impact of overhead on reliability and AoI of LT codes in time-critical scenarios is studied.

关键词： Measurement Codes Simulation Reliability theory Information age Communications technology Time factors Low latency communication Industrial Internet of Things

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Collaborative Auto-encoding for Blind Image Quality Assessment

Collaborative Auto-encoding for Blind Image Quality Assessme...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zehong Zhou Fei Zhou Guoping Qiu College of Electronic and Information Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen Institute for Artificial Intelligence and Robotics for Society Shenzhen China Peng Cheng Laboratory Shenzhen China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen China School of Computer Science University of Nottingham Nottingham U.K

Blind image quality assessment (BIQA) is a challenging problem with important real-world applications. Recent efforts attempting to exploit powerful representations by deep neural networks (DNN) are hindered by the lack of subjectively annotated data. This paper presents a novel BIQA method which overcomes this fundamental obstacle. Specifically, we design a pair of collaborative autoencoders (COAE) consisting of a content autoencoder (CAE) and a distortion autoencoder (DAE) that work together to extract content and distortion representations, which are shown to be highly descriptive of image quality. While the CAE follows a standard codec procedure, we introduce the CAE-encoded feature as an extra input to the DAE's decoder for reconstructing distorted images, thus effectively forcing DAE's encoder to extract distortion representations. The self-supervised learning framework allows the COAE including two feature extractors to be trained by almost unlimited amount of data, thus leaving limited samples with annotations to finetune a BIQA model. We will show that the proposed BIQA method achieves state-of-the-art performance and has superior generalization capability over other learning based models. All the related codes, trained models, and the supplementary material are available at: https://***/Macro-Zhou/NRIQA-VISOR/.

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Enhancing the McEliece Scheme Based on Concatenation of Polar Codes and Blocked QC-LDPC Codes

Enhancing the McEliece Scheme Based on Concatenation of Pola...

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IEEE Conference on Communications and Network Security (CNS)

作者： Xin Lin Yusun Fu Zihao Wang Junpeng Yin Ningbo Artificial Intelligence Institute Shanghai Jiao Tong University Ningbo China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350375961

ISBN: (纸本)9798350375978

This paper proposes an enhanced McEliece cryptosystem, leveraging a concatenation scheme of Polar codes and blocked QC-LDPC codes. During the key generation phase, the generator matrices for both Polar and blocked QC-LDPC codes are employed as private keys. These matrices are subsequently multiplied by reversible and random permutation matrices respectively. The products of these multiplications are then combined to form the public key, effectively concealing the structural properties of the generator matrices. In the encryption phase, the plaintext is encrypted sequentially through the Polar codes and the blocked QC-LDPC codes. This process introduces two layers of random error vectors, culminating in the generation of the encrypted ciphertext. The proposed scheme reduces the public key length by 54% and doubles the security level compared to the original McEliece scheme with similar parameters. It also significantly reduces the total key length compared to improved schemes based on Polar codes and QC-LDPC/MDPC codes at an 80-bit security level, and shortens the private key length compared to schemes based on Polar-LDPC concatenated codes. Notably, the scheme is secure against known structural and decoding attacks while increases resistance to information set decoding (ISD) attacks. Through the randomization and hashing of the plaintext, the proposed scheme can achieve IND-CCA2 security.

关键词： Resistance Codes Public key Network security Generators Vectors Decoding Encryption Concatenated codes Polar codes

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HU-Net: Hessian-incorporated U-Net for CBCT projection-domain metal segmentation with guidance from SAM

HU-Net: Hessian-incorporated U-Net for CBCT projection-domai...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Chen Jiang Tianling Lyu Gege Ma Yan Xi Yang Chen Wentao Zhu Zhejiang Lab Hangzhou China Hangzhou Institute for Advanced Study of UCAS Hangzhou China Shanghai First-Imaging Tech. Shanghai China Laboratory of Image Science and Technology Southeast University Nanjing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

Metal artifact is a prevailing factor reducing the image quality of CBCT in minimally invasive spine surgery. In CBCT, conventional metal artifact reduction algorithms pay more attention to the inpainting of the metal traces, but metal segmentation is also challenging. Despite the current success in image segmentation with deep learning, the substantial expense associated with annotating metal traces in the projection domain makes most of these approaches impractical for this task. To address this, we propose a Hessian-incorporated U-Net (HU-Net) for CBCT projection-domain metal segmentation with guidance from SAM. The proposed method has been tested on both digital phantom data and clinical CBCT data. According to the experimental results, our method has demonstrated notable improvement in the segmentation of metal traces across both datasets. This paper presents a feasible approach for metal trace segmentation in the projection domain with deep learning and has the potential to be applied in clinical applications.

关键词： Deep learning Image quality Image segmentation Minimally invasive surgery Mechatronics Metals Phantoms

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