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A Compact Dead-Zone-Free Multi-Channel Stimulation and Recording System for Precision Peripheral Nerve Modulation 46

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A Compact Dead-Zone-Free Multi-Channel Stimulation and Recor...

46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Xing, Yanjie Chen, Weihuang Yu, Danlei Gao, Xiang Bao, Jieting Xu, Kedi Zhejiang China Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Key Laboratory of Biomedical Engineering of Education Ministry Department of Biomedical Engineering Zhejiang University Hangzhou China Institute of Advanced Digital Technology and Instrument Zhejiang University Hangzhou310027 China Zhejiang Provincial Key Laboratory for Network Multimedia Technologies Zhejiang University Hangzhou310027 China Binjiang Institude of Zhejiang University Hangzhou310027 China Nanhu Brain-computer interface institute Hangzhou311100 China Zhejiang University Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China

ISBN: (纸本)9798350371499

Peripheral nerve modulation has a certain potential in motor recovery, but the majority of systems suffer from the drawback of dead time caused by switching channels to achieve multi-channel stimulation. As a response to this challenge, we have developed a peripheral nerve modulation device with 64-channel recording and 16-channel stimulation without dead time. Through modulation of the rat sciatic nerve, we identified the crucial role of initial phase and amplitude in the outcomes of dual-channel combined stimulation. This design not only enhanced neural activation capabilities with constant power supply but also achieves more precise neural activation and reduces unintentional activation. © 2024 IEEE.

关键词： Recording instruments

Self-supervised Image-specific Prototype Exploration for Weakly Supervised Semantic Segmentation

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arXiv 2022年

作者： Chen, Qi Yang, Lingxiao Lai, Jianhuang Xie, Xiaohua School of Computer Science and Engineering Sun Yat-Sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Weakly Supervised Semantic Segmentation (WSSS) based on image-level labels has attracted much attention due to low annotation costs. Existing methods often rely on Class Activation Mapping (CAM) that measures the correlation between image pixels and classifier weight. However, the classifier focuses only on the discriminative regions while ignoring other useful information in each image, resulting in incomplete localization maps. To address this issue, we propose a Self-supervised Image-specific Prototype Exploration (SIPE) that consists of an Image-specific Prototype Exploration (IPE) and a General-Specific Consistency (GSC) loss. Specifically, IPE tailors prototypes for every image to capture complete regions, formed our Image-Specific CAM (IS-CAM), which is realized by two sequential steps. In addition, GSC is proposed to construct the consistency of general CAM and our specific IS-CAM, which further optimizes the feature representation and empowers a self-correction ability of prototype exploration. Extensive experiments are conducted on PASCAL VOC 2012 and MS COCO 2014 segmentation benchmark and results show our SIPE achieves new state-of-the-art performance using only image-level labels. The code is available at https://***/chenqi1126/SIPE. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

Texture-guided Saliency Distilling for Unsupervised Salient Object Detection

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arXiv 2022年

作者： Zhou, Huajun Qiao, Bo Yang, Lingxiao Lai, Jianhuang Xie, Xiaohua School of Computer Science and Engineering Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Deep Learning-based Unsupervised Salient Object Detection (USOD) mainly relies on the noisy saliency pseudo labels that have been generated from traditional handcraft methods or pre-trained networks. To cope with the noisy labels problem, a class of methods focus on only easy samples with reliable labels but ignore valuable knowledge in hard samples. In this paper, we propose a novel USOD method to mine rich and accurate saliency knowledge from both easy and hard samples. First, we propose a Confidence-aware Saliency Distilling (CSD) strategy that scores samples conditioned on samples’ confidences, which guides the model to distill saliency knowledge from easy samples to hard samples progressively. Second, we propose a Boundary-aware Texture Matching (BTM) strategy to refine the boundaries of noisy labels by matching the textures around the predicted boundaries. Extensive experiments on RGB, RGBD, RGB-T, and video SOD benchmarks prove that our method achieves state-of-the-art USOD performance. Code is available at ***/moothes/A2S-v2. Copyright © 2022, The Authors. All rights reserved.

关键词： Textures

Exploring Instance Relation for Decentralized Multi-Source Domain Adaptation

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Exploring Instance Relation for Decentralized Multi-Source D...

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

作者： Yikang Wei Yahong Han College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Lab of Machine Learning Tianjin University Tianjin China Peng Cheng Laboratory Shenzhen China

Multi-source domain adaptation aims to transfer knowledge from multiple labeled source domains to an unlabeled target domain and reduce the domain shift. Considering data privacy and storage cost, the data from different domains are isolated, which leads to the difficulty of domain adaptation. To reduce the domain shift on the decentralized source domains and target domain, we propose an instance relation consistency method for decentralized multi-source domain adaptation. Specifically, we utilize the models from other domains as bridges to conduct domain adaptation. We impose inter-domain instance relation consistency on the isolated source and target domain to transfer the semantic relation knowledge across different domain models. Meanwhile, we exploit intra-domain instance relation consistency to learn the intrinsic semantic relation across different data views. Experiments on three benchmarks indicate the effectiveness of our method for decentralized multi-source domain adaptation.

关键词： Bridges Adaptation models Data privacy Costs Semantics Signal processing Benchmark testing

Two Model-Free Schemes for Solving Kinematic Tracking Control of Redundant Robot Manipulators Based on Cmac Networks

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SSRN

SSRN 2022年

作者： Tan, Ning Li, Chaoyuan Yu, Peng Ni, Fenglei School of Computer Science and Engineering Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University Guangzhou510006 China Harbin China

Kinematic tracking control of robotic manipulators is a basic and vital issue in robotics. Existing studies show that cerebellum, a pivotal region of brain, plays a major role in human motion control, which prompts us to devise two model-free CMAC-based (MFCB) schemes for the kinematic tracking control of redundant manipulators in this article. The first MFCB scheme is based on a CMAC network and a GND algorithm. The CMAC network is utilized to generate approximate joint angle commands for the manipulator. By using the Jacobian matrix of the manipulator which is estimated by the GND algorithm, the task space error is transformed into joint space error, which is taken as the teaching signal for training the CMAC. For the second scheme, another CMAC network replaces the GND algorithm to achieve the estimation of the Jacobian matrix of the manipulator. The effectiveness and robustness of the proposed control schemes are verified by both simulations and physical experiments. © 2022, The Authors. All rights reserved.

关键词： Redundant manipulators

Exploring Dual-task Correlation for Pose Guided Person Image Generation

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arXiv 2022年

作者： Zhang, Pengze Yang, Lingxiao Lai, Jianhuang Xie, Xiaohua School of Computer Science and Engineering Sun Yat-Sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Pose Guided Person Image Generation (PGPIG) is the task of transforming a person image from the source pose to a given target pose. Most of the existing methods only focus on the ill-posed source-to-target task and fail to capture reasonable texture mapping. To address this problem, we propose a novel Dual-task Pose Transformer Network (DPTN), which introduces an auxiliary task (i.e., source-to-source task) and exploits the dual-task correlation to promote the performance of PGPIG. The DPTN is of a Siamese structure, containing a source-to-source self-reconstruction branch, and a transformation branch for source-to-target generation. By sharing partial weights between them, the knowledge learned by the source-to-source task can effectively assist the source-to-target learning. Furthermore, we bridge the two branches with a proposed Pose Transformer Module (PTM) to adaptively explore the correlation between features from dual tasks. Such correlation can establish the fine-grained mapping of all the pixels between the sources and the targets, and promote the source texture transmission to enhance the details of the generated target images. Extensive experiments show that our DPTN outperforms state-of-the-arts in terms of both PSNR and LPIPS. In addition, our DPTN only contains 9.79 million parameters, which is significantly smaller than other approaches. Our code is available at: https://***/PangzeCheung/Dual-task-Pose-Transformer-Network. © 2022, CC BY.

关键词： Textures

One-step Bipartite Graph Cut: A Normalized Formulation and Its Application to Scalable Subspace Clustering

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arXiv 2023年

作者： Fang, Si-Guo Huang, Dong Wang, Chang-Dong Lai, Jian-Huang The College of Mathematics and Informatics South China Agricultural University Guangzhou China The School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

The bipartite graph structure has shown its promising ability in facilitating the subspace clustering and spectral clustering algorithms for large-scale datasets. To avoid the post-processing via k-means during the bipartite graph partitioning, the constrained Laplacian rank (CLR) is often utilized for constraining the number of connected components (i.e., clusters) in the bipartite graph, which, however, neglects the distribution (or normalization) of these connected components and may lead to imbalanced or even ill clusters. Despite the significant success of normalized cut (Ncut) in general graphs, it remains surprisingly an open problem how to enforce a one-step normalized cut for bipartite graphs, especially with linear-time complexity. In this paper, we first characterize a novel one-step bipartite graph cut (OBCut) criterion with normalized constraints, and theoretically prove its equivalence to a trace maximization problem. Then we extend this cut criterion to a scalable subspace clustering approach, where adaptive anchor learning, bipartite graph learning, and one-step normalized bipartite graph partitioning are simultaneously modeled in a unified objective function, and an alternating optimization algorithm is further designed to solve it in linear time. Experiments on a variety of general and large-scale datasets demonstrate the effectiveness and scalability of our approach. © 2023, CC BY.

关键词： K-means clustering

Attention-Guided Siamese Network for Clothes-Changing Person Re-identification 11th

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Attention-Guided Siamese Network for Clothes-Changing Person...

11th International Conference on Image and Graphics, ICIG 2021

作者： Feng, Zhanxiang Huang, Sien Lai, Jianhuang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangzhou Xinhua University Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9783030873578

Person re-identification (re-id) has achieved significant progresses in recent years. However, the existing methods generally assume that the clothes of pedestrians remain unchanged throughout the surveillance periods, which is contradict to realistic environment where pedestrians may change their clothes. Current re-id techniques may encounter a dramatic performance degradation when the pedestrians change the clothes. In this paper, we propose a novel attention-guided siamese network (AGS-Net) to solve the cross-clothes re-id challenge. The AGS-Net integrates the visual and contour information together by developing a dual-branch structure, among which one extracts powerful features from raw inputs while the other learns robust features from the sketch image. Moreover, we exploit the attention modules to emphasize reliable identity-related features considering changing clothes and avoid generating features sensitive to clothes. Specifically, we propose a clothes-change invariant constraint to learn clothes-invariant features. Experimental results verify the effectiveness of our approach. © 2021, Springer Nature Switzerland AG.

关键词： Computers

Preparation and Simulation of Quantum States of Matrices 6

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Preparation and Simulation of Quantum States of Matrices

6th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2022

作者： Liu, Zhengyu Liu, Xiaonan Xie, Haoshan Zhao, Chenyan School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450000 China National Supercomputing Center in Zhengzhou Zhengzhou450000 China State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou450000 China

ISBN: (纸本)9781450397148

A basic problem of quantum computing is how to effectively express classical data in quantum systems. This problem is called state preparation problem, and the process of preparing quantum states is called coding. In this paper, the classical data of matrix is transformed into quantum states in the form of amplitude coding and simulated. This paper first introduces the detailed process of matrix transformation into the linear combination of quantum ground state, then designs a quantum circuit model to realize amplitude coding according to the idea of binary tree model and quantum random walk algorithm, and finally simulates the three matrices on IBM quantum cloud platform, and analyzes and introduces many situations. The experimental results verify the feasibility of converting the matrix into quantum states by amplitude coding, analyze the causes of errors in the experimental process, and put forward the corresponding solutions. This paper introduces in detail the whole process of matrix conversion to quantum states and the construction of quantum circuits for simulation. Compared with the existing experiments, the quantum circuits constructed in this paper are shorter in length, less in number of quantum gates, and more versatile. © 2022 Association for computing machinery.

关键词： Matrix algebra