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IEEE International Conference on systems, Man and Cybernetics

作者： Jinbiao Liu Gansheng Tan Yixuan Sheng Jiaole Wang Wenjie Lu Honghai Liu State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai China State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China School of Computing University of Portsmouth Portsmouth UK

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

It is evident that corticomuscular coherence (CMC), representing the functional coupling between motor cortex and muscle tissues, plays a crucial role in neurophysiologic studies and applications. It is hypothesized that there is an unknown time delay comprising at least neural conduction time in the process of corticomuscular interaction. In this study, we developed a novel delay estimation method, defined as the rate of voxels change (RVC) for the estimation of time delay in two coupled physiological signals. The RVC is the dynamic variation of the local CMCs observed in different time offsets. Both simulation and physiological data confirm the capability of RVC in estimating cortical-muscular delay. The underlying mechanisms of individual discrepancy of the latency is also investigated via exploring the correlation between delays, brain activity and motor performance. Correlation analyses indicate an intrinsic link between the connectivity strength of the brain network and the length of time delay in cortical-muscular interactions.

关键词： Correlation Delay effects Delay estimation Muscles Brain modeling Physiology Data models

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A Theoretical Framework for Value Co-creation Analysis of Ecological Interaction between 6G and Its Enabling Technologies 24

A Theoretical Framework for Value Co-creation Analysis of Ec...

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Proceedings of the 2024 10th International Conference on Communication and Information Processing

作者： Jiao Zhang Xuyu Zhao Fengshuai Du Xinyue Yan Tianjian Yang Haiyan Yan Jiayin Qi School of Economics and Management Beijing University of Posts and Telecommunications Beijing Beijing China China Mobile Research Institute Beijing Beijing China Scienze Politiche e delle relazioni internazionali University of Palermo Palermo Italy and Huangpu Research School of Guangzhou University Guangzhou University Guangzhou Guangdong China School of Management Fudan University Shanghai Shanghai China School of Management Shanghai University of International Business and Economics Shanghai Shanghai China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou Guangdong China and Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing Beijing China

ISBN: (纸本)9798400717444

As demand for communication increases in terms of rate, reliability and security, 6G networks will provide more extreme communication experiences and richer communication services. It is therefore crucial that 6G enabling technologies are developed in order to meet the performance requirements of 6G networks. This study firstly analyses the core elements and development of the 6G ecosystem, and secondly reviews the ecological interactions of 6G and its enabling technologies. Notably, this study, based on the resource orchestration theory, has explored the three-phase evolution and value co-creation of the ecological interactions of 6G and its enabling technologies and provides a summary of the challenges for future development. The study is expected to provide theoretical support for the future system planning and evolutionary development of 6G ecology and its enabling technology ecology.

关键词： 6G ecology

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Feature Fusion of sEMG and Ultrasound Signals in Hand Gesture Recognition

Feature Fusion of sEMG and Ultrasound Signals in Hand Gestur...

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IEEE International Conference on systems, Man and Cybernetics

作者： Jia Zeng Yu Zhou Yicheng Yang Jiaole Wang Honghai Liu State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai China State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China School of Computing University of Portsmouth Portsmouth UK

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Multi-modal sensory fusion is believed to obtain higher accuracy in gesture recognition. Its difficulty lies in mining discriminative features and fusing features from different modalities. Surface electromyography(sEMG) and ultrasound signals are typical signal modalities in gesture recognition. It is expected that the fusion of them can take advantage of the complementarity of electrophysiological information and muscle morphology information. This paper proposed two kinds of feature fusion method. The one is concatenating the manual designed sEMG and ultrasound features, and the other is a convolutional neural network (CNN) based feature exaction and fusion method for sEMG and ultrasound signals. Eight able-bodied subjects were involved to participate in the experiments. In the experiments, four channels of sEMG and A-mode ultrasound signals corresponding to 20 gestures were collected synchronously to evaluate the proposed method. The experimental results demonstrated that the fusion sEMG-ultrasound feature always outperformed the separate sEMG or ultrasound feature regardless of the feature extraction method, and as for fusion sEMG-ultrasound feature, the CNN based method achieve a high accuracy (97.38±1.49%) in 20 gestures, which surpassed the method of concatenating the manual designed features and applying machine learning algorithm (LDA, KNN, SVM).

关键词： Support vector machines Ultrasonic imaging Surface morphology Gesture recognition Manuals Feature extraction Task analysis

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Face Recognition for Embedded system Based on Optimized Triplet Loss Neural Network

Face Recognition for Embedded System Based on Optimized Trip...

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Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), International Conference on

作者： Yubo Zhao Cheng Yang Yushi Wang Jing Cai Yanbing Xue Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China Beijng-Dublin International College Beijing University of Technology Beijing China

ISBN: (数字)9781728181431

ISBN: (纸本)9781728181448

Algorithms based on Convolution neural networks are significantly better than artificially designed features. Among these methods, the network trained with the triplet loss function can usually obtain better features than the direct classification, and thus get better recognition accuracy. The input of the triplet loss function consists of an anchor picture, a positive picture and a negative picture. The distance between the anchor picture and the positive picture is reduced by training, thereby achieving the purpose of face recognition. However, the parameters of the network are usually too much that it cannot be directly applied to embedded devices. In this regard, this paper proposes a solution for compression optimization based on triplet loss network. This scheme can reduce the resource overhead of the network, improve the processing speed, and realize high-precision real-time face recognition on the embedded device. This solution greatly improves the performance of the network on embedded devices, and achieves the effect of high recognition accuracy and low resource overhead.

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Enhancement of oxygen evolution reaction activity and durability of Ba0.5Sr0.5Co0.8Fe0.2O3-δ by CO2 thermal treatment

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Journal of Materials Science & technology 2019年第6期35卷 1184-1191页

作者： Fengli Liang Ziqiong Yang Haipeng Deng Jaka Sunarso Lili Yang Junkui Mao College of Energy and Power Engineering.Jiangsu Province Key Laboratory of Aerospace Power System Nanjing University of Aeronautics and Astronautics Nanjing 210016 China Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga93350 Kuching Sarawak Malaysia

This work demonstrates that in situ formation of carbonate layer on the surface of Ba0.5Sr0.5Co0.8Fe0.2O3-δ(BSCF)obtained by exposure to CO2 during heating between 500℃ and 700℃ can provide enhanced oxygen evolution reaction(OER)performance and durability in an alkaline solution relative to the original *** temperatures,i.e., 500℃,600℃,and 700℃ were chosen to perform the CO2 thermal treatment,resulting into BSCF-500,BSCF-600,and BSCF-700 *** OER was enhanced in the order of BSCF-500< BSCF-700 < BSCF-600. BSCF-600 showed the best OER performance, i.e., a low overpotential of 0.36 V required to attain 10 mA cm^-2 current density as well as a mass activity of 74.14 Agcmcat-1 and a specific activity of 5.04 mA cmcat-2 at an overpotential of 0.4 V. The OER performance durability of BSCF-600 was highlighted by its ability to maintain a stable potential of around 1.61 V vs. RHE (RHE: reversible hydrogen electrode) when charged at a constant current density of 10 mA cm^-2 throughout the 800 min continuous chronopotentiometry test. The enhanced OER performance for BSCF-600 relative to the original BSCF is attributed to three factors:(i) higher electrochemically active surface area;(ii) faster charge transfer rate and higher electrical conductivity;and (iii) modified oxidation state of cobalt ions. The formation of thin carbonate layer in BSCF-600 appears to suppress the durability issue observed in BSCF.

关键词： Carbonate Electrocatalysis Oxygen evolution reaction Perovskite Water splitting

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A zero-shot based fingerprint presentation attack detection system

arXiv

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arXiv 2020年

作者： Liu, Haozhe Zhang, Wentian Liu, Guojie Liu, Feng National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of IntelligentInformation Processing College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

With the development of presentation attacks, Automated Fingerprint Recognition systems(AFRSs) are vulnerable to presentation attack. Thus, numerous methods of presentation attack detection(PAD) have been proposed to ensure the normal utilization of AFRS. However, the demand of large-scale presentation attack images and the low-level generalization ability always astrict existing PAD methods' actual performances. Therefore, we propose a novel Zero-Shot Presentation Attack Detection Model to guarantee the generalization of the PAD model. The proposed ZSPAD-Model based on generative model does not utilize any negative samples in the process of establishment, which ensures the robustness for various types or materials based presentation attack. Different from other auto-encoder based model, the Fine-grained Map architecture is proposed to refine the reconstruction error of the auto-encoder networks and a task-specific gaussian model is utilized to improve the quality of clustering. Meanwhile, in order to improve the performance of the proposed model, 9 confidence scores are discussed in this article. Experimental results showed that the ZSPAD-Model is the state of the art for ZSPAD, and the MS-Score is the best confidence score. Compared with existing methods, the proposed ZSPAD-Model performs better than the feature-based method and under the multi-shot setting, the proposed method overperforms the learning based method with little training data. When large training data is available, their results are similar. Copyright © 2020, The Authors. All rights reserved.

关键词： Palmprint recognition

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N-Docker: A NVM-HDD Hybrid Docker Storage Framework to Improve Docker Performance 16th

N-Docker: A NVM-HDD Hybrid Docker Storage Framework to Impro...

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16th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2019

作者： Gu, Lin Tang, Qizhi Wu, Song Jin, Hai Zhang, Yingxi Shi, Guoqiang Lin, Tingyu Rao, Jia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China State Key Laboratory of Intelligent Manufacturing System Technology Beijing100854 China The University of Texas at Arlington ArlingtonTX76019 United States

ISBN: (纸本)9783030307080

Docker has been widely adopted in production environment, but unfortunately deployment and cold-start of container are limited by the low speed of disk. The emerging non-volatile memory (NVM) technology, which has high speed and can store data permanently, brings a new chance to accelerate the deployment and cold-start of container. However, it is expensive to replace the whole hard disk driver (HDD) with NVM. To achieve the fastest deployment and cold-start with lowest cost, we conduct in-depth analysis on the Top-134 images in Docker Hub and obtain two main insights as: (1) the storing latency of layered image has become the bottleneck of container deployment;(2) only a few image layers are required for container cold-start. Based on these two findings, we propose a NVM-HDD hybrid docker storage framework as N-Docker. It can effectively accelerate container cold-start by detecting the bottleneck layers as well as cold-start required layers and storing them into NVM for faster container startup with limited NVM capacity. Experimental results show that N-Docker can accelerate the container deployment by 1.21X and cold-start by 2.96X. Compared to NVM-Docker, which stores all images into NVM, N-Docker achieves the same performance improvements while reducing the usage of NVM by 88.22%. © 2019, IFIP International Federation for Information Processing.

关键词： Containers

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A simple low-degree optimal finite element scheme for the elastic transmission eigenvalue problem

arXiv

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arXiv 2021年

作者： Xi, Yingxia Ji, Xia Zhang, Shuo School of Science Nanjing University of Science and Technology Nanjing210094 China School of Mathematics and Statistics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory on MCAACI Beijing Institute of Technology Beijing100081 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and System Sciences Chinese Academy of Sciences Beijing100190 China

The paper presents a finite element scheme for the elastic transmission eigenvalue problem written as a fourth order eigenvalue problem. The scheme uses piecewise cubic polynomials and obtains optimal convergence rate. Compared with other low-degree and nonconforming finite element schemes, the scheme inherits the continuous bilinear form which does not need extra stabilizations and is thus simple to *** Codes 31A30, 65N30, 47B07 Copyright © 2021, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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CSformer: Bridging Convolution and Transformer for Compressive Sensing

arXiv

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arXiv 2021年

作者： Ye, Dongjie Ni, Zhangkai Wang, Hanli Zhang, Jian Wang, Shiqi Kwong, Sam The Department of Computer Science City University of Hong Kong 999077 Hong Kong The Department of Computer Science & Technology Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China The Peng Cheng Laboratory Shenzhen518052 China The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China

Convolution neural networks (CNNs) have succeeded in compressive image sensing. However, due to the inductive bias of locality and weight sharing, the convolution operations demonstrate the intrinsic limitations in modeling the long-range dependency. Transformer, designed initially as a sequence-to-sequence model, excels at capturing global contexts due to the self-attention-based architectures even though it may be equipped with limited localization abilities. This paper proposes CSformer, a hybrid framework that integrates the advantages of leveraging both detailed spatial information from CNN and the global context provided by transformer for enhanced representation learning. The proposed approach is an end-to-end compressive image sensing method, composed of adaptive sampling and recovery. In the sampling module, images are measured block-by-block by the learned sampling matrix. In the reconstruction stage, the measurement is projected into dual stems. One is the CNN stem for modeling the neighborhood relationships by convolution, and the other is the transformer stem for adopting global self-attention mechanism. The dual branches structure is concurrent, and the local features and global representations are fused under different resolutions to maximize the complementary of features. Furthermore, we explore a progressive strategy and window-based transformer block to reduce the parameter and computational complexity. The experimental results demonstrate the effectiveness of the dedicated transformer-based architecture for compressive sensing, which achieves superior performance compared to state-of-the-art methods on different datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Compressed sensing

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A Hybrid Routing Algorithm based on Destruction Resistance Cost and Reliability in Flying Ad-Hoc Networks

A Hybrid Routing Algorithm based on Destruction Resistance C...

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International Conference on Computer and Communications (ICCC)

作者： Kun Guo Dongbin Wang Hui Zhi Yibo Gao Tao Zhang Tiejun Lv Yueming Lu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China National Engineering Laboratory for Mobile Network Beijing University of Posts and Telecommunications Beijing China TravelSky Technology Limited Beijing China Cyberspace Security Research Center Peng Cheng Laboratory Beijing University of Posts and Telecommunications Beijing China China Academy of Launch Vehicle Technology Beijing China

ISBN: (纸本)9781665409513

Existing routing algorithms in Flying Ad-Hoc Net- works (FANETs) lack the optimization consideration of destruction resistance cost caused by routing recovery when some nodes fail suddenly. In this paper, we propose a hybrid routing algorithm based on destruction resistance cost and reliability. Based on the real-time network topology and network state, the algorithm can provide a multi-parameters-based routing path with low destruction resistance cost for communication transmission. It can ensure that the main routing path can be quickly recovered after interruption and reduce the process of discovering new routing paths. We have constructed a variety of simulation scenarios to analyze the proposed algorithm and the existing algorithms. The simulation results show that the pro- posed algorithm has good performance in reducing destruction resistance cost.

关键词： Resistance Analytical models Costs Computer network reliability Simulation Routing Real-time systems

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