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Recent advances in wireless epicortical and intracortical neuronal recording systems

science China(information sciences) 2022年第4期65卷 5-22页

作者： Bowen JI Zekai LIANG Xichen YUAN Honglai XU Minghao WANG Erwei YIN Zhejun GUO Longchun WANG Yuhao ZHOU Huicheng FENG Honglong CHANG Jingquan LIU Unmanned System Research Institute Northwestern Polytechnical University Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace School of Mechanical EngineeringNorthwestern Polytechnical University Collaborative Innovation Center of Northwestern Polytechnical University NEURACLE TECH (China) Co. Ltd. College of Electronics and Information Hangzhou Dianzi University Defense Innovation Institute Academy of Military Sciences (AMS) Tianjin Artificial Intelligence Innovation Center (TAIIC) National Key Laboratory of Science and Technology on Micro/Nano Fabrication Department of Micro/Nano ElectronicsShanghai Jiao Tong University

An implantable brain-computer interface(BCI) has proven to be effective in the field of sensory and motor function restoration and in the treatment of neurological disorders. Using a BCI recording system,we can transform current methods of human interaction with machines and the environment, especially to help those with cognitive and mobility disabilities regain mobility and reintegrate into society. However,most reported work has focused on a simple aspect of the whole system, such as electrodes, circuits, or data transmission, and only a very small percentage of systems are wireless. A miniature, lightweight, wireless,implantable microsystem is key to realizing long-term, real-time, and stable monitoring on freely moving animals or humans in their natural conditions. Here, we summarize typical wireless recording systems, from recording electrodes, processing chips and controllers, wireless data transmission, and power supply to the system-level package for either epicortical electrocorticogram(ECo G) or intracortical local field potentials(LFPs)/spike acquisition, developed in recent years. Finally, we conclude with our vision of challenges in next-generation wireless neuronal recording systems for chronic and safe applications.

关键词： wireless implant neuronal recording system recording electrodes processing chips wireless data transmission power supply system-level package

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DFCM: A Distributed Floor Control Mechanism in PoC

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Chinese Journal of Electronics 2023年第4期20卷 739-746页

作者： LIAO Jianxin LIU Haipeng LI Tonghong WANG Jingyu ZHU Xiaomin State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China EBUPT Information Technology Co. Ltd. Beijing China Department of Computer Science Technical University of Madrid Spain

With the fast progress of Collaborative multimedia application (CMA) running on overlay network, Push to talk over cellular (PoC) attracts significant interest in the field of mobile cellular communications. PoC is an on-demand, resource efficient, half-duplex multimedia service. Due to the half-duplex nature, only one user is allowed to speak at a time, and the others listen. If listeners want to speak, they have to contend for the right to speak and wait for the permission if request waiting is supported. Open mobile alliance (OMA) proposed a centralized controlling based floor control mechanism for speak right coordination in PoC, which is called Talk burst control protocol (TBCP). But it has many limitations in scalability, reliability and robustness as all centralized approaches have. In addition, none of available distributed approaches well meets mobile users' requirements in PoC, especially for the request waiting function. In this paper we put forward a novel distributed floor control mechanism with request waiting for PoC, which is called Distributed floor control mechanism (DFCM). From the performance evaluation of DFCM and TBCP, we find that DFCM outperforms TBCP especially when performance of the central node, Controlling server (CS), drops down due to heavy load. Finally, this paper is summarized and future work is forecasted.

关键词： Performance evaluation Protocols Overlay networks Scalability Half-duplex system Collaboration Robustness

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A Software-Defined Reconfigurable Battery system Prototype

A Software-Defined Reconfigurable Battery System Prototype

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Asia-Pacific Conference on Communications

作者： Siyu Jin Baochang Liu Yanglin Zhou Junrui Liang Song Ci Department of Electrical Engineering Tsinghua University Beijing China School of Information Science and Technology ShanghaiTech University Shanghai China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing China

ISBN: (数字)9798331517816

ISBN: (纸本)9798331517823

In the energy internet era, distributed energy storage systems will be widely used in various industrial, commercial, and residential scenarios. However, the modeling of energy flow and the digital flow of information operate on different temporal and spatial scales in traditional energy storage systems, lacking deep integration between energy flow and information flow. This prevents it from completely addressing the complex battery cell balancing and low safety issues in current distributed energy storage systems. The proposal of software-defined energy storage provides a new approach to solve the inherent problems in the battery industry by introducing the idea of internet shielding terminal differences into energy storage system. We proposed a software-defined reconfiguration battery system (SRBS) in this paper, which can manage different types of energy storage media to adapt to varying loads and operating conditions. Energy-type batteries, power-type batteries, and supercapacitors are integrated together to form an efficient and safe energy storage and power supply system. The hardware architecture and software control method of SRBS are described in detail. Finally, a real-world prototype is charged and discharged to show the successful coexistence and operation of different energy storage media within a single system.

关键词： Prototypes Energy Internet systems architecture computer architecture Transforms Media Software Hardware Batteries Energy storage

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Lung Detection Based on YOLOv3 6th

Lung Detection Based on YOLOv3

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6th Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2019

作者： Zhao, Qingda Zhang, Fuquan Ma, Linjuan Traditional Chinese Medicine Department of Chinese PLA General Hospital Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350117 China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9789811650352

Lung detection is attached much importance to in medical imaging to improve the efficiency and accuracy of the doctor’s work. Recently, it has become very popular among the researchers to explore deep medical technology. In our work, we present a new method of lung detection, which based on YOLOv3 Model. Our goal is to make a contribution to a lung detection system to make it more efficiency and accuracy for the doctor’s work. ChestX-ray14 dataset is used to perfrom the experiments and the result is sound. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Efficiency

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The Weapon Target Assignment in Adversarial Environments 1

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2nd International Conference on Cognitive Computation and systems, ICCCS 2023

作者： Gao, Rui Zheng, Shaoqiu Wang, Kai Zhang, Longlong Zhu, Zhiyuan Yuan, Xiaohu College of Electronic and Information Engineering Southwest University Chongqing400715 China Key Lab of Information System Requirement Nanjing Research Institute of Electronics Engineering Nanjing210007 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

ISBN: (数字)9789819708857

ISBN: (纸本)9789819708840

In context of the current weapon target assignment (WTA) problem, consideration of target capability is often disregarded. To address this issue, we propose an optimized model for weapon target assignment in complex environments. Our model provides a comprehensive description and conducts a detailed analysis of constraints and objective functions in the context of adversarial weapon target assignment (AWTA). To efficiently tackle the assignment problem, we employ the multi-particle swarm optimization algorithm (MOPSO). During the optimization process, we incorporate learning factors and inertia weights to update particle positions, seamlessly implementing the model with a set of adjustable parameters. Experimental results demonstrate that our model effectively captures the concept of adversarial scenarios. Furthermore, by utilizing the MOPSO optimization algorithm, we achieve a superior Pareto front even for small-scale adversarial scenarios, successfully resolving the adversarial WTA problem. Our research contributes an effective optimization approach to address the weapon task assignment problem and offers valuable inspiration for future investigations into multi-objective research. In conclusion, our study reveals the potential of multi-objective optimization research in the context of weapon target assignment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multiobjective optimization

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Cross-heterogeneous Domain Authentication Scheme Based on Blockchain

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Journal of Artificial Intelligence and technology 2021年第2期1卷 92-100页

作者： Jing Liu Yixin Liu Yingxu Lai Rongchen Li Siyu Wu Sami Mian College of Computer Science Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Shaanxi Key Laboratory of Network and System Security Xi’an 710071China Science and Technology on Information Assurance Laboratory Beijing 100072China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of EducationBeijing 100124China Fan Gongxiu Honors College Beijing University of TechnologyBeijing 100124China University of Pittsburgh PittsburghPA 15260USA

With the rising popularity of the Internet and the development of big data technology,an increasing number of organizations are opting to cooperate across domains to maximize their *** organizations use public key infrastructure to ensure security in accessing their data and ***,with the continuous development of identity-based encryption(IBE)technology,small-and medium-sized enterprises are increasingly using IBE to deploy internal authentication *** solve the problems that arise when crossing heterogeneous authentication domains and to guarantee the security of the certification process,we propose using blockchain technology to establish a reliable cross-domain authentication *** the distributed and tamper-resistant characteristics of the blockchain,we design a cross-domain authentication model based on blockchain to guarantee the security of the heterogeneous authentication process and present a cross-domain authentication protocol based on *** model does not change the internal trust structure of each authentication domain and is highly ***,on the premise of ensuring security,the process of verifying the signature of the root certificate in the traditional cross-domain authentication protocol is improved to verify the hash value of the root certificate,thereby improving the authentication *** developed prototype exhibits generality and simplicity compared to previous methods.

关键词： public key infrastructure identity-based encryption cross-domain authentication blockchain

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Ensemble Learning-based Traffic Classification with Small-Scale Datasets for Wireless Networks

Ensemble Learning-based Traffic Classification with Small-Sc...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Xiaorong Wang Wenting Wei Xiaoshan Yu Danyang Zheng Neeraj Kuma Lei Liu State Key Laboratory of Integrated Service Networks Xidian University Xi'an China School of Computing and Artificial Intelligence Southwest Jiaotong University China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology India Guangzhou Institute of Technology Xidian University Xi'an China Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications Xi'an China

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Traffic classification is an essential part of intelligent network management in Fifth generation (5G) networks, from quality of service guarantees to security monitoring. With the evolution of diverse services and applications over 5G networks, traffic patterns and their implications also becoming more complicated, especially in wireless networks. How to dynamically manage the growing traffic demand and satisfy the quality of service (QoS) needs of new applications is particularly critical. The biggest challenge is how to identify traffic types accurately without application information and manual features. However, the existing deep learning (DL)-based classifiers heavily rely on extensive and high-quality samples, which proves impractical to obtain in wireless networks. To overcome this limitation, we propose an ensemble Learning based traffic classification model to provide accurate identification with small-scale datasets, where Attention-based Long Short-Term Memory Networks are bagged to train this classifier. The ensemble learning approach employs a combinatorial design involving five base models to construct more accurate and robust learning models. This enables effective feature absorption, making it feasible to train classifiers on limited sample sets. Comprehensive experiments are conducted using a real-world dataset covering 16 applications. Results demonstrate that, even with a mere 10% of training samples, our proposed model achieves a classification accuracy of 95.57% and a precision of 95.83%, surpassing multiple cutting-edge methods.

关键词： Training Accuracy 5G mobile communication Wireless networks Computational modeling Quality of service Telecommunication traffic

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Traffic Pattern Sharing for Federated Traffic Flow Prediction with Personalization

Traffic Pattern Sharing for Federated Traffic Flow Predictio...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Hang Zhou Wentao Yu Sheng Wan Yongxin Tong Tianlong Gu Chen Gong School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China SKLSDE Beihang University China Engineering Research Center of Trustworthy AI (Ministry of Education) Jinan University China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Accurate Traffic Flow Prediction (TFP) is crucial for enhancing the efficiency and safety of transportation systems, so it has attracted intensive researches by exploiting spatial-temporal dependencies within road networks. However, existing works only consider the case of centralized data collection with all traffic data observed, which may raise privacy concerns as each region of a city may have its own traffic administration department and the traffic data is not allowed to distribute. Therefore, this paper proposes to use Federated Learning (FL) to address this issue by allowing all clients (i.e., traffic administration departments in all regions in our problem) to collaboratively train TFP models without exchanging raw data, thereby offering a solution in maintaining data privacy. Nevertheless, most existing FL methods aim to learn a global model that performs well universally, so they cannot well handle the non-Independent and Identically Distributed (non-IID) traffic data naturally over different regions. To cope with this problem, this paper develops a new FL framework termed “personalized Federated learning with Traffic Pattern Sharing” (FedTPS) to solve federated TFP problem. Our FedTPS critically exploits the underlying common traffic patterns (e.g., morning and evening rush hours) shared across different city regions and meanwhile maintaining the region-specific data characteristics in a personalized FL manner. Specifically, to extract the common traffic patterns, we decompose the traffic data in each client via using discrete wavelet transform, where the low-frequency components uncover the stable traffic dynamics of different regions and thus can be considered as the common traffic patterns. These common patterns are then shared among different clients through traffic pattern repositories on the server side to aid the global collaborative traffic flow modeling. Moreover, the model components capturing spatial-temporal dependencies in traffic data are r

关键词： Training Data privacy Federated learning Urban areas Transportation Transforms Traffic control Data models Safety Servers

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Research on Optimization and Scheduling of MPTCP Data Networks in GNSS Network Reference Stations

Research on Optimization and Scheduling of MPTCP Data Networ...

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IEEE International Conference on systems, Man and Cybernetics

作者： Fengqi Hao Yuxin Wang Hoiio Kong Dexin Ma Xiyuan Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Data Science City University of Macau Macau China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Communication College Qingdao Agricultural University Qingdao China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The Global Navigation Satellite system Network Reference Stations (GNSS-NRS) are pivotal in modern po-sitioning and navigation applications. However, GNSS-NRS face significant communication challenges, including instability during rapid user movement and inconsistent coverage by different operators, which often results in data transmission interruptions and delays. Moreover, the conventional Multipath Transmission Control Protocol (MPTCP) scheduling strategy does not accommodate the diversity of the data types managed by GNSS-NRS. This default approach treats all data uniformly, even under deteriorating network conditions. To overcome these limitations, this paper introduces a ‘Multiple Reception’ strategy based on MPTCP that ensures timely and reliable GNSS data transmission. This novel strategy differentiates between observational and control data based on their real-time criticality and applies tailored transmission techniques. Under adverse conditions, it utilizes multiple transmission paths and selectively receives control data, thus prioritizing the transfer of critical information. We conducted simulation experiments using MiniNet to compare the ‘Multiple Reception’ method against conventional and redundant strategies, demonstrating that it significantly reduces data loss rates in GNSS-NRS.

关键词： Global navigation satellite system Protocols Real-time systems Delays MPTCP Reliability Data communication Kernel Optimization Faces

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CSCL-DTI: predicting drug-target interaction through cross-view and self-supervised contrastive learning

CSCL-DTI: predicting drug-target interaction through cross-v...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xuan Lin Xi Zhang Zu-Guo Yu Yahui Long Xiangxiang Zeng Philip S. Yu College of Computer Science Xiangtan University China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University China Singapore Immunology Network (SIgN) Agency for Science Technology and Research (A*STAR) Singapore College of Information Science and Engineering Hunan University China Department of Computer Science University of Illinois Chicago USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Accurately predicting drug-target interactions (DTI) is a critical step in drug discovery. Existing methods of DTI prediction primarily employ Simplified Molecular-Input Line-Entry system (SMILES) sequences or molecular graphs to learn drug representations. However, the features learned by such single-view approach is prone to incomplete. While some multiview methods that consider the views of both SMILES sequences and molecular graphs have been developed, these methods often fall in short in capturing potential interactions between views. In this work, we propose a novel dual contrastive learning framework CSCL-DTI for DTI prediction. First, we design a contrastive-enhanced cross-view representation learning (CVRL) to learn representations for drugs. In this module, Transformer-based and graph convolutional network (GCN)-based encoders are separately adopted to learn view-specific representations, followed by contrastive learning to enrich the representations by accounting for the potential interplay between local chemical context and topological structure. Second, we combine Transformer with self-supervised contrastive learning (SSCL) to learn representations for targets by modelling protein amino acids sequences. The scheme allows to effectively preserve the intrinsic characteristics of the sequences. Finally, we introduce a bilinear attention network to obtain an integrated representation by adaptively incorporating drug and target representations. Benchmarking experiments on two datasets demonstrated that CSCL-DTI 1 outperforms six state-of-the-art methods.

关键词： Drugs Proteins Representation learning Protein engineering Adaptation models Contrastive learning Predictive models Transformers Amino acids Diffusion tensor imaging

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