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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Defeng Zhao Zejun Ye Wei-Shi Zheng Ruixuan Wang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE China Department of Network Intelligence Peng Cheng Laboratory Shenzhen China

Current intelligent diagnosis systems are often trained to diagnose a small number of diseases and lack the ability of continually learning new disease knowledge. To have such continual learning ability, the deployed intelligent system needs to be continually updated based on training data of only new diseases, without accessing old data of previously learned diseases due to privacy concerns and challenges in data sharing across medical centers. In this study, a novel and effective prompt learning strategy is proposed to help a pretrained and fixed Vision Transformer (ViT) continually learn new diseases. In particular, a set of unique prompts for each disease are effectively learned such that discriminative disease features can be well extracted from the fixed ViT under the instructions of prompts during feature extraction, even though the ViT feature extractor is pretrained in the natural image domain. Extensive empirical evaluations on two medical image datasets and one natural image dataset demonstrate the superior performance of the proposed method. The source code is available at https://***/zhaoedf/CSPrompt.

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SE-PifPaf: A Multi-Person Human Pose Estimation Methond for Complex Scenes

SE-PifPaf: A Multi-Person Human Pose Estimation Methond for ...

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Image Processing and computer Vision (IPCV), International Conference on

作者： Tianbao Wang Caixia Meng Lei Shi Yufei Gao Qingxian Wang Lin Wei School of Cyber Science and Engineering Zhengzhou University Zhengzhou China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Department of Image and Network Investigation Technology Railway Police College Zhengzhou China

Images in complex scenes are prone to cross-obscuration of people, which brings great challenges to multi-person human pose estimation. Previous multi-person human pose estimation algorithms suffer from limited accuracy and human skeleton loss in complex scenes. To solve the above problems, a novel pose estimation optimization algorithm SE-PifPaf is proposed. Firstly, the grouped convolution network is employed to mine semantic features relevant to the human pose estimation task. Secondly, the attention mechanism is applied to realize the interaction among channels, obtaining the weight of each channel on the pose estimation task in complex scenes. Experimental results show that SE-PifPaf achieves an accuracy of 72.3% on COCO 2017 dataset. More importantly, it improves the accuracy of pose estimation by 1.1% in complex scenes on CrowdPose dataset.

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Computational Offloading in Semantic-Aware Cloud-Edge-End Collaborative networks

arXiv

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arXiv 2024年

作者： Ji, Zelin Qin, Zhijin School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electronic Engineering Tsinghua University Beijing China The Beijing National Research Center for Information Science and Technology Beijing China The State Key Laboratory of Space Network and Communications Beijing China

The trend of massive connectivity pushes forward the explosive growth of end devices. The emergence of various applications has prompted a demand for pervasive connectivity and more efficient computing paradigms. On the other hand, the lack of computational capacity of the end devices restricts the implementation of the intelligent applications, and becomes a bottleneck of the multiple access for supporting massive connectivity. Mobile cloud computing (MCC) and mobile edge computing (MEC) techniques enable end devices to offload local computation-intensive tasks to servers by networks. In this paper, we consider the cloud-edge-end collaborative networks to utilize distributed computing resources. Furthermore, we apply task-oriented semantic communications to tackle the fast-varying channel between the end devices and MEC servers and reduce the communication cost. To minimize long-term energy consumption on constraints queue stability and computational delay, a Lyapunov-guided deep reinforcement learning hybrid (DRLH) framework is proposed to solve the mixed integer nonlinear programming (MINLP) problem. The long-term energy consumption minimization problem is transformed into the deterministic problem in each time frame. The DRLH framework integrates a model-free deep reinforcement learning algorithm with a model-based mathematical optimization algorithm to mitigate computational complexity and leverage the scenario information, so that improving the convergence performance. Numerical results demonstrate that the proposed DRLH framework achieves near-optimal performance on energy consumption while stabilizing all queues. © 2024, CC BY-NC-ND.

关键词： Reinforcement learning

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Energy Management for Single Machine Permanent Magnet Linear Generator (PMLG) for Stand-Alone Application

Energy Management for Single Machine Permanent Magnet Linear...

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International Conference on Control Communication & Computing India (ICCC)

作者： Tuanku Badzlin Hashfi Addy Wahyuddie Abdin Y. Elamin Bisni Fahad Mon Siddiqul Akbar National Water and Energy Center United Arab Emirates University (UAEU) Al Ain UAE Electrical Department College of Engineering United Arab Emirates University (UAEU) Al Ain UAE Computer and Network Engineering United Arab Emirates University (UAEU) Al Ain UAE

This article proposes a novel operation and control scheme for a wave energy system intended for standalone use. The suggested system comprises a battery storage system, loads, and a wave energy conversion (WEC) system. The battery energy management and power regulation are at the highest level. This system generates reference dynamic operating points, which is damping and stiffness value, to regulate the power flow of the WEC system at the DC-link terminal based on state of charge of the battery. To prevent a system blackout when there is insufficient energy storage, and adverse wave circumstances, the energy management and power regulation system also controls the inverter load operation. A MATLAB/Simulink implementation of the suggested control system is used to test it under various study circumstances. The findings are displayed and discussed.

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A 60-GHz Two-Way Transformer-Coupled Power Amplifier in 65-nm CMOS

A 60-GHz Two-Way Transformer-Coupled Power Amplifier in 65-n...

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IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP)

作者： Yihui Wang Tinghuan Chen Liang Wu School of Science and Engineering and Future Network of Intelligence Institute The Chinese University of Hong Kong Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin Hong Kong China

This paper presents a 60-GHz transformer-coupled power amplifier (PA). It is composed of three-stage PA and a distributed-active-transformer-based (DAT-based) structure is proposed to increase the output power (P out ) with supply voltage limited to 1 V. Implemented in a 65-nm CMOS technology, the PA measures a peak gain (S 21 ) of 20.8 dB at 54.4 GHz, and a 3-dB bandwidth (BW 3dB ) of 9.2 GHz from 50.2 to 59.4 GHz. The measured saturated output power (P SAT ) is 13.0 dBm with 7.5% peak power-added efficiency (PAE) at 60 GHz.

关键词： Microwave measurement Radio frequency Voltage measurement Power measurement Power amplifiers CMOS technology Microwave transistors

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New SAR Imaging Algorithm via the Optimal Time-Frequency Transform Domain

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computers, Materials & Continua 2020年第12期65卷 2351-2363页

作者： Zhenli Wang Qun Wang Jiayin Liu Zheng Liang Jingsong Xu Department of Computer Information and Network Security Jiangsu Police InstituteNanjingChina Research Fellow Faculty of Engineering and Information Technology University of Technology SydneyNew South WalesAustralia

To address the low-resolution imaging problem in relation to traditional Range Doppler(RD)algorithm,this paper intends to propose a new algorithm based on Fractional Fourier Transform(FrFT),which proves highly advantageous in the acquisition of high-resolution Synthetic Aperture Radar(SAR)*** expression of the optimal order of SAR range signals using FrFT is deduced in detail,and the corresponding expression of the azimuth signal is also *** analysis shows that,the optimal order in range(azimuth)direction,which turns out to be very unique,depends on the known imaging parameters of SAR,therefore the engineering practicability of FrFT-RD algorithm can be greatly improved without the need of order *** FrFT-RD algorithm is established after an analysis of the optimal time-frequency *** results demonstrate that,compared with traditional RD algorithm,the main-lobe width of the peak-point target of FrFT-RD algorithm is narrow in both range and azimuth *** the peak amplitude of the first side-lobe is reduced significantly,those of other side-lobes also drop in various *** this way,the imaging resolution of range and azimuth can be increased considerably.

关键词： Fourier transform fractional Fourier transform synthetic aperture radar range doppler algorithm

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CoRaiS: Lightweight Real-Time Scheduler for Multi-Edge Cooperative Computing

arXiv

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arXiv 2024年

作者： Hu, Yujiao Jia, Qingmin Chen, Jinchao Yao, Yuan Pan, Yan Xie, Renchao Yu, F. Richard Future Network Research Center Purple Mountain Laboratories Nanjing211111 China The School of Computer Science Northwestern Polytechnical University Xi'An710029 China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology China The State Key Laboratory of networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Systems and Computer Engineering Carleton University Ottawa Canada

Multi-edge cooperative computing that combines constrained resources of multiple edges into a powerful resource pool has the potential to deliver great benefits, such as a tremendous computing power, improved response time, more diversified services. However, the mass heterogeneous resources composition and lack of scheduling strategies make the modeling and cooperating of multi-edge computing system particularly complicated. This paper first proposes a system-level state evaluation model to shield the complex hardware configurations and redefine the different service capabilities at heterogeneous edges. Secondly, an integer linear programming model is designed to cater for optimally dispatching the distributed arriving requests. Finally, a learning-based lightweight real-time scheduler, CoRaiS, is proposed. CoRaiS embeds the real-time states of multi-edge system and requests information, and combines the embeddings with a policy network to schedule the requests, so that the response time of all requests can be minimized. Evaluation results verify that CoRaiS can make a high-quality scheduling decision in real time, and can be generalized to other multi-edge computing system, regardless of system scales. Characteristic validation also demonstrates that CoRaiS successfully learns to balance loads, perceive real-time state and recognize heterogeneity while scheduling. © 2024, CC BY-NC-ND.

关键词： Edge computing

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Semantic Communication-Empowered Physical-layer network Coding

Semantic Communication-Empowered Physical-layer Network Codi...

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IEEE Conference on Wireless Communications and networking

作者： Shuai Yang Haoyuan Pan Tse-Tin Chan Zhaorui Wang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong SAR China School of Science and Engineering Future Network of Intelligence Institute The Chinese University of Hong Kong (Shenzhen) Shenzhen China

In a two-way relay channel (TWRC), physical-layer network coding (PNC) doubles the system throughput by turning superimposed signals transmitted simultaneously by different end nodes into useful network-coded information (known as PNC de-coding). Prior works indicated that the PNC decoding performance is affected by the relative phase offset between the received signals from different nodes. In particular, some "bad" relative phase offsets could lead to huge performance degradation. Previous solutions to mitigate the relative phase offset effect were limited to the conventional bit-oriented communication paradigm, aiming at delivering a given information stream as quickly and reliably as possible. In contrast, this paper puts forth the first semantic communication-empowered PNC-enabled TWRC to address the relative phase offset issue, referred to as SC-PNC. Despite the bad relative phase offsets, SC-PNC directly extracts the semantic meaning of transmitted messages rather than ensuring accurate bit stream transmission. We jointly design deep neural network (DNN)-based transceivers at the end nodes and propose a semantic PNC decoder at the relay. Taking image delivery as an example, experimental results show that the SC-PNC TWRC achieves high and stable image reconstruction quality under different channel conditions and relative phase offsets, compared with the conventional bit-oriented counterparts.

关键词： Degradation Semantics Streaming media network coding Turning Throughput Reliability engineering

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Roland: Robust In-band Parallel Communication for Magnetic MIMO Wireless Power Transfer System

Roland: Robust In-band Parallel Communication for Magnetic M...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Wangqiu Zhou Hao Zhou Xiang Cui Xinyu Wang Xiaoyan Wang Zhi Liu LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou China Graduate School of Science and Engineering Ibaraki University Ibaraki Japan Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan

In recent years, receiver (RX) feedback communication has attracted increasing attention to enhance the charging performance for magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems. People prefer to adopt the in-band implementation with minimal overhead costs. However, the influence of RX-RX coupling couldn’t be directly ignored like that in the RFID field, i.e., strong couplings and relay phenomenon. In order to solve these two critical issues, we propose a Robust layer-level in-band parallel communication protocol for MIMO MRC-WPT systems (called Roland). Technically, we first utilize the observed channel decomposability to construct group-level channel relationship graph for eliminating the interference caused by strong RX-RX couplings. Then, we generalize such method to deal with the RX dependency due to relay phenomenon. Finally, we conduct extensive experiments on a prototype testbed to evaluate the effectiveness of the proposed scheme. The results demonstrate that our Roland could provide ≥95% average decoding accuracy for concurrent feedback communication of 14 devices. Compared with the state-of-the-art solution, the proposed protocol Roland can achieve an average decoding accuracy improvement of 20.41%.

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Data-Driven Rate Control for RDMA networks: A Lightweight Online Learning Approach

Data-Driven Rate Control for RDMA Networks: A Lightweight On...

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International Conference on Distributed Computing Systems

作者： Jiancheng Ye Dong Lin Kechao Cai Chao Zhou Jianfei He John C.S. Lui Network Technology Lab and Hong Kong Research Center Huawei Technologies Co. Ltd. School of Electronics and Communication Engineering Sun Yat-Sen University China Computing Product Line Huawei Technologies Co. Ltd. Department of Computer Science City University of Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong

Link speed in datacenter networks (DCNs) keeps growing rapidly, inducing an increasingly large portion of network flows to become short flows which can be finished within one round-trip time (RTT). This phenomenon makes many existing congestion control schemes ineffective because they iteratively adjust the sending rate based on the latest congestion feedback in multiple rounds. We find that the representative DCQCN scheme for RDMA exhibits substantial performance degradation when there are many short flows, and this is specially true in High Performance Computing (HPC) scenarios where most of Message Passing Interface (MPI) messages are small. In this paper, we propose a data-driven rate control framework which can learn from long-term online data about past rate control decisions via a lightweight online learning technique named Multi-Armed Bandit (MAB) which has a provable performance guarantee. Utilizing the framework, we devise a rate control scheme named Dolce-RC, which dynamically controls the rate increase and reduction by learning from online data. We implement Dolce-RC in commodity smart NICs, and show via testbed experiments and large-scale simulations that compared to DCQCN, Dolce-RC reduces average completion time of MPI messages by up to 68%, while not requiring any modification to switches.

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