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30th International Conference on Parallel and Distributed Processing Techniques, PDPTA 2024, held as part of the World Congress in computer Science, computer engineering and Applied Computing, CSCE 2024

作者： Yaguchi, Kazuki Maeda, Eriko Kawai, Shunya Teruya, Daichi Osana, Yasunori Miyoshi, Takefumi Nakajo, Hironori Department of Electrical Engineering and Computer Science Graduate School of Engineering Tokyo University of Agriculture and Technology 2-24-16 Nakacho Tokyo Koganei-shi184-8588 Japan Research and Education Institute for Semiconductors and Informatics Kumamoto University 2-39-1 Kurokami Kumamoto Chuo-ku860-8555 Japan WasaLabo LLC. K-2 Buransyu 6-5-20 Minaminaruse Tokyo Machida-shi194-0045 Japan Division of Advanced Information Technology and Computer Science Institute of Engineering Tokyo University of Agriculture and Technology 2-24-16 Nakacho Tokyo Koganei-shi184-8588 Japan

ISBN: (纸本)9783031856372

In recent years, hardware acceleration in large-scale computing fields such as Artificial Intelligence and High Performance Computing, faces hardware resource shortages. To overcome this problem, we propose Reconfigurable Virtual Accelerator (ReVA), which allows a large-scale acceleration circuit built using multiple FPGAs, processors and memory subsystems, to accelerate application programs. We have designed and implemented a prototype of Virtual Accelerator (VA) Generator for ReVA to investigate its performance. The VA Generator prototype employs an open-source HLS automated split compilation tool, RapidStream, to automatically generate placed-and-routed virtual accelerators that can be implemented on multiple FPGAs based on HLS dataflow designs. The VA Generator, which places VAs on the appropriate regions of FPGAs, allows large circuits to be performed like a single accelerator using several FPGAs. In addition, RapidStream’s parallel compilation technology allows the VA Generator to suppress the increasing compilation time according to the circuit size. Moreover, with our VA Generator prototype we have built and evaluated a VA of a large-scale circuit which cannot be fit in a single FPGA with multiple FPGAs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Reconfigurable hardware

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Outage Performance of Multi-tier UAV Communication with Random Beam Misalignment

arXiv

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arXiv 2023年

作者： Wang, Weihao Fei, Zesong Guo, Jing Durrani, Salman Yanikomeroglu, Halim The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314000 China The School of Engineering The Australian National University CanberraACT2601 Australia Lab Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada

By exploiting the degree of freedom on the altitude, unmanned aerial vehicle (UAV) communication can provide ubiquitous communication for future wireless networks. In the case of concurrent transmission of multiple UAVs, the directional beamforming formed by multiple antennas is an effective way to reduce co-channel interference. However, factors such as airflow disturbance or estimation error for UAV communications can cause the occurrence of beam misalignment. In this paper, we investigate the system performance of a multi-tier UAV communication network with the consideration of unstable beam alignment. In particular, we propose a tractable random model to capture the impacts of beam misalignment in the 3D space. Based on this, by utilizing stochastic geometry, an analytical framework for obtaining the outage probability in the downlink of a multi-tier UAV communication network for the closest distance association scheme and the maximum average power association scheme is established. The accuracy of the analysis is verified by Monte-Carlo simulations. The results indicate that in the presence of random beam misalignment, the optimal number of UAV antennas needs to be adjusted to be relatively larger when the density of UAVs increases or the altitude of UAVs becomes higher. Copyright © 2023, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Neural Network-Assisted Hybrid Model Based Message Passing for Parametric Holographic MIMO Near Field Channel Estimation

arXiv

引用

arXiv 2024年

作者： Yuan, Zhengdao Guo, Yabo Gao, Dawei Guo, Qinghua Wang, Zhongyong Huang, Chongwen Jin, Ming Wong, Kai-Kit Artificial Intelligence Technology Engineering Research Center Henan Open University Zhengzhou450002 China School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2522 Australia School of Information Engineering Zhengzhou University Zhengzhou450002 China Hangzhou Institute of Technology Xidian University Hangzhou311200 China School of Electrical Computer and Telecommunications Engineering University of Wollongong NSW2522 Australia College of Information Science and Electronic Engineering Zhejiang University Hangzhou310007 China Hangzhou310007 China The International Joint Innovation Center Zhejiang University Haining314400 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Department of Electronic and Electrical Engineering University College London Torrington Place WC1E 7JE United Kingdom Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of

Holographic multiple-input and multiple-output (HMIMO) is a promising technology with the potential to achieve high energy and spectral efficiencies, enhance system capacity and diversity, etc. In this work, we address the challenge of HMIMO near field (NF) channel estimation, which is complicated by the intricate model introduced by the dyadic Green’s function. Despite its complexity, the channel model is governed by a limited set of parameters. This makes parametric channel estimation highly attractive, offering substantial performance enhancements and enabling the extraction of valuable sensing parameters, such as user locations, which are particularly beneficial in mobile networks. However, the relationship between these parameters and channel gains is nonlinear and compounded by integration, making the estimation a formidable task. To tackle this problem, we propose a novel neural network (NN) assisted hybrid method. With the assistance of NNs, we first develop a novel hybrid channel model with a significantly simplified expression compared to the original one, thereby enabling parametric channel estimation. Using the readily available training data derived from the original channel model, the NNs in the hybrid channel model can be effectively trained offline. Then, building upon this hybrid channel model, we formulate the parametric channel estimation problem with a probabilistic framework and design a factor graph representation for Bayesian estimation. Leveraging the factor graph representation and unitary approximate message passing (UAMP), we develop an effective message passing-based Bayesian channel estimation algorithm. Extensive simulations demonstrate the superior performance of the proposed method. © 2024, CC BY.

关键词： Channel estimation

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Data Identification Optimal Control for Quantized Linear Systems under DoS Attacks

引用

IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Wang, Xianming Li, Liwei Shen, Mouquan Zhao, Xudong Wang, Qing-Guo Zhu, Zheng Hong Nanjing Tech University School of Mechanical and Power Engineering Nanjing211816 China Nanjing Tech University College of Electrical Engineering and Control Science Nanjing211816 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116024 China Beijing Normal University Institute of Ai and Future Networks Zhuhai519087 China BNU-HKBU United International College Guangdong Provincial Key Laboratory Irads Department of Computer Science Zhuhai519087 China York University Department of Mechanical Engineering TorontoONM3J 1P3 Canada

This paper is devoted to data identification optimal control for quantized linear systems under denial of service (DoS) attacks. A least-squares algorithm is provided to identify the unknown system matrices using a data-based representation. An output estimation algorithm utilizing identified matrices is supplied to offset DoS attacks. Consequently, an optimal controller is built on the quantized and attacked data by adaptive dynamic programming. Sufficient conditions are presented to guarantee that the resultant closed-loop system is ultimately uniformly bounded. An example is produced to verify the validity of the proposed approach. © 1965-2011 IEEE.

关键词： Optimal control systems

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Optically-Sampled Superconducting-Nanostrip Photon-Number Resolving Detector for Non-Classical Quantum State Generation

arXiv

引用

arXiv 2024年

作者： Endo, Mamoru Takahashi, Kazuma Nomura, Takefumi Sonoyama, Tatsuki Yabuno, Masahiro Miki, Shigehito Terai, Hirotaka Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Nehra, Rajveer Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi Kobe651-2492 Japan NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan Department of Electrical and Computer Engineering University of Massachusetts Amherst AmherstMA01003 United States Department of Physics University of Massachusetts Amherst AmherstMA01003 United States College of Information and Computer Science University of Massachusetts Amherst AmherstMA01003 United States

Photon number-resolving detectors (PNRDs) are the ultimate optical sensors. Superconducting-nanostrip photon detectors (SNSPDs), traditionally known as ON-OFF detectors, have recently been found to have photon number resolving capability without multiplexing. This discovery positions them to become true PNRDs. However, their practical use is limited by the need to precisely detect tiny signal differences with low signal-to-noise ratios within sub-nanosecond time frames. We overcome this challenge using optical sampling with a dual-output Mach Zehnder modulator (DO-MZM) and ultra-short pulsed laser. By adjusting the DO-MZM’s bias voltage to nearly balance the outputs, this method enables sensitive detection of picosecond-order signal differences, achieving a temporal resolution of 1.9 ps and facilitating real-time photon number resolution. We applied this method to produce various non-classical quantum states, enhancing their non-classicality through photon number resolution. This advancement marks a significant shift from principle verification to practical application for SNSPD-type PNRDs in diverse quantum optics fields. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum optics

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Next Generation Advanced Transceiver Technologies for 6G and Beyond

arXiv

引用

arXiv 2024年

作者： You, Changsheng Cai, Yunlong Liu, Yuanwei Di Renzo, Marco Duman, Tolga M. Yener, Aylin Swindlehurst, A. Lee Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes Gif-sur-Yvette91192 France Department of Electrical and Electronics Engineering Bilkent University Ankara06800 Turkey Departments of Electrical and Computer Engineering Computer Science and Engineering and Integrated Systems Engineering The Ohio State University OH United States Center for Pervasive Communications and Computing University of California IrvineCA92697 United States

To accommodate new applications such as extended reality, fully autonomous vehicular networks and the metaverse, next generation wireless networks are going to be subject to much more stringent performance requirements than the fifth-generation (5G) in terms of data rates, reliability, latency, and connectivity. It is thus necessary to develop next generation advanced transceiver (NGAT) technologies for efficient signal transmission and reception. In this tutorial, we explore the evolution of NGAT from three different perspectives. Specifically, we first provide an overview of new-field NGAT technology, which shifts from conventional far-field channel models to new near-field channel models. Then, three new-form NGAT technologies and their design challenges are presented, including reconfigurable intelligent surfaces, flexible antennas, and holographic multi-input multi-output (MIMO) systems. Subsequently, we discuss recent advances in semantic-aware NGAT technologies, which can utilize new metrics for advanced transceiver designs. Finally, we point out other promising transceiver technologies for future research. Copyright © 2024, The Authors. All rights reserved.

关键词： Near field communication

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Comparison and development of advanced machine learning tools to predict nonalcoholic fatty liver disease:An extended study

引用

Hepatobiliary & Pancreatic Diseases International 2021年第5期20卷 409-415页

作者： Yuan-Xing Liu Xi Liu Chao Cen Xin Li Ji-Min Liu Zhao-Yan Ming Song-Feng Yu Xiao-Feng Tang Lin Zhou Jun Yu Ke-Jie Huang Shu-Sen Zheng Department of Hepatobiliary and Pancreatic Surgery the First Affiliated HospitalZhejiang University School of MedicineHangzhou 310003China College of Information Science and Electrical Engineering Zhejiang UniversityHangzhou 310027China Department of Pathology and Molecular Medicine Faculty of Health SciencesMcMaster University1200 Main Street WestHamilton L8S 4K1Canada College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China

Background:Nonalcoholic fatty liver disease(NAFLD)is a public health challenge and significant cause of morbidity and mortality *** identification is crucial for disease *** recently proposed a nomogram-based NAFLD prediction model from a large population *** aimed to explore machine learning tools in predicting ***:A retrospective cross-sectional study was performed on 15315 Chinese subjects(10373 training and 4942 testing sets).Selected clinical and biochemical factors were evaluated by different types of machine learning algorithms to develop and validate seven predictive *** evaluation indicators including area under the receiver operating characteristic curve(AUROC),area under the precision-recall curve(AUPRC),accuracy,positive predictive value,sensitivity,F1 score,Matthews correlation coefficient(MCC),specificity and negative prognostic value were applied to compare the performance among the *** selected clinical and biochemical factors were ranked according to the importance in prediction ***:Totally 4018/10373(38.74%)and 1860/4942(37.64%)subjects had ultrasound-proven NAFLD in the training and testing sets,*** machine learning based models were developed and demonstrated good performance in predicting *** these models,the XGBoost model revealed the highest AUROC(0.873),AUPRC(0.810),accuracy(0.795),positive predictive value(0.806),F1 score(0.695),MCC(0.557),specificity(0.909),demonstrating the best prediction ability among the built *** mass index was the most valuable indicator to predict NAFLD according to the feature ranking ***:The XGBoost model has the best overall prediction ability for diagnosing *** novel machine learning tools provide considerable beneficial potential in NAFLD screening.

关键词： Nonalcoholic fatty liver disease Machine learning Population screening Prediction model Body mass index

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GNN-Based Beamforming for Sum-Rate Maximization in MU-MISO Networks

arXiv

引用

arXiv 2023年

作者： Li, Yuhang Lu, Yang Ai, Bo Dobre, Octavia A. Ding, Zhiguo Niyato, Dusit School of Computer and Information Technology Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China State Key Laboratory of Rail Traffic Control and Safety the School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China Faculty of Engineering and Applied Science Memorial University St. John’sNLA1C 5S7 Canada Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Department of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

The advantages of graph neural networks (GNNs) in leveraging the graph topology of wireless networks have drawn increasing attentions. This paper studies the GNN-based learning approach for the sum-rate maximization in multiple-user multiple-input single-output (MU-MISO) networks subject to the users’ individual data rate requirements and the power budget of the base station. By modeling the MU-MISO network as a graph, a GNN-based architecture named CRGAT is proposed to directly map the channel state information to the beamforming vectors. The attention-enabled aggregation and the residual-assisted combination are adopted to enhance the learning capability and avoid the oversmoothing issue. Furthermore, a novel activation function is proposed for the constraint due to the limited power budget at the base station. The CRGAT is trained in an unsupervised learning manner with two proposed loss functions. An evaluation method is proposed for the learning-based approach, based on which the effectiveness of the proposed CRGAT is validated in comparison with several convex optimization and learning based approaches. Numerical results are provided to reveal the advantages of the CRGAT including the millisecond-level response with limited optimality performance loss, the scalability to different number of users and power budgets, and the adaptability to different system settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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Design of Evanescent Mode Band-Pass Filters Based on Groove Gap Waveguide technology

SSRN

引用

SSRN 2023年

作者： Hinojosa, Juan Máximo-Gutiérrez, Clara Alvarez-Melcon, Alejandro Universidad Politécnica de Cartagena Department of Information and Communications Technology Campus Universitario Muralla del Mar Plaza del Hospital 1 Cartagena Murcia30202 Spain Universidad Politécnica de Cartagena Department of Electronics and Computer Engineering Campus Universitario Muralla del Mar Plaza del Hospital 1 Cartagena Murcia30202 Spain

This paper proposes for the first time a design method of Chebyshev GGW band-pass filters based on evanescent mode. The filter structure has been obtained by inserting resonators of metallic pins along the central part of an evanescent mode GGW section according to a coupling matrix synthesis method. The proposed method has allowed reducing the design time of this filter, by solving the discretization problem of the different lengths of the evanescent mode section. This design technique has been applied to a fourth-order Chebyshev band-pass GGW filter. The design has been performed in the recommended frequency band of the WR75 standard (10GHz–15GHz) to facilitate its manufacture. The design specifications have been a central frequency GHz, a fractional bandwidth FBW=2.5% and return loss RL=20dB. Measurement and EM simulation results have shown a good agreement. Return and insertion losses in the pass-band region of the measured prototype have been, respectively, RL=20dB and IL=0.3dB, while maximum attenuation in the stop-band regions reached ASB=83dB. A comparison has been performed between the measured prototype and other GGW bandpass filters. The proposed GGW band-pass filter has shown to have a reduced size and high-performance characteristics. Future emerging communications systems could benefit from this band-pass filter. © 2023, The Authors. All rights reserved.

关键词： Bandpass filters

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Verification of Usefulness of Student Modeling with Real Educational Data using Convex Factorization Machines

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Procedia computer Science 2021年 192卷 804-811页

作者： Shinichi Oeda Daiki Shimizu Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Kisarazu City Chiba Japan Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Kisarazu City Chiba Japan

Extracting useful information generated from educational settings involves the application of data mining, machine learning, and statistics to the large amount of electronic data collected by educational systems. To generate better higher learning outcomes using an intelligent tutoring system, such as an e-learning system, it is necessary to more accurately understand the state of student knowledge. The purpose of student modeling is to estimate the students’ skills from log data, such as examination results, and to predict whether or not a student will be able to solve a problem. In this study, we propose a student performance prediction method using convex factorization machines. Factorization machines offer a combination of the advantages of support vector machines and factorization models such as matrix factorization. The results of conventional methods, which predict student performance using factorization machines, exhibited better results than they have before. However, because factorization machines are not convex optimizations, they acquire local minima, which is a disadvantage. Therefore, we used convex factorization machines in order to improve the performance of student modeling predictions.

关键词： Educational Data Mining Student Modeling Convex Factorization Machines Factorization Machines Convex Optimization Recommendation Intelligent Tutoring Systems

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