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arXiv 2023年

作者： Li, Zheng Zhu, Zhengyu Chu, Zheng Guan, Yingying Mi, De Liu, Fan Yang, Lie-Liang School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China National Mobile Communications Research Laboratory Southeast University Nanjing210019 China University of Surrey GuildfordGU2 7XH United Kingdom School of Computer Science and Engineering Northeastern University Shenyang110819 China School of Computing and Digital Technology Birmingham City University BirminghamB4 7XG United Kingdom Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Wireless sensing and wireless energy are enablers to pave the way for smart transportation and a greener future. In this paper, an intelligent reflecting surface (IRS) assisted integrated sensing and wireless power transfer (ISWPT) system is investigated, where the transmitter in transportation infrastructure networks sends signals to sense multiple targets and simultaneously to multiple energy harvesting devices (EHDs) to power them. In light of the performance tradeoff between energy harvesting and sensing, we propose to jointly optimize the system performance via optimizing the beamforming and IRS phase shift. However, the coupling of optimization variables makes the formulated problem non-convex. Thus, an alternative optimization approach is introduced and based on which two algorithms are proposed to solve the problem. Specifically, the first one involves a semi-definite program technique, while the second one features a low-complexity optimization algorithm based on successive convex approximation and majorization minimization. Our simulation results validate the proposed algorithms and demonstrate the advantages of using IRS to assist wireless power transfer in ISWPT systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy transfer

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Performance Optimization of Energy Efficient Semantic Communications over Wireless Networks

Performance Optimization of Energy Efficient Semantic Commun...

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IEEE Conference on Vehicular Technology (VTC)

作者： Zhaohui Yang Mingzhe Chen Zhaoyang Zhang Chongwen Huang Qianqian Yang College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Info. Proc. Commun. & Netw. (IPCAN) Hangzhou China Department of Electrical and Computer Engineer Institute of Data Science and Computing University of Miami Coral Gables FL USA

ISBN: (纸本)9781665454698

In this paper, the problem of wireless resource allocation and semantic information extraction for energy efficient semantic communications over wireless networks is investigated. In the considered model, each user first extracts the semantic information from its large-scale data, and then transmits the small-sized semantic information to the base station (BS) which recovers the original data. Due to the limited energy budget of wireless users, both local computational energy and transmission energy must be considered. This joint computation and communication problem is formulated as an optimization problem whose goal is to minimize the total energy consumption of the network under a latency constraint. To solve this problem, an iterative algorithm is proposed where the optimal solution for joint bandwidth allocation, power control, and computation frequency optimization problem can be obtained. Numerical results show the effectiveness of the proposed algorithm.

关键词： Wireless networks Computational modeling Semantics Information retrieval Energy efficiency Data models Resource management

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A DNA Based Colour Image Encryption Scheme Using A Convolutional Autoencoder

arXiv

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arXiv 2022年

作者： Ahmed, Fawad Rehman, Muneeb Ur Ahmad, Jawad Khan, Muhammad Shahbaz Boulila, Wadii Srivastava, Gautam Lin, Jerry Chun-Wei Buchanan, William J. Department of Cyber Security Pakistan Navy Engineering College NUST Karachi75350 Pakistan Department of Electrical Engineering HITEC University Taxila Pakistan School of Computing Edinburgh Napier University Edinburgh United Kingdom Department of Electrical Engineering HITEC University Taxila47080 Pakistan Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh Saudi Arabia RIADI Laboratory University of Manouba Manouba Tunisia Math and Computer Science Brandon University Brandon Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Dept of Computer Science and Math Lebanese American University Beirut Lebanon Western Norway University of Applied Sciences Bergen Norway

With the advancement in technology, digital images can easily be transmitted and stored over the Internet. Encryption is used to avoid illegal interception of digital images. Encrypting large-sized colour images in their original dimension generally results in low encryption/decryption speed along with exerting a burden on the limited bandwidth of the transmission channel. To address the aforementioned issues, a new encryption scheme for colour images employing convolutional autoencoder, DNA and chaos is presented in this paper. The proposed scheme has two main modules, the dimensionality conversion module using the proposed convolutional autoencoder, and the encryption/decryption module using DNA and chaos. The dimension of the input colour image is first reduced from N × M × 3 toP × Q gray-scale image using the encoder. Encryption and decryption are then performed in the reduced dimension space. The decrypted gray-scale image is upsampled to obtain the original colour image having dimension N × M × 3. The training and validation accuracy of the proposed autoencoder is 97% and 95%, respectively. Once the autoencoder is trained, it can be used to reduce and subsequently increase the dimension of any arbitrary input colour image. The efficacy of the designed autoencoder has been demonstrated by the successful reconstruction of the compressed image into the original colour image with negligible perceptual distortion. The second major contribution presented in this paper is an image encryption scheme using DNA along with multiple chaotic sequences and substitution boxes. The security of the proposed image encryption algorithm has been gauged using several evaluation parameters, such as histogram of the cipher image, entropy, NPCR, UACI, key sensitivity, contrast, etc. The experimental results of the proposed scheme demonstrate its effectiveness to perform colour image encryption. © 2022, CC BY.

关键词： DNA

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SpEx+: A complete time domain speaker extraction network 21

SpEx+: A complete time domain speaker extraction network

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21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020

作者： Ge, Meng Xu, Chenglin Wang, Longbiao Chng, Eng Siong Dang, Jianwu Li, Haizhou Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Engineering Nanyang Technological University Singapore Japan Advanced Institute of Science and Technology Ishikawa Japan Department of Electrical and Computer Engineering National University of Singapore Singapore Machine Listening Lab University of Bremen Germany

ISBN: (纸本)9781713820697

Speaker extraction aims to extract the target speech signal from a multi-talker environment given a target speaker's reference speech. We recently proposed a time-domain solution, SpEx, that avoids the phase estimation in frequency-domain approaches. Unfortunately, SpEx is not fully a time-domain solution since it performs time-domain speech encoding for speaker extraction, while taking frequency-domain speaker embedding as the reference. The size of the analysis window for time-domain and the size for frequency-domain input are also different. Such mismatch has an adverse effect on the system performance. To eliminate such mismatch, we propose a complete time-domain speaker extraction solution, that is called SpEx+. Specifically, we tie the weights of two identical speech encoder networks, one for the encoder-extractor-decoder pipeline, another as part of the speaker encoder. Experiments show that the SpEx+ achieves 0.8dB and 2.1dB SDR improvement over the state-of-the-art SpEx baseline, under different and same gender conditions on WSJ0-2mix-extr database respectively. Copyright © 2020 ISCA

关键词： Extraction

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Lightweight Trust Model with Machine Learning scheme for secure privacy in VANET

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Procedia computer science 2021年 194卷 45-59页

作者： Muhammad Haleem Junejo Ab Al-Hadi Ab Rahman Riaz Ahmed Shaikh Kamaludin Mohamad Yusof Dileep Kumar Imran Memon Faculty of Electrical Engineering Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia State Key Laboratory of Industrial Control Technology College of Control Science & Engineering Zhejiang University Hangzhou china Department of computer science bahria university Karachi campus sindh Pakistan

A vehicular ad hoc network (VANETs) is transforming public transport into a safer wireless network, increasing its safety and efficiency. The VANET consists of several nodes which include RSU (Roadside Units), vehicles, traffic signals, and other wireless communication devices that are communicating sensitive information in a network. Nevertheless, security threats are increasing day by day because of dependency on network infrastructure, dynamic nature, and control technologies used in VANET. The security threats could be addressed widely by using machine learning and artificial intelligence on the road transport nodes. In this paper, a comparison of trust and cryptography was presented based on applications and security requirements of VANET.

关键词： VANET Trust Model Machine Learning

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Magnon-mediated perpendicular magnetization switching by topological crystalline insulator SnTe with high spin Hall conductivity

arXiv

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arXiv 2025年

作者： Zhao, Pengnan Shi, Guoyi Lu, Wentian Yang, Lihuan Tan, Hui Ru Guo, Kaiwei Lai, Jia-Min Yu, Zhonghai Soumyanarayanan, Anjan Yuan, Zhe Wang, Fei Xu, Xiaohong Yang, Hyunsoo Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education School of Chemistry and Materials Science Shanxi Normal University Taiyuan030006 China Department of Electrical and Computer Engineering National University of Singapore Singapore117576 Singapore Singapore138634 Singapore Department of Physics National University of Singapore Singapore117551 Singapore Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China Interdisciplinary Center for Theoretical Physics and Information Sciences Fudan University Shanghai200433 China

Magnons possess the ability to transport spin angular momentum in insulating magnetic materials, a characteristic that sets them apart from traditional electronics where power consumption arises from the movement of electrons. However, the practical application of magnon devices demands room temperature operation and low switching power of perpendicular magnetization. Here we demonstrate the low-power manipulation of perpendicular magnetization via magnon torques in SnTe/NiO/CoFeB devices at room temperature. Topological crystalline insulator SnTe exhibits a high spin Hall conductivity of σs ≈ 6.1 x 104 (ℏ /2e) (Ω \. m)-1, which facilitates the generation of magnon currents in an antiferromagnetic insulator NiO. The magnon currents traverse the 20-nm-thick NiO layer and subsequently exert magnon torques on the adjacent ferromagnetic layer, leading to magnetization switching. Notably, we achieve a 22-fold reduction in power consumption in SnTe/NiO/CoFeB heterostructures compared to Bi2Te3/NiO/CoFeB control samples. Our findings establish the low-power perpendicular magnetization manipulation through magnon torques, significantly expanding the range of topological materials with practical applications. © 2025, CC BY-NC-ND.

关键词： Tin alloys

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APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service

arXiv

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arXiv 2023年

作者： Li, Zilinghan He, Shilan Chaturvedi, Pranshu Hoang, Trung-Hieu Ryu, Minseok Huerta, E.A. Kindratenko, Volodymyr Fuhrman, Jordan Giger, Maryellen Chard, Ryan Kim, Kibaek Madduri, Ravi Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Computing and Augmented Intelligence Arizona State University TempeAZ85281 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science The University of Chicago ChicagoIL60637 United States University of Chicago Consortium for Advanced Science and Engineering ChicagoIL60637 United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States Department of Radiology University of Chicago Hyde ParkIL60616 United States

Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e.g., healthcare of financial) local data. To ease and accelerate the adoption of PPFL, we introduce APPFLx, a ready-to-use platform that provides privacy-preserving cross-silo federated learning as a service. APPFLx employs Globus authentication to allow users to easily and securely invite trustworthy collaborators for PPFL, implements several synchronous and asynchronous FL algorithms, streamlines the FL experiment launch process, and enables tracking and visualizing the life cycle of FL experiments, allowing domain experts and ML practitioners to easily orchestrate and evaluate cross-silo FL under one platform. APPFLx is available online at https://*** Index Terms—Federated learning, privacy preserving, federation as a service, AI for science, science as a service, HPC, IAM Copyright © 2023, The Authors. All rights reserved.

关键词： Life cycle

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Exploring lossy compressibility through statistical correlations of scientific datasets

arXiv

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arXiv 2021年

作者： Krasowska, David Bessac, Julie Underwood, Robert Calhoun, Jon C. Di, Sheng Cappello, Franck Holcombe Department of Electrical and Computing Engineering Clemson University Clemson United States Mathematics and Computer Science Division Argonne National Laboratory Lemont United States School of Computing Clemson University Clemson United States

Lossy compression plays a growing role in scientific simulations where the cost of storing their output data can span terabytes. Using error bounded lossy compression reduces the amount of storage for each simulation;however, there is no known bound for the upper limit on lossy compressibility. Correlation structures in the data, choice of compressor and error bound are factors allowing larger compression ratios and improved quality metrics. Analyzing these three factors provides one direction towards quantifying lossy compressibility. As a first step, we explore statistical methods to characterize the correlation structures present in the data and their relationships, through functional models, to compression ratios. We observed a relationship between compression ratios and statistics summarizing correlation structure of the data, which are a first step towards evaluating the theoretical limits of lossy compressibility used to eventually predict compression performance and adapt compressors to correlation structures present in the data. © 2021, CC BY.

关键词： Compressibility

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Task Offloading with Multi-Tier computing Resources in Next Generation Wireless Networks

arXiv

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arXiv 2022年

作者： Wang, Kunlun Jin, Jiong Yang, Yang Zhang, Tao Nallanathan, Arumugam Tellambura, Chintha Jabbari, Bijan The Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China The School of Communication and Electronic Engineering East China Normal University Shanghai200241 China The School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia Terminus Group Beijing100027 China ShanghaiTech University Shanghai201210 China Peng Cheng Laboratory Shenzhen518055 China United States The School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom The Department of Electrical and Computer Engineering University of Alberta Canada The Department of Electrical and Computer Engineering George Mason University FairfaxVA United States

With the development of next-generation wireless networks, the Internet of Things (IoT) is evolving towards the intelligent IoT (iIoT), where intelligent applications usually have stringent delay and jitter requirements. In order to provide low-latency services to heterogeneous users in the emerging iIoT, multi-tier computing was proposed by effectively combining edge computing and fog computing. More specifically, multi-tier computing systems compensate for cloud computing through task offloading and dispersing computing tasks to multi-tier nodes along the continuum from the cloud to things. In this paper, we investigate key techniques and directions for wireless communications and resource allocation approaches to enable task offloading in multi-tier computing systems. A multi-tier computing model, with its main functionality and optimization methods, is presented in details. We hope that this paper will serve as a valuable reference and guide to the theoretical, algorithmic, and systematic opportunities of multi-tier computing towards next-generation wireless networks. Copyright © 2022, The Authors. All rights reserved.

关键词： Resource allocation

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Simulating nonlinear optical processes on a superconducting quantum device

arXiv

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arXiv 2024年

作者： Shi, Yuan Evert, Bram Brown, Amy F. Tripathi, Vinay Sete, Eyob A. Geyko, Vasily Cho, Yujin DuBois, Jonathan L. Lidar, Daniel Joseph, Ilon Reagor, Matt Department of Physics Center for Integrated Plasma Studies University of Colorado Boulder BoulderCO80309 United States Rigetti Computing 775 Heinz Ave BerkeleyCA94710 United States Department of Physics & Astronomy Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Electrical & Computer Engineering Department of Chemistry University of Southern California Los AngelesCA90089 United States

Simulating plasma physics on quantum computers is difficult because most problems of interest are nonlinear, but quantum computers are not naturally suitable for nonlinear operations. In weakly nonlinear regimes, plasma problems can be modeled as wave-wave interactions. In this paper, we develop a quantization approach to convert nonlinear wave-wave interaction problems to Hamiltonian simulation problems. We demonstrate our approach using two qubits on a superconducting device. Unlike a photonic device, a superconducting device does not naturally have the desired interactions in its native Hamiltonian. Nevertheless, Hamiltonian simulations can still be performed by decomposing required unitary operations into native gates. To improve experimental results, we employ a range of error mitigation techniques. Apart from readout error mitigation, we use randomized compilation to transform undiagnosed coherent errors into well-behaved stochastic Pauli channels. Moreover, to compensate for stochastic noise, we rescale exponentially decaying probability amplitudes using rates measured from cycle benchmarking. We carefully consider how different choices of product-formula algorithms affect the overall error and show how a trade-off can be made to best utilize limited quantum resources. This study provides an example of how plasma problems may be solved on near-term quantum computing platforms. Copyright © 2024, The Authors. All rights reserved.

关键词： Hamiltonians

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