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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Guo, Yunhui Zhang, Yang Pang, Lihua Liu, Yuanwei Ding, Zhiguo Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Xian University of Science and Technology School of Information Engineering Xian710054 China Khalifa University 6G Center Department of Computer and Communication Engineering Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350362244

In this paper, the design of non-uniform antenna topology for 3D massive MIMO arrays in near-field communications is investigated. Specifically, the near-field spherical wavefront radiation characteristics are considered to accurately model the variations of signal phase across array elements. Subsequently, the closed-form expressions of the per-user signal-to-interference noise ratio (SINR) and achievable sum rate for a multi-user MIMO system with maximum-ratio transmission (MRT) precoding are derived. The focus is on the maximization of the achievable sum rate by optimizing the non-uniform 3D antenna array topology. Since the optimization problem exhibits highly nonlinear and nonconvex characteristics, an enhanced particle swarm optimization (EPSO) algorithm is proposed to effectively solve it. Numerical results demonstrate the superiority of the proposed non-uniform 3D array topology in near-field communications for enhancing the achievable sum rate. © 2024 IEEE.

关键词： Signal to noise ratio

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Research on recognition algorithm for gesture page turning based on wireless sensing

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Intelligent and Converged networks 2023年第1期4卷 15-27页

作者： Lin Tang Sumin Wang Meng Zhou Yinfan Ding Chao Wang Shengbo Wang Zhen Sun Jie Wu Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced CommunicationShanghai Institute for Advanced Communication and Data ScienceSchool of Communication and Information EngineeringShanghai UniversityShanghai 200444China School of Electronic Information Engineering Gannan University of Science and TechnologyGanzhou 341000China Affiliated High School of Peking University Beijing 100086China Peking University&National Engineering Laboratory for Big Data Analysis and Applications Beijing 100871China Department of Computer and Information Sciences Temple UniversityPhiladelphiaPA 19122-6096USA

When a human body moves within the coverage range of Wi-Fi signals,the reflected Wi-Fi signals by the various parts of the human body change the propagation path,so analysis of the channel state data can achieve the perception of the human *** extracting the Channel State Information(CSI)related to human motion from the Wi-Fi signals and analyzing it with the introduced machine learning classification algorithm,the human motion in the spatial environment can be *** the basis of this theory,this paper proposed an algorithm of human behavior recognition based on CSI wireless sensing to realize deviceless and over-the-air slide *** algorithm collects the environmental information containing upward or downward wave in a conference room scene,uses the local outlier factor detection algorithm to segment the actions,and then the time domain features are extracted to train Support Vector Machine(SVM)and eXtreme Gradient Boosting(XGBoost)classification *** experimental results show that the average accuracy of the XGBoost module sensing slide flipping can reach 94%,and the SVM module can reach 89%,so the module could be extended to the field of smart classroom and significantly improve speech efficiency.

关键词： Wi-Fi signal Channel State Information(CSI) wireless sensing human behavior recognition

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Terabit Ultra-high-speed NFDM Transmission over G.654E Fiber with 103Gbaud Probabilistic Shaped 64QAM Signals

Terabit Ultra-high-speed NFDM Transmission over G.654E Fiber...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Chen, Xinyu Fang, Xiansong Cai, Xiang Zhou, Lingjun Li, Chao He, Zhixue Zhang, Fan State Key Laboratory of Advanced Optical Communication System and Networks Frontiers Science Center for Nano-optoelectronics School of Electronics Peking University Beijing100871 China School of Electronic and Computer Engineering Peking University Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China

We experimentally demonstrate a record ultra-high-speed 1.004 Tb/s dual-polarization NFDM transmission system over 300 km G.654E fiber with probabilistic shaped 64QAM signals, achieving a spectral efficiency at 7.65 b... 详细信息

ISBN: (纸本)9781957171258

关键词： Quadrature amplitude modulation

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Comparative E-Voting Security Evaluation: Multi-Modal Authentication Approaches 6

Comparative E-Voting Security Evaluation: Multi-Modal Authen...

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6th International Conference on Blockchain Computing and Applications, BCCA 2024

作者： Ahmadieh, Elsi El Madhoun, Nour Lebanese University Faculty of Technology Department Communications and Computer Networks Engineering Beirut Lebanon LISITE Laboratory ISEP 10 Rue de Vanves Issy-les-Moulineaux92130 France Sorbonne Université CNRS LIP6 4 place Jussieu Paris75005 France

ISBN: (纸本)9798350351538

The decentralized, distributed ledger, a fundamental aspect of blockchain technology, plays a pivotal role in various sectors, including electronic voting (e-voting) systems. When considering e-voting, the significance of blockchain technology in boosting the security, transparency, and integrity of the entire process is evident. This ranges from the initial steps of user registration and authentication to the final stages of voting and result validation. Unlike conventional voting systems, which are plagued by issues such as fraud, tampering, and a lack of transparency in the result tallying and viewing processes, not to mention the delays, blockchain-enabled e-voting mitigates these challenges within government frameworks. It ensures a secure, tamper-proof, and transparent procedure, where each vote is recorded as an immutable transaction, thereby enhancing the process’s reliability. This paper explores the integration of voice recognition and fingerprinting using blockchain technology for enhancing the security of e-voting systems, comparing alternative authentication methods, and emphasizing the need for a multi-modal approach to achieve better security and reliability of the voting systems. © 2024 IEEE.

关键词： Voting machines

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50-km Weakly-coupled Transmission with L-band 4-LP-mode FM-EDFA

50-km Weakly-coupled Transmission with L-band 4-LP-mode FM-E...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical Communications, IPOC 2024

作者： Huang, Shuailuo Zhu, Baolong Qiao, Gang Gao, Yuyang Zuo, Mingqing Shen, Shikui Wang, Zelin He, Yongqi Chen, Zhangyuan Li, Juhao Chen, Zhangyuan Li, Juhao China Unicom Research Institute Beijing China State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Fundamental Network Technology China Mobile Research Institute Beijing China Pengcheng National Laboratory Shenzhen China

ISBN: (纸本)9798350379266

关键词： Pulse amplitude modulation

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LDVNet: Lightweight and Detail-Aware Vision Network for Image Recognition Tasks in Resource-Constrained Environments 29

LDVNet: Lightweight and Detail-Aware Vision Network for Imag...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Yuchen Liang, Hu Qiu, Ran Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

In many underwater application scenarios, recognition tasks need to be executed promptly on computationally limited platforms. However, models designed for this field often exhibit spatial locality, and existing works lack the ability to capture crucial details in images. Therefore, a lightweight and detail-aware vision network (LDVNet) for resource-constrained environments is proposed to overcome the limitations of these approaches. Firstly, in order to enhance the accuracy of target image recognition, we introduce transformer modules to acquire global information, thus addressing the issue of spatial locality inherent in traditional convolutional neural networks (CNNs). Secondly, to maintain the network's lightweight nature, we integrate the transformer module with convolutional operations, thereby mitigating the substantial parameter and floating point operations (FLOPs) overhead. Thirdly, for the efficient extraction of crucial fine-grained details from feature maps, we have devised a channel and spatial attention module (C&SA). This module aids in recognizing intricate and fine-grained visual tasks and enhances image understanding. It is seamlessly integrated into LDVNet with nearly negligible parameter overhead. The experimental results demonstrate that LDVNet outperforms other lightweight networks and hybrid networks in different recognition tasks, while being suitable for resource-constrained environments. © 2023 IEEE.

关键词： Detail-aware Image recognition Lightweight

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GAF-GAN: Gated Attention Feature Fusion Image Inpainting Network Based on Generative Adversarial Network 29

GAF-GAN: Gated Attention Feature Fusion Image Inpainting Net...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qiu, Ran Liang, Hu Liu, Yuchen Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Image inpainting, which aims to reconstruct reasonably clear and realistic images from known pixel information, is one of the core problems in computer vision. However, due to the complexity and variability of the underwater environment, the inability to extract valid pixel points and insufficient correlation between feature information in existing image inpainting techniques lead to blurring in the generated images. Therefore, a novel gated attention feature fusion image inpainting network based on generative adversarial networks (GAF-GAN) is proposed. The accuracy of feature similarity matching depends heavily on the validity of the information contained in the features. On the one hand, gating values are dynamically generated by gated convolution to reduce the interference of invalid information. On the other hand, semantic information at distant locations in an image is accurately acquired by the attention mechanism. For these reasons, we designed an improved gated attention mechanism. Gated attention mechanism make the network focus on effective information such as high-frequency texture and color fidelity of restored images. In addition, the dense feature fusion module is added to expand the overall receptive field of the network to fully learn the image features. Experimental results show that the proposed method can effectively repair defective images with complex texture structures and improve the reality and integrity of image details and structures. © 2023 IEEE.

关键词： Dense feature fusion module Gated attention mechanism Image inpainting Underwater environment

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Denoising Diffusion Model-Based Channel Estimation in IRS-Assisted ISAC Systems 16

Denoising Diffusion Model-Based Channel Estimation in IRS-As...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Yi, Lan Miao, Jiansong Zhang, Tao Yuan, Zhenhui Kang, Jiawen Niyato, Dusit Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing China School of Software Engineering Beijing Jiaotong University Beijing China School of Engineering University of Warwick CV4 7AL United Kingdom School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798350390643

In Integrated Sensing and Communications (ISAC) systems assisted by Intelligent Reflecting Surfaces (IRS), precise channel estimation is crucial for optimal system performance and maximizing the potential benefits. The estimation problem, however, becomes intricate due to complex channel coupling and specific environmental conditions. This paper addresses these challenges by formulating channel estimation as a denoising problem and proposing a novel diffusion-based framework. This approach mitigates the impact of ill-posedness, which is typically problematic for general neural network (NN)-based techniques. By modeling the joint distribution and sampling from the learned posterior distribution, we accurately recover the channel coefficients from noisy pilot-based observations. Simulation results demonstrate significant performance improvements of the proposed method compared to both least-squares and NN-based benchmarks. Moreover, thanks to the carefully designed architecture and residual prediction strategy, the number of sampling steps in the diffusion reverse process is greatly reduced, enabling real-time CSI acquisition. © 2024 IEEE.

关键词： Least squares approximations

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Automatic Energy-Hotspot Detection and Elimination in Real-Time Deeply Embedded Systems 42

Automatic Energy-Hotspot Detection and Elimination in Real-T...

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42nd IEEE Real-Time Systems Symposium, RTSS 2021

作者： Shekarisaz, Mohsen Thiele, Lothar Kargahi, Mehdi School of Electrical and Computer Engineering University of Tehran Tehran Iran Computer Engineering and Networks Laboratory ETH Zürich Zürich Switzerland

ISBN: (纸本)9781665428026

Today’s deeply embedded systems, with real-time interactions to the environment, are largely battery-operated, and peripheral modules like LTE, WiFi, and GPS are among the most energy-hungry components of them. These components are often under the direct control of an embedded software. Some pieces of the software program are called energy hotspots if they can be transformed towards better system energy consumption while leaving it logically- and temporally-correct. This paper focuses on three such energy hotspots from the peripheral module perspective. The root causes of the hotspots in the software program are misplaced driver calls: Early acquiring or late releasing of the module causes it to waste energy in the active state, having unnecessary distance between the use operations causes extra tail energy overhead, and unaccounted releasing and re-acquiring of the module causes more energy consumption in comparison to leaving the module unreleased. We provide static analysis methods for the detection and elimination of such energy hotspots automatically with regard to some relations between temporal requirements of the real-time embedded software, the time and energy specifications of the module, and the extreme (worst-case/best-case) execution times of specific pieces of the software program. Our simulation results show about 4.7 to 20 percent of energy reductions after elimination of the energy hotspots of the test programs using our proposed method. © 2021 IEEE

关键词： Real time systems

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Anonym-Recognizer: Relationship-preserving Face Anonymization and Recognition 3

Anonym-Recognizer: Relationship-preserving Face Anonymizatio...

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3rd International Workshop on Human-Centric Multimedia Analysis, HCMA 2022, held in conjunction with the ACM Multimedia 2022

作者： Peng, Chunlei Wan, Shuang Miao, Zimin Liu, Decheng Zheng, Yu Wang, Nannan School of Cyber Engineering Xidian University Xi'an China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China State Key Laboratory of Integrated Services Networks Xidian University Xi'an China

ISBN: (纸本)9781450394925

With the widespread application of big data technology, we are exposed to more and more video monitoring. To prevent serious social problems caused by face data leakage, face anonymization has become an important kind of method to protect face privacy. The face anonymization mentioned in this paper refers to the anonymization generation of the visual appearance in face images. Existing face anonymization methods mainly focus on removing identity information. However, in the scenario of face recognition technology that needs to protect privacy, existing face anonymization technology makes anonymized faces that can no longer be used for face recognition, limiting the application scope of face anonymization. Therefore, when using face anonymization, it is equally important to ensure that the anonymized face images can still be used for downstream tasks such as face recognition. To this end, we propose Anonym-Recognizer, a relationship-preserving face anonymization and recognition method. Our method uses relationship cyphertext which can be any binary identity number representing the identity of the image owner and designs a generative adversarial network to perform face anonymization and relationship cyphertexts embedding. In our framework, we first use Visual Anonymizer to manipulate the visual appearance of the input image, then use Cyphertext Embedder to get the anonymized image with the identity information embedded. With the help of Anonym Recognizer, the face recognition system can extract the relationship cyphertexts from the anonymized image as the credentials to match the identity information. The proposed Anonym-Recognizer provides a new perspective for the recognition and application of anonymized face images. Experiments on the Megaface dataset show that our method can encourage a 100% recognition accuracy on anonymized faces while finishing the task of face anonymization with high qualitative and quantitative quality. © 2022 ACM.

关键词： Face recognition

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