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Near Optimal Hybrid Digital-Analog Beamforming for mmWave Point-to-Point MIMO Transmissions Using OTFS Waveforms

IEEE Transactions on Communications 2025年

作者： Liu, Mengmeng Li, Shuangyang Wei, Zhiqiang Bai, Baoming Caire, Giuseppe Ng, Derrick Wing Kwan Xidian University State Key Lab. of ISN Xi'an710071 China Technische Universität Berlin Electrical Engineering and Computer Science Department Berlin10587 Germany Xi'an Jiaotong University School of Mathematics and Statistics Xi'an710049 China Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Guangzhou510555 China University of New South Wales School of Electrical Engineering and Telecommunications SydneyNSW2052 Australia

In this paper, a point-to-point (P2P) orthogonal time frequency space (OTFS)-based multiple-input multiple-output (MIMO-OTFS) transmission scheme is devised for millimeter wave (mmWave) channels. The proposed transmission scheme relies on a low-complexity hybrid digital-analog beamforming (HBF) scheme that exploits the delay-Doppler (DD) domain channel properties, where detailed design criteria for different channel conditions are presented, including the case where paths are indistinguishable by angles. Thanks to the proposed HBF scheme, approximate path-wise interference-free transmission of multiple data streams is achieved, and consequently, only little pre-equalization is required for combating the residual channel impairments. The achievable rate of the proposed scheme is studied and compared with the orthogonal frequency-division multiplexing (OFDM) counterpart. In particular, we unveil that the condition number of the effective angular domain matrix for OTFS is smaller than that for OFDM, due to the enhanced path separability in the DD domain. As a result, the proposed MIMO-OTFS transmission scheme demonstrates superior performance over the MIMO-OFDM transmission scheme. Our numerical results corroborate our theoretical analysis and show a near-optimal rate performance with significantly reduced complexity compared to the optimal singular value decomposition (SVD) precoding method. © 1972-2012 IEEE.

关键词： Orthogonal frequency division multiplexing

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Federated Edge Learning via Integrated Sensing, Computation, and Communication

Federated Edge Learning via Integrated Sensing, Computation,...

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IEEE International Conference on Communications (ICC)

作者： Peixi Liu Guangxu Zhu Shuai Wang Miaowen Wen Wu Luo H. Vincent Poor Shuguang Cui State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Shenzhen Research Institute of Big Data Chinese Academy of Sciences Shenzhen Institute of Advanced Technology School of Electronic and Information Engineering South China University of Technology Department of Electrical and Computer Engineering Princeton University the Guangdong Provincial Key Laboratory of Future Networks of Intelligence School of Science and Engineering (SSE) Future Network of Intelligence Institute (FNii) the Chinese University of Hong Kong Shenzhen

Sensing, computation, and communication (SC 2 ) are highly coupled processes in federated edge learning (FEEL) and need to be jointly designed in a task-oriented manner for pursuing the best FEEL performance under the stringent resource constraints at edge devices. However, this remains an open problem as there is a lack of theoretical understanding on how the SC 2 resources jointly affect the FEEL performance. In this paper, we address the problem of joint SC 2 resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. Specifically, the joint SC 2 resource allocation problem is cast to maximize the convergence speed of FEEL, under the constraints on training time and energy supply of each edge device. Solving this problem entails solving two subproblems in order: the first one reduces to determining a joint sensing and communication resource allocation that maximizes the total number of samples sensed during the entire training process; the second one concerns the partition of the total number of sensed samples over communication rounds to determine the batch size at each round for convergence speed maximization. Finally, extensive simulation results are provided to validate the superiority of the proposed scheme over several baseline schemes.

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Design of Classification of Human Stress Levels Based on Brain Wave Observation Using EEG with K-NN Algorithm

Design of Classification of Human Stress Levels Based on Bra...

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International Conference on electrical, Telecommunication and computer engineering (ELTICOM)

作者： Fahmi Fahmi Veny Aprianti Baihaqi Siregar Muhammad Aziz Department of Electrical Engineering Universitas Sumatera Utara Medan Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Faculty of Advanced Engineering Universitas Tokyo Tokyo Japan

ISBN: (纸本)9798350320725

Stress is a condition that can suppress the person’s psychic state in achieving something. Stress, at some level, can harm human health since it can cause various diseases that humans often underestimate. These diseases include headaches, cramps, heart attacks, high blood pressure, and even strokes can occur. Stress levels are detected by manually filling out questionnaires or conducting self-assessment tests. However, it seems subjective because the results depend on honesty in answering the questionnaire. Therefore, in this study, we conduct a study to classify human stress levels by observing brain wave activity using an Electroencephalogram (EEG). Since EEG signals can directly reflect the brain’s electrical activity, they can be used as an objective measure for classifying stress levels. In this study, the method used in classification is K-Nearest Neighbour. The signal processing stages include pre-processing, feature extraction using Independent Component Analysis (ICA), and then classification using K-Nearest Neighbour. This research used 62 data as training and testing data. After testing the system, it was concluded that the K-Nearest Neighbour method could classify stress into normal and high levels with an accuracy of 75% with a k-value of 7.

关键词： Training System testing Signal processing algorithms Stroke (medical condition) Signal processing Electroencephalography Classification algorithms

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Perceptual Quality Assessment of Octree-RAHT Encoded 3D Point Clouds

arXiv

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arXiv 2024年

作者： Duan, Dongshuai Su, Honglei Liu, Qi Yuan, Hui Gao, Wei Song, Jiarun Wang, Zhou College of Electronics and Information Qingdao University Qingdao266071 China School of Control Science and Engineering Shandong University Ji’nan250061 China School of Electronic and Computer Engineering Peking University Shenzhen518055 China Peng Cheng Laboratory Shenzhen518066 China School of Telecommunications Engineering Xidian University Xi’an710071 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

No-reference bitstream-layer point cloud quality assessment (PCQA) can be deployed without full decoding at any network node to achieve real-time quality monitoring. In this work, we focus on the PCQA problem dedicated to Octree-RAHT encoding mode. First, to address the issue that existing PCQA databases have a small scale and limited distortion levels, we establish the WPC5.0 database which is the first one dedicated to Octree-RAHT encoding mode with a scale of 400 distorted point clouds (PCs) including 4 geometric multiplied by 5 attitude distortion levels. Then, we propose the first PCQA model dedicated to Octree-RAHT encoding mode by parsing PC bitstreams without full decoding. The model introduces texture bitrate (TBPP) to predict texture complexity (TC) and further derives the texture distortion factor. In addition, the Geometric Quantization Parameter (PQS) is used to estimate the geometric distortion factor, which is then integrated into the model along with the texture distortion factor to obtain the proposed PCQA model named streamPCQ-OR. The proposed model has been compared with other advanced PCQA methods on the WPC5.0, BASICS and M-PCCD databases, and experimental results show that our model has excellent performance while having very low computational complexity, providing a reliable choice for time-critical applications. To facilitate subsequent research, the database and source code will be publicly released at https://***/qdushl/Waterloo-Point-Cloud-Database-5.0. Copyright © 2024, The Authors. All rights reserved.

关键词： Database systems

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Light-Field Image Multiple Reversible Robust Watermarking Against Geometric Attacks

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wang, Chunpeng Zhang, Qinghua Wang, Xiaoyu Zhou, Linna Li, Qi Xia, Zhiqiu Ma, Bin Shi, Yun-Qing 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 250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan 250353 China Beijing University of Posts and Telecommunications School of Cyberspace Security Beijing 100876 China New Jersey Institute of Technology Department of Electrical and Computer Engineering Newark 07102 NJ United States

Light-field (LF) images contain rich visual information and have broader application scenarios than traditional images. However, their complex structure also makes their copyright protection more challenging. Currently, there are few watermarking schemes suitable for LF images, and most of them fail to restore the original image after embedding the watermark. In addition, geometric attacks remain a difficult problem in the field of LF image watermarking. In this study, we propose a multiple reversible robust LF image watermarking scheme based on code division multiplexing (CDM) and quaternion polar harmonic Fourier moments (QPHFMs). This scheme embeds multiple identical watermarks into the LF macro-pixel image, and the compensation information for information loss caused by watermark embedding is reversibly embedded into the LF sub-aperture images. The watermark can be extracted and the original LF image can be fully recovered if the image has not been attacked. The watermark can be extracted to verify the copyright ownership of the LF image even when the image has been attacked. Experimental results demonstrate that the proposed watermarking scheme is resistant to various attacks and exhibits strong robustness. © 2004-2012 IEEE.

关键词： Code division multiplexing Light-field images Multiple watermarking Quaternion polar harmonic Fourier moments Reversible data hiding Robust watermarking

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An optimal power supervising strategy for a smart home assessed based on BPMN Framework

An optimal power supervising strategy for a smart home asses...

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International Conference on sciences and Techniques of Automatic Control and computer engineering (STA)

作者： Leila Lanouar Mouna Rekik Olfa Bouchaala Lotfi Krichen Multimédia & Web Department University of Gabes Higher Institute of Computer Science and Multimedia of Gabes (ISIMG) Gabes Tunis Electrical Engineering Department Electrical systems and renewable energies Laboratory (LSEER) University of Sfax University of Gabes National Engineering School of Sfax (ENIS) Higher Institute of Computer Science and Multimedia of Gabes (ISIMG) Sfax Gabes Tunis Department of Computer Engineering and Applied Mathematics Research Laboratory on Development and Control and Distributed Applications (ReDCAD) University of Sfax National Engineering School of Sfax (ENIS) Sfax Tunis Electrical Engineering Department Electrical systems and renewable energies Laboratory (LSEER) University of Sfax National Engineering School of Sfax (ENIS) Sfax Tunis

ISBN: (纸本)9781665482622

For each smart home, the need of energy consumption supervision is necessary, which plays an important role to ensure the highest power quality and to enhance the stability of the whole grid. The current document implements a smart home supply strategy based on endless resources to reduce the electricity bill and confirm the energy balance. In this context, a proposed supervision algorithm operates in eight cases to reach optimal energy flow between renewable generators, home battery and grid in a smart home concept is presented. The system is evaluated using the framework “Business Process Model and Notation” (BPMN) Camunda basing on information stored in Firebase Cloud and results are presented in order to manifest the efficiency of this control strategy.

关键词： Renewable energy sources Energy consumption Power quality Process control Smart homes Power system stability Stability analysis

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A robust three-way classifier with shadowed granular-balls based on justifiable granularity

arXiv

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arXiv 2024年

作者： Yang, Jie Xiaodiao, Lingyun Wang, Guoyin Pedrycz, Witold Xia, Shuyin Zhang, Qinghua Wu, Di The Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China The School of Physics and Electronic Science Zunyi Normal University Zunyi563002 China The Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and Telecommunications Chongqing400065 China The Department of Electrical and Computer Engineering University of Alberta EdmontonAB Canada College of Computer and Information Science Southwest University Chongqing400715 China

The granular-ball (GB)-based classifier introduced by Xia, exhibits adaptability in creating coarse-grained information granules for input, thereby enhancing its generality and flexibility. Nevertheless, the current GB-based classifiers rigidly assign a specific class label to each data instance and lacks of the necessary strategies to address uncertain instances. These far-fetched certain classification approachs toward uncertain instances may suffer considerable risks. To solve this problem, we construct a robust three-way classifier with shadowed GBs for uncertain data. Firstly, combine with information entropy, we propose an enhanced GB generation method with the principle of justifiable granularity. Subsequently, based on minimum uncertainty, a shadowed mapping is utilized to partition a GB into Core region, Important region and Unessential region. Based on the constructed shadowed GBs, we establish a three-way classifier to categorize data instances into certain classes and uncertain case. Finally, extensive comparative experiments are conducted with 2 three-way classifiers, 3 state-of-the-art GB-based classifiers, and 3 classical machine learning classifiers on 12 public benchmark datasets. The results show that our model demonstrates robustness in managing uncertain data and effectively mitigates classification risks. Furthermore, our model almost outperforms the other comparison methods in both effectiveness and efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Joint Active and Passive Beamforming Design for IRS-aided MIMO ISAC Based on Sensing Mutual Information

arXiv

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arXiv 2024年

作者： Li, Jin Zhou, Gui Gong, Tantao Liu, Nan Zhang, Rui The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China Erlangen91054 Germany School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

In this paper, we investigate the intelligent reflecting surface (IRS)/reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system based on sensing mutual information (MI). Specifically, the base station (BS) perceives the sensing target via the reflected sensing signal by the IRS, while communicating with the users simultaneously. Our aim is to maximize the sensing MI, subject to the quality of service (QoS) constraints for all communication users, the transmit power constraint at the BS, and the unit-modulus constraint on the IRS’s passive reflection. We solve this problem under two cases: one simplified case assuming a line-of-sight (LoS) channel between the BS and IRS and no clutter interference to sensing, and the other generalized case considering the Rician fading channel of the BS-IRS link and the presence of clutter interference to sensing. For the first case, we show that the dedicated sensing beamformer cannot enhance the sensing MI if the BS-user direct links are blocked, and develop a low-complexity iterative algorithm to jointly optimize the BS and IRS active/passive beamformers. In contrast, for the second case, we propose an alternative iterative algorithm, which can also be applied to the first case, to solve the beamforming design problem under the general setup. Numerical results are provided to validate the performance of the proposed algorithms, as compared to various benchmark schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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End-to-end Semantic Segmentation Network for Low-Light Scenes

End-to-end Semantic Segmentation Network for Low-Light Scene...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Hongmin Mu Gang Zhang MengChu Zhou Zhengcai Cao College of Information Science and Technology Beijing University of Chemical Technology Beijing China Department of Macao Institute of Systems Engineering Macau University of Science and Technology Macao China Helen and John C. Hartmann Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In the fields of robotic perception and computer vision, achieving accurate semantic segmentation of low-light or nighttime scenes is challenging. This is primarily due to the limited visibility of objects and the reduced texture and color contrasts among them. To address the issue of limited visibility, we propose a hierarchical gated convolution unit, which simultaneously expands the receptive field and restores edge texture. To address the issue of reduced texture among objects, we propose a dual closed-loop bipartite matching algorithm to establish a total loss function consisting of the unsupervised illumination enhancement loss and supervised intersection-over-union loss, thus enabling the joint minimization of both losses via the Hungarian algorithm. We thus achieve end-to-end training for a semantic segmentation network especially suitable for handling low-light scenes. Experimental results demonstrate that the proposed network surpasses existing methods on the Cityscapes dataset and notably outperforms state-of-the-art methods on both Dark Zurich and Nighttime Driving datasets.

关键词： Training computer vision Accuracy Convolution Semantic segmentation Lighting Logic gates

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Magnetic uniformity and limits on room-temperature magnetization in strained RuO2/TiO2 films

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Physical Review Applied 2025年第5期23卷 054082-054082页

作者： Xuezhao Wu Alexander J. Grutter Ruizi Liu Purnima P. Balakrishnan Christy J. Kinane Andrew J. Caruana Yiyang Zhang Xiaolin Ren Yifan Jiang Rolf Lortz Shiming Lei Qiming Shao Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong China State Key Laboratory of Advanced Displays and Optoelectronics Technologies The Hong Kong University of Science and Technology Clear Water Bay Hong Kong China NIST Center for Neutron Research National Institute of Standards and Technology Gaithersburg Maryland 20899 USA ISIS Neutron Facility STFC Rutherford Appleton Laboratory Chilton Oxfordshire OX11 0QX United Kingdom Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Hong Kong China

Altermagnets attract much attention due to their unique spin-splitting energy band and potential applications in spintronics. Ruthenium oxide (RuO2) has been reported as a metallic altermagnet, exhibiting long-range spin-splitting torque without considerable spin-orbit coupling. However, recently, various independent experimental observations of RuO2, including by angle-resolved photoemission spectroscopy and neutron diffraction, suggest the absence of altermagnetic order and a preference for the nonmagnetic phase in RuO2. Here we investigate the strain relaxation along the thickness of [101]-oriented RuO2 films on TiO2 substrates using x-ray diffraction and analyze the potential nonuniform strain-induced net magnetization profile through superconducting quantum interference device magnetometry and polarized neutron reflectometry (PNR). Four models with different constraints were used to fit the PNR data, revealing no significant net magnetization signal in either the film or the interface. Our findings could contribute to the future clarification of the altermagnetic origins of RuO2.

关键词： Titanium dioxide

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