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Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Lower Bound optimization

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Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao ...

IEEE Globecom Workshops

作者： Xianxin Song Jie Xu Fan Liu Tony Xiao Han Yonina C. Eldar School of Science and Engineering Future Network of Intelligence Institute and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong (Shenzhen) China Department of Electrical and Electronic Engineering Southern University of Science and Technology China wireless Technology Lab 2012 Laboratories Huawei China Faculty of Mathematics and Computer Science Weizmann Institute of Science Israel

ISBN: (纸本)9781665459761

This paper investigates intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) wireless sensing, in which an IRS is deployed to assist an access point (AP) to sense a target in its NLoS region. It is assumed that the AP is equipped with multiple antennas and the IRS is equipped with a uniform linear array. The AP aims to estimate the target’s direction-of-arrival (DoA) with respect to the IRS, based on the echo signals from the AP-IRS-target-IRS-AP link. Under this setup, we jointly design the transmit beamforming at the AP and the reflective beamforming at the IRS to minimize the CramérRao lower bound (CRLB) on estimation error. Towards this end, we first obtain the CRLB expression for estimating the DoA in closed form. Next, we optimize the joint beamforming design to minimize the CRLB, via alternating optimization, semi-definite relaxation, and successive convex approximation. Numerical results show that the proposed design based on CRLB minimization achieves improved sensing performance in terms of mean squared error, as compared to the traditional schemes with signal-to-noise ratio maximization and separate beamforming.

关键词： Wireless communication Wireless sensor networks Estimation error Direction-of-arrival estimation Array signal processing Minimization Sensors

Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Lower Bound Optimization

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arXiv 2022年

作者： Song, Xianxin Xu, Jie Liu, Fan Han, Tony Xiao Eldar, Yonina C. School of Science and Engineering Future Network of Intelligence Institute Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen China Department of Electrical and Electronic Engineering Southern University of Science and Technology China Wireless Technology Lab Laboratories 2012 Huawei China Faculty of Mathematics and Computer Science Weizmann Institute of Science Israel

This paper investigates intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) wireless sensing, in which an IRS is deployed to assist an access point (AP) to sense a target in its NLoS region. It is assumed that the AP is equipped with multiple antennas and the IRS is equipped with a uniform linear array. The AP aims to estimate the target's direction-of-arrival (DoA) with respect to the IRS, based on the echo signals from the AP-IRS-target-IRS-AP link. Under this setup, we jointly design the transmit beamforming at the AP and the reflective beamforming at the IRS to minimize the Cramér-Rao lower bound (CRLB) on estimation error. Towards this end, we first obtain the CRLB expression for estimating the DoA in closed form. Next, we optimize the joint beamforming design to minimize the CRLB, via alternating optimization, semi-definite relaxation, and successive convex approximation. Numerical results show that the proposed design based on CRLB minimization achieves improved sensing performance in terms of mean squared error, as compared to the traditional schemes with signal-to-noise ratio maximization and separate beamforming. Copyright © 2022, The Authors. All rights reserved.

关键词： Beamforming

Prox-DBRO-VR: A Unified Analysis on Byzantine-Resilient Decentralized Stochastic Composite Optimization with Variance Reduction

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arXiv 2023年

作者： Hu, Jinhui Chen, Guo Li, Huaqing Guo, Xiaoyu Ran, Liang Huang, Tingwen School of Automation Central South University Hunan Changsha410083 China School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia Department of Mechanical Engineering City University of Hong Kong Kowloon Tong Hong Kong Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China

Decentralized stochastic gradient algorithms efficiently solve large-scale finite-sum optimization problems when all agents in the network are reliable. However, most of these algorithms are not resilient to adverse conditions, such as malfunctioning agents, software bugs, and cyber attacks. This paper aims to handle a class of general composite optimization problems over multi-agent systems (MASs) in the presence of an unknown number of Byzantine agents. Building on a resilient model-aggregation mechanism and the proximal-gradient mapping method, a Byzantine-resilient decentralized stochastic proximal-gradient algorithmic framework is proposed, dubbed ProxDBRO-VR, which achieves an optimization and control goal using only local computations and communications. To asymptotically reduce the noise variance arising from local gradient estimation and accelerate the convergence, we incorporate two localized variance-reduced (VR) techniques (SAGA and LSVRG) into ProxDBRO-VR to design Prox-DBRO-SAGA and Prox-DBRO-LSVRG. By analyzing the contraction relationships among the gradient-learning error, resilient consensus condition, and convergence error in a unified theoretical framework, it is proved that both Prox-DBRO-SAGA and Prox-DBRO-LSVRG, with a well-designed constant (resp., decaying) step-size, converge linearly (resp., sub-linearly) inside an error ball around the optimal solution to the original problem under standard assumptions. A trade-off between convergence accuracy and Byzantine resilience in both linear and sub-linear cases is also characterized. In numerical experiments, the effectiveness and practicability of the proposed algorithms are manifested via resolving a decentralized sparse machine-learning problem under various Byzantine attacks. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous agents

Nanowires for UV–vis–IR Optoelectronic Synaptic Devices

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Advanced Functional Materials 2023年第1期33卷 2208807-2208807页

作者： Chen, Xue Chen, Bingkun Jiang, Bei Gao, Tengfei Shang, Gang Han, Su-Ting Kuo, Chi-Ching Roy, Vellaisamy A. L. Zhou, Ye Institute of Microscale Optoelectronics Shenzhen University Shenzhen518060 China College of Physics and Optoelectronic Engineering Harbin Engineering University Harbin150001 China Faculty of Physics and Electronic Science Hubei University Wuhan430062 China Department of Electrical and Computer Engineering University of California DavisCA95616 United States College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Institute of Organic and Polymeric Materials Research and Development Center of Smart Textile Technology National Taipei University of Technology Taipei10608 Taiwan James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom Institute for Advanced Study Shenzhen University Shenzhen518060 China

Simulating biological synaptic functionalities through artificial synaptic devices opens up an innovative way to overcome the von Neumann bottleneck at the device level. Artificial optoelectronic synapses provide a non-contact method to operate the devices and overcome the shortcomings of electrical synaptic devices. With the advantages of high photoelectric conversion efficiency, adjustable light absorption coefficient, and broad spectral range, nanowires (NWs)-based optoelectronic synapses have attracted wide attention. Herein, to better promote the applications of nanowires-based optoelectronic synapses for future neuromorphic systems, the functionalities of optoelectronic synaptic devices and the current progress of NWs optoelectronic synaptic devices in UV–vis–IR spectral range are introduced. Furthermore, a bridge between NWs-based optoelectronic synaptic device and the neuromorphic system is established. Challenges for the forthcoming development of NWs optoelectronic synapses are also discussed. This review may offer a vision into the design and neuromorphic applications of NWs-based optoelectronic synaptic devices. © 2022 Wiley-VCH GmbH.

关键词： Nanowires

Density-aware and Depth-aware Visual Representation for Zero-Shot Object Counting

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Density-aware and Depth-aware Visual Representation for Zero...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Fang Nan Feng Tian Ni Zhang Nian Liu Haonan Miao Guang Dai Mengmeng Wang Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Automation Northwestern Polytechnical University Xi’an China Computer Vision Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi UAE SGIT AI Lab State Grid Corporation of China Zhejiang University of Technology Hang Zhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Previous methods often utilize CLIP semantic classifiers with class names for zero-shot object counting. However, they ignore crucial density and depth knowledge for counting tasks. Thus, we propose a density-aware and depth-aware prompt counting model, which captures density information via learning density-aware prompts based on density-aware contrastive loss and incorporates depth guidance with predefined depth-aware prompts. To facilitate the training process, we design two strategies for standard counting loss and the contrastive loss, where the former prioritizes larger and sparser objects initially, gradually focusing on smaller and denser objects, and the latter adopts coarse-to-fine density learning. Besides, we construct a dataset named LVIS-372 with more real-world scenarios and balanced instance distribution compared to existing ones. Finally, the experimental results demonstrate the effectiveness of our proposed method.

关键词： Training Visualization Semantics Focusing Signal processing Acoustics Power capacitors Speech processing Standards

Focused X-ray Beams from Van der Waals Heterostructures

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Focused X-ray Beams from Van der Waals Heterostructures

Conference on Lasers and Electro-Optics (CLEO)

作者： Xihang Shi Yaniv Kurman Michael Shentcis Liang Jie Wong F. Javier García de Abajo Ido Kaminer Solid State Institute and Faculty of Electrical and Computer Engineering Technion – Israel Institute of Technology Haifa Israel School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore ICFO—Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Castelldefels Spain ICFO—Institució Catalana de Recerca i Estudis Avançats Barcelona Spain

We present the concept of customized van der Waals heterostructures with periodicity tailored to generate pre-designed X-ray patterns from free electrons. As an example, we demonstrate focused X-ray beams.

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Free electron lasers X-ray lasers Lasers and electrooptics Laser beams Electrooptical waveguides

Proposing Algorithm to Localize and Extract Facial Information Using FaceNet and MTCNN

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Proposing Algorithm to Localize and Extract Facial Informati...

Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Phat Nguyen Huu Anh Pham Thi Quy Mai Danh Nhi Chu Quynh Tran Manh Hoang Quang Tran Minh School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Vietnam 11 English 1 Classs Hanoi-Amsterdam Highschool for the Gifted Hanoi Vietnam Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology (VNU-HCM) Hochiminh Vietnam Vietnam National University Ho Chi Minh City (VNU-HCM) Hochiminh Vietnam

ISBN: (纸本)9781665490597

Facial recognition systems from photos or videos are widely applied in daily life such as surveillance systems, access management, and security investigations. It can be compared with other forms of biometrics such as fingerprint or iris recognition systems. Among the facial recognition systems developed so far, Facenet introduced by Google brings a high accuracy rate. Therefore, in this paper, we will build a face recognition system using MTCNN| to detect faces and extract features using Facenet and SVM to classify and recognize faces. The proposed algorithm consists of 5 steps, namely, preparing data for training; face detection from data with MTCNN; extracting the features of each face with the FaceNet Keras model; classifying feature vectors by SVM, and performing face recognition. The algorithm results in an accuracy of up to 99.63% with data from only 5 to 15 images for a person. This proves that the algorithm is feasible when applied in practice.

关键词： Support vector machines Training Face recognition Surveillance Feature extraction Cameras Data models

Lifting query complexity to time-space complexity for two-way finite automata

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arXiv 2023年

作者： Zheng, Shenggen Li, Yaqiao Pan, Minghua Gruska, Jozef Li, Lvzhou Peng Cheng Laboratory Shenzhen518055 China Shenzhen MSU-BIT University Shenzhen518172 China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin541004 China Faculty of Informatics Masaryk University Brno60200 Czech Republic School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China

Time-space tradeoff has been studied in a variety of models, such as Turing machines, branching programs, and finite automata, etc. While communication complexity as a technique has been applied to study finite automata, it seems it has not been used to study time-space tradeoffs of finite automata. We design a new technique showing that separations of query complexity can be lifted, via communication complexity, to separations of time-space complexity of two-way finite automata. As an application, one of our main results exhibits the first example of a language L such that the time-space complexity of two-way probabilistic finite automata with a bounded error (2PFA) is Ω˜(n2), while of exact two-way quantum finite automata with classical states (2QCFA) is Õ(n5/3), that is, we demonstrate for the first time that exact quantum computing has an advantage in time-space complexity comparing to classical computing. © 2023, CC BY.

关键词： Finite automata

Compacting deep neural networks for internet of things: Methods and applications

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arXiv 2021年

作者： Zhang, Ke Ying, Hanbo Dai, Hong-Ning Li, Lin Peng, Yuangyuang Guo, Keyi Yu, Hongfang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Science and Technology on Electronic Information Control Laboratory Chengdu China School of Communication and Information Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology Macau University of Science and Technology China Courant Institute of Mathematical Science New York University New York United States

Deep Neural Networks (DNNs) have shown great success in completing complex tasks. However, DNNs inevitably bring high computational cost and storage consumption due to the complexity of hierarchical structures, thereby hindering their wide deployment in Internet-of-Things (IoT) devices, which have limited computational capability and storage capacity. Therefore, it is a necessity to investigate the technologies to compact DNNs. Despite tremendous advances in compacting DNNs, few surveys summarize compacting-DNNs technologies, especially for IoT applications. Hence, this paper presents a comprehensive study on compacting-DNNs technologies. We categorize compacting-DNNs technologies into three major types: 1) network model compression, 2) Knowledge Distillation (KD), 3) modification of network structures. We also elaborate on the diversity of these approaches and make side-by-side comparisons. Moreover, we discuss the applications of compacted DNNs in various IoT applications and outline future *** Codes 68T07 © 2021, CC BY-NC-SA.

关键词： Internet of things