检索结果-内蒙古大学图书馆

Unfolding-Aided Bootstrapped Phase Retrieval in Optical Imaging: Explainable AI reveals new imaging frontiers

IEEE signal processing MAGAZINE 2023年第2期40卷 46-60页

作者： Pinilla, Samuel Mishra, Kumar Vijay Shevkunov, Igor Soltanalian, Mojtaba Katkovnik, Vladimir Egiazarian, Karen Univ Manchester Harwell Sci & Innovat Campus Manchester OX11 0FA Oxon England US DEVCOM Army Res Lab Elect & Comp Engn Adelphi MD 20783 USA Univ Illinois Elect Engn Chicago IL 60607 USA Tampere Univ Fac Informat Technol & Commun Sci Tampere 33720 Finland

Phase retrieval in optical imaging refers to the recovery of a complex signal from phaseless data acquired in the form of its diffraction patterns. These patterns are acquired through a system with a coherent light source that employs a diffractive optical element (DOE) to modulate the scene, resulting in coded diffraction patterns (CDPs) at the sensor. Recently, the hybrid approach of a model-driven network or deep unfolding has emerged as an effective alternative to conventional model- and learning-based phase-retrieval techniques because it allows for bounding the complexity of algorithms while also retaining their efficacy. Additionally, such hybrid approaches have shown promise in improving the design of DOEs that follow theoretical uniqueness conditions. There are opportunities to exploit novel experimental setups and resolve even more complex DOE phase-retrieval applications. This article presents an overview of algorithms and applications of deep unfolding for bootstrapped-regardless whether near, middle, or far zones-phase retrieval.

关键词： Optical diffraction Image resolution Diffraction signal processing algorithms Optical imaging Optical sensors Optical modulation

来源：评论

学校读者我要写书评

暂无评论

Joint Activity Detection and Channel Estimation for Massive IoT Access Based on Millimeter-Wave/Terahertz Multi-Panel Massive MIMO

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第1期72卷 1349-1354页

作者： Xiu, Hanlin Gao, Zhen Liao, Anwen Mei, Yikun Zheng, Dezhi Tan, Shufeng Di Renzo, Marco Hanzo, Lajos Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Adv Res Inst Multidisciplinary Sci Beijing 100081 Peoples R China Univ Paris Saclay Lab Signaux & Syst CNRS Cent SuplecUniv Paris Sud F-91192 Paris France Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England

The multi-panel array, as a state-of-the-art antenna-in-package technology, is very suitable for millimeter-wave (mmWave)/ terahertz (THz) systems, due to its low-cost deployment and scalable configuration. But in the context of non-uniform array structures it leads to intractable signal processing. Based on such an array structure at the base station, this paper investigates a joint active user detection (AUD) and channel estimation (CE) scheme based on compressive sensing (CS) for application to the massive Internet of Things (IoT). Specifically, by exploiting the structured sparsity of mmWave/THz massive IoT access channels, we firstly formulate the multi-panel massive multiple-input multiple-output (mMIMO)-based joint AUD and CE problem as a multiple measurement vector (MMV)-CS problem. Then, we harness the expectation maximization (EM) algorithm to learn the prior parameters (i.e., the noise variance and the sparsity ratio) and an orthogonal approximate message passing (OAMP)-EM-MMV algorithm is developed to solve this problem. Our simulation results verify the improved AUD and CE performance of the proposed scheme compared to conventional CS-based algorithms.

关键词： Antenna arrays signal processing algorithms Radio frequency Millimeter wave communication Uplink Internet of Things Millimeter wave technology Active user detection channel estimation massive IoT access millimeter-wave multi-panel mMIMO terahertz

来源：评论

学校读者我要写书评

暂无评论

Distributed Subspace Projection Graph signal Estimation With Anomaly Interference

引用

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第6期10卷 3883-3894页

作者： Liu, Zhao Chen, Feng Duan, Shukai Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China Southwest Univ Brain Inspired Comp & Intelligent Control Key Lab Chongqing 400715 Peoples R China Chongqing Collaborat Innovat Ctr Brain Sci Chongqing 400715 Peoples R China

The estimation of graph signal is a vital problem in many distributed networks, such as vehicular networks, smart grids, unmanned aerial vehicles (UAVs), and the Internet of Things. In those networks, anomaly interference widely exists, such as network attack, noise, device fault, which will hazard the healthy of the entire system. In the article, the estimation of graph signals with anomaly interference is investigated. We show that the graph signal estimation problem can be treated as a bandlimited subspace optimization problem, and propose a distributed subspace projection graph signal estimation algorithm based on the graph filter (DispGF), which can achieve better performance with less communication burden. In addition, a graph filter matrix that produces subspace projection is proposed to replace the nonsparse projection matrix, which guarantees distributed implementation and projection accuracy. Different from previous work, here, graph signal estimation is studied with no prior anomaly information. To this end, for FDI attack, random attack, noise interference, we propose anomaly detection and node localization scheme based on smoothness, that can achieve similar performance compared with the case of prior anomaly information known. Numerical experiments verify the effectiveness of the proposed DispGF algorithm. The convergence of the algorithm is theoretically analyzed.

关键词： Estimation signal processing algorithms Filtering algorithms Symmetric matrices Interference Wireless sensor networks Perturbation methods Graph filter distributed estimation graph signal estimation subspace projection anomaly interference secure estimation

来源：评论

学校读者我要写书评

暂无评论

Byzantine-Robust Distributed Learning With Compression

引用

IEEE TRANSACTIONS ON signal AND INFORMATION processing OVER NETWORKS 2023年 9卷 280-294页

作者： Zhu, Heng Ling, Qing Univ Calif San Diego San Diego CA 92093 USA Sun Yat Sen Univ Guangdong Prov Key Lab Computat Sci Guangzhou 510275 Peoples R China Sun Yat Sen Univ Pazhou Lab Guangzhou 510275 Peoples R China

Communication between workers and the master node to collect local stochastic gradients is a key bottleneck in a large-scale distributed learning system. Various recent works have proposed to compress the local stochastic gradients to mitigate the communication overhead. However, robustness to malicious attacks is rarely considered in such a setting. In this work, we investigate the problem of Byzantine-robust compressed distributed learning, where the attacks from Byzantine workers can be arbitrarily malicious. We theoretically point out that different to the attacks-free compressed stochastic gradient descent (SGD), its vanilla combination with geometric median-based robust aggregation seriously suffers from the compression noise in the presence of Byzantine attacks. In light of this observation, we propose to reduce the compression noise with gradient difference compression so as to improve the Byzantine-robustness. We also observe the impact of the intrinsic stochastic noise caused by selecting random samples, and adopt the stochastic average gradient algorithm (SAGA) to gradually eliminate the inner variations of regular workers. We theoretically prove that the proposed algorithm reaches a neighborhood of the optimal solution at a linear convergence rate, and the asymptotic learning error is in the same order as that of the state-of-the-art uncompressed method. Finally, numerical experiments demonstrate the effectiveness of the proposed method.

关键词： Stochastic processes Distance learning Computer aided instruction Compressors Convergence Robustness signal processing algorithms Distributed learning communication efficiency Byzantine-robustness gradient compression

来源：评论

学校读者我要写书评

暂无评论

De-Noising of Sparse signals Using Mixture Model Shrinkage Function

引用

IEEE ACCESS 2023年 11卷 7551-7563页

作者： Ullah, Hayat Amir, Muhammad Iqbal, Muhammad Malik, Suheel Abdullah Jadoon, Muhammad Mohsin Khan NYU Tandon Sch Engn Dept Elect & Comp Engn New York NY 11201 USA Int Islamic Univ Islamabad Dept Elect Engn Islamabad 44000 Pakistan Tampere Univ Dept Automat & Mech Engn Tampere 33720 Finland Pak Austria Fachhsch Inst Fac Elect Com IT & Design Haripur 22653 Pakistan

In this work a new thresholding function referred to as 'mixture model shrinkage' (MMS) based on the minimization of a convex cost function is proposed. Normally, thresholding functions underestimate larger signal amplitudes during the de-noising process. The proposed model is a more flexible shrinkage function as it solves the underestimation problem to a greater extent and thus efficiently de-noises the signal without affecting signal amplitudes. The Expectation minimization (EM) algorithm is used to find the model parameters along with the majorization-minimization (MM) algorithm that minimize the monotonic cost function. The proposed model is then applied for de-noising group sparse signals and Shepp Logan phantom images. Our experimental study shows that MMS outclasses current thresholding functions and overlapping group shrinkage algorithm without results suffering from underestimation. Furthermore, the proposed model has the smallest Root Mean Square Error (RMSE) for de-noising group sparse signals.

关键词： Noise reduction Transforms Thresholding (Imaging) Mixture models signal processing algorithms Noise measurement Wavelet coefficients Convex optimization EM algorithm maximum a posteriori estimator (MAP) MM algorithm PSNR RMSE sparse signal processing

来源：评论

学校读者我要写书评

暂无评论

Twenty-Five Years of Advances in Beamforming: From convex and nonconvex optimization to learning techniques

引用

IEEE signal processing MAGAZINE 2023年第4期40卷 118-131页

作者： Elbir, Ahmet M. Mishra, Kumar Vijay Vorobyov, Sergiy A. Heath Jr, Robert W. W. Univ Luxembourg L-1855 Luxembourg Luxembourg Duzce Univ TR-81620 Duzce Turkiye US DEVCOM Army Res Lab Adelphi MD 20783 USA Aalto Univ Dept Informat & Commun Engn Espoo 02150 Finland North Carolina State Univ Raleigh NC 27695 USA

Beamforming is a signal processing technique to steer, shape, and focus an electromagnetic (EM) wave using an array of sensors toward a desired direction. It has been used in many engineering applications, such as radar, sonar, acoustics, astronomy, seismology, medical imaging, and communications. With the advent of multiantenna technologies in, say, radar and communication, there has been a great interest in designing beamformers by exploiting convex or nonconvex optimization methods. Recently, machine learning (ML) is also leveraged for obtaining attractive solutions to more complex beamforming scenarios. This article captures the evolution of beamforming in the last 25 years from convex to nonconvex optimization and optimization to learning approaches. It provides a glimpse into these important signal processing algorithms for a variety of transmit-receive architectures, propagation zones, propagation paths, and multidisciplinary applications.

关键词： Array signal processing Shape Sonar applications Seismology signal processing algorithms Optimization methods Machine learning

来源：评论

学校读者我要写书评

暂无评论

Reconfigurable-Intelligent-Surface-Aided OTFS: Transmission Scheme and Channel Estimation

引用

IEEE INTERNET OF THINGS JOURNAL 2023年第22期10卷 19518-19532页

作者： Li, Zhongjie Yuan, Weijie Li, Buyi Wu, Jun You, Changsheng Meng, Fanke Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Tianjin Yinyuan Informat Technol Co Ltd Tianjin 300457 Peoples R China

In this article, we study the uplink transmission scheme and channel estimation design for reconfigurable intelligent surfaces (RIS)-aided orthogonal time-frequency space (OTFS) systems in high-mobility scenarios. To this end, we first propose an efficient and reliable transmission scheme that utilizes the delay-Doppler (DD) information in OTFS to facilitate the configuration of RIS. Specifically, the proposed scheme exploits the estimated delay and Doppler shifts of the cascaded channel to sense the channel parameters, and the sensing parameters are then used for RIS passive beamforming. It is noteworthy that we estimate the channel state information (CSI) by employing only one OTFS frame and configure the RIS based on the predicted channel parameters, leading to substantially reduced channel training overhead and more real-time RIS configuration. To obtain the essential information for channel information sensing, we then propose a low-complexity algorithm which determines the Doppler and delay shifts of the channel between the user and RIS based on linear systems and the mapping relationship of the DD pairs, respectively. With the DD information in hand, a user localization algorithm constructed by the least square (LS) and a channel tracking method relying on extended Kalman filter (EKF) are then presented to obtain the spatial angle information. By making use of the channel parameters acquired at the base station (BS), the RIS reflection vector is designed to maximize the achievable rate. The results obtained from the simulation experiments affirm the efficacy of the proposed scheme, thereby confirming its capability to attain efficient communications under high Doppler channels.

关键词： Channel estimation Delays Doppler shift Training OFDM signal processing algorithms Sensors orthogonal time-frequency space (OTFS) passive beamforming reconfigurable intelligent surfaces (RIS)

来源：评论

学校读者我要写书评

暂无评论

Fairness-Aware Regression Robust to Adversarial Attacks

引用

IEEE TRANSACTIONS ON signal processing 2023年 71卷 4092-4105页

作者： Jin, Yulu Lai, Lifeng Univ Calif Davis Dept Elect & Comp Engn Davis CA 95616 USA

In this paper, we take a first step towards answering the question of how to design fair machine learning algorithms that are robust to adversarial attacks. Using a minimax framework, we aim to design an adversarially robust fair regression model that achieves optimal performance in the presence of an attacker who is able to add a carefully designed adversarial data point to the dataset or perform a rank-one attack on the dataset. By solving the proposed nonsmooth nonconvex-nonconcave minimax problem, the optimal adversary as well as the robust fairness-aware regression model are obtained. For both synthetic data and real-world datasets, numerical results illustrate that the proposed adversarially robust fair models have better performance on poisoned datasets than other fair machine learning models in both prediction accuracy and group-based fairness measure.

关键词： Data models Predictive models Numerical models Training Robustness Linear programming signal processing algorithms Fairness minimax problem adversarial robustness

来源：评论

学校读者我要写书评

暂无评论

Joint Generalized Inner Product Method for Main Lobe Jamming Suppression in Distributed Array Radar

引用

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第5期59卷 6940-6953页

作者： Pu, Weiming Liang, Zhennan Wu, Jianxin Liu, Quanhua Beijing Inst Technol Minist Educ Key Lab Elect & Informat Technol Satellite Nav Beijing 100081 Peoples R China Sun Yat Sen Univ Sch Elect & Commun Engn Guangzhou 510275 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401135 Peoples R China

Modern radar detection faces an increasingly complex electromagnetic environment, and strong clutter and strong jamming have severely restricted radar performance. This article proposes a robust jamming suppression method that is based on the joint generalized inner product (JGIP) method in a distributed array radar, which utilizes the characteristics of the extremely high angular resolution of the distributed array radar to effectively suppress main lobe jamming. Thus, the advantages of flexible aperture synthesis of distributed array radar are investigated to design a JGIP to detect targets in a cluttered environment. This method is not sensitive to the various nonideal factors that exist in distributed array radar engineering practice and is highly robust. Simulation and actual measured data verify the correctness and effectiveness of the proposed method.

关键词： Jamming Radar Radar antennas Clutter Radar clutter Spaceborne radar signal processing algorithms Distributed array radar joint generalized inner product (JGIP) main lobe jamming measured data

来源：评论

学校读者我要写书评

暂无评论

Iterative Difference Hard-Thresholding Algorithm for Sparse signal Recovery

引用

IEEE TRANSACTIONS ON signal processing 2023年 71卷 1093-1102页

作者： Cui, Angang He, Haizhen Xie, Zhiqi Yan, Weijun Yang, Hong Yulin Univ Sch Math & Stat Yulin 719000 Peoples R China Yulin Univ Sch Int Educ Yulin 719000 Peoples R China

In this paper, a nonconvex surrogate function, namely, Laplace norm, is studied to recover the sparse signals. Firstly, we discuss the equivalence of the optimal solutions of $l_{0}$-norm minimization problem, Laplace norm minimization problem and regularization Laplace norm minimization problem. It is proved that the $l_{0}$-norm minimization problem can be solved by solving the regularization Laplace norm minimization problem if the certain conditions are satisfied. Secondly, an iterative difference hard-thresholding algorithm and its adaptive version algorithm are proposed to solve the regularization Laplace norm minimization problem. Finally, we provide some numerical experiments to test the performance of the adaptive iterative difference hard-thresholding algorithm, and the numerical results show that the adaptive iterative difference hard-thresholding algorithm performs better than some state-of-art methods in recovering the sparse signals.

关键词： Minimization signal processing algorithms Iterative algorithms Thresholding (Imaging) Indexes Eigenvalues and eigenfunctions Linear matrix inequalities Laplace norm equivalence iterative difference hard-thresholding algorithm adaptive iterative difference hard-thresholding algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：