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IEEE OPEN JOURNAL OF signal processing 2023年 4卷 294-303页

作者： Zou, Yanbin Shantou Univ Dept Elect & Informat Engn Shantou 515063 Guangdong Peoples R China

By taking different kinds of measurements at the same time, it may be possible to improve the accuracy of target localization or reduce the number of sensors needed. Time-of-arrival (TOA), time-difference-of-arrival (TDOA), time delay (TD), received signal strength (RSS), and angle-of-arrival (AOA) are the commonly used measurements in wireless localization systems. This article first presents a new weighted least squares (WLS) algorithm for the AOA-only localization problem. Then, a unified solution based on semidefinite programming (SDP) is developed for hybrid AOA/TOA, hybrid AOA/TDOA, hybrid AOA/TD, and hybrid AOA/RSS localization problems. Additionally, thorough simulation results show that the proposed SDP algorithms perform better than state-of-the-art techniques.

关键词： Location awareness signal processing algorithms Sensors Maximum likelihood estimation Noise measurement Azimuth Wireless sensor networks Angle-of-arrival (AOA) semidefinite programming (SDP) time-of-arrival (TOA) time-difference-of-arrival (TDOA) time delay (TD) received signal strength (RSS) weighted least-squares (WLS)

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Windowed Quantum Phase Estimation: signal processing Approach to a Quantum Algorithm

Windowed Quantum Phase Estimation: Signal Processing Approac...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Xue Wen Samsung Research China Beijing

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Quantum phase estimation (QPE) is a key ingredient to some of the most important results of quantum computing. In this paper we examine the QPE algorithm through the looking glass of signal processing. This perspective helps to reveal a relation between the distribution of quantum phase estimates and the shape of the discrete Fourier transform (DFT) of a pure complex sinusoid. Guided by spectral shaping theory, we design a class of quantum circuits that are the quantum equivalents to classical windowing operators, for a family of window functions that have compact support in the DFT. These circuits have complexity linear in the number of target qubits, strictly lower than QPE itself. Using these circuits we are able to reshape QPE's performance characteristics. In particular, the asymptotic marginal cost for 2x reduction in fail rate (2x improvement in reliability) is reduced from one qubit to 1/3 qubit or lower, which can help reduce the qubit budget for a preset accuracy-reliability target, especially in high-reliability scenarios. This makes a demonstration of the value of signal processing principles in designing quantum algorithms.

关键词： Quantum algorithm Shape Qubit Discrete Fourier transforms Phase estimation signal processing algorithms Quantum mechanics signal processing Windows Speech processing

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Dynamic Orthogonal Matching Pursuit for Sparse Data Reconstruction

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2023年 4卷 242-256页

作者： Zhao, Yun-Bin Luo, Zhi-Quan Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China

The orthogonal matching pursuit (OMP) is one of the mainstream algorithms for sparse data reconstruction or approximation. It acts as a driving force for the development of several other greedy methods for sparse data reconstruction, and it also plays a vital role in the development of compressed sensing theory for sparse signal and image reconstruction. In this article, we propose the so-called dynamic orthogonal matching pursuit (DOMP) and enhanced dynamic orthogonal matching pursuit (EDOMP) algorithms which are more efficient than OMP for sparse data reconstruction from a numerical point of view. We carry out a rigorous analysis to establish the reconstruction error bound for DOMP under the restricted isometry property of the measurement matrix. The main result claims that the reconstruction error via DOMP can be controlled and measured in terms of the number of iterations, sparsity level of data, and the noise level of measurements. Moveover, the finite convergence of DOMP for a class of large-scale compressed sensing problems is also shown.

关键词： Matching pursuit algorithms signal processing algorithms Image reconstruction Indexes Heuristic algorithms Measurement uncertainty Approximation algorithms Data reconstruction dynamic orthogonal matching pursuit error bound orthogonal matching pursuit restricted isometry property sparse optimization

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Multidimensional Data Classification using Graph signal processing

Multidimensional Data Classification using Graph Signal Proc...

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International Conference for Convergence of Technology (I2CT)

作者： Pratistha Gaur Prrathyush Kedeelaya Aishwary Anurag C. Narendra Babu Dept. of CSE MS Ramaiah University of Applied Sciences Bengaluru Karnataka India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Conventional classification methods encounter difficulties in handling non-linear relationships and high-dimensional feature spaces, highlighting the need for alternative *** signal processing (GSP) has attracted many researchers the recent times owing to its ability to capture complexities associated with capturing intricate relationships within multidimensional data. In this paper, GSP is employed for the classification of digits. Graph signal is constructed from MNIST handwritten digit data. Fourier transformation is used to represent the signal variation on the graph obtained *** the optimized graph signal regularization, the model captures intricate patterns, demonstrating superior adaptability for handwritten digit classification.A comparative analysis is performed on contemporary models such as Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), and U-Net. The experimental results reveal that the proposed model based on GSP for classification is more efficient than the contemporary models for character recognition in terms of precision, recall and F-Score.

关键词： Adaptation models Analytical models Machine learning algorithms Heuristic algorithms Neural networks signal processing algorithms Generative adversarial networks

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Multivariate Data Acquisition Strategy by Employing signal processing on Graph in Edge Computing Based Industrial Internet of Things

Multivariate Data Acquisition Strategy by Employing Signal P...

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Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Lishan Yang Yining Zhang Chenglin Bai Hengying Xu Guizhen Wang Xuezhen Wang School of Physics Science and Information Engineering Liaocheng University Liaocheng China School of Mathematical Sciences Liaocheng University Liaocheng China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

Multivariate data acquisition strategy is a crucial issue in edge computing based Industrial Internet of Things (EC-IIoT), which should be carefully designed to improve energy efficiency and prolong network lifetime of EC-IIoT. Sampling on graph provides a promising opportunity to solve resource limited sensor selection problem via forming the appropriate sampling set and reconstructing original graph signal. In this paper, we translate the multivariate data acquisition strategy to the graph sampling set forming problem, which could exploit the adjacent correlation of the vertices in the spatial and variable domain. The simulation result demonstrates the effectiveness of the proposed data acquisition strategy.

关键词： Correlation coefficient Sufficient conditions Simulation Data acquisition signal processing algorithms signal processing Reconstruction algorithms

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Hybrid Quantum-Classical Approaches to Optimize signal processing in Massive MIMO Arrays

Hybrid Quantum-Classical Approaches to Optimize Signal Proce...

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Communication, Computer Sciences and Engineering (IC3SE), International Conference on

作者： Shaik Anjimoon Swathi Baswaraju Rajeev Sobti Sudhakar Ajmera Ajay Rana Ali Abduhussien Hameed Institute of Aeronautical Engineering Dundigal Hyderabad India Department of Computer Science Engineering (Date Science) New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad Telangana India Amity School of Engineering and Technology Amity University Greater Noida India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

This paper introduces a pioneering method in the realm of signal processing for Massive Multiple-Input Multiple-Output (MIMO) systems, leveraging the synergistic potential of hybrid quantum-classical computing paradigms. The escalating demand for high data rate communications necessitates the advancement of MIMO technologies. However, the computational complexity associated with signal processing in such systems, especially in scenarios involving large array sizes, presents a significant challenge. This study proposes an innovative framework that integrates quantum computing algorithms with traditional classical methods to address this challenge. The quantum algorithms are specifically designed to expedite matrix operations and optimization tasks that are central to MIMO signal processing, such as channel estimation and precoding. A comparative analysis with purely classical approaches demonstrates a substantial improvement in computational efficiency and latency reduction, while maintaining, and in certain aspects enhancing, the accuracy of signal detection and system throughput. Furthermore, this hybrid approach offers scalability and adaptability to varying channel conditions and system configurations, a crucial feature for next-generation wireless communication systems. The theoretical foundation is complemented by simulation results, showcasing the feasibility and effectiveness of this approach in practical scenarios. This research not only paves the way for advanced signal processing techniques in Massive MIMO systems but also opens new horizons in the application of quantum computing in telecommunications.

关键词： Wireless communication Quantum algorithm Scalability Simulation signal processing algorithms Massive MIMO Throughput

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An Adaptive Parameter Estimation Algorithm of Radar Linear Frequency Modulation signal Based on Nonlinear Transform Under Different Alpha Stable Distribution Noise Environments

IEEE JOURNAL ON MINIATURIZATION FOR AIR AND SPACE SYSTEMS

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IEEE JOURNAL ON MINIATURIZATION FOR AIR AND SPACE SYSTEMS 2023年第4期4卷 389-399页

作者： Zhang, Yuhong Zhang, Yixin Xian Polytech Univ Sch Elect Informat Xian 710048 Peoples R China

In order to address the impact of alpha stable distribution noise in the field of parameter estimation of radar linear frequency modulation (LFM) signal, the Lv's distribution (LVD) class algorithms have been proposed in recent works. However, they just can be applied under the single noisy environment and suffered severe performance degradation at low signal-to-noise ratios (SNRs). In this article, an adaptive nonlinear function LVD (ANF-LVD) algorithm is proposed, different from the traditional LVD algorithms, which makes full use of the geometric information of the LFM signal to adapt to different alpha stable distribution noise environments. Then, based on the geometric information of the LFM signal, an appropriate nonlinear function is selected to suppress the noise under different alpha stable distribution noise environments, which has high parameter estimation accuracy even under an extremely low SNR environment. Simulation experiments show that the proposed algorithm has stronger adaptability and higher parameter estimation accuracy than the traditional LVD algorithm under different alpha stable distribution noise environments.

关键词： Parameter estimation signal processing algorithms signal to noise ratio Time-frequency analysis Radar Chirp signal resolution Alpha stable distribution noise linear frequency modulation (LFM) Lv's distribution (LVD) nonlinear function parameter estimation

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Fast 3D Joint Superresolution Algorithm for Millimeter Wave FMCW Radars

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2023年 4卷 346-365页

作者： Patole, Sujeet Baral, Ashwin Bhobani Torlak, Murat Univ Texas Dallas Dept Elect & Comp Engn Richardson TX 75080 USA

This article formulates a novel fast three-dimensional joint superresolution algorithm for millimeter wave (mmWave) imaging. The existing three-dimensional superresolution techniques for joint estimation are inherently slow. Firstly, the size of the correlation matrix, used for the subspace-based superresolution algorithms such as MUltiple signal Classification (MUSIC), increases with a dimension. This escalates the computational cost of singular value decomposition (SVD) or eigenvalue decomposition (EVD) required for the subspace estimation. Secondly, the existing approaches involve an exhaustive joint search over the entire grid of multiple dimensions. To reduce these cost complexities for the joint 3D estimation problem, we propose a two-stage imaging algorithm based on the Fourier transform and beamspace MUSIC to jointly estimate the range, azimuth, and elevation of the targets. Due to the reduced implementation complexity, the proposed algorithm becomes an attractive option for the silicon implementation of superresolution techniques. Our novel 3D imaging algorithm is explained with a detailed mathematical framework by utilizing the mmWave frequency modulated continuous wave (FMCW) radar, which is popular in the automotive industry. In addition, we derive the asymptotic analytical expressions for analyzing the statistical performance of the proposed method in terms of mean square error (MSE). The performance of the proposed approach is verified through various simulations and a real radio-frequency (RF) experiment using a 60 GHz FMCW radar.

关键词： Three-dimensional displays Estimation Multiple signal classification signal processing algorithms Superresolution Costs Radar 2D direction of arrival estimation azimuth estimation beamspace elevation estimation frequency modulated continuous wave low-complexity mean square error range estimation superresolution algorithms

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Efficient Partial Recomputation-Based Fault Detection Approaches for Z-transform

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2025年

作者： Aghapour, Saeed Ahmadi, Kasra Kermani, Mehran Mozaffari Azarderakhsh, Reza Univ S Florida Dept Comp Sci & Engn Tampa FL 33620 USA Florida Atlantic Univ Dept Comp & Elect Engn & Comp Sci Boca Raton FL 33431 USA

The Z-transform is a fundamental and strong tool being widely utilized in signal processing and various other applications such as communications and networking. By analyzing the Z-transform of a signal, one can extract critical information about its stability, causality, frequency response, energy and power, and overall behavior of the signal. However, errors caused either by environmental changes or malicious injections in large-scale integration (VLSI) implementations can critically compromise the integrity and reliability of its output. Failure to detect such faults may result in unpredictable, erroneous, and misleading function analyses. Therefore, the ability to detect soft errors and faults before accepting the results is of paramount importance. In this article, we propose an efficient fault detection method that combines algorithmic-level checks with partial recomputation to identify both transient and permanent faults with a high error coverage rate across various injection scenarios. The AMD/Xilinx field-programmable gate array (FPGA) implementation of our design demonstrated only a modest increase in time and area overhead. To the best of our knowledge, fault detection for the Z-transform function has not been previously studied.

关键词： Fault detection Reliability Field programmable gate arrays Cryptography Computer architecture signal processing algorithms Fault diagnosis Very large scale integration Transforms Encoding field-programmable gate array (FPGA) reliability Z-transform

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Iterative Broadband Source Localization

IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY

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IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY 2023年 4卷 453-469页

作者： DeLude, Coleman Srinivasa, Rakshith S. Karnik, Santhosh Hood, Christopher Davenport, Mark A. Romberg, Justin Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Samsung Res Amer AI Ctr Mountain View CA 94043 USA Michigan State Univ Dept Computat Math Sci & Engn E Lansing MI 48824 USA

In this paper we consider the problem of localizing a set of broadband sources from a finite window of measurements. In the case of narrowband sources this can be reduced to the problem of spectral line estimation, where our goal is simply to estimate the active frequencies from a weighted mixture of pure sinusoids. There exists a plethora of modern and classical methods that effectively solve this problem. However, for a wide variety of applications the underlying sources are not narrowband and can have an appreciable amount of bandwidth. In this work, we extend classical greedy algorithms for sparse recovery (e.g., orthogonal matching pursuit) to localize broadband sources. We leverage models for samples of broadband signals based on a union of Slepian subspaces, which are more aptly suited for dealing with spectral leakage and dynamic range disparities. We show that by using these models, our adapted algorithms can successfully localize broadband sources under a variety of adverse operating scenarios. Furthermore, we show that our algorithms outperform complementary methods that use more standard Fourier models. We also show that we can perform estimation from compressed measurements with little loss in fidelity as long as the number of measurements are on the order of the signal's implicit degrees of freedom. We conclude with an in-depth application of these ideas to the problem of localization in multi-sensor arrays.

关键词： Array signal processing parameter estimation frequency estimation direction-of-arrival estimation signal processing algorithms compressed sensing

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