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A DIGITAL signal-processing ALGORITHM FOR DETECTING ARCING FAULTS ON POWER DISTRIBUTION FEEDERS

IEEE TRANSACTIONS ON POWER DELIVERY 1989年第1期4卷 132-140页

作者： RUSSELL, BD CHINCHALI, RP Texas A and M University College Station TX USA

signal processing hardware and software that can be used to improve the detection of certain power system faults using computer relays are discussed. Integrated systems and architectures for monitoring several fault-sensitive parameters have been investigated. A suggested architecture utilizing several processors is presented. Several fault-sensitive parameters for the detection of arcing faults are presented. A detection methodology based on these parameters is described, and a partial solution to the problem of directionality is discussed. The use of a knowledge-based environment to modify protection criteria is suggested.< >

关键词： Digital signal processing signal processing algorithms Fault detection Electrical fault detection Computer architecture Hardware Power system faults Digital relays Power system relaying Computerized monitoring

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Reproducible Research in signal processing

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IEEE signal processing MAGAZINE 2009年第3期26卷 37-47页

作者： Vandewalle, Patrick Kovacevic, Jelena Vetterli, Martin Carnegie Mellon Univ Ctr Bioimage Informat Pittsburgh PA 15213 USA Columbia Univ New York NY USA Univ Calif Berkeley Berkeley CA 94720 USA Ecole Polytech Fed Lausanne Commun Syst Dept Lausanne Switzerland

What should we do to raise the quality of signal processing publications to an even higher level? We believe it to be crucial to maintain the precision in describing our work in publications, ensured through a high-quality reviewing process. We also believe that if the experiments are performed on a large data set, the algorithm is compared to the state-of-the-art methods, the code and/or data are well documented and available online, we will all benefit and make it easier to build upon each other's work. It is a clear win-win situation for our community: we will have access to more and more algorithms and can spend time inventing new things rather than recreating existing ones.

关键词： signal processing signal processing algorithms Scholarships Digital signal processing Wikipedia Testing Advertising Programming Education Reproducibility of results

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APPLICATION OF FAST SUBSPACE DECOMPOSITION TO signal-processing AND COMMUNICATION PROBLEMS

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IEEE TRANSACTIONS ON signal processing 1994年第6期42卷 1453-1461页

作者： XU, GG CHO, YM KAILATH, T STANFORD UNIV INFORMAT SYST LABSTANFORDCA 94305

In [1], we described a class of fast subspace decomposition (FSD) algorithms. Though these algorithms can be applied to solve a variety of signal processing and communication problems with significant computational reduction, we shall focus our discussion on two typical applications. i.e., sensor array processing and time series analysis. In many cases, replacing usual eigenvalue decomposition (EVD) or singular value decomposition (SVD) by FSD is quite straightforward. However, the FSD approach can exploit more structure of some special problems to further simplify the implementation. In this paper, we shall first discuss the implementation details of FSD such as how to choose an optimal starting vector, how to handle correlated noise, and how to exploit additional matrix structure for further computational reduction. Then, we describe an FSD approach targeted at data matrices (rectangular N x M, N greater-than-or-equal-to M), which requires only O(NMd) flops where d denotes the signal subspace dimension versus a regular O(N M2 + M3) SVD. The computational reduction is substantial in tropical scenarios d much less than M less-than-or-equal-to N. In the spectrum estimation problems, the data matrix has additional structure such as Toeplitz or Hankel, we shall finally show how FSD can exploit such structure for further computational reduction.

关键词： signal processing signal processing algorithms Array signal processing Sensor arrays signal analysis Time series analysis Algorithm design and analysis Eigenvalues and eigenfunctions Singular value decomposition Noise reduction

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Two-dimensional signal processing techniques for airborne laser bathymetry

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1996年第1期34卷 57-66页

作者： Wong, HC Antoniou, A Department of Electrical and Computer Engineering University of Victoria Victoria BC Canada

The application of two-dimensional (2-D) signal processing to data collected in airborne laser bathymetry is investigated. Specifically, a type of 2-D filter for the suppression of impulsive noise in irregularly-spaced data based on order-statistics filtering is developed. An algorithm which incorporates this type of filter along with a sophisticated 2-D interpolation technique is constructed to automate the filtering process. An adaptive 2-D filtering technique that can be applied to raw bathymetric profiles to remove wideband noise is then discussed. The results obtained show that each type of filtering enhances the accuracy of bathymetric measurement quite significantly.

关键词： signal processing Oceanographic techniques Sea measurements Geologic measurements Laser noise Filtering Oceans Acoustic noise signal processing algorithms Filters

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Statistical reference criteria for adaptive signal processing in digital communications

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IEEE TRANSACTIONS ON signal processing 1997年第1期45卷 14-31页

作者： SalaAlvarez, J VazquezGrau, G Department of Signal Theory and Communications Campus Nord UPC Mòdul D5 Universitat Poliltècnica de Catalunya Barcelona Spain

A general criterion for the design of adaptive systems in digital communications called the statistical reference criterion is proposed. The criterion is based on imposition of the probability density function of the signal of interest at the output of the adaptive system, with its application to the scenario of highly powerful interferers being the main focus of this paper, The knowledge of the pdf of the wanted signal is used as a discriminator between signals so that interferers with differing distributions are rejected by the algorithm, Its performance is studied over a range of scenarios, Equations for gradient-based coefficient updates are derived, and the relationship with other existing algorithms like the minimum variance and the Wiener criterion are examined.

关键词： Adaptive signal processing Digital communication Cost function signal processing algorithms Adaptive systems Density functional theory signal processing Robustness Convergence Digital signal processing

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Using wavelet transforms for ECG characterization - An on-line digital signal processing system

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IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE 1997年第1期16卷 77-83页

作者： Sahambi, JS Tandon, SN Bhatt, RKP INDIAN INST TECHNOL CTR BIOMED ENGN NEW DELHI 110016 INDIA

The rapid and objective measurement of timing intervals of the electrocardiogram (ECG) by automated systems is superior to the subjective assessment of ECG morphology. The timing interval measurements are usually made from the onset to the termination of any component of the EGG, after accurate detection of the QRS complex. This article describes a real-time system that uses wavelet transforms to overcome the limitations of other methods of detecting QRS and the onsets and offsets of P- and T-waves. Wavelet transformation is briefly discussed, and detection methods and hardware and software aspects of the system are presented, as well as experimental results.

关键词： Wavelet transforms Electrocardiography Frequency signal processing algorithms Databases Biomedical engineering Biomedical measurements Timing Filtering Artificial intelligence

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Sparse Representation for Brain signal processing [A tutorial on methods and applications]

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IEEE signal processing MAGAZINE 2014年第3期31卷 96-106页

作者： Li, Yuanqing Yu, Zhu Liang Bi, Ning Xu, Yong Gu, Zhenghui Amari, Shun-ichi Nanyang Technol Univ Ctr Signal Proc Singapore Singapore S China Univ Technol Coll Automat Sci & Engn Guangzhou Guangdong Peoples R China S China Univ Technol Guangzhou Guangdong Peoples R China Inst Infocomm Res Singapore Singapore Kyushu Univ Fukuoka 812 Japan RIKEN Brain Sci Inst Wako Saitama Japan

In many cases, observed brain signals can be assumed as the linear mixtures of unknown brain sources/components. It is the task of blind source separation (BSS) to find the sources. However, the number of brain sources is generally larger than the number of mixtures, which leads to an underdetermined model with infinite solutions. Under the reasonable assumption that brain sources are sparse within a domain, e.g., in the spatial, time, or time-frequency domain, we may obtain the sources through sparse representation. As explained in this article, several other typical problems, e.g., feature selection in brain signal processing, can also be formulated as the underdetermined linear model and solved by sparse representation. This article first reviews the probabilistic results of the equivalence between two important sparse solutions?the 0-norm and 1-norm solutions. In sparse representation-based brain component analysis including blind separation of brain sources and electroencephalogram (EEG) inverse imaging, the equivalence is related to the recoverability of the sources. This article also focuses on the applications of sparse representation in brain signal processing, including components extraction, BSS and EEG inverse imaging, feature selection, and classification. Based on functional magnetic resonance imaging (fMRI) and EEG data, the corresponding methods and experimental results are reviewed.

关键词： Tutorials Electroencephalography Source separation Brain modeling signal processing algorithms Minimization Sparse matrices Medical image processing Brain models

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signal processing and general-purpose computing on GPUs

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IEEE signal processing MAGAZINE 2007年第3期24卷 109-114页

作者： McCool, Michael D. Univ Waterloo Waterloo ON N2L 3G1 Canada

Not Available.

关键词： signal processing Digital signal processing signal processing algorithms Streaming media Computer graphics Kernel Rendering (computer graphics) Application software Image processing Convolution

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Polynomial Eigenvalue Decomposition for Multichannel Broadband signal processing: A mathematical technique offering new insights and solutions

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IEEE signal processing MAGAZINE 2023年第7期40卷 18-37页

作者： Neo, Vincent W. Redif, Soydan Mcwhirter, John G. Pestana, Jennifer Proudler, Ian K. Weiss, Stephan Naylor, Patrick A. Singapore Def Sci & Technol Singapore Singapore Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Amer Univ Middle East Coll Engn & Technol Dasman 15453 Kuwait Univ Cardiff Cardiff CF24 3AA Wales Univ Strathclyde Dept Math & Stat Glasgow G1 1XH Scotland Univ Strathclyde Glasgow G1 1XW Scotland Imperial Coll London London SW7 2AZ England

This article is devoted to the polynomial eigenvalue decomposition (PEVD) and its applications in broadband multichannel signal processing, motivated by the optimum solutions provided by the EVD for the narrowband case [1], [2]. In general, we would like to extend the utility of the EVD to also address broadband problems. Multichannel broadband signals arise at the core of many essential commercial applications, such as telecommunications, speech processing, health-care monitoring, astronomy and seismic surveillance, and military technologies, including radar, sonar, and communications [3]. The success of these applications often depends on the performance of signal processing tasks, including data compression [4], source localization [5], channel coding [6], signal enhancement [7], beamforming [8], and source separation [9]. In most cases and for narrowband signals, performing an EVD is the key to the signal processing algorithm. Therefore, this article aims to introduce the PEVD as a novel mathematical technique suitable for many broadband signal processing applications.

关键词： Source separation Surveillance Sonar applications signal processing algorithms Eigenvalues and eigenfunctions Broadband communication Telecommunications

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Stochastic Vector Approximate Message Passing with Applications to Phase Retrieval

Stochastic Vector Approximate Message Passing with Applicati...

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

作者： Hajime Ueda Shun Katakami Masato Okada Graduate School of Frontier Sciences The University of Tokyo Kashiwa Chiba Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Phase retrieval refers to the problem of recovering a high-dimensional vector ${\mathbf{x}} \in {\mathbb{C}^N}$ from the magnitude of its linear transform z = Ax, observed through a noisy channel. To improve the ill-posed nature of the inverse problem, it is a common practice to observe the magnitude of linear measurements z (1) = A (1) x,…, z (L) = A (L) x using multiple sensing matrices A (1) ,…, A (L) , with ptychographic imaging being a remarkable example of such strategies. Inspired by existing algorithms for ptychographic reconstruction, we introduce stochasticity to Vector Approximate Message Passing (VAMP), a computationally efficient algorithm applicable to a wide range of Bayesian inverse problems. By testing our approach in the setup of phase retrieval, we show the superior convergence speed of the proposed algorithm.

关键词： Inverse problems Message passing signal processing algorithms Imaging Approximation algorithms Vectors Bayes methods Probes Image reconstruction Convergence

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