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

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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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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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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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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Edge Learning for B5G Networks With Distributed signal processing: Semantic Communication, Edge Computing, and Wireless Sensing

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2023年第1期17卷 9-39页

作者： Xu, Wei Yang, Zhaohui Ng, Derrick Wing Kwan Levorato, Marco Eldar, Yonina C. Debbah, Merouane Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Zhejiang Lab Hangzhou 311121 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Zhejiang Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA Weizmann Inst Sci Fac Math & CS IL-7610001 Rehovot Israel Technol Innovat Inst Masdar U Arab Emirates Mohamed Bin Zayed Univ Artificial Intelligence Masdar 9639 Abu Dhabi U Arab Emirates

To process and transfer large amounts of data in emerging wireless services, it has become increasingly appealing to exploit distributed data communication and learning. Specifically, edge learning (EL) enables local model training on geographically disperse edge nodes and minimizes the need for frequent data exchange. However, the current design of separating EL deployment and communication optimization does not yet reap the promised benefits of distributed signal processing, and sometimes suffers from excessive signalling overhead, long processing delay, and unstable learning convergence. In this paper, we provide an overview on practical distributed EL techniques and their interplay with advanced communication optimization designs. In particular, typical performance metrics for dual-functional learning and communication networks are discussed. Also, recent achievements of enabling techniques for the dual-functional design are surveyed with exemplifications from the mutual perspectives of "communications for learning " and "learning for communications. " The application of EL techniques within a variety of future communication systems are also envisioned for beyond 5G (B5G) wireless networks. For the application in goal-oriented semantic communication, we present a first mathematical model of the goal-oriented source entropy as an optimization problem. In addition, from the viewpoint of information theory, we identify fundamental open problems of characterizing rate regions for communication networks supporting distributed learning-and-computing tasks. We also present technical challenges as well as emerging application opportunities in this field, with the aim of inspiring future research and promoting widespread developments of EL in B5G.

关键词： Wireless communication Optimization Training Reinforcement learning Measurement Deep learning signal processing algorithms Artificial intelligence (AI) deep learning (DL) edge learning (EL) federated learning (FL) multi-agent reinforcement learning (MARL) communication optimization Internet-of-Everything (IoE) beyond 5G (B5G)

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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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Forensic Applications of signal processing Bridging the gap between scientific and legal requirements

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IEEE signal processing MAGAZINE 2009年第2期26卷 104-111页

作者： Tibbitts, Jo Lu, YiBin University of Sydney Australia University of New South Wales Australia Shanǵhai Jiao Tonǵ University China

This article highlights some problems encountered by forensic signal processing experts in the area of speech and video processing. We have demonstrated that there is a need for speech, video, and other signal processing experts within the I II community to work together to provide standardized guidelines to court systems around the world to assist them in dealing with this complex form of evidence. We also need to develop and provide law enforcement agencies with supplemental tools to assist them to improve the quality of speech and video evidence gathered for the court. In particular, quantitative measures need to be developed to allow law enforcement agents to determine during the investigation whether speech and video material is above or below a standard expected of court evidence.

关键词： Forensics signal processing Law Legal factors signal processing algorithms Data engineering Appropriate technology Documentation Qualifications Ethics

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COMPLEX DIGITAL signal-processing USING QUADRATIC RESIDUE NUMBER-SYSTEMS

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IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND signal processing 1986年第1期34卷 166-177页

作者： KRISHNAN, R JULLIEN, GA MILLER, WC UNIV WINDSOR DEPT ELECT ENGNWINDSOR N9B 3P4ONTARIOCANADA

Recently, the quadratic residue number system (QRNS) has been introduced [6], [7] which allows the multiplication of complex integers with two real multiplications. The restriction is that the number system has either all prime moduli of the form 4K + 1, or composite numbers with prime factors of that form. If an increase in real multiplications from two to three can be tolerated, then the restriction can be lifted to allow moduli of any form: the resulting number system is termed the modified quadratic residue number system (MQRNS). In this paper, the MQRNS is defined, and residue-to-binary conversion techniques in both the QRNS and MQRNS are presented. Hard-ware implementations of multiplication intensive, complex nonrecursive, and recursive digital filters are also presented in this paper where the QRNS and MQRNS structures are realized using a bit-slice architecture.

关键词： Digital signal processing Dynamic range Hardware Digital filters Digital arithmetic Throughput signal processing Decoding Cathode ray tubes signal processing algorithms

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Kernel methods and their potential use in signal processing -: An overview and guidelines for future development

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IEEE signal processing MAGAZINE 2004年第3期21卷 57-65页

作者： Pérez-Cruz, F Bousquet, O Univ Carlos III Madrid Dept Signal Theory & Commun Madrid Spain Max Planck Inst Biol Cybernet D-72076 Tubingen Germany

The notion of kernels, recently introduced, has drawn much interest as it allows one to obtain nonlinear algorithms from linear ones in a simple and elegant manner. This, in conjunction with the introduction of new linear classification methods such as the support vector machines (SVMs), has produced significant progress in machine learning and related research topics. The success of such algorithms is now spreading as they are applied to more and more domains. signal processing procedures can benefit from a kernel perspective, making them more powerful and applicable to nonlinear processing in a simpler and nicer way. We present an overview of kernel methods and provide some guidelines for future development in kernel methods, as well as, some perspectives to the actual signal processing problems in which kernel methods are being applied.

关键词： Kernel signal processing Vectors signal processing algorithms Data analysis Hilbert space Symmetric matrices Machine learning Principal component analysis Least squares methods

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THE INTEGRATED signal-processing SYSTEM ISP

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IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND signal processing 1984年第4期32卷 842-851页

作者： KOPEC, GE Fairchild Laboratory for Artificial Intelligence Research Palo Alto CA USA

The Integrated signal processing System (ISP) is a Lisp machine-based workstation which provides a unified environment for signal data processing and the development of signal processing algorithms. ISP is based on a model of signal processing computation in which the fundamental activities are creating and manipulating abstract signal objects. ISP consists of three main subsystems. The signal representation language (SRL) formalizes the semantic foundation of ISP and provides a set of facilities for defining signal classes and creating instances of signal objects. The ISP environment provides a signal stack, display windows, and signal pictures which are used to create and view selected signals from the universe defined by SRL. Finally, the user interface consists of a number of interactive mechanisms for manipulating components of the environment.

关键词： signal processing signal processing algorithms Data processing Programming profession Speech processing Speech analysis Libraries Command languages Workstations Displays

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