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Adaptive signal processing

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  • Signals represent information about data, voice, audio, image, video… There are many ways to classify signals but here we categorize signals as either analog (continuous-time) or digital (discretetime). Signal processing is to use circuits and systems (hardware and software) to act on input signal to give output signal which differs from the input, the way we would like to.

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  • The main thrust is to provide students with a solid understanding of a number of important and related advanced topics in digital signal processing such as Wiener filters, power spectrum estimation, signal modeling and adaptive filtering. Scores of worked examples illustrate fine points, compare techniques and algorithms and facilitate comprehension of fundamental concepts. Also features an abundance of interesting and challenging problems at the end of every chapter.

    pdf622p loi09dt1 27-01-2013 68 14   Download

  • Digital filters, together with signal processing, are being employed in the new technologies and information systems, and are implemented in different areas and applications. Digital filters and signal processing are used with no costs and they can be adapted to different cases with great flexibility and reliability. This book presents advanced developments in digital filters and signal process methods covering different cases studies.

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Complex-Valued Adaptive Signal Processing Using Nonlinear Functions

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  • The document Processing Fundamentals and applications Digital signal present the content: introduction to digital signal processing, signal sampling and quantization, digital signals and systems, discrete fourier transform and signal spectrum, the Z-transform, digital signal processing systems, basic filtering types, and digital filter realizations, finite impulse response filter design, infinite impulse response filter design, hardware and software for digital signal processors, adaptive filters and applications, waveform quantization and compression, subband- and wavelet-based coding, ima...

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  • Real Time Digital Signal Processing Adaptive filters are time varying, filter characteristics such as bandwidth and frequency response change with time. Thus the filter coefficients cannot be determined when the filter is implemented. The coefficients of the adaptive filter are adjusted automatically by an adaptive algorithm based on incoming signals. This has the important effect of enabling adaptive filters

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  • This book intends to provide highlights of the current research in signal processing area and to offer a snapshot of the recent advances in this field. This work is mainly destined to researchers in the signal processing related areas but it is also accessible to anyone with a scientific background desiring to have an up-to-date overview of this domain.

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  • Digital signal processing is currently in a period of rapid growth caused by recent advances inVLSI technology. This is especially true of three areas of optimum signal pro- cessing; namely, real-time adaptive signal processing, eigenvector methods of spectrum estimation, and parallel processor implementations of optimum filtering and prediction algorithms. In this edition the book has been brought up to date by increasing the emphasis on the above areas and including several new developments.

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  • Chapter 10 introduce the pipelined and parallel pecursive and adaptive filters. This chapter includes content: Pipelining in 1st-Order IIR Digital Filters, Pipelining in Higher-Order IIR Digital Filters, Parallel Processing for IIR Filters, Combined Pipelining and Parallel Processing for IIR Filters.

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  • This book provides a comprehensive introduction to the underlying theory, design techniques and analytical tools of wireless communications, focusing primarily on the core principles of wireless system design. The book begins with an overview of wireless systems and standards. The characteristics of the wireless channel are then described, including their fundamental capacity limits. Various modulation, coding, and signal processing schemes are then discussed in detail, including state-of-the-art adaptive modulation, multicarrier, spread spectrum, and multiple antenna techniques....

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  • The book is an edited collection of research articles covering the current state of sonar systems, the signal processing methods and their applications prepared by experts in the field. The first section is dedicated to the theory and applications of innovative synthetic aperture, interferometric, multistatic sonars and modeling and simulation.

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  • The use of neural networks is permeating every area of signal processing. They can provide powerful means for solving many problems, especially in nonlinear, real-time, adaptive, and blind signal processing. The Handbook of Neural Network Signal Processing brings together applications that were previously scattered among various publications to provide an up-to-date, detailed treatment of the subject from an engineering point of view.

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  • EURASIP Journal on Applied Signal Processing 2003:8, 733–739 c 2003 Hindawi Publishing Corporation Editorial Riccardo Poli Department of Computer Science, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK Email: rpoli@essex.ac.uk Stefano Cagnoni Department of Computer Engineering, University of Parma, 43100 Parma, Italy Email: cagnoni@ce.unipr.it 1. INTRODUCTION Darwinian evolution is probably the most intriguing and powerful mechanism of nature mankind has ever discovered.

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  • Tham khảo sách 'adaptive filtering', kỹ thuật - công nghệ, điện - điện tử phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

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  • EURASIP Journal on Applied Signal Processing 2003:8, 834–840 c 2003 Hindawi Publishing Corporation A Comparison of Evolutionary Algorithms for Tracking Time-Varying Recursive Systems Michael S. White Royal Holloway, University of London, Egham Hill, Egham, Surrey, TW20 0EX, UK Email: mike@whitem.com Stuart J. Flockton Royal Holloway, University of London, Egham Hill, Egham, Surrey, TW20 0EX, UK Email: s.flockton@rhul.ac.

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  • EURASIP Journal on Applied Signal Processing 2003:3, 244–251 c 2003 Hindawi Publishing Corporation An Adaptive Video Coding Control Scheme for Real-Time MPEG Applications Shih-Chang Hsia Department of Computer and Communication Engineering, National Kaohsiung First University of Science and Technology, Kaohsiung 824, Taiwan Email: hsia@ccms.nkfust.edu.tw Received 27 February 2002 and in revised form 16 September 2002 This paper proposes a new rate control scheme to increase the coding efficiency for MPEG systems.

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  • EURASIP Journal on Applied Signal Processing 2003:11, 1157–1166 c 2003 Hindawi Publishing Corporation Equivalence between Frequency-Domain Blind Source Separation and Frequency-Domain Adaptive Beamforming for Convolutive Mixtures Shoko Araki NTT Communication Science Laboratories, NTT Corporation, 2-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237, Japan Email: shoko@cslab.kecl.ntt.co.jp Shoji Makino NTT Communication Science Laboratories, NTT Corporation, 2-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237, Japan Email: maki@cslab.kecl.ntt.co.

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  • EURASIP Journal on Applied Signal Processing 2003:12, 1167–1180 c 2003 Hindawi Publishing Corporation Spatially Adaptive Intensity Bounds for Image Restoration Kaaren L. May Snell and Wilcox Ltd., Liss Research Centre, Liss Mill, Mill Road, Liss, Hampshire, GU33 7BD, UK Email: kaaren.may@snellwilcox.com Tania Stathaki Communications and Signal Processing Group, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2BT, UK Email: t.stathaki@ic.ac.uk Aggelos K.

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  • EURASIP Journal on Applied Signal Processing 2003:11, 1110–1124 c 2003 Hindawi Publishing Corporation Robust Adaptive Time Delay Estimation for Speaker Localization in Noisy and Reverberant Acoustic Environments Simon Doclo Department of Electrical Engineering, Katholieke Universiteit Leuven, ESAT-SISTA, Kasteelpark Arenberg 10, B-3001 Heverlee, Belgium Email: simon.doclo@esat.kuleuven.ac.be Marc Moonen Department of Electrical Engineering, Katholieke Universiteit Leuven, ESAT-SISTA, Kasteelpark Arenberg 10, B-3001 Heverlee, Belgium Email: marc.moonen@esat.kuleuven.ac.

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  • EURASIP Journal on Applied Signal Processing 2003:12, 1229–1237 c 2003 Hindawi Publishing Corporation Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent Mohamed Ibnkahla Electrical and Computer Engineering Department, Queen’s University, Kingston, Ontario, Canada K7L 3N6 Email: mohamed.ibnkahla@ece.queensu.ca Received 13 December 2002 and in revised form 17 May 2003 We use natural gradient (NG) learning neural networks (NNs) for modeling and identifying nonlinear systems with memory.

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