Intelligent Methods in Signal Processing and Communications / Edition 1

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1997 Hard cover New. No dust jacket as issued. Sewn binding. Cloth over boards. 340 p. Contains: Illustrations. Audience: General/trade.

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Birkhauser Verlag, 1997. Hardcover. Brand new book, still in shrinkwrap. ISBN: 0817639608.

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Editorial Reviews

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Contains 14 chapters on intelligent techniques applied to signal processing and communications engineering problems, based on topics presented at a June 1996 workshop held in Bayona, Vigo, Spain. Subjects include antenna arrays, spread spectrum, adaptive systems, bimetric identification, neural networks, nonlinear and chaotic systems, filter banks, and genetic algorithms. Of interest to scientists and practitioners in electrical and computer engineering. Annotation c. by Book News, Inc., Portland, Or.
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Product Details

  • ISBN-13: 9780817639600
  • Publisher: Birkhauser Verlag
  • Publication date: 5/1/1997
  • Edition description: 1997
  • Edition number: 1
  • Pages: 318
  • Product dimensions: 0.81 (w) x 6.14 (h) x 9.21 (d)

Table of Contents

1 Adaptive Antenna Arrays in Mobile Communications.- 1.1 Introduction.- 1.2 Adaptive Arrays in Base Station Antennas.- 1.3 Adaptive Array Details.- 1.4 LMS Adaptive Array Examples.- 1.5 Desired Signal Availability.- 1.6 Discussion and Observations.- 1.7 References.- 2 Demodulation in the Presence of Multiuser Interference: Progress and Misconceptions.- 2.1 Introduction.- 2.2 Single-user Matched Filter.- 2.3 Optimum Multiuser Detection.- 2.4 Linear Multiuser Detection.- 2.5 Decision-based Multiuser Detection.- 2.6 Noncoherent Multiuser Detection.- 2.7 Multiuser Detection combined with Array Processing.- 2.8 Multiuser Detection with Error Control Coded Data.- 2.9 References.- 3 Intelligent Signal Detection.- 3.1 Introduction.- 3.2 Three Basic Elements Of The Intelligent Detection System.- 3.3 Neural Network-Based Two-Channel Receiver.- 3.4 Rationale For The Modular Detection Strategy.- 3.5 Case Study.- 3.6 Summary And Discussion.- 3.7 References.- 4 Biometric Identification for Access Control.- 4.1 Introduction.- 4.2 Feature Extraction for Biometric Identification.- 4.2.1 Geometric Features.- 4.2.2 Template Features.- 4.3 Pattern Classification for Biometric Identification.- 4.3.1 Statistical Pattern Recognition.- 4.3.2 Neural Networks.- 4.4 Probabilistic Decision-Based Neural Network.- 4.4.1 Discriminant Functions of PDBNN.- 4.4.2 Learning Rules for PDBNN.- 4.4.3 Extension of PDBNN to Multiple-Class Pattern Recognition.- 4.5 Biometric Identification by Human Faces.- 4.5.1 Face Detection.- 4.5.2 Eye Localization.- 4.5.3 Face Recognition.- 4.6 Biometric Identification by Palm Prints.- 4.6.1 Feature Extraction for Palm Print Recognition...- 4.6.2 Pattern Classification for Palm Print Recognition. •.- 4.6.3 Experimental Results.- 4.7 Concluding Remarks.- 4.8 References.- 5 Multidimensional Nonlinear Myopic Maps, Volterra Series, and Uniform Neural-Network Approximations.- 5.1 Introduction.- 5.2 Approximation of Myopic Maps.- 5.2.1 Preliminaries.- 5.2.2 Our Main Result.- 5.2.3 Comments.- 5.2.4 Finite Generalized Volterra-Series Approximations.- 5.3 Appendices.- 5.3.1 H.1. Preliminaries and the Approximation Result.- 5.4 References.- 6 Monotonicity: Theory and Implementation.- 6.1 Introduction.- 6.2 Representation of hints.- 6.3 Monotonicity hints.- 6.4 Theory.- 6.4.1 Capacity results.- 6.4.2 Decision boundaries.- 6.5 Conclusion.- 6.6 References.- 7 Analysis and Synthesis Tools for Robust SPRness.- 7.1 Introduction.- 7.2 SPR Analysis of Uncertain Systems.- 7.2.1 The Polytopic Case.- 7.2.2 The 1v-Ball Case.- 7.2.3 The Roots Space Case.- 7.3 Synthesis of LTI Filters for Robust SPR Problems.- 7.3.1 Algebraic Design for Two Plants.- 7.3.2 Algebraic Design for Three or More Plants.- 7.3.3 Approximate Design Methods.- 7.4 Experimental results.- 7.5 Conclusions.- 7.6 References.- 8 Boundary Methods for Distribution Analysis.- 8.1 Introduction.- 8.1.1 Building a Classifier System.- 8.2 Motivation.- 8.3 Boundary Methods as Feature-Set Evaluation.- 8.3.1 Results.- 8.3.2 Feature Set Evaluation using Boundary Methods: Summary.- 8.4 Boundary Methods as a Sample-Pruning (SP) Mechanism.- 8.4.1 Description of the simulations.- 8.4.2 Results.- 8.4.3 Sample Pruning using Boundary Methods: Summary.- 8.5 Boundary Methods as Fisher’s Linear Discriminant (FLD)..- 8.6 Conclusions.- 8.7 Apendix: Proof of the Theorem Relating FLD and Boundary Methods.- 8.7.1 Assumptions and Definitions.- 8.7.2 Fisher’s Linear Discriminant (FLD) Analysis.- 8.7.3 Unicity of the Tangent Point Equivalent to FLD.- 8.7.4 Elliptic Tangent Point with the Equal Magnitude and Opposite Sign of the Gradient.- 8.8 References.- 9 Constructive Function Approximation: Theory and Practice.- 9.1 Introduction.- 9.2 Overview of Constructive Approximation.- 9.3 Constructive Solutions.- 9.3.1 Discussion.- 9.4 Limits and Bounds of the Approximation.- 9.4.1 Minimum Global Error.- 9.4.2 Fixingem.- 9.4.3 Fixing the rate of convergence.- 9.5 The Sigmoidal Class of Approximators.- 9.6 Practical Considerations.- 9.6.1 Projection Pursuit Methods.- 9.6.2 Projection Pursuit with Neural Networks.- 9.7 Conclusions.- 9.8 Acknowledgments.- 9.9 References.- 10 Decision Trees Based on Neural Networks.- 10.1 Introduction.- 10.2 Adaptive modular classifiers.- 10.2.1 The classification problem.- 10.2.2 Splitting the input space.- 10.2.3 Supervised and non-supervised learning.- 10.3 A survey on tree classification.- 10.3.1 Hypercubic cells.- 10.3.2 Thresholding attributes.- 10.3.3 Linear Combinations of the Attributes.- 10.4 Neural Decision Trees.- 10.5 Hierarchical mixtures of experts.- 10.5.1 Soft decision classifiers.- 10.5.2 Training HME classifiers.- 10.5.3 Applying the EM algorithm.- 10.6 Lighting the hidden variables.- 10.7 Conclusions.- 10.8 References.- 11 Applications of Chaos in Communications.- 11.1 Introduction.- 11.2 Deterministic dynamical systems and chaos.- 11.3 Chua’s oscillator: a paradigm for chaos.- 11.4 Periodicity, quasiperiodicity, and chaos.- 11.5 Applications of chaos in communications.- 11.6 Digital communication.- 11.7 Spreading.- 11.7.1 Pseudorandom spreading sequences.- 11.7.2 Chaotic spreading signals.- 11.8 Chaotic synchronization: state of the art.- 11.8.1 Drive-response synchronization.- 11.8.2 Inverse systems.- 11.8.3 Error-feedback synchronization.- 11.8.4 Performance evaluation.- 11.9 Chaotic modulation: state of the art.- 11.9.1 Chaotic masking.- 11.9.2 Inverse systems.- 11.9.3 Predictive Poincaré Control (PPC) modulation.- 11.9.4 Chaos Shift Keying (CSK).- 11.9.5 Differential Chaos Shift Keying (DCSK).- 11.10Chaotic demodulation: state of the art.- 11.10.1 Coherent demodulation by chaos synchronization.- 11.10.2Noncoherent demodulation.- 11.11Additional considerations.- 11.11.1 Security issues.- 11.11.2 Multiple access.- 11.12Engineering challenges.- 11.13References.- 12 Design of Near PR Non-Uniform Filter Banks.- 12.1 Introduction.- 12.2 The MPEG audio coder.- 12.3 Non-uniform filter banks with rational sampling factors.- 12.3.1 Aliasing cancellation in non-uniform filter banks.- 12.3.2 Use of cosine-modulated filter banks.- 12.4 Examples of non-uniform filter banks design.- 12.5 Conclusions.- 12.6 References.- 13 Source Coding of Stereo Pairs.- 13.1 Introduction.- 13.2 Stereo Image Coding.- 13.2.1 Theory of Stereo Image Coding.- 13.2.2 Historical Perspective.- 13.3 The Subspace Projection Technique.- 13.4 Experimental Results.- 13.5 Conclusion.- 13.6 References.- 14 Design Methodology for VLSI Implementation of Image and Video Coding Algorithms — A Case Study.- 14.1 Introduction.- 14.2 JPEG Baseline Algorithm.- 14.3 High Level Modeling.- 14.4 VLSI Architectures.- 14.4.1 FDCT.- 14.4.2 Quantizer.- 14.4.3 Entropy coder.- 14.5 Bit-true Level Modeling.- 14.6 Layout Design.- 14.7 Results.- 14.7.1 Extensions for Video Coding.- 14.8 Conclusions.- 14.9 Acknowledgements.- 14. l0 References.

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