Intelligent Methods in Signal Processing and Communications by Domingo DoCampoIntelligent Methods in Signal Processing and Communications by Domingo DoCampo

Intelligent Methods in Signal Processing and Communications

EditorDomingo DoCampo, Anibal Figueiras-vidal, Fernando Perez-González

Hardcover | May 1, 1997

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129 6.2 Representation of hints. 131 6.3 Monotonicity hints .. . 134 6.4 Theory ......... . 139 6.4.1 Capacity results 140 6.4.2 Decision boundaries 144 6.5 Conclusion 145 6.6 References....... ... 146 7 Analysis and Synthesis Tools for Robust SPRness 147 C. Mosquera, J.R. Hernandez, F. Perez-Gonzalez 7.1 Introduction.............. 147 7.2 SPR Analysis of Uncertain Systems. 153 7.2.1 The Poly topic Case . 155 7.2.2 The ZP-Ball Case ...... . 157 7.2.3 The Roots Space Case ... . 159 7.3 Synthesis of LTI Filters for Robust SPR Problems 161 7.3.1 Algebraic Design for Two Plants ..... . 161 7.3.2 Algebraic Design for Three or More Plants 164 7.3.3 Approximate Design Methods. 165 7.4 Experimental results 167 7.5 Conclusions 168 7.6 References ..... . 169 8 Boundary Methods for Distribution Analysis 173 J.L. Sancho et aZ. 8.1 Introduction ............. . 173 8.1.1 Building a Classifier System . 175 8.2 Motivation ............. . 176 8.3 Boundary Methods as Feature-Set Evaluation 177 8.3.1 Results ................ . 179 8.3.2 Feature Set Evaluation using Boundary Methods: S- mary. . . . . . . . . . . . . . . . . . . .. . . 182 . . .
Title:Intelligent Methods in Signal Processing and CommunicationsFormat:HardcoverDimensions:334 pagesPublished:May 1, 1997Publisher:Birkhäuser Boston

The following ISBNs are associated with this title:

ISBN - 10:0817639608

ISBN - 13:9780817639600

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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.