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140122 ||| eng |
| 020 |
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|a 9781461220183
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| 100 |
1 |
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|a DoCampo, Domingo
|e [editor]
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| 245 |
0 |
0 |
|a Intelligent Methods in Signal Processing and Communications
|h Elektronische Ressource
|c edited by Domingo DoCampo, Anibal Figueiras-Vidal, Fernando Perez-González
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| 250 |
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|a 1st ed. 1997
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| 260 |
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|a Boston, MA
|b Birkhäuser
|c 1997, 1997
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| 300 |
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|a XVI, 318 p
|b online resource
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| 505 |
0 |
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|a 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 -- 11.8 Chaotic synchronization: state of the art -- 11.9 Chaotic modulation: state of the art -- 11.10Chaotic demodulation: state of the art -- 11.11Additional considerations -- 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 -- 12.4 Examples of non-uniform filterbanks design -- 12.5 Conclusions -- 12.6 References -- 13 Source Coding of Stereo Pairs -- 13.1 Introduction -- 13.2 Stereo Image Coding -- 13.3 The Subspace Projection Technique -- 13.4 Experimental Results -- 13.5 Conclusion -- 13.6 References --
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| 505 |
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|a 8 Boundary Methods for Distribution Analysis -- 8.1 Introduction -- 8.2 Motivation -- 8.3 Boundary Methods as Feature-Set Evaluation -- 8.4 Boundary Methods as a Sample-Pruning (SP) Mechanism -- 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 -- 8.8 References -- 9 Constructive Function Approximation: Theory and Practice -- 9.1 Introduction -- 9.2 Overview of Constructive Approximation -- 9.3 Constructive Solutions -- 9.4 Limits and Bounds of the Approximation -- 9.5 The Sigmoidal Class of Approximators -- 9.6 Practical Considerations -- 9.7 Conclusions -- 9.8 Acknowledgments -- 9.9 References -- 10 Decision Trees Based on Neural Networks -- 10.1 Introduction -- 10.2 Adaptive modular classifiers -- 10.3 A survey on tree classification -- 10.4 Neural Decision Trees -- 10.5 Hierarchical mixtures of experts -- 10.6 Lighting the hidden variables -- 10.7 Conclusions -- 10.8 References --
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| 505 |
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|a 4 Biometric Identification for Access Control -- 4.1 Introduction -- 4.2 Feature Extraction for Biometric Identification -- 4.3 Pattern Classification for Biometric Identification -- 4.4 Probabilistic Decision-Based Neural Network -- 4.5 Biometric Identification by Human Faces -- 4.6 Biometric Identification by Palm Prints -- 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 -- 5.3 Appendices -- 5.4 References -- 6 Monotonicity: Theory and Implementation -- 6.1 Introduction -- 6.2 Representation of hints -- 6.3 Monotonicity hints -- 6.4 Theory -- 6.5 Conclusion -- 6.6 References -- 7 Analysis and Synthesis Tools for Robust SPRness -- 7.1 Introduction -- 7.2 SPR Analysis of Uncertain Systems -- 7.3 Synthesis of LTI Filters for Robust SPR Problems -- 7.4 Experimental results -- 7.5 Conclusions -- 7.6 References --
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| 505 |
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|a 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 --
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| 505 |
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|a 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 -- 14.5 Bit-true Level Modeling -- 14.6 Layout Design -- 14.7 Results -- 14.8 Conclusions -- 14.9 Acknowledgements -- 14. l0 References
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| 653 |
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|a Signal, Speech and Image Processing
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| 653 |
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|a Telecommunication
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| 653 |
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|a Communications Engineering, Networks
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| 653 |
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|a Signal processing
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| 700 |
1 |
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|a Figueiras-Vidal, Anibal
|e [editor]
|
| 700 |
1 |
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|a Perez-González, Fernando
|e [editor]
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 028 |
5 |
0 |
|a 10.1007/978-1-4612-2018-3
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-1-4612-2018-3?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 621.382
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| 520 |
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|a 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 . .
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