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9780817639600

Intelligent Methods in Signal Processing and Communications

by ; ;
  • ISBN13:

    9780817639600

  • ISBN10:

    0817639608

  • Format: Hardcover
  • Copyright: 1997-04-01
  • Publisher: Birkhauser

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Summary

Modern Historiographyis the essential introduction to the history of historical writing. It explains the broad philosophical background to the different historians and historical schools of the modern era. In a unique overview of modern historiography, the book includes surveys on the Enlightenment and Counter Enlightenment; Romanticism; the voice of Science and the process of secularization within Western intellectual thought; the influence of, and broadening contact with, the New World; theAnnalesschool in France; and the effects of the repression and exile of the inter-war years and the Post-War 'moods.'Modern Historiographyprovides a clear and concise account of this modern period of historical writing.

Table of Contents

List of Contributors xi(4)
Preface xv
1 Adaptive Antenna Arrays in Mobile Communications
1(14)
L. J. Griffiths
1.1 Introduction
1(2)
1.2 Adaptive Arrays in Base Station Antennas
3(2)
1.3 Adaptive Array Details
5(2)
1.4 LMS Adaptive Array Examples
7(1)
1.5 Desired Signal Availability
8(3)
1.6 Discussion and Observations
11(2)
1.7 References
13(2)
2 Demodulation in the Presence of Multiuser Interference: Progress and Misconceptions
15(32)
S. Verdu
2.1 Introduction
15(1)
2.2 Single-user Matched Filter
16(3)
2.3 Optimum Multiuser Detection
19(3)
2.4 Linear Multiuser Detection
22(5)
2.5 Decision-based Multiuser Detection
27(1)
2.6 Noncoherent Multiuser Detection
28(1)
2.7 Multiuser Detection combined with Array Processing
28(1)
2.8 Multiuser Detection with Error Control Coded Data
29(3)
2.9 References
32(15)
3 Intelligent Signal Detection
47(16)
S. Haykin
3.1 Introduction
47(1)
3.2 Three Basic Elements Of The Intelligent Detection System
48(5)
3.3 Neural Network-Based Two-Channel Receiver
53(1)
3.4 Rationale For The Modular Detection Strategy
54(3)
3.5 Case Study
57(1)
3.6 Summary And Discussion
58(2)
3.7 References
60(3)
4 Biometric Identification for Access Control
63(36)
S. Lin
S. Y. Kung
4.1 Introduction
64(2)
4.2 Feature Extraction for Biometric Identification
66(4)
4.2.1 Geometric Features
67(1)
4.2.2 Template Features
68(2)
4.3 Pattern Classification for Biometric Identification
70(4)
4.3.1 Statistical Pattern Recognition
70(2)
4.3.2 Neural Networks
72(2)
4.4 Probabilistic Decision-Based Neural Network
74(6)
4.4.1 Discriminant Functions of PDBNN
74(1)
4.4.2 Learning Rules for PDBNN
75(3)
4.4.3 Extension of PDBNN to Multiple-Class Pattern Recognition
78(2)
4.5 Biometric Identification by Human Faces
80(7)
4.5.1 Face Detection
82(2)
4.5.2 Eye Localization
84(2)
4.5.3 Face Recognition
86(1)
4.6 Biometric Identification by Palm Prints
87(6)
4.6.1 Feature Extraction for Palm Print Recognition
87(1)
4.6.2 Pattern Classification for Palm Print Recognition
88(3)
4.6.3 Experimental Results
91(2)
4.7 Concluding Remarks
93(1)
4.8 References
94(5)
5 Multidimensional Nonlinear Myopic Maps, Volterra Series, and Uniform Neural-Network Approximations
99(30)
I. W. Sandberg
5.1 Introduction
99(3)
5.2 Approximation of Myopic Maps
102(9)
5.2.1 Preliminaries
102(1)
5.2.2 Our Main Result
103(4)
5.2.3 Comments
107(2)
5.2.4 Finite Generalized Volterra-Series Approximations
109(2)
5.3 Appendices
111(15)
5.3.1 H.1. Preliminaries and the Approximation Result
122(4)
5.4 References
126(3)
6 Monotonicity: Theory and Implementation
129(18)
J. Sill
Y. Abu-Mostafa
6.1 Introduction
129(2)
6.2 Representation of hints
131(3)
6.3 Monotonicity hints
134(5)
6.4 Theory
139(6)
6.4.1 Capacity results
140(4)
6.4.2 Decision boundaries
144(1)
6.5 Conclusion
145(1)
6.6 References
146(1)
7 Analysis and Synthesis Tools for Robust SP Rness
147(26)
C. Mosquera
J.R. Hernandez
F. Perez-Gonzalez
7.1 Introduction
147(6)
7.2 SPR Analysis of Uncertain Systems
153(8)
7.2.1 The Polytopic Case
155(2)
7.2.2 The l(p)-Ball Case
157(2)
7.2.3 The Roots Space Case
159(2)
7.3 Synthesis of LTI Filters for Robust SPR Problems
161(6)
7.3.1 Algebraic Design for Two Plants
161(3)
7.3.2 Algebraic Design for Three or More Plants
164(1)
7.3.3 Approximate Design Methods
165(2)
7.4 Experimental results
167(1)
7.5 Conclusions
168(1)
7.6 References
169(4)
8 Boundary Methods for Distribution Analysis
173(26)
J.L. Sancho et al.
8.1 Introduction
173(3)
8.1.1 Building a Classifier System
175(1)
8.2 Motivation
176(1)
8.3 Boundary Methods as Feature-Set Evaluation
177(5)
8.3.1 Results
179(3)
8.3.2 Feature Set Evaluation using Boundary Methods: Summary
182(1)
8.4 Boundary Methods as a Sample-Pruning (SP) Mechanism
182(3)
8.4.1 Description of the simulations
184(1)
8.4.2 Results
184(1)
8.4.3 Sample Pruning using Boundary Methods: Summary
185(1)
8.5 Boundary Methods as Fisher's Linear Discriminant (FLD)
185(1)
8.6 Conclusions
186(1)
8.7 Apendix: Proof of the Theorem Relating FLD and Boundary Methods
186(5)
8.7.1 Assumptions and Definitions
186(1)
8.7.2 Fisher's Linear Discriminant (FLD) Analysis
187(1)
8.7.3 Unicity of the Tangent Point Equivalent to FLD
188(1)
8.7.4 Elliptic Tangent Point with the Equal Magnitude and Opposite Sign of the Gradient
189(2)
8.8 References
191(8)
9 Constructive Function Approximation: Theory and Practice
199(22)
D. Docampo
D. Hush
C.T. Abdallah
9.1 Introduction
199(1)
9.2 Overview of Constructive Approximation
200(1)
9.3 Constructive Solutions
201(4)
9.3.1 Discussion
203(2)
9.4 Limits and Bounds of the Approximation
205(4)
9.4.1 Minimum Global Error
206(1)
9.4.2 Fixing XXXn
207(1)
9.4.3 Fixing the rate of convergence
208(1)
9.5 The Sigmoidal Class of Approximators
209(1)
9.6 Practical Considerations
210(7)
9.6.1 Projection Pursuit Methods
212(1)
9.6.2 Projection Pursuit with Neural Networks
213(4)
9.7 Conclusions
217(1)
9.8 Acknowledgments
217(1)
9.9 References
217(4)
10 Decision Trees Based on Neural Networks
221(22)
J. Cid-Sueiro
J. Ghattas
A.R. Figueiras-Vidal
10.1 Introduction
221(2)
10.2 Adaptive modular classifiers
223(2)
10.2.1 The classification problem
223(1)
10.2.2 Splitting the input space
223(2)
10.2.3 Supervised and non-supervised learning
225(1)
10.3 A survey on tree classification
225(3)
10.3.1 Hypercubic cells
225(2)
10.3.2 Thresholding attributes
227(1)
10.3.3 Linear Combinations of the Attributes
227(1)
10.4 Neural Decision Trees
228(1)
10.5 Hierarchical mixtures of experts
229(6)
10.5.1 Soft decision classifiers
229(1)
10.5.2 Training HME classifiers
230(1)
10.5.3 Applying the EM algorithm
231(4)
10.6 Lighting the hidden variables
235(1)
10.7 Conclusions
235(4)
10.8 References
239(4)
11 Applications of Chaos in Communications
243(20)
M. P. Kennedy
11.1 Introduction
243(1)
11.2 Deterministic dynamical systems and chaos
243(1)
11.3 Chua's oscillator: a paradigm for chaos
244(1)
11.4 Periodicity, quasiperiodicity, and chaos
245(2)
11.5 Applications of chaos in communications
247(1)
11.6 Digital communication
247(2)
11.7 Spreading
249(2)
11.7.1 Pseudorandom spreading sequences
250(1)
11.7.2 Chaotic spreading signals
251(1)
11.8 Chaotic synchronization: state of the art
251(1)
11.8.1 Drive-response synchronization
251(1)
11.8.2 Inverse systems
252(1)
11.8.3 Error-feedback synchronization
252(1)
11.8.4 Performance evaluation
252(1)
11.9 Chaotic modulation: state of the art
252(3)
11.9.1 Chaotic masking
252(1)
11.9.2 Inverse systems
253(1)
11.9.3 Predictive Poincare Control (PPC) modulation
253(1)
11.9.4 Chaos Shift Keying (CSK)
254(1)
11.9.5 Differential Chaos Shift Keying (DCSK)
255(1)
11.10 Chaotic demodulation: state of the art
255(1)
11.10.1 Coherent demodulation by chaos synchronization
255(1)
11.10.2 Noncoherent demodulation
256(1)
11.11 Additional considerations
256(1)
11.11.1 Security issues
256(1)
11.11.2 Multiple access
256(1)
11.12 Engineering challenges
257(1)
11.13 References
257(6)
12 Design of Near PR Non-Uniform Filter Banks
263(18)
F. Argenti
B. Brogelli
E. Del Re
12.1 Introduction
263(3)
12.2 The MPEG audio coder
266(2)
12.3 Non-uniform filter banks with rational sampling factors
268(6)
12.3.1 Aliasing cancellation in non-uniform filter banks
268(4)
12.3.2 Use of cosine-modulated filter banks
272(2)
12.4 Examples of non-uniform filter banks design
274(2)
12.5 Conclusions
276(1)
12.6 References
276(5)
13 Source Coding of Stereo Pairs
281(20)
H. Aydinoglu
M. H. Hayes
13.1 Introduction
281(1)
13.2 Stereo Image Coding
282(5)
13.2.1 Theory of Stereo Image Coding
283(2)
13.2.2 Historical Perspective
285(2)
13.3 The Subspace Projection Technique
287(6)
13.4 Experimental Results
293(1)
13.5 Conclusion
294(4)
13.6 References
298(3)
14 Design Methodology for VLSI Implementation of Image and Video Coding Algorithms - A Case Study
301
J. Bracamonte
M. Ansorge
F. Pellandini
14.1 Introduction
301(1)
14.2 JPEG Baseline Algorithm
302(3)
14.3 High Level Modeling
305(3)
14.4 VLSI Architectures
308(5)
14.4.1 FDCT
309(3)
14.4.2 Quantizer
312(1)
14.4.3 Entropy coder
312(1)
14.5 Bit-true Level Modeling
313(2)
14.6 Layout Design
315(1)
14.7 Results
316(1)
14.7.1 Extensions for Video Coding
316(1)
14.8 Conclusions
316(1)
14.9 Acknowledgements
317(1)
14.10 References
317

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