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Advances in heuristic signal processing and applications
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Advances in heuristic signal processing and applications
Author :
Publisher :
Springer
Pub. Year :
2013
Subjects :
Signal processing Digital techniques Data processing Computer science Artificial...
Call Number :
TK 5102 .9 .A38 2013
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Advances in Heuristic Signal Processing and Applications
(3)
Preface
(5)
Contents
(10)
Contributors
(12)
Chapter 1: Nonconvex Optimization via Joint Norm Relaxed SQP and Filled Function Method with Application to Minimax Two-Channel Linear Phase FIR QMF Bank Design
(15)
1.1 Introduction
(15)
1.2 Problem Formulation
(17)
1.3 Joint Norm Relaxed Sequential Quadratic Programming and Filled Function Method
(21)
1.3.1 Filled Function Method
(21)
1.3.2 Norm Relaxed Sequential Quadratic Programming
(24)
1.4 Computer Numerical Simulation Results
(26)
1.5 Conclusions
(28)
References
(29)
Chapter 2: Robust Reduced-Rank Adaptive LCMV Beamforming Algorithms Based on Joint Iterative Optimization of Parameters
(31)
2.1 Introduction
(31)
2.2 System Model
(33)
2.3 Problem Statement and Design of Adaptive Beamformers
(34)
2.3.1 Adaptive LCMV Beamformers
(34)
2.3.2 Robust Adaptive LCMV Beamformers
(35)
2.4 Robust Reduced-Rank Beamforming Based on Joint Iterative Optimization of Parameters
(36)
2.5 Adaptive Algorithms
(38)
2.5.1 Stochastic Gradient Algorithm
(38)
2.5.2 Recursive Least-Squares Algorithms
(39)
2.5.3 Complexity of RJIO Algorithms
(41)
2.5.4 Rank Adaptation
(42)
2.6 Simulations
(43)
2.7 Conclusions
(46)
References
(47)
Chapter 3: Designing OFDM Radar Waveform for Target Detection Using Multi-objective Optimization
(49)
3.1 Introduction
(50)
3.2 Problem Description and Modeling
(53)
3.2.1 OFDM Signal Model
(54)
3.2.2 Measurement Model
(54)
3.2.3 Statistical Assumptions
(56)
3.3 Sparse-Estimation Approach
(56)
3.3.1 Sparse Model
(57)
3.3.2 Sparse Recovery
(57)
3.4 Adaptive Waveform Design
(58)
3.4.1 Maximizing the Mahalanobis Distance
(59)
3.4.2 Minimizing the Weighted Trace of CRB Matrix
(59)
3.4.3 Minimizing the Upper Bound on Sparse Error
(61)
3.4.4 Multi-objective Optimization
(63)
3.5 Numerical Results
(63)
3.5.1 Results of the MOO Problem
(65)
3.5.2 Improvement in Detection and Estimation Performance
(68)
3.5.3 Redistributions of Signal and Target Energies
(69)
3.6 Conclusions
(72)
References
(73)
Chapter 4: Multi-object Tracking Using Particle Swarm Optimization on Target Interactions
(76)
4.1 Introduction
(76)
4.2 Particle Swarm Optimization
(78)
4.3 PSO-Based Object Tracking
(79)
4.3.1 Multi-patch Based Object Tracking Using Region Covariance
(79)
4.3.2 Foreground Prior
(81)
4.3.3 Foreground Segmentation
(81)
4.3.4 Re-diversification of the Swarm
(82)
4.4 Multiple Object Tracking
(83)
4.4.1 Multiple Object Tracking by Multiple Particle Swarms
(84)
4.4.2 Refinement of the Tracklets by Particle Swarm Optimization
(86)
4.5 Experiments
(87)
4.6 Conclusions
(90)
References
(90)
Chapter 5: A Comparative Study of Modified BBO Variants and Other Metaheuristics for Optimal Power Allocation in Wireless Sensor Networks
(92)
5.1 Introduction
(93)
5.2 Problem Statement
(94)
5.2.1 Fusion Problem Formulation
(95)
5.2.2 Node Decision Rules
(96)
5.2.3 Transmission of Local Decisions
(96)
5.2.4 The Decision Fusion Problem
(97)
5.3 Optimal Power Allocation
(98)
5.3.1 Independent Observations
(98)
5.3.2 Correlated Observations
(100)
5.4 Constrained BBO for Optimal Power Allocation
(102)
5.4.1 Standard Unconstrained Biogeography-Based Optimization (BBO)
(102)
5.4.2 Constrained Optimization
(105)
5.4.3 Description of the Proposed Algorithms
(108)
5.4.3.1 Solution Representation
(108)
5.4.3.2 Objective Function
(109)
5.4.3.3 Constraint Handling Approach
(109)
5.4.3.4 Individual Initialization
(109)
5.4.3.5 Individual Stochastic Ranking
(110)
5.4.3.6 Population Update Strategy
(111)
5.4.3.7 Selection
(113)
5.4.3.8 Elitism
(113)
5.5 Experimental Results and Analysis
(114)
5.5.1 Parameter Configuration
(115)
5.5.2 Numerical Results
(116)
5.6 Conclusion
(122)
References
(122)
Chapter 6: Joint Optimization of Detection and Tracking in Adaptive Radar Systems
(124)
6.1 Introduction
(124)
6.1.1 Related Work
(127)
6.1.2 Chapter Outline
(129)
6.2 Offline Performance Evaluation of Tracking Algorithms
(130)
6.2.1 Target and Measurement Models
(132)
6.2.2 NSPP Techniques for the PDAF
(133)
6.2.3 The Modified Riccati Equation (MRE)
(134)
6.2.4 The Hybrid Conditional Averaging (HYCA) Algorithm
(135)
6.3 NSPP-Based Detector Threshold Optimization
(136)
6.3.1 Static Threshold Optimization (STOP)
(137)
6.3.2 Dynamic Threshold Optimization (DTOP)
(139)
6.3.2.1 MRE-Based Dynamic Threshold Optimization
(139)
6.3.2.2 HYCA-Based Dynamic Threshold Optimization
(142)
6.4 Simulations
(143)
6.4.1 Static Threshold Optimization Based on MRE and HYCA Algorithms
(144)
6.4.2 Experiment 1: Comparison with Heuristic Approaches
(146)
6.4.3 Experiment 2: Comparison of MRE-Based DTOP Schemes
(150)
6.5 Conclusion and Future Directions
(153)
References
(154)
Chapter 7: Iterative Design of FIR Filters
(157)
7.1 Introduction
(157)
7.2 Particle Swarm Optimization with Quantum Infusion
(159)
7.3 Digital FIR Filter
(160)
7.4 FIR Filter Design Using PSO-QI
(161)
7.5 Studies and Results
(162)
7.6 Conclusion
(176)
References
(177)
Chapter 8: A Metaheuristic Approach to Two Dimensional Recursive Digital Filter Design
(179)
8.1 Introduction
(179)
8.2 The Design Problem Formulation
(181)
8.3 An Outline of IWO Algorithm
(183)
8.3.1 Generation of an Initial Population
(183)
8.3.2 Reproduction
(184)
8.3.3 Dispersal of Seeds Through the Search Space
(184)
8.3.4 Competitive Exclusion
(184)
8.4 Differential Q-Learning
(185)
8.5 IIWO: The Proposed Approach
(186)
8.5.1 Initialization
(187)
8.5.2 Adaptive Selection of Memes
(187)
8.5.3 Invasive Weed Optimization
(188)
8.5.4 State Assignment
(188)
8.5.5 Updating the Q-table
(188)
8.6 The Filter Design Experiments and Results
(188)
8.7 Conclusions
(192)
References
(193)
Chapter 9: A Survey of Kurtosis Optimization Schemes for MISO Source Separation and Equalization
(195)
9.1 Introduction
(196)
9.1.1 Channel Equalization and Source Separation
(196)
9.1.2 Why Kurtosis?
(197)
9.1.3 Historical Overview
(198)
9.1.4 Chapter Outline
(199)
9.1.5 Mathematical Notations
(199)
9.2 Blind Source Separation: Model and Assumptions
(199)
9.2.1 Convolutive Mixtures
(200)
9.2.2 Instantaneous Mixtures
(201)
9.3 Deflationary Source Separation
(201)
9.3.1 Source Extraction with MISO Contrast Functions
(202)
9.3.2 Deflation Procedure
(204)
9.4 Optimization Methods
(205)
9.4.1 Gradient Search Algorithms
(206)
9.4.2 Projected Gradient Search
(207)
9.4.3 Gradient Algorithm with Filter Parametrization
(208)
9.4.4 Approximate Newton Search: The FastICA Algorithm
(210)
9.4.4.1 Derivation of the Algorithm
(210)
9.4.4.2 FastICA as a Constant Step-Size Gradient Algorithm
(212)
9.4.5 Algorithms with Optimal Step-Size Selection
(214)
9.4.5.1 Step-Size Optimization
(214)
9.4.5.2 Algebraic Exact Line Search: The RobustICA Algorithm
(215)
9.4.6 Algorithms Based on Reference Signals
(218)
9.4.6.1 Quadratic Contrast Functions
(218)
9.4.6.2 Monotonically Convergent Algorithms Based on Quadratic Contrasts
(220)
9.5 Illustrative Results
(221)
9.5.1 Source Separation and Equalization in Digital Communications
(221)
9.5.2 Artifact Rejection in Biomedical Recordings
(222)
9.6 Conclusions
(224)
References
(225)
Chapter 10: Swarm Intelligence Techniques Applied to Nonlinear Systems State Estimation
(230)
10.1 Introduction
(230)
10.2 Estimation Problem Formulation
(232)
10.3 Generic Particle Filter and Limitations
(233)
10.3.1 Initialization
(234)
10.3.2 Propagation of Particles' Location
(234)
10.3.3 Weight Computation and Normalization
(235)
10.3.4 Resampling
(235)
10.3.5 State Estimation
(236)
10.3.6 Limitations
(237)
10.3.6.1 Particle Impoverishment
(237)
10.3.6.2 Sample Size Dependency
(238)
10.4 Swarm Filters
(238)
10.4.1 Particle Swarm Optimized Particle Filter (PSOPF)
(239)
10.4.1.1 Computation of Cost Function
(239)
10.4.1.2 Local Best and Global Best Update
(239)
10.4.1.3 Velocity and Position Update Rules
(241)
10.4.1.4 Weight Computation and Normalization
(241)
10.4.1.5 Stopping Condition
(241)
10.4.2 Ant Colony Optimization Assisted Particle Filter (PFACO)
(242)
10.4.2.1 Computation of Probability Function
(242)
10.4.2.2 Movement of the Ants
(243)
10.4.2.3 Update Pheromone Distribution
(244)
10.4.2.4 Stopping Condition
(244)
10.4.3 Particle Filter with Ant Colony for Continuous Domains
(244)
10.4.3.1 Initialization
(245)
10.4.3.2 Computation of Cost Function
(246)
10.4.3.3 External Archive Update
(246)
10.4.3.4 Pheromone Update
(246)
10.4.3.5 Movement of the Ants
(247)
10.4.3.6 Computation of Weights
(247)
10.4.3.7 State Estimation
(247)
10.4.4 Continuous Ant Colony Filter (CACF)
(248)
10.4.4.1 Initialization
(249)
10.4.4.2 Computation of Cost Function
(249)
10.4.4.3 Updating Pheromone Distribution
(249)
10.4.4.4 Movement of Ants
(250)
10.4.4.5 State Estimation
(250)
10.5 Conclusion
(250)
References
(251)
Chapter 11: Heuristic Optimal Design of Multiplier-less Digital Filter
(253)
11.1 Introduction
(253)
11.2 The Accumulated Hybrid Code (AHC)
(255)
11.2.1 The Traditional Hybrid Code Method
(255)
11.2.2 New Hybrid Code Method
(256)
11.3 CSD-Based Genetic Algorithm
(257)
11.3.1 Definition of the Fitness Function
(257)
11.3.2 CSD-Based Crossover
(257)
11.3.3 CSD-Based Mutation
(258)
11.4 CSD-Based Design of FIR Filter
(259)
11.4.1 Overview of Finite-Impulse Response (FIR) Filter
(259)
11.4.2 Estimation of the Order
(260)
11.4.3 Design Example of an FIR Filter [4]
(261)
11.5 CSD-Based Design of IIR Filter
(264)
11.5.1 Overview of Infinite Impulse Response (IIR) Filters
(265)
11.5.2 Stability Criterion for an IIR Filter
(266)
11.5.3 Design of an IIR Filter
(267)
11.5.4 Design Example of an IIR Filter [5]
(267)
References
(270)
Chapter 12: Hybrid Correlation-Neural Network Synergy for Gait Signal Classification
(272)
12.1 Introduction
(273)
12.2 The Acquisition of Gait Signals
(274)
12.3 Cross-Correlation Based Feature Extraction Methodology
(277)
12.3.1 Time Domain Features
(278)
12.3.2 Frequency Domain Features
(279)
12.4 Elman's Recurrent Neural Network Based Classification
(279)
12.5 Time Domain Cross-Correlation Based Scheme for Gait Signal Classification
(282)
12.5.1 Performance Evaluation
(285)
12.6 Frequency Domain Cross-Correlation Based Scheme for Gait Signal Classification
(289)
12.6.1 Performance Evaluation
(290)
12.7 Conclusions
(291)
References
(292)
Chapter 13: Image Denoising Using Wavelets: Application in Medical Imaging
(295)
13.1 Introduction to Multiresolution Analysis
(295)
13.1.1 Discovery and Contributions of Wavelets
(296)
13.1.2 Continuous Wavelet Transforms
(296)
13.1.3 Discrete Wavelet Transforms
(298)
13.1.4 The Concept of MRA
(299)
13.1.5 Implementation of MRA: Mallat Algorithm
(300)
13.2 Redundant Multiresolution Analysis
(301)
13.2.1 Undecimated Discrete Wavelet Transform
(302)
13.2.2 Wavelet Packets
(302)
13.2.3 Contourlet Transform
(303)
13.3 Denoising or Noise Reduction
(304)
13.3.1 Additive Gaussian White Noise Model
(305)
13.3.2 Sigmoidal Wavelet Shrinkage
(308)
13.3.3 Parametric Denoising: Wiener Filtering
(310)
13.3.4 Suppression of Correlated Noise
(311)
13.4 Applications in Medical Imaging
(312)
13.4.1 Medical Imaging Methods and Techniques
(313)
13.4.2 Wavelet-Based Denoising in fMRI, MRI, and Echography
(316)
13.4.2.1 MRI Illustration
(316)
13.4.2.2 Echography Illustration
(319)
13.5 Conclusion
(320)
References
(320)
Chapter 14: Signal Separation with A Priori Knowledge Using Sparse Representation
(322)
14.1 Introduction
(322)
14.2 Signal Separation Using Sparse Representation
(323)
14.2.1 Dictionary Construction
(324)
14.2.2 Pursuit Algorithms
(325)
14.2.2.1 Greedy Algorithms
(325)
14.2.2.2 Algorithms Based on Norm Minimization
(327)
14.3 MRS Spectra Separation with A Priori Knowledge Using Sparse Representation
(328)
14.3.1 Signal Models
(329)
14.3.2 Dictionary Construction Based on the A Priori Knowledge
(330)
14.3.3 Resonance Estimation with FOCUSS Algorithm
(332)
14.3.4 Experiments and Results
(333)
14.3.4.1 Simulated Experiments
(333)
14.3.4.2 Quantitation of Human Brain 1H MRS Data
(336)
14.3.4.3 Quantitation of Prostate 1H MRS Data
(336)
14.4 Summary
(337)
References
(338)
Chapter 15: Definition of a Discrete Color Monogenic Wavelet Transform
(340)
15.1 Introduction
(340)
Notations
(341)
15.2 Analytical Signal and 2D Generalization
(342)
15.2.1 Analytic Signal (1D)
(342)
15.2.2 Monogenic Signal (2D)
(342)
15.2.3 Monogenic Multiresolution
(343)
15.2.3.1 Primary Transform
(344)
15.2.3.2 The Monogenic Transform
(344)
15.3 First Extension of Color Monogenic Wavelet
(345)
15.3.1 The Color Monogenic Signal
(345)
15.3.2 The Color Monogenic Wavelet Transform
(345)
15.4 Second Approach for Color Monogenic Signal: A Tensor Approach
(348)
15.4.1 Link Between Riesz and Gradient
(348)
15.4.2 Color Riesz Analysis
(349)
15.4.3 Tensor Based Color Monogenic Wavelet Transform
(351)
15.4.4 Algorithm Discussion
(352)
15.5 The Radon Domain for Numerical Monogenic Transform
(353)
15.5.1 The Radon Transform
(353)
15.5.2 Discrete Radon
(354)
15.5.3 Discrete Radon Based Riesz Transform
(356)
15.5.4 Discrete Radon Based Monogenic Wavelet Transform
(357)
15.6 Conclusion
(359)
References
(361)
Chapter 16: On Image Matching and Feature Tracking for Embedded Systems: A State-of-the-Art
(363)
16.1 Introduction
(364)
16.2 Matching: Concepts, Algorithms, and Architectures
(365)
16.2.1 Matching: Basic Concepts
(365)
16.2.2 Characteristics for Matching
(365)
16.2.3 Popular Matching Methods
(367)
16.2.3.1 Raw Data/Area Based Methods
(367)
16.2.3.2 Feature Based Approaches
(369)
16.2.4 Hardware Systems for Image Matching
(373)
16.3 Feature Tracking
(375)
16.3.1 Correlation-Based Feature Tracking
(376)
16.3.2 Bayesian Approaches
(380)
16.4 Final Comments and Potential Future Developments
(381)
References
(382)
Index
(387)
