| Orthogonal Wavelets and Signal Processing |
| Multiresolution Analysis |
| Two Orthogonal Wavelet Bases |
| Discrete Wavelets and the Daubechies Construction |
| The Discrete Wavelet Transform Algorithm |
| Wavelets and Non-Stationary Signal Analysis |
| Signal Compression Using Wavelets |
| Adaptive Filtering Using Vector Spaces of Systems: Fundamentals of Adaptive Filtering |
| The Vector Space Adaptive Filter |
| Algorithm Convergence and AsymptoticPerformance |
| Choosing the Vector Space |
| Choosing the Basis |
| Examples |
| Order Statistics and Adaptive Filtering: Median and Order Statistic Filters |
| Adaptive Filters and Order Statistics |
| OS Filters and Robustness |
| Rapid Adaptation--An Ad-Hoc Estimator |
| Multi-Layer Perceptron Neural Networks with Application to Speech Recognition |
| The Multi-Layer Perceptron |
| Signal Classification Design Examples |
| Perceptron Architecture and Learning |
| Statistical Training of Multi-Layer Perceptrons |
| Combining Multi-Layer Perceptrons for Speech Recognition |
| Auditory Localization Using Spectral Information: A Localization Model Based on HRTFs |
| Template Matching and a Matching Measure |
| Normalized Correlation Matching |
| Optimal DMM Matching |
| Matching Using Backpropagation Neural Networks |
| Experiments |
| Signal Processing by Projection Methods: Applications to Color Matching, Resolution Enhancement, and Blind Deconvolution |
| The Method of Projections Onto Convex Sets |
| Color Matching Problems |
| Resolution Enhancement |
| Generalized Projections |
| Projection-Based Blind Deconvolution |
| Projection Based Image Reconstruction from Compressed Data |
| Principles of the Proposed Recovery Approach |
| Constraint Set Based on the Transmitted Data |
| Constraint Sets Based on Prior Knowledge |
| The Recovery Algorithm |
| A Simplified Algorithm |
| Computational Complexity Analysis |
| Non-Orthogonal Expansion for Template Matching and Edge Detection |
| The Correlation Approach: A Brief Review |
| The Discriminative Signal-to-Noise Ratio and Expansion Matching |
| EXM Related to Minimum Squared Error Restoration |
| EXM Related to Non-Orthogonal Expansion |
| Experimental Results |
| EXM-based Optimal DSNR Edge Detection |
| Locally Optimum Detection and Its Application to Communicationsand Signal Processing: Derivation of the Memoryless Locally Optimum Detector |
| Derivation of the Locally Optimum Detector with Memory |
| LO Detection Applied to a Spread Spectrum System |
| Estimation of Probability Density Functions Using Projections Onto Convex Sets: Constraint Sets for Probability Density Function Estimation |
| Determining the Parameters in Constraint Sets |
| Competing Algorithms |
| Numerical Results |
| Subject |
| Index |
| Orthogonal Wavelets and Signal Processing |
| Multiresolution Analysis |
| Two Orthogonal Wavelet Bases |
| Discrete Wavelets and the Daubechies Construction |
| The Discrete Wavelet Transform Algorithm |
| Wavelets and Non-Stationary Signal Analysis |
図書
Sensor array and multichannel signal processing, signal processing theory and methods (: softbound)