Matches in SemOpenAlex for { <https://semopenalex.org/work/W62719046> ?p ?o ?g. }
Showing items 1 to 99 of
99
with 100 items per page.
- W62719046 abstract "Preface. Acknowledgments. Acronyms. 1. Introduction. 1.1 Non--Gaussian Random Processes. 1.1.1 Generalized Gaussian Distributions and Weighted Medians. 1.1.2 Stable Distributions and Weighted Myriads. 1.2 Statistical Foundations. 1.3 The Filtering Problem. 1.3.1 Moment Theory. PART I: STATISTICAL FOUNDATIONS. 2. Non--Gaussian Models. 2.1 Generalized Gaussian Distributions. 2.2 Stable Distributions. 2.2.1 Definitions. 2.2.2 Symmetric Stable Distributions. 2.2.3 Generalized Central Limit Theorem. 2.2.4 Simulation of Stable Sequences. 2.3 Lower Order Moments. 2.3.1 Fractional Lower Order Moments. 2.3.2 Zero Order Statistics. 2.3.3 Parameter Estimation of Stable Distributions. Problems. 3. Order Statistics. 3.1 Distributions of Order Statistics. 3.2 Moments of Order Statistics. 3.2.1 Order Statistics From Uniform Distributions. 3.2.2 Recurrence Relations. 3.3 Order Statistics Containing Outliers. 3.4 Joint Statistics of Ordered and Non--Ordered Samples. Problems. 4. Statistical Foundations of Filtering. 4.1 Properties of Estimators. 4.2 Maximum Likelihood Estimation. 4.3 Robust Estimation. Problems. PART II: SIGNAL PROCESSING WITH ORDER STATISTICS. 5. Median and Weighted Median Smoothers. 5.1 Running Median Smoothers. 5.1.1 Statistical Properties. 5.1.2 Root Signals (Fixed Points). 5.2 Weighted Median Smoothers. 5.2.1 The Center Weighted Median Smoother. 5.2.2 Permutation Weighted Median Smoothers. 5.3 Threshold Decomposition Representation. 5.3.1 Stack Smoothers. 5.4 Weighted Medians in Least Absolute Deviation (LAD) Regression. 5.4.1 Foundation and Cost Functions. 5.4.2 LAD Regression with Weighted Medians. 5.4.3 Simulation. Problems. 6. Weighted Median Filters. 6.1 Weighted Median Filters With Real--Valued Weights. 6.1.1 Permutation Weighted Median Filters. 6.2 Spectral Design of Weighted Median Filters. 6.2.1 Median Smoothers and Sample Selection Probabilities. 6.2.2 SSPs for Weighted Median Smoothers. 6.2.3 Synthesis of WM Smoothers. 6.2.4 General Iterative Solution. 6.2.5 Spectral Design of Weighted Median Filters Admitting Real--Valued Weights. 6.3 The Optimal Weighted Median Filtering Problem. 6.3.1 Threshold Decomposition for Real--Valued Signals. 6.3.2 The Least Mean Absolute (LMA) Algorithm. 6.4 Recursive Weighted Median Filters. 6.4.1 Threshold Decomposition Representation of Recursive WM Filters. 6.4.2 Optimal Recursive Weighted Median Filtering. 6.5 Mirrored Threshold Decomposition and Stack Filters. 6.5.1 Stack Filters. 6.5.2 Stack Filter Representation of Recursive WM Filters. 6.6 Complex Valued Weighted Median Filter. 6.6.1 Phase Coupled Complex WM Filters. 6.6.2 Marginal Phase Coupled Complex WM Filter. 6.6.3 Complex Threshold Decomposition. 6.6.4 Optimal Marginal Phase Coupled Complex WM. 6.6.5 Spectral Design of Complex Valued Weighted Medians. 6.7 Weighted Median Filters for Multichannel Signals. 6.7.1 Marginal WM Filter. 6.7.2 Vector WM Filter. 6.7.3 Weighted Multichannel Median Filtering Structures. 6.7.4 Filter Optimization. Problems. 7. Linear Combination or Order Statistics. 7.1 L--Estimates of Location. 7.2 L--Smoothers. 7.3 L --Filters. 7.3.1 Design and Optimization of L Filters. 7.4 Lj Permutation Filters. 7.5 Hybrid Median/Linear FIR Filters. 7.5.1 Median and FIR Affinity Trimming. 7.6 Linear Combination of Weighted Medians. 7.6.1 LCWM Filters. 7.6.2 Design of LCWM Filters. 7.6.3 Symmetric LCWM Filters. Problems. PART III: SIGNAL PROCESSING WITH THE STABLE MODEL. 8. Myriad Smoothers. 8.1 FLOM Smoothers. 8.2 Running Myriad Smoothers. 8.3 Optimality of the Sample Myriad. 8.4 Weighted Myriad Smoothers. 8.5 Fast Weighted Myriad Computation. 8.6 Weighted Myriad Smoother Design. 8.6.1 Center Weighted Myriads for Image Denoising. 8.6.2 Myriadization. Problems. 9. Weighted Myriad Filters. 9.1 Weighted Myriad Filters with Real--Valued Weights. 9.2 Fast Real--Valued Weighted Myriad Computation. 9.3 Weighted Myriad Filter Design. 9.3.1 Myriadization. 9.3.2 Optimization. Problems. References. Appendix A: Software Guide. Index." @default.
- W62719046 created "2016-06-24" @default.
- W62719046 creator A5005357824 @default.
- W62719046 date "2004-11-12" @default.
- W62719046 modified "2023-10-06" @default.
- W62719046 title "Nonlinear Signal Processing: A Statistical Approach" @default.
- W62719046 hasPublicationYear "2004" @default.
- W62719046 type Work @default.
- W62719046 sameAs 62719046 @default.
- W62719046 citedByCount "169" @default.
- W62719046 countsByYear W627190462012 @default.
- W62719046 countsByYear W627190462013 @default.
- W62719046 countsByYear W627190462014 @default.
- W62719046 countsByYear W627190462015 @default.
- W62719046 countsByYear W627190462016 @default.
- W62719046 countsByYear W627190462017 @default.
- W62719046 countsByYear W627190462018 @default.
- W62719046 countsByYear W627190462019 @default.
- W62719046 countsByYear W627190462020 @default.
- W62719046 countsByYear W627190462021 @default.
- W62719046 crossrefType "book" @default.
- W62719046 hasAuthorship W62719046A5005357824 @default.
- W62719046 hasConcept C104267543 @default.
- W62719046 hasConcept C105795698 @default.
- W62719046 hasConcept C115961682 @default.
- W62719046 hasConcept C121332964 @default.
- W62719046 hasConcept C136368487 @default.
- W62719046 hasConcept C154945302 @default.
- W62719046 hasConcept C156460124 @default.
- W62719046 hasConcept C163716315 @default.
- W62719046 hasConcept C166785042 @default.
- W62719046 hasConcept C179254644 @default.
- W62719046 hasConcept C18294631 @default.
- W62719046 hasConcept C185429906 @default.
- W62719046 hasConcept C2524010 @default.
- W62719046 hasConcept C2780576426 @default.
- W62719046 hasConcept C28826006 @default.
- W62719046 hasConcept C33923547 @default.
- W62719046 hasConcept C41008148 @default.
- W62719046 hasConcept C44082924 @default.
- W62719046 hasConcept C55352655 @default.
- W62719046 hasConcept C554190296 @default.
- W62719046 hasConcept C62520636 @default.
- W62719046 hasConcept C74650414 @default.
- W62719046 hasConcept C76155785 @default.
- W62719046 hasConcept C79337645 @default.
- W62719046 hasConcept C9417928 @default.
- W62719046 hasConceptScore W62719046C104267543 @default.
- W62719046 hasConceptScore W62719046C105795698 @default.
- W62719046 hasConceptScore W62719046C115961682 @default.
- W62719046 hasConceptScore W62719046C121332964 @default.
- W62719046 hasConceptScore W62719046C136368487 @default.
- W62719046 hasConceptScore W62719046C154945302 @default.
- W62719046 hasConceptScore W62719046C156460124 @default.
- W62719046 hasConceptScore W62719046C163716315 @default.
- W62719046 hasConceptScore W62719046C166785042 @default.
- W62719046 hasConceptScore W62719046C179254644 @default.
- W62719046 hasConceptScore W62719046C18294631 @default.
- W62719046 hasConceptScore W62719046C185429906 @default.
- W62719046 hasConceptScore W62719046C2524010 @default.
- W62719046 hasConceptScore W62719046C2780576426 @default.
- W62719046 hasConceptScore W62719046C28826006 @default.
- W62719046 hasConceptScore W62719046C33923547 @default.
- W62719046 hasConceptScore W62719046C41008148 @default.
- W62719046 hasConceptScore W62719046C44082924 @default.
- W62719046 hasConceptScore W62719046C55352655 @default.
- W62719046 hasConceptScore W62719046C554190296 @default.
- W62719046 hasConceptScore W62719046C62520636 @default.
- W62719046 hasConceptScore W62719046C74650414 @default.
- W62719046 hasConceptScore W62719046C76155785 @default.
- W62719046 hasConceptScore W62719046C79337645 @default.
- W62719046 hasConceptScore W62719046C9417928 @default.
- W62719046 hasLocation W627190461 @default.
- W62719046 hasOpenAccess W62719046 @default.
- W62719046 hasPrimaryLocation W627190461 @default.
- W62719046 hasRelatedWork W1540920731 @default.
- W62719046 hasRelatedWork W1543826209 @default.
- W62719046 hasRelatedWork W1663973292 @default.
- W62719046 hasRelatedWork W1991666417 @default.
- W62719046 hasRelatedWork W2022894179 @default.
- W62719046 hasRelatedWork W2045021643 @default.
- W62719046 hasRelatedWork W2094903560 @default.
- W62719046 hasRelatedWork W2101248405 @default.
- W62719046 hasRelatedWork W2114998197 @default.
- W62719046 hasRelatedWork W2115522319 @default.
- W62719046 hasRelatedWork W2125324936 @default.
- W62719046 hasRelatedWork W2129611343 @default.
- W62719046 hasRelatedWork W2142253570 @default.
- W62719046 hasRelatedWork W2144750054 @default.
- W62719046 hasRelatedWork W2151768996 @default.
- W62719046 hasRelatedWork W2164388682 @default.
- W62719046 hasRelatedWork W2164792216 @default.
- W62719046 hasRelatedWork W2296319761 @default.
- W62719046 hasRelatedWork W2489822048 @default.
- W62719046 hasRelatedWork W42203131 @default.
- W62719046 isParatext "false" @default.
- W62719046 isRetracted "false" @default.
- W62719046 magId "62719046" @default.
- W62719046 workType "book" @default.