Nonlinear Digital Filtering with Python An Introduction 1st editon by Ronald Pearson, Moncef Gabbouj – Ebook PDF Instant Download/Delivery: 1498714110, 9781498714112
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Product details:
ISBN 10: 1498714110
ISBN 13: 9781498714112
Author: Ronald K. Pearson; Moncef Gabbouj
Nonlinear Digital Filtering with Python: An Introduction discusses important structural filter classes including the median filter and a number of its extensions (e.g., weighted and recursive median filters), and Volterra filters based on polynomial nonlinearities. Adopting both structural and behavioral approaches in characterizing and designing nonlinear digital filters, this book: Begins with an expedient introduction to programming in the free, open-source computing environment of Python Uses results from algebra and the theory of functional equations to construct and characterize behaviorally defined nonlinear filter classes Analyzes the impact of a range of useful interconnection strategies on filter behavior, providing Python implementations of the presented filters and interconnection strategies Proposes practical, bottom-up strategies for designing more complex and capable filters from simpler components in a way that preserves the key properties of these components Illustrates the behavioral consequences of allowing recursive (i.e., feedback) interconnections in nonlinear digital filters while highlighting a challenging but promising research frontier Nonlinear Digital Filtering with Python: An Introduction supplies essential knowledge useful for developing and implementing data cleaning filters for dynamic data analysis and time-series modeling.
Nonlinear Digital Filtering with Python An Introduction 1st Table of contents:
Chapter 1: Introduction
- Linear vs. Nonlinear Filters: An Example
- Why Nonlinearity? Data Cleaning Filters
- The Many Forms of Nonlinearity
- Python and Reproducible Research
- Organization of This Book
Chapter 2: Python
- A High-Level Overview of the Language
- Key Language Elements
- Caveat Emptor: A Few Python Quirks
- A Few Filtering Examples
- Learning More about Python
Chapter 3: Linear and Volterra Filters
- Linear Digital Filters
- Linearity, Smoothness, and Harmonics
- Volterra Filters
- Universal Approximations
Chapter 4: Median Filters and Some Extensions
- The Standard Median Filter
- Median Filter Cascades
- Order Statistic Filters
- The Recursive Median Filter
- Weighted Median Filters
- Threshold Decompositions and Stack Filters
- The Hampel Filter
- Python Implementations
- Chapter Summary
Chapter 5: Forms of Nonlinear Behavior
- Linearity vs. Additivity
- Homogeneity and Positive Homogeneity
- Generalized Homogeneity
- Location-Invariance
- Restricted Linearity
- Summary: Nonlinear Structure vs. Behavior
Chapter 6: Composite Structures: Bottom-up Design
- A Practical Overview
- Cascade Interconnections and Categories
- Parallel Interconnections and Groupoids
- Clones: More General Interconnections
- Python Implementations
- Extensions to More General Settings
Chapter 7: Recursive Structures and Stability
- What Is Different about Recursive Filters?
- Recursive Filter Classes
- Initializing Recursive Filters
- BIBO Stability
- Steady-State Responses
- Asymptotic Stability
- Inherently Nonlinear Behavior
- Fading Memory Filters
- Structured Lipschitz Filters
- Behavior of Key Nonlinear Filter Classes
- Stability of Interconnected Systems
- Challenges and Potential of Recursive Filters
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