Spectral Analysis Parametric and Non-Parametric Digital Methods

DSP

James V. Candy, “Bayesian Signal Processing: Classical, Modern, and Particle Filtering Methods, 2nd edition”
A Simplified Approach to Image Processing: Classical and Modern Techniques in C by Randy Crane
Spectral Analysis: Parametric and Non-Parametric Digital Methods by Francis Castanié
Digital Signal Processing (DSP) with Python Programming
Haidi Ibrahim and Shahid Iqbal, “9th International Conference on Robotic, Vision, Signal Processing and Power Applications”

James V. Candy, “Bayesian Signal Processing: Classical, Modern, and Particle Filtering Methods, 2nd edition”

English | ISBN: 1119125456 | 2016 | 640 pages | PDF | 19 MB
Presents the Bayesian approach to statistical signal processing for a variety of useful model sets
This book aims to give readers a unified Bayesian treatment starting from the basics (Baye s rule) to the more advanced (Monte Carlo sampling), evolving to the next–generation model–based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on Sequential Bayesian Detection, a new section on Ensemble Kalman Filters as well as an expansion of Case Studies that detail Bayesian solutions for a variety of applications. These studies illustrate Bayesian approaches to real–world problems incorporating detailed particle filter designs, adaptive particle filters and sequential Bayesian detectors. In addition to these major developments a variety of sections are expanded to fill–in–the gaps of the first edition. Here metrics for particle filter (PF) designs with emphasis on classical sanity testing lead to ensemble techniques as a basic requirement for performance analysis. The expansion of information theory metrics and their application to PF designs is fully developed and applied. These expansions of the book have been updated to provide a more cohesive discussion of Bayesian processing with examples and applications enabling the comprehension of alternative approaches to solving estimation/detection problems.
The second edition of Bayesian Signal Processing features:
Classical Kalman filtering for linear, linearized, and nonlinear systems; modern unscented and ensemble Kalman filters: and the next–generation Bayesian particle filters
Sequential Bayesian detection techniques incorporating model–based schemes for a variety of real–world problems
Practical Bayesian processor designs including comprehensive methods of performance analysis ranging from simple sanity testing and ensemble techniques to sophisticated information metrics
New case studies on adaptive particle filtering and sequential Bayesian detection are covered detailing more Bayesian approaches to applied problem solving
MATLAB notes at the end of each chapter help readers solve complex problems using readily available software commands and point out other software packages available
Problem sets included to test readers knowledge and help them put their new skills into practice Bayesian
Signal Processing, Second Edition is written for all students, scientists, and engineers who investigate and apply signal processing to their everyday problems.

A Simplified Approach to Image Processing: Classical and Modern Techniques in C by Randy Crane

English | 1996 | ISBN: 0132264161 | 336 Pages | DJVU | 23.8 MB
Image processing, the use of computers to process pictures, has revolutionized the fields of medicine, space exploration, geology, and oceanography, and has become the hottest area in digital signal processing. This book provides a comprehensive introduction to the most popular image processing techniques used today, without getting bogged down in the complex mathematical presentations found in most image processing books and journals. The book covers the hottes t topics in image proessing, including whole chapters on the processing of color images, image warping and morphing techniques, and image compression. For computer programmers and electrical engineers who need to enhance image processing applications.

Spectral Analysis: Parametric and Non-Parametric Digital Methods by Francis Castanié

English | 2006 | ISBN: 1905209053 | 264 Pages | PDF | 4.4 MB
This book deals with these parametric methods, first discussing those based on time series models, Capon’s method and its variants, and then estimators based on the notions of sub-spaces. However, the book also deals with the traditional “analog” methods, now called non-parametric methods, which are still the most widely used in practical spectral analysis.

Digital Signal Processing (DSP) with Python Programming

Wiley ISTE | English | February 2017 | ISBN-10: 1786301261 | 290 pages | PDF | 2.46 mb
by Maurice Charbit (Author)
The parameter estimation and hypothesis testing are the basic tools in statistical inference. These techniques occur in many applications of data processing., and methods of Monte Carlo have become an essential tool to assess performance. For pedagogical purposes the book includes several computational problems and exercices. To prevent students from getting stuck on exercises, detailed corrections are provided.

Haidi Ibrahim and Shahid Iqbal, “9th International Conference on Robotic, Vision, Signal Processing and Power Applications”

English | ISBN: 9811017190 | 2017 | 861 pages | PDF | 29 MB
The proceeding is a collection of research papers presented, at the 9th International Conference on Robotics, Vision, Signal Processing & Power Applications (ROVISP 2016), by researchers, scientists, engineers, academicians as well as industrial professionals from all around the globe to present their research results and development activities for oral or poster presentations. The topics of interest are as follows but are not limited to:
• Robotics, Control, Mechatronics and Automation
• Vision, Image, and Signal Processing
• Artificial Intelligence and Computer Applications
• Electronic Design and Applications
• Telecommunication Systems and Applications
• Power System and Industrial Applications
• Engineering Education

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