Time-Frequency Domain for Segmentation and Classification of Non-stationary Signals The Stockwell Transform Applied on Bio-signals and Electric Signals

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  • Format: Hardcover
  • Copyright: 3/31/2014
  • Publisher: Iste/Hermes Science Pub
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This book focuses on signal processing algorithms based on the timefrequency domain. Original methods and algorithms are presented which are able to extract information from non-stationary signals such as heart sounds and power electric signals. The methods proposed focus on the time-frequency domain, and most notably the Stockwell Transform for the feature extraction process and to identify signatures. For the classification method, the Adaline Neural Network is used and compared with other common classifiers. Theory enhancement, original applications and concrete implementation on FPGA for real-time processing are also covered in this book.

Table of Contents

1. Introduction

2. Non-stationary Signals

a. Time representation and Frequency representation

b. The Heisenberg uncertainty principle

3. Time-Frequency Analysis

a. State of art

b. Short time Frequency Transform

c. Adaptive Neural Network (Adaline)

d. The Wavelet Transform

e. The Wigner-Ville Transform

f. The S-Transform

i. Why use the S-Transform?

ii. The DOST

4. Segmentation and Features Extraction for bio-signals

a. Introduction

b. Heart-Sounds

c. Lung Sounds

d. ECG

5. Applications for electrical signals

a. Introduction

b. Electric events

c. Harmonic currents

d. Adaline for Harmonic currents detection

e. S-Transform Harmonic currents detection

6. FPGA implementation of the S-Transform

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