Time-frequency domain for segmentation and classification of non-stationary signals : the Stockwell transform applied on bio-signals and electric signals / Ali Moukadem, Djaffar Ould Abdeslam, Alain Dieterlen.

Format:

Book

Author/Creator:

Moukadem, Ali, author.

Abdeslam, Djaffar Ould, author.

Dieterlen, Alain, author.

Series:

Focus series in digital signal and image processing.

Focus series (London, England)

FOCUS : Digital Signal and Image Processing Series, 2051-249X

FOCUS Series

Language:

English

Subjects (All):

Signal processing--Digital techniques.

Signal processing.

Signal processing--Mathematics.

Physical Description:

1 online resource (149 p.)

Edition:

1st ed.

Place of Publication:

London, England ; Hoboken, New Jersey : ISTE : Wiley, 2014.

Language Note:

English

Summary:

This book focuses on signal processing algorithms based on the time frequency 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 fo

Contents:

Cover; Title Page; Contents; Preface; Chapter 1. The Need for Time-Frequency Analysis; 1.1. Introduction; 1.2. Stationary and non-stationary concepts; 1.2.1. Stationarity; 1.2.2. Non-stationarity; 1.3. Temporal representations; 1.4. Frequency representations of signals; 1.4.1. Fourier transform; 1.4.2. Mean frequency, bandwidth and frequency average; 1.5. Uncertainty principle; 1.6. Limitation of time analysis and frequency analysis: the need for time-frequency representation; 1.6.1. Instantaneous frequency; 1.7. Conclusion; 1.8. Bibliography

Chapter 2. Time-Frequency Analysis:The S-Transform 2.1. Introduction; 2.2. Synthetic signals; 2.3. The STFT; 2.4. The WT; 2.5. The Wigner-Ville distribution; 2.5.1. The pseudo-WVD; 2.5.2. The smoothed PWVD; 2.6. Cohen's class; 2.7. The S-transform; 2.7.1. Properties of the S-transform; 2.7.2. The discrete S-transform; 2.7.3. The improvement of the S-transform energy concentration; 2.7.4. The ST-spectrogram; 2.8. Conclusion; 2.9. Bibliography; Chapter 3. Segmentation and Classification of Heart Sounds Based on the S-Transform; 3.1. Introduction; 3.2. Methods and materials; 3.2.1. Data sets

3.2.2. Localization and segmentation of heart sounds 3.2.3. Classification of heart sounds; 3.3. Results and discussion; 3.3.1. Localization and segmentation results; 3.3.2. S1 and S2 classification results; 3.3.3. Murmur detection results; 3.4. Conclusion; 3.5. Bibliography; Chapter 4. Adaline for the Detection of Electrical Events in Electrical Signals; 4.1. Introduction; 4.2. Electric events; 4.2.1. Power quality; 4.2.2. Electric events; 4.3. Adaline; 4.4. Adaline for frequency estimation; 4.4.1. Adaline method; 4.4.2. Results

4.5. Adaline for voltage component extraction in unbalanced system 4.5.1. Model of the unbalanced voltage system; 4.5.2. Extraction of the voltage components in the DQ-space; 4.5.3. Online estimation of the instantaneous phases θd and θi; 4.5.4. Filtering the AC components in the DQ-space; 4.5.5. Results; 4.6. Adaline for harmonic current identification and compensation; 4.6.1. Adaline method; 4.6.2. Results; 4.7. Conclusion; 4.8. Bibliography; Chapter 5. FPGA Implementation of the Adaline; 5.1. Introduction; 5.2. Instantaneous power theory (IPT) in the APF

5.3. Adaline for the computing of the IPT in the PLL5.3.1. Adaline-based PLL; 5.3.2. A multiplexing approach for hardware consumption reduction; 5.4. Results; 5.4.1. Simulation; 5.4.2. FPGA implementation results; 5.5. Conclusion; 5.6. Bibliography; Index

Notes:

Description based upon print version of record.

Includes bibliographical references at the end of each chapters and index.

Description based on print version record.

ISBN:

9781118908709

1118908708

9781118908686

1118908686

9781118908778

1118908775

OCLC:

874321835