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Time-frequency domain for segmentation and classification of non-stationary signals : the Stockwell Transform applied on bio-signals and electric signals /
"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,...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London : Hoboken, NJ :
ISTE ; Wiley,
2014.
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| Colección: | Focus nanoscience and nanotechnology series.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 049 | |a UAMI | ||
| 100 | 1 | |a Moukadem, Ali, |e author. | |
| 245 | 1 | 0 | |a Time-frequency domain for segmentation and classification of non-stationary signals : |b the Stockwell Transform applied on bio-signals and electric signals / |c Ali Moukadem, Djaffar Ould Abdeslam, Alain Dieterlen. |
| 264 | 1 | |a London : |b ISTE ; |a Hoboken, NJ : |b Wiley, |c 2014. | |
| 264 | 4 | |c ©2014 | |
| 300 | |a 1 online resource (xi, 135 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a FOCUS Series | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |6 880-01 |a The Need for Time-Frequency Analysis / Ali Moukadem, Djaffar Ould Abdeslam, Alain Dieterlen -- Time-Frequency Analysis:The S-Transform / Ali Moukadem, Djaffar Ould Abdeslam, Alain Dieterlen -- Segmentation and Classification of Heart Sounds Based on the S-Transform / Ali Moukadem, Djaffar Ould Abdeslam, Alain Dieterlen -- Adaline for the Detection of Electrical Events in Electrical Signals / Ali Moukadem, Djaffar Ould Abdeslam, Alain Dieterlen -- FPGA Implementation of the Adaline / Ali Moukadem, Djaffar Ould Abdeslam, Alain Dieterlen. | |
| 520 | |a "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."--Provided by publisher | ||
| 588 | 0 | |a Online resource; title from PDF title page (Wiley, viewed May 5, 2014). | |
| 546 | |a English. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Signal processing |x Digital techniques |x Data processing. | |
| 650 | 0 | |a Software engineering. | |
| 650 | 0 | |a Signal processing |x Digital techniques. | |
| 650 | 0 | |a Signal processing |x Mathematics. | |
| 650 | 6 | |a Traitement du signal |x Techniques numériques. | |
| 650 | 6 | |a Traitement du signal |x Mathématiques. | |
| 650 | 6 | |a Traitement du signal |x Techniques numériques |x Informatique. | |
| 650 | 6 | |a Génie logiciel. | |
| 650 | 7 | |a COMPUTERS |x Programming |x General. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Programming |x Software Development. |2 bisacsh | |
| 650 | 7 | |a Signal processing |x Digital techniques |2 fast | |
| 650 | 7 | |a Signal processing |x Digital techniques |x Data processing |2 fast | |
| 650 | 7 | |a Software engineering |2 fast | |
| 700 | 1 | |a Abdeslam, Djaffar Ould, |e author. | |
| 700 | 1 | |a Dieterlen, Alain, |e author. | |
| 758 | |i has work: |a Time-frequency domain for segmentation and classification of non-stationary signals (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGQM88yvktXVpKxgrgmMHd |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Moukadem, Ali. |t Time-frequency domain for segmentation and classification of non-stationary signals. |d London : ISTE Ltd ; Hoboken, NJ : John Wiley & Sons, Inc., 2014 |z 9781848216136 |w (DLC) 2014930208 |w (OCoLC)876882922 |
| 830 | 0 | |a Focus nanoscience and nanotechnology series. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1650849 |z Texto completo |
| 880 | 0 | 0 | |6 505-01/(S |g Machine generated contents note: |g ch. 1 |t Need for Time--Frequency Analysis -- |g 1.1. |t Introduction -- |g 1.2. |t Stationary and non-stationary concepts -- |g 1.2.1. |t Stationarity -- |g 1.2.2. |t Non-stationarity -- |g 1.3. |t Temporal representations -- |g 1.4. |t Frequency representations of signals -- |g 1.4.1. |t Fourier transform -- |g 1.4.2. |t Mean frequency, bandwidth and frequency average -- |g 1.5. |t Uncertainty principle -- |g 1.6. |t Limitation of time analysis and frequency analysis: the need for time--frequency representation -- |g 1.6.1. |t Instantaneous frequency -- |g 1.7. |t Conclusion -- |g 1.8. |t Bibliography -- |g ch. 2 |t Time--Frequency Analysis: The S-Transform -- |g 2.1. |t Introduction -- |g 2.2. |t Synthetic signals -- |g 2.3. |t STFT -- |g 2.4. |t WT -- |g 2.5. |t Wigner--Ville distribution -- |g 2.5.1. |t pseudo-WVD -- |g 2.5.2. |t smoothed PWVD -- |g 2.6. |t Cohen's class -- |g 2.7. |t S-transform -- |g 2.7.1. |t Properties of the S-transform -- |g 2.7.2. |t discrete S-transform -- |g 2.7.3. |t improvement of the S-transform energy concentration -- |g 2.7.4. |t ST-spectrogram -- |g 2.8. |t Conclusion -- |g 2.9. |t Bibliography -- |g ch. 3 |t Segmentation and Classification of Heart Sounds Based on the S-Transform -- |g 3.1. |t Introduction -- |g 3.2. |t Methods and materials -- |g 3.2.1. |t Data sets -- |g 3.2.2. |t Localization and segmentation of heart sounds -- |g 3.2.3. |t Classification of heart sounds -- |g 3.3. |t Results and discussion -- |g 3.3.1. |t Localization and segmentation results -- |g 3.3.2. |t S1 and S2 classification results -- |g 3.3.3. |t Murmur detection results -- |g 3.4. |t Conclusion -- |g 3.5. |t Bibliography -- |g ch. 4 |t Adaline for the Detection of Electrical Events in Electrical Signals -- |g 4.1. |t Introduction -- |g 4.2. |t Electric events -- |g 4.2.1. |t Power quality -- |g 4.2.2. |t Electric events -- |g 4.3. |t Adaline -- |g 4.4. |t Adaline for frequency estimation -- |g 4.4.1. |t Adaline method -- |g 4.4.2. |t Results -- |g 4.5. |t Adaline for voltage component extraction in unbalanced system -- |g 4.5.1. |t Model of the unbalanced voltage system -- |g 4.5.2. |t Extraction of the voltage components in the DQ-space -- |g 4.5.3. |t Online estimation of the instantaneous phases θd and θi -- |g 4.5.4. |t Filtering the AC components in the DQ-space -- |g 4.5.5. |t Results -- |g 4.6. |t Adaline for harmonic current identification and compensation -- |g 4.6.1. |t Adaline method -- |g 4.6.2. |t Results -- |g 4.7. |t Conclusion -- |g 4.8. |t Bibliography -- |g ch. 5 |t FPGA Implementation of the Adaline -- |g 5.1. |t Introduction -- |g 5.2. |t Instantaneous power theory (IPT) in the APF -- |g 5.3. |t Adaline for the computing of the IPT in the PLL -- |g 5.3.1. |t Adaline-based PLL -- |g 5.3.2. |t multiplexing approach for hardware consumption reduction -- |g 5.4. |t Results -- |g 5.4.1. |t Simulation -- |g 5.4.2. |t FPGA implementation results -- |g 5.5. |t Conclusion -- |g 5.6. |t Bibliography. |
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