Iglesias Mart�inez, Miguel Enrique,
Algorithms for noise reduction in signals : theory and practical examples based on statistical and convolutional analysis / Miguel Enrique Iglesias Mart�inez, Miguel �Angel Garc�ia March, Carles Mili�an Enrique and Pedro Fern�andez de C�ordoba. - 1 online resource (various pagings) : illustrations. - [IOP release $release] IOP ebooks. [2022 collection] . - IOP (Series). Release 22. IOP ebooks. 2022 collection. .
"Version: 20221201"--Title page verso.
Includes bibliographical references.
1. Introduction -- 2. Current trends in signal processing techniques applied to noise reduction -- 2.1. Signals and noise -- 2.2. Current trends in signal processing techniques applied to noise reduction -- 2.3. Introduction to higher-order statistical analysis 3. Noise reduction in periodic signals based on statistical analysis -- 3.1. Basic approach to noise reduction using higher-order noise reduction statistics -- 3.2. Amplitude correction in the spectral domain -- 3.3. Experimental results applying the phase recovery algorithm -- 3.4. Computational cost analysis of the proposed method compared with others -- 3.5. SNR levels processed by the proposed algorithm compared with others developed for noise reduction and phase retrieval -- 3.6. Comparative analysis according to other noise reduction methods not based on HOSA -- 3.7. Application to noise reduction in real signals -- 3.8. Conclusions of the chapter Appendix A. Properties of cumulants -- Appendix B. Moments, cumulants, and higher-order spectra -- Appendix C. Calculation of the one-dimensional component of the fourth-order cumulative of a harmonic signal -- Appendix D. Calculation of the autocorrelation function of a harmonic signal -- Appendix E. Examples of codes.
This book is the result of an exhaustive review of the general algorithms used for noise reduction using two general application criteria: one-input, one-output systems, and two-input, one-output systems.
Engineers and scientists involved with nose reduction and signal processing.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.
Miguel Enrique Iglesias Mart�inez: received a degree in Telecommunications and Electronics Engineering from the University of Pinar del R�io (UPR) in 2008 and a Master's Degree in Digital Systems from the Technological University of Havana, Cuba, in 2011.
9780750335911 9780750335904
10.1088/978-0-7503-3591-1 doi
Signal processing--Digital techniques.
Electronic noise.
Signal processing.
TECHNOLOGY & ENGINEERING / Signals & Signal Processing.
TK5102.9 / .I453 2022eb
621.382/2
Algorithms for noise reduction in signals : theory and practical examples based on statistical and convolutional analysis / Miguel Enrique Iglesias Mart�inez, Miguel �Angel Garc�ia March, Carles Mili�an Enrique and Pedro Fern�andez de C�ordoba. - 1 online resource (various pagings) : illustrations. - [IOP release $release] IOP ebooks. [2022 collection] . - IOP (Series). Release 22. IOP ebooks. 2022 collection. .
"Version: 20221201"--Title page verso.
Includes bibliographical references.
1. Introduction -- 2. Current trends in signal processing techniques applied to noise reduction -- 2.1. Signals and noise -- 2.2. Current trends in signal processing techniques applied to noise reduction -- 2.3. Introduction to higher-order statistical analysis 3. Noise reduction in periodic signals based on statistical analysis -- 3.1. Basic approach to noise reduction using higher-order noise reduction statistics -- 3.2. Amplitude correction in the spectral domain -- 3.3. Experimental results applying the phase recovery algorithm -- 3.4. Computational cost analysis of the proposed method compared with others -- 3.5. SNR levels processed by the proposed algorithm compared with others developed for noise reduction and phase retrieval -- 3.6. Comparative analysis according to other noise reduction methods not based on HOSA -- 3.7. Application to noise reduction in real signals -- 3.8. Conclusions of the chapter Appendix A. Properties of cumulants -- Appendix B. Moments, cumulants, and higher-order spectra -- Appendix C. Calculation of the one-dimensional component of the fourth-order cumulative of a harmonic signal -- Appendix D. Calculation of the autocorrelation function of a harmonic signal -- Appendix E. Examples of codes.
This book is the result of an exhaustive review of the general algorithms used for noise reduction using two general application criteria: one-input, one-output systems, and two-input, one-output systems.
Engineers and scientists involved with nose reduction and signal processing.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.
Miguel Enrique Iglesias Mart�inez: received a degree in Telecommunications and Electronics Engineering from the University of Pinar del R�io (UPR) in 2008 and a Master's Degree in Digital Systems from the Technological University of Havana, Cuba, in 2011.
9780750335911 9780750335904
10.1088/978-0-7503-3591-1 doi
Signal processing--Digital techniques.
Electronic noise.
Signal processing.
TECHNOLOGY & ENGINEERING / Signals & Signal Processing.
TK5102.9 / .I453 2022eb
621.382/2