Algorithms for Noise Reduction in Signals. Theory and practical examples based on statistical and convolutional analysis

✍ Scribed by Miguel Enrique Iglesias Martı´nez, Miguel A´ngel Garcı´a March, Carles Milia´n Enrique and Pedro Ferna´ndez de Co´rdoba

• Publisher: IOP Publishing
• Year: 2022
• Tongue: English
• Leaves: 109
• Category: Library

✦ Table of Contents

PRELIMS.pdf
Preface
Author biographies
Miguel Enrique Iglesias Martínez
Miguel Ángel García March
Carles Milián Enrique
Pedro Fernández de Córdoba
Glossary
CH001.pdf
Chapter 1 Introduction
References
CH002.pdf
Chapter 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
2.2.1 Filtering methods based on FIR and IIR system impulse response
2.2.2 Adaptive noise reduction methods
2.2.3 Machine learning methods: neural networks
2.2.4 Wavelet-based methods
2.3 Introduction to higher-order statistical analysis
2.3.1 Higher-order statistics: definition and properties
2.3.2 Higher-order spectra
2.3.3 Use of HOSA applied to noise reduction
2.3.4 Use of HOSA applied to phase information retrieval
2.3.5 Conclusions of chapter
References
CH003.pdf
Chapter 3 Noise reduction in periodic signals based on statistical analysis
3.1 Basic approach to noise reduction using higher-order noise reduction statistics
3.1.1 Working with the fourth-order cumulant
3.1.2 Experimental results on noise reduction applying only higher-order (fourth-order) statistics
3.1.3 Phase recovery algorithm
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
3.4.1 Computational cost of methods based on bispectrum computation
3.4.2 Computational cost of methods based on trispectrum computation
3.4.3 Computational cost of a method based on the combination of one- and
3.4.4 Computational cost of the proposed phase recovery algorithm
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
3.7.1 Vibration sensor ADXL203
3.7.2 Application to noise reduction in digital modulations
3.7.3 Noise reduction in the human tremor signal
3.8 Conclusions of the chapter
References
APPA.pdf
Chapter
References
APPB.pdf
Chapter
B.1 Moments
B.2 Cumulants
B.3 Higher-order spectra
APPC.pdf
Chapter
Reference
APPD.pdf
Chapter
APPE.pdf
Chapter