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

Description

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. The text describes theoretically and experimentally the processes related to high-order statistical analysis algorithms, as well as its practical combination with the convolutional analysis. On the other hand, the results of applications in real telecommunications signals, sensing of variables and biosignals such as human tremor are also shown. The reader will benefit from both the theoretical foundations described in the book, and the practical examples including generic codes of all the functions described and modifiable for use in different applications. This book is an ideal text for engineers, graduate students and scientists involved with noise reduction and signal processing. 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