Neural Advances In Processing Nonlinear Dynamic Signals

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Neural Advances In Processing Nonlinear Dynamic Signals

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Author : Anna Esposito
language : en
Publisher: Springer
Release Date : 2018-07-21

Neural Advances In Processing Nonlinear Dynamic Signals written by Anna Esposito and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-21 with Technology & Engineering categories.

This book proposes neural networks algorithms and advanced machine learning techniques for processing nonlinear dynamic signals such as audio, speech, financial signals, feedback loops, waveform generation, filtering, equalization, signals from arrays of sensors, and perturbations in the automatic control of industrial production processes. It also discusses the drastic changes in financial, economic, and work processes that are currently being experienced by the computational and engineering sciences community. Addresses key aspects, such as the integration of neural algorithms and procedures for the recognition, the analysis and detection of dynamic complex structures and the implementation of systems for discovering patterns in data, the book highlights the commonalities between computational intelligence (CI) and information and communications technologies (ICT) to promote transversal skills and sophisticated processing techniques. This book is a valuable resource for a. The academic research community b. The ICT market c. PhD students and early stage researchers d. Companies, research institutes e. Representatives from industry and standardization bodies

Advances In Cardiac Signal Processing

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Author : U. Rajendra Acharya
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-04-25

Advances In Cardiac Signal Processing written by U. Rajendra Acharya and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-04-25 with Technology & Engineering categories.

This book provides a comprehensive review of progress in the acquisition and extraction of electrocardiogram signals. The coverage is extensive, from a review of filtering techniques to measurement of heart rate variability, to aortic pressure measurement, to strategies for assessing contractile effort of the left ventricle and more. The book concludes by assessing the future of cardiac signal processing, leading to next generation research which directly impact cardiac health care.

Concept Of Adaptive Filtering

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Author : Shivendra Nandan
language : en
Publisher: TSG Publications
Release Date :

Concept Of Adaptive Filtering written by Shivendra Nandan and has been published by TSG Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on with Education categories.

A spline adaptive filter (SAF) based nonlinear active noise control (ANC) system is proposed in this paper. The SAF consists of a linear network of adaptive weights in a cascade with an adaptive nonlinear network. The nonlinear network, in turn consists of an adaptive look-up table followed by a spline interpolation network and forms an adaptive activation function. An update rule has been derived for the proposed ANC system, which not only updates the weights of the linear network, but also updates the nature of the activation function. Linear Network is based on improvement in FxLMS algorithm. FxLMS algorithm is used because it is computationally simple like the most commonly used Least Mean Square (LMS) algorithm. In addition, it includes secondary path effects. To make the FxLMS algorithm more effective, the secondary path estimation should be more precise and accurate. The nonlinear function involved in the adaptation process is based on a spline function that can be modified during learning. The spline control points are adaptively changed using gradient-based techniques. B-splines and Catmull-Rom splines are used, because they allow imposing simple constraints on control parameters. This new kind of adaptive function is then applied to the output of a linear adaptive filter and it is used for the identification of Wiener-type nonlinear systems. In addition, we derive a simple form of the adaptation algorithm and an upper bound on the choice of the step-size. An extensive simulation study has been conducted to evaluate the noise mitigation performance of the proposed scheme and the new method has been shown to provide improved noise cancellation efficiency with a lesser computational load in comparison with other popular ANC systems.

Advances In Non Linear Modeling For Speech Processing

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Author : Raghunath S. Holambe
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-02-21

Advances In Non Linear Modeling For Speech Processing written by Raghunath S. Holambe and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-02-21 with Technology & Engineering categories.

Advances in Non-Linear Modeling for Speech Processing includes advanced topics in non-linear estimation and modeling techniques along with their applications to speaker recognition. Non-linear aeroacoustic modeling approach is used to estimate the important fine-structure speech events, which are not revealed by the short time Fourier transform (STFT). This aeroacostic modeling approach provides the impetus for the high resolution Teager energy operator (TEO). This operator is characterized by a time resolution that can track rapid signal energy changes within a glottal cycle. The cepstral features like linear prediction cepstral coefficients (LPCC) and mel frequency cepstral coefficients (MFCC) are computed from the magnitude spectrum of the speech frame and the phase spectra is neglected. To overcome the problem of neglecting the phase spectra, the speech production system can be represented as an amplitude modulation-frequency modulation (AM-FM) model. To demodulate the speech signal, to estimation the amplitude envelope and instantaneous frequency components, the energy separation algorithm (ESA) and the Hilbert transform demodulation (HTD) algorithm are discussed. Different features derived using above non-linear modeling techniques are used to develop a speaker identification system. Finally, it is shown that, the fusion of speech production and speech perception mechanisms can lead to a robust feature set.

Advanced Methods Of Biomedical Signal Processing

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Author : Sergio Cerutti
language : en
Publisher: John Wiley & Sons
Release Date : 2011-05-10

Advanced Methods Of Biomedical Signal Processing written by Sergio Cerutti and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-05-10 with Science categories.

This book grew out of the IEEE-EMBS Summer Schools on Biomedical Signal Processing, which have been held annually since 2002 to provide the participants state-of-the-art knowledge on emerging areas in biomedical engineering. Prominent experts in the areas of biomedical signal processing, biomedical data treatment, medicine, signal processing, system biology, and applied physiology introduce novel techniques and algorithms as well as their clinical or physiological applications. The book provides an overview of a compelling group of advanced biomedical signal processing techniques, such as multisource and multiscale integration of information for physiology and clinical decision; the impact of advanced methods of signal processing in cardiology and neurology; the integration of signal processing methods with a modelling approach; complexity measurement from biomedical signals; higher order analysis in biomedical signals; advanced methods of signal and data processing in genomics and proteomics; and classification and parameter enhancement.

Nonlinear Dynamics In Computational Neuroscience

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Author : Fernando Corinto
language : en
Publisher: Springer
Release Date : 2018-06-19

Nonlinear Dynamics In Computational Neuroscience written by Fernando Corinto and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-19 with Technology & Engineering categories.

This book provides an essential overview of computational neuroscience. It addresses a broad range of aspects, from physiology to nonlinear dynamical approaches to understanding neural computation, and from the simulation of brain circuits to the development of engineering devices and platforms for neuromorphic computation. Written by leading experts in such diverse fields as neuroscience, physics, psychology, neural engineering, cognitive science and applied mathematics, the book reflects the remarkable advances that have been made in the field of computational neuroscience, an emerging discipline devoted to the study of brain functions in terms of the information-processing properties of the structures forming the nervous system. The contents build on the workshop “Nonlinear Dynamics in Computational Neuroscience: from Physics and Biology to ICT,” which was held in Torino, Italy in September 2015.

Advanced Signal Processing Technology By Softcomputing

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Author : Hsu Charles
language : en
Publisher: World Scientific
Release Date : 2000-11-07

Advanced Signal Processing Technology By Softcomputing written by Hsu Charles and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-11-07 with Computers categories.

This book presents worldwide outstanding research and recent progress in the applications of neural networks, fuzzy logic, chaos, independent component analysis, etc to fields related to speech recognition enhancement, supervised Fourier demixing noise elimination, acoustic databases, the human hearing system, cancer detection, image processing, and visual communications.

Advanced Models Of Neural Networks

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Author : Gerasimos G. Rigatos
language : en
Publisher: Springer
Release Date : 2014-08-27

Advanced Models Of Neural Networks written by Gerasimos G. Rigatos and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-27 with Technology & Engineering categories.

This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical systems theory. It is suitable for researchers and postgraduate students engaged with neural networks and dynamical systems theory.

Advanced State Space Methods For Neural And Clinical Data

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Author : Zhe Chen
language : en
Publisher: Cambridge University Press
Release Date : 2015-10-15

Advanced State Space Methods For Neural And Clinical Data written by Zhe Chen and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-15 with Computers categories.

An authoritative and in-depth treatment of state space methods, with a range of applications in neural and clinical data.

Reservoir Computing

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Author : Kohei Nakajima
language : en
Publisher: Springer Nature
Release Date : 2021-08-05

Reservoir Computing written by Kohei Nakajima and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-05 with Computers categories.

This book is the first comprehensive book about reservoir computing (RC). RC is a powerful and broadly applicable computational framework based on recurrent neural networks. Its advantages lie in small training data set requirements, fast training, inherent memory and high flexibility for various hardware implementations. It originated from computational neuroscience and machine learning but has, in recent years, spread dramatically, and has been introduced into a wide variety of fields, including complex systems science, physics, material science, biological science, quantum machine learning, optical communication systems, and robotics. Reviewing the current state of the art and providing a concise guide to the field, this book introduces readers to its basic concepts, theory, techniques, physical implementations and applications. The book is sub-structured into two major parts: theory and physical implementations. Both parts consist of a compilation of chapters, authored by leading experts in their respective fields. The first part is devoted to theoretical developments of RC, extending the framework from the conventional recurrent neural network context to a more general dynamical systems context. With this broadened perspective, RC is not restricted to the area of machine learning but is being connected to a much wider class of systems. The second part of the book focuses on the utilization of physical dynamical systems as reservoirs, a framework referred to as physical reservoir computing. A variety of physical systems and substrates have already been suggested and used for the implementation of reservoir computing. Among these physical systems which cover a wide range of spatial and temporal scales, are mechanical and optical systems, nanomaterials, spintronics, and quantum many body systems. This book offers a valuable resource for researchers (Ph.D. students and experts alike) and practitioners working in the field of machine learning, artificial intelligence, robotics, neuromorphic computing, complex systems, and physics.