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Time-Frequency Domain for Segmentation and Classification of Non-stationary Signals

time-frequency domain, and most notably the Stockwell Transform for the feature extraction process and to identify signatures. For the;

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Time-Frequency Based Feature Extraction and Classification

insufficient to provide comprehensive information about such signals. On the contrary, time-frequency analysis is more suitable for non-stationary;

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Applications in Time-Frequency Signal Processing

given frequency component. A more suitable approach for those studying non-stationary signals is the use of time frequency representations that;

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The Pseudo Wigner Distribution for the Analysis of Doppler Ultrasound Signals

The time-frequency representations of the signals are a powerful tool for the analysis of non-stationary signals such as the biological;

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Pragmatic Circuits

of the sinusoidal steady state, is an important topic with unique techniques and terminology. Pragmatic Circuits: Frequency Domain is focused on;

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Analysis And Control Of Nonlinear Systems With Stationary Sets

been based on the frequency-domain for this class of systems. However, few results on robustness analysis and controller design for these;

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Fourier Transform - Signal Processing

frequency estimation, spectral leakage, Mellin and Scale transforms which are of great usefulness in stationary signal processing. However, FT data;

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Time-Frequency Signal Analysis and Processing

Time-Frequency Signal Analysis and Processing (TFSAP) is a collection of theory, techniques and algorithms used for the analysis and;

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Digital Spectral Analysis

in spectral analysis, and time-series models. An entire chapter is devoted to the non-parametric methods most widely used in industry. High resolution;

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Signals and Systems 2. In discrete Time

The book begins by introducing signals and systems, and then discusses Time-Domain analysis and Frequency-Domain analysis for Continuous;

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Multidimensional Stationary Time Series

. * Provides a unified treatment for time and frequency domain inferences by using machinery of complex and harmonic analysis, spectral and Smith;

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Signals and Systems

A compact overview on signals and systems, with emphasis on analysis of continuous and discrete systems in time domain. Frequency-domain;

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Discrete Stochastic Processes and Optimal Filtering

Optimal filtering applied to stationary and non-stationary signals provides the most efficient means of dealing with problems arising from;

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Discrete Stochastic Processes and Optimal Filtering

Optimal filtering applied to stationary and non-stationary signals provides the most efficient means of dealing with problems arising from;

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Pragmatic Circuits

Time Domain and Frequency Domain. These short lecture books will be of use to students at any level of electrical engineering and for;

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Signal Processing with Free Software

An ideal resource for students, industrial engineers, and researchers, Signal Processing with Free Software Practical Experiments presents;

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Time-Frequency Analysis

to researchers and engineers interested in non-stationary signal analysis and processing. It is written by recognized experts in the field.;

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Non-Stationary Electromagnetics

application in electromagnetic devices, optoelectronics, and photonics, where time-domain methods become a powerful tool for modeling. Particular;

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New Trends in Quantum Coherence & Nonlinear Optics

of atomic media, Doppler-free spectroscopy, time-domain spectroscopy of dense atomic vapours, frequency comb spectroscopy and atom-surface;

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Characterizing Interdependencies of Multiple Time Series

of multiple time series in the frequency domain. Detecting causal directions between a pair of time series and the extent of their effects, as well as;

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Non-Stationary Electromagnetics

This book is devoted to the investigations of non-stationary electromagnetic processes. The investigations are undertaken analytically;

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Pragmatic Circuits

. The two follow-up volumes in the Pragmatic Circuits series include titles on Frequency Domain and Signals and Filters. These short lecture;

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EEG-Based Brain-Computer Interfaces

control applications, along with the EEG-related advances in BCI. The research and techniques in this book discuss time and frequency domain;

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Signals and Systems

signals, system modelling and system analysis. System analysis in frequency domain using Fourier transform and Laplace transform is explained with;

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Developments and Applications for ECG Signal Processing

Developments and Applications for ECG Signal Processing: Modeling, Segmentation, and Pattern Recognition covers reliable techniques for ECG;

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Condition Monitoring with Vibration Signals

processing in the time-frequency domain, methods for linear subspace learning, and the basic principles of the learning method Artificial Neural;

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Recent Trends in Signal and Image Processing

processing applications involving filtering, encoding, classification, segmentation, clustering, feature extraction, denoising, watermarking, object;

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