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Libros sobre el tema "WAVELETE DOMAIN"

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Autor: Grafiati
Publicado: 11 de septiembre de 2023

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1

Hesthaven, J. S. A wavelet optimized adaptive multi-domain method. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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2

Function spaces and wavelets on domains. Zürich: European Mathematical Society, 2008.

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3

M, Jameson Leland y Langley Research Center, eds. A waverlet optimized adaptive multi-domain method. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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4

M, Jameson Leland y Langley Research Center, eds. A waverlet optimized adaptive multi-domain method. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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5

Hesthaven, Jan S. A waverlet optimized adaptive multi-domain method. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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6

Rapp, R. Ocean domains and maximum degree of spherical harmonic and orthonormal expansions. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 1999.

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7

Rapp, R. Ocean domains and maximum degree of spherical harmonic and orthonormal expansions. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 1999.

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8

Castillejos, Heydy. Fuzzy Image Segmentation Algorithms in Wavelet Domain. INTECH Open Access Publisher, 2012.

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9

Jouini, Abdellatif. Wavelet Bases in Bounded Domains and Applications. Alpha Science International, Limited, 2018.

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10

Wavelet Domain Communication System (WDCS): Packet-Based Wavelet Spectral Estimation and M-ARY Signaling. Storming Media, 2002.

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11

Wavelet Domain Communication System (WDCS): Design, Model, Simulation, and Analysis. Storming Media, 2001.

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12

Multiresolution Time Domain Scheme for Electromagnetic Engineering. Wiley-Interscience, 2005.

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13

Bushyager, Nathan y Manos Tentzeris. MRTD (Multi Resolution Time Domain) Method in Electromagnetics. Springer International Publishing AG, 2007.

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14

Bushyager, Nathan y Manos M. Tentzeris. MRTD (Multi Resolution Time Domain) Method in Electromagnetics. Morgan & Claypool Publishers, 2006.

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15

Bushyager, Nathan y Emmanuil M. Tentzeris. MRTD (Multi Resolution Time Domain) Method in Electromagnetics (Synthesis Lectures in Computational Electromagnetics). Morgan & Claypool Publishers, 2006.

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16

Pabel, Roland. Adaptive Wavelet Methods for Variational Formulations of Nonlinear Elliptic PDEs on Tensor-Product Domains. Logos Verlag Berlin, 2015.

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17

Mittra, Raj, Yinchao Chen y Qunsheng Cao. Multiresolution Time Domain Scheme for Electromagnetic Engineering (Wiley Series in Microwave and Optical Engineering). Wiley-Interscience, 2005.

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18

Congendo, Marco y Fernando H. Lopes da Silva. Event-Related Potentials. Editado por Donald L. Schomer y Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0039.

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Event-related potentials (ERPs) can be elicited by a variety of stimuli and events in diverse conditions. This chapter covers the methodology of analyzing and quantifying ERPs in general. Basic models (additive, phase modulation and resetting, potential asymmetry) that account for the generation of ERPs are discussed. The principles and requirements of ensemble time averaging are presented, along with several univariate and multivariate methods that have been proposed to improve the averaging procedure: wavelet decomposition and denoising, spatial, temporal and spatio-temporal filtering. We emphasize basic concepts of principal component analysis, common spatial pattern, and blind source separation, including independent component analysis. We cover practical questions related to the averaging procedure: overlapping ERPs, correcting inter-sweep latency and amplitude variability, alternative averaging methods (e.g., median), and estimation of ERP onset. Some specific aspects of ERP analysis in the frequency domain are surveyed, along with topographic analysis, statistical testing, and classification methods.
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19

Wendling, Fabrice, Marco Congendo y Fernando H. Lopes da Silva. EEG Analysis. Editado por Donald L. Schomer y Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0044.

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This chapter addresses the analysis and quantification of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Topics include characteristics of these signals and practical issues such as sampling, filtering, and artifact rejection. Basic concepts of analysis in time and frequency domains are presented, with attention to non-stationary signals focusing on time-frequency signal decomposition, analytic signal and Hilbert transform, wavelet transform, matching pursuit, blind source separation and independent component analysis, canonical correlation analysis, and empirical model decomposition. The behavior of these methods in denoising EEG signals is illustrated. Concepts of functional and effective connectivity are developed with emphasis on methods to estimate causality and phase and time delays using linear and nonlinear methods. Attention is given to Granger causality and methods inspired by this concept. A concrete example is provided to show how information processing methods can be combined in the detection and classification of transient events in EEG/MEG signals.
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