Results 301 to 310 of about 441,365 (362)
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Proceedings of IEEE Antennas and Propagation Society International Symposium and URSI National Radio Science Meeting, 1995
Summary form only given, as follows. One attractive application of the wavelet is as a basis function that is of compact support both in the original and in the transform domain. However, this is not possible from a theoretical point of view. Namely, a pulse that is limited in time cannot simultaneously be limited in frequency.
T. K. Sarkar +4 more
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Summary form only given, as follows. One attractive application of the wavelet is as a basis function that is of compact support both in the original and in the transform domain. However, this is not possible from a theoretical point of view. Namely, a pulse that is limited in time cannot simultaneously be limited in frequency.
T. K. Sarkar +4 more
openaire +2 more sources
Wavelets, Wavelet Filters, and Wavelet Transforms [PDF]
, 2013 Spectral characteristics of speech are known to be particularly useful in describing a speech signal such that it can be efficiently reconstructed after coding or identified for recognition. The wavelets are considered one of such efficient methods for representing the spectrum of speech signals.
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Wavelet and wavelet Stieltjes transforms
Proceedings of 32nd IEEE Conference on Decision and Control, 2002Some fundamental and useful properties of wavelet transforms are presented. A unified approach for both discrete and continuous time-frequency localization is introduced. >
Jie Chen +3 more
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Wavelets and Wavelet Transform
2017Wavelet transforms are the most powerful and the most widely used tool in the field of image processing. Wavelet transform has received considerable attention in the field of image processing due to its flexibility in representing non-stationary image signals and its ability in adapting to human visual characteristics. Wavelet transform is an efficient
Soohwan Yu, Aparna Vyas, Joonki Paik
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IFAC Proceedings Volumes, 1994
Abstract This is a tutorial about nonparametric nonlinear system identification. Advantages and limitations of this approach are discussed from the engineer’s point of view. Classical as well as modem techniques are discussed. Both practical and mathematical issues are investigated.
P-Y. Glorennec +4 more
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Abstract This is a tutorial about nonparametric nonlinear system identification. Advantages and limitations of this approach are discussed from the engineer’s point of view. Classical as well as modem techniques are discussed. Both practical and mathematical issues are investigated.
P-Y. Glorennec +4 more
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On trigonometric wavelets [PDF]
Constructive Approximation, 1993 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hrushikesh N. Mhaskar, Charles K. Chui
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Multisensor Image Fusion Using the Wavelet Transform
CVGIP: Graphical Models and Image Processing, 1995The goal of image fusion is to integrate complementary information from multisensor data such that the new images are more suitable for the purpose of human visual perception and computer-processing tasks such as segmentation, feature extraction, and ...
Hui Li, B. S. Manjunath, S. Mitra
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Super-Wavelets and Decomposable Wavelet Frames
Journal of Fourier Analysis and Applications, 2005A wavelet frame is called decomposable whenever it is equivalent to a superwavelet frame of length greater than one. Decomposable wavelet frames are closely related to some problems on super-wavelets. In this article we first obtain some necessary or sufficient conditions for decomposable Parseval wavelet frames.
Gu, Qing, Han, Deguang, Heil, Chris
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Multifrequency channel decompositions of images and wavelet models
IEEE Transactions on Acoustics Speech and Signal Processing, 1989The author reviews recent multichannel models developed in psychophysiology, computer vision, and image processing. In psychophysiology, multichannel models have been particularly successful in explaining some low-level processing in the visual cortex ...
S. Mallat
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Wavelet-based statistical signal processing using hidden Markov models
IEEE Transactions on Signal Processing, 1998Wavelet-based statistical signal processing techniques such as denoising and detection typically model the wavelet coefficients as independent or jointly Gaussian. These models are unrealistic for many real-world signals.
M. Crouse, R. Nowak, Richard Baraniuk
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