Results 231 to 240 of about 72,581 (258)

Sparse Multiple Kernel Learning for Signal Processing Applications

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010
In many signal processing applications, grouping of features during model development and the selection of a small number of relevant groups can be useful to improve the interpretability of the learned parameters. While a lot of work based on linear models has been reported to solve this problem, in the last few years, multiple kernel learning has come
Niranjan A. Subrahmanya, Yung C. Shin
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Waveform Design for Sparse Signal Processing in Radar

2021 IEEE Radar Conference (RadarConf21), 2021
In the past decades, there has been an extensive research interest in the areas of both waveform diversity/design and advanced signal processing algorithms departing from the more classical solutions based on Linear Frequency Modulated (LFM) pulses and Matched Filters (MF).
Anitori, L., Ender, J.
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Sparse representation in speech signal processing

SPIE Proceedings, 2003
We review the sparse representation principle for processing speech signals. A transformation for encoding the speech signals is learned such that the resulting coefficients are as independent as possible. We use independent component analysis with an exponential prior to learn a statistical representation for speech signals.
Te-Won Lee, Gil-Jin Jang, Oh-Wook Kwon
openaire   +1 more source

Distributed and sparse signal processing

2019
The 21st century will be remembered for the ubiquity of data. Data analysis has become an indispensable tool for finding patterns in high-dimensional datasets, and the steep increase in computational power allows us to execute ever more sophisticated algorithms.
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Sparse Image and Signal Processing

2010
This book presents the state of the art in sparse and multiscale image and signal processing, covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators.
Starck, Jean-Luc, Murtagh, Fionn, Fadili, Jalal M.   +2 more
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Basis Selection for Wavelet Processing of Sparse Source Signals

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007
An attractive property of wavelet bases is their ability to sparsely represent piecewise polynomial signals. The sparsity of a wavelet-domain representation depends on several factors such as the mother wavelet, the number of decomposition levels, and the structure of the original signal.
Ian C. Atkinson, Farzad Kamalabadi
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Sparse Signal Processing for Massive MIMO Communications

Zhen Gao, Yikun Mei, Li Qiao
exaly   +2 more sources

Finite Frames for Sparse Signal Processing

2013
Over the last decade, considerable progress has been made toward developing new signal processing methods to manage the deluge of data caused by advances in sensing, imaging, storage, and computing technologies. Most of these methods are based on a simple but fundamental observation: high-dimensional data sets are typically highly redundant and live on
Waheed U. Bajwa, Ali Pezeshki
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Sonar array signal processing for sparse linear arrays

ISSPA '99. Proceedings of the Fifth International Symposium on Signal Processing and its Applications (IEEE Cat. No.99EX359), 2003
Acoustic signals which propagate through the ocean have wavefronts which can differ significantly from the "planar wavefronts" assumed in array signal processing. In this paper we investigate the performance of the the minimum variance distortionless response (MVDR) beamformer and the previously introduced Fourier integral method (FIM), when applied to
I. S. D. Solomon, A. J. Knight, M. V. Greening   +2 more
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Processing of Sparse Signals and Mutual Coherence of ‘‘Measurable’’ Vectors

Lobachevskii Journal of Mathematics, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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