Results 251 to 260 of about 322,181 (285)

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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Signal Processing for Sparse Discrete Time Systems

2013
In recent years compressive sampling (CS) has appeared in the signal processing literature as a legitimate contender for processing of sparse signals. Natural signals such as speech, image and video are compressible. In most signal processing systems dealing with these signals the signal is first sampled and later on compressed.
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Sparse stochastic processes: A statistical framework for modern signal processing

2013 20th International Conference on Systems, Signals and Image Processing (IWSSIP), 2013
Summary form only given. We introduce an extended family of sparse processes that are specified by a generic (non-Gaussian) innovation model or, equivalently, as solutions of linear stochastic differential equations driven by white Levy noise. We present the mathematical tools for their characterization.
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False alarms in radar detection within sparse-signal processing

2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa), 2016
Radar-detection metrics are assessed in outcomes of sparse-signal processing (SSP) with test statistics based on the subgradient and the dual feasibility in the SSP optimization via an approach separating false alarms (FAs) from targets. In radar, SSP is aimed for estimating a sparse solution whose FAs are fixed and whose detection of targets is ...
Radmila Pribic, Han Lun Yap
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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 Atkinson, Farzad Kamalabadi
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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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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 Signal Processing Based Image Compression and Inpainting

2016
VCU Theses and ...
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Sparse Signal Processing

2020
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SignalP 6.0 predicts all five types of signal peptides using protein language models

Nature Biotechnology, 2022
Felix Teufel, Jose Juan Almagro Armenteros, Alexander Rosenberg Johansen   +2 more
exaly