Results 261 to 270 of about 18,243 (295)

Compressive sampling in array processing

2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013
In this paper, we propose a sampling architecture for the efficient acquisition of multiple signals lying in a subspace. We show that without the knowledge of the signal subspace, the proposed sampling architecture acquires the signals at a sub-Nyquist rate.
Ali Ahmed 0004, Justin K. Romberg
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Compressive Sampling for Signal Classification

2006 Fortieth Asilomar Conference on Signals, Systems and Computers, 2006
Compressive sampling (CS), also called compressed sensing, entails making observations of an unknown signal by projecting it onto random vectors. Recent theoretical results show that if the signal is sparse (or nearly sparse) in some basis, then with high probability such observations essentially encode the salient information in the signal.
Haupt, J., Castro, R.M., Nowak, R., Fudge, G., Yeh, A.   +4 more
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Universal algorithm for compressive sampling

2015 23rd European Signal Processing Conference (EUSIPCO), 2015
In a standard compressive sampling (CS) setup, we develop a universal algorithm where multiple CS reconstruction algorithms participate and their outputs are fused to achieve a better reconstruction performance. The new method is called universal algorithm for CS (UACS) that is iterative in nature and has a restricted isometry property (RIP) based ...
Ahmed Zaki, Saikat Chatterjee, Lars K. Rasmussen   +2 more
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Compressive sampling hardware reconstruction

Proceedings of 2010 IEEE International Symposium on Circuits and Systems, 2010
Compressive Sampling reconstruction techniques require computationally intensive algorithms, often using L1 optimization to reconstruct a signal that was originally sampled at a sub-Nyquist rate. In this work we present a VLSI implementation of a computationally efficient algorithm named Orthogonal Matching Pursuit. We further optimize the algorithm to
Avi Septimus, Raphael Steinberg
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Compressive sampling of correlated signals

2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2011
The recently developed theory of Compressive sensing (CS) has shown that sparse signals can be reconstructed from a much smaller number of measurements than their bandwidth suggests. In this paper we present a sampling scheme to acquire ensembles of correlated signals at a sub-Nyquist rate.
Ali Ahmed 0004, Justin K. Romberg
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Compressive Sampling for Signal Detection

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007
Compressive sampling (CS) refers to a generalized sampling paradigm in which observations are inner products between an unknown signal vector and user-specified test vectors. Among the attractive features of CS is the ability to reconstruct any sparse (or nearly sparse) signal from a relatively small number of samples, even when the observations are ...
Jarvis D. Haupt, Robert D. Nowak
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Compressive sampling for microwave tomography

2011 IEEE International Geoscience and Remote Sensing Symposium, 2011
This communication deals with the solution of microwave imaging problems exploiting a Compressive Sampling (CS) based method, an emerging technique for data acquisition and signal recovery based on its property of requiring lower dimensional data. In particular, the inversion procedure was tested on the Contrast Source-Extended Born model.
Autieri, Roberta, D'Urso, Michele, Malanga, Stefano, PASCAZIO, Vito   +3 more
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Recurrent networks for compressive sampling

Neurocomputing, 2014
This paper develops two neural network models, based on Lagrange programming neural networks (LPNNs), for recovering sparse signals in compressive sampling. The first model is for the standard recovery of sparse signals. The second one is for the recovery of sparse signals from noisy observations.
Chi-Sing Leung, John Sum, Anthony G. Constantinides   +2 more
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Compressive Sampling With Generalized Polygons

IEEE Transactions on Signal Processing, 2011
We consider the problem of compressed sensing and propose new deterministic low-storage constructions of compressive sampling matrices based on classical finite-geometry generalized polygons. For the noiseless measurements case, we develop a novel exact-recovery algorithm for strictly sparse signals that utilizes the geometry properties of generalized ...
Kanke Gao, Stella N. Batalama, Dimitrios A. Pados, Bruce W. Suter   +3 more
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Compressive sampling for phenotype classification

2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017
Genome classification has become an increasingly important genomic research method for cancer identification and treatment. One challenge associated with genome classification is feature selection; which genes can be used for phenotyping. This challenge is made more complicated considering affected gene mutate at different rates and schedules.
Eric L. Brooks, Ryan D. Kappedal
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