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Compressive sampling of correlated signals
2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2011The 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, 2007Compressive 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, 2011This 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 +3 more
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Recurrent networks for compressive sampling
Neurocomputing, 2014This 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 +2 more
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Compressive Sampling With Generalized Polygons
IEEE Transactions on Signal Processing, 2011We 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 +3 more
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Compressive sampling for phenotype classification
2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017Genome 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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Compressive sampling experiments
2014 6th European Embedded Design in Education and Research Conference (EDERC), 2014Compressive sampling theory describes methods to reconstruct signals sampled at sub-Nyquist rates. The theory assumes that the signals are sparse in the frequency domain or in the time domain and requires a random sampling process. This paper describes compressive sampling experiments using a 6 Msps ADC (THS1206) and a C6000 DSP.
Carsten Roppel, Martin Danz
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Variational methods for compressive sampling
SPIE Proceedings, 2007ABSTRACT We consider two natural extensions of the standard 1 minimization framework for compressive sampling. Thetwo methods, one based on penalizing second-order derivatives and one based on the redundant wavelet transform,can also be viewed as variational methods which extend the basic total-variation recovery program. A numericalexample illustrates
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Compressive Sampling with Coefficients Random Permutations for Image Compression
2011 International Conference on Multimedia and Signal Processing, 2011The different image block has different sparsity or compressibility in transform domain; in general, the blocks in smooth region have stronger sparsity while those in texture or edge region have weaker sparsity. Based on this observation, a novel block DCT based sampling scheme with coefficients random permutations for image compressive sensing has ...
Zhirong Gao +3 more
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Knowledge Distillation by Compressive Sampling
2023 57th Asilomar Conference on Signals, Systems, and Computers, 2023Shreyas Chaudhari, José M. F. Moura
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