2017 51st Annual Conference on Information Sciences and Systems (CISS) 2017
DOI: 10.1109/ciss.2017.7926148
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Adaptive non-uniform compressive sampling for time-varying signals

Abstract: In this paper, adaptive non-uniform compressive sampling (ANCS) of time-varying signals, which are sparse in a proper basis, is introduced. ANCS employs the measurements of previous time steps to distribute the sensing energy among coefficients more intelligently. To this aim, a Bayesian inference method is proposed that does not require any prior knowledge of importance levels of coefficients or sparsity of the signal. Our numerical simulations show that ANCS is able to achieve the desired non-uniform recover… Show more

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Cited by 11 publications
(3 citation statements)
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“…It can be observed that the proposed sampling method outperforms the uniform CS upto tp 01 ≤ 0.62. Unlike Bayesian ANCS [16], RL-NCS has significant performance gains over uniform CS for high transition probabilities. We have also shown the TNMSE only for the coefficients in ROI and it is observable that ROI coefficients are reconstructed with more accuracy compared to Non-ROI counterpart.…”
Section: Simulations With Sparse Signals In the Canonical Basismentioning
confidence: 99%
See 1 more Smart Citation
“…It can be observed that the proposed sampling method outperforms the uniform CS upto tp 01 ≤ 0.62. Unlike Bayesian ANCS [16], RL-NCS has significant performance gains over uniform CS for high transition probabilities. We have also shown the TNMSE only for the coefficients in ROI and it is observable that ROI coefficients are reconstructed with more accuracy compared to Non-ROI counterpart.…”
Section: Simulations With Sparse Signals In the Canonical Basismentioning
confidence: 99%
“…We also take a different approach than dynamic CS methods [13,14] that focus only on the reconstruction phase. Furthermore, there have been studies that capitalize the idea of utilization of knowledge from prior estimations and they seem to work only if the signal ROI changes very slowly [15,16]. However, our focus is to obtain adaptive design of CS measurement matrices that show effectiveness even for rapidly varying signals.…”
Section: Introductionmentioning
confidence: 99%
“…Recently, non-uniform sampling approaches such as Compressive Sensing (CS) have been proposed to reduce the energy consumption of sampling operation by reducing number of samples in each frame, reduce required storage to save the sampled data, and reduce the data transmission due to lower number of samples taken [15,19,20]. Additionally, event-driven sampling, such as level-crossing sampling, has been widely adopted as a promising CS technique to maximize the performance of sampling operation while reducing energy consumption [18].…”
Section: Introductionmentioning
confidence: 99%