Results 221 to 230 of about 83,836 (252)

SwinTCS: A Swin Transformer Approach to Compressive Sensing with Non-Local Denoising. [PDF]

J Imaging
Li X, Li H, Liao H, Suo Z, Chen X, Han J.   +5 more
europepmc   +1 more source

Experimental study on the homogeneity of microbial grouting to reinforce calcareous sand. [PDF]

PLoS One
Ding X, Li P, Mao X, Zhang X.
europepmc   +1 more source

The Effects of Calcium Phosphate Bone Cement Preparation Parameters on Injectability and Compressive Strength for Minimally Invasive Surgery. [PDF]

Bioengineering (Basel)
Qiao Q, Zhao Q, Wang J, Li M, Zhou H, Yang L.   +5 more
europepmc   +1 more source

Tolerance driven lightweight design and interface robustness of multi material aircraft horizontal tail structures. [PDF]

Sci Rep
Lin M, Wang B, Lin C.
europepmc   +1 more source

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Compressive Sampling and Lossy Compression

IEEE Signal Processing Magazine, 2008
Recent results in compressive sampling have shown that sparse signals can be recovered from a small number of random measurements. This property raises the question of whether random measurements can provide an efficient representation of sparse signals in an information-theoretic sense.
V.K. Goyal, A.K. Fletcher, S. Rangan
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Reweighted Compressive Sampling for image compression

2009 Picture Coding Symposium, 2009
Compressive Sampling (CS), is an emerging theory which points us a promising direction of designing novel efficient data compression techniques. However, the conventional CS adopts a non-discriminated sampling scheme which usually gives poor performance on realistic complex signals.
null Yi Yang, Oscar C. Au, null Lu Fang, null Xing Wen, null Weiran Tang   +4 more
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Compressive covariance sampling

2013 Information Theory and Applications Workshop (ITA), 2013
Most research efforts in the field of compressed sensing have been pointed towards analyzing sampling and reconstruction techniques for sparse signals, where sampling rates below the Nyquist rate can be reached. When only second-order statistics or, equivalently, covariance information is of interest, perfect signal reconstruction is not required and ...
D. Romero, G. Leus
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Compressive sampling experiments

2014 6th European Embedded Design in Education and Research Conference (EDERC), 2014
Compressive 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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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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