Results 251 to 260 of about 18,243 (295)

Integrated transcriptomic and proteomic analysis reveals the regulatory role of exogenous gibberellin in sugarcane internode maturation. [PDF]

Front Plant Sci
Chen R, Wu Z, Lai Z, Wen J, Dong W, Liao J, Wu Y, Chen J, Que Y, Wu J, Fan Y.   +10 more
europepmc   +1 more source

A multifunctional 2D goldene monolayer with strain-sensitive optoelectronic and thermoelectric properties. [PDF]

Sci Rep
Kumar K, Dhasmana A, Mishra AK.
europepmc   +1 more source

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.
Yi Yang 0041, Oscar C. Au, Lu Fang 0001, Xing Wen, Weiran Tang   +4 more
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Compressive covariance sampling [PDF]

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 ...
Daniel Romero 0004, Geert Leus
openaire   +2 more sources

Watermarking for Compressive Sampling Applications [PDF]

2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2012
Compressive sampling is a newly developed topic in the field of data compression. For current researches, compressive sampling techniques focus on compression performances. There are very few papers aiming at the integration of watermarking into compressive sampling systems.
Hsiang-Cheh Huang, Feng-Cheng Chang, Chun-Hsien Wu, Wei-Hao Lai   +3 more
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Compressive image sampling with side information

2009 16th IEEE International Conference on Image Processing (ICIP), 2009
Compressive sampling is a novel framework that exploits sparsity of a signal in a transform domain to perform sampling below the Nyquist rate. In this paper, we apply compressive sampling to reduce the sampling rate of images/video. The key idea is to exploit the intra- and inter-frame correlation to improve signal recovery algorithms.
Vladimir Stankovic 0001, Lina Stankovic, Samuel Cheng 0001   +2 more
openaire   +2 more sources

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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.
Vivek K. Goyal, Alyson K. Fletcher, Sundeep Rangan   +2 more
openaire   +1 more source

Adaptive sampling for compressed sensing based image compression

2014 IEEE International Conference on Multimedia and Expo (ICME), 2014
A jointly-reweighted block-based compressed sensing scheme.A generic measurement allocation algorithm to assign CS-measurements.Statistical parameters as allocation factors.Two solutions to implement the adaptive measurement allocation.Remarkable quality improvement over the traditional reweighted BCS scheme. The compressed sensing (CS) theory has been
Shuyuan Zhu, Bing Zeng 0001, Moncef Gabbouj   +2 more
openaire   +2 more sources