Results 311 to 320 of about 204,120 (332)
Substrate Engineering for Durable Omniphobic Liquid‐Like Surfaces
Advanced Functional Materials, EarlyView.The significant yet less investigated role of substrates in determining the liquid‐repellency and mechanical durability of liquid‐like surfaces (LLSs) is explored. Thick and crack‐free sol–gel silica intermediary layers are developed that can smoothen substrate asperity roughness even at the micron scale, enabling omniphobic polydimethylsiloxane‐based ...
Tao Wen +6 more
wiley +1 more source
Influence of Hysteresis on Magnetocaloric Performance at Cryogenic Temperatures: A Tb3Ni Case Study
Advanced Functional Materials, EarlyView.The existence of an inverse magnetocaloric effect in metamagnetic materials at cryogenic temperatures is investigated using multiple methods. Contrary to predictions from magnetization data, neither specific heat nor pulsed‐field measurements show such a cooling effect, instead indicating irreversible heating.
Timo Niehoff +10 more
wiley +1 more source
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Compressive sensing: To compress or not to compress
2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2011In this paper, we consider the compressive sensing scheme from the information theory point of view and derive the lower bound of the probability of error for CS when length N of the information vector is large. The result has been shown that, for an i.i.d.
Qilian Liang, Davis Kirachaiwanich
openaire +2 more sources
IEEE Transactions on Image Processing, 2012
Compressive sensing (CS) is an emerging approach for the acquisition of signals having a sparse or compressible representation in some basis. While the CS literature has mostly focused on problems involving 1-D signals and 2-D images, many important applications involve multidimensional signals; the construction of sparsifying bases and measurement ...
Marco F. Duarte, Richard G. Baraniuk
openaire +3 more sources
Compressive sensing (CS) is an emerging approach for the acquisition of signals having a sparse or compressible representation in some basis. While the CS literature has mostly focused on problems involving 1-D signals and 2-D images, many important applications involve multidimensional signals; the construction of sparsifying bases and measurement ...
Marco F. Duarte, Richard G. Baraniuk
openaire +3 more sources
In Situ Compressive Sensing [PDF]
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2007 Compressive sensing (CS) is a framework that exploits the compressible character of most natural signals, allowing the accurate measurement of an m-dimensional real signal u in terms of n«m real measurements v. The CS measurements may be represented in terms of an n×m matrix that defines the linear relationship between v and u.
Lawrence Carin, Ya Xue, Dehong Liu
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Compressed sensing radar [PDF]
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008 A stylized compressed sensing radar is proposed in which the time-frequency plane is discretized into an N by N grid. Assuming that the number of targets K is small (i.e. KLtN2), then we can transmit a sufficiently ldquoincoherentrdquo pulse and employ the techniques of compressed sensing to reconstruct the target scene.
M.A. Herman, Thomas Strohmer
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Cosparsity in Compressed Sensing
2015Analysis l1-recovery is a strategy of acquiring a signal, that is sparse in some transform domain, from incomplete observations. In this chapter we give an overview of the analysis sparsity model and present theoretical conditions that guarantee successful nonuniform and uniform recovery of signals from noisy measurements.
Maryia Kabanava, Holger Rauhut
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Compressed sensing of compressible signals
2017 IEEE International Symposium on Information Theory (ISIT), 2017A novel low-complexity robust-to-noise iterative algorithm named compression-based gradient descent (C-GD) algorithm is proposed. C-GD is a generic compressed sensing recovery algorithm, that at its core, employs compression codes, such as JPEG2000 and MPEG4.
Sajjad Beygi +3 more
openaire +2 more sources
2017
We consider the problem of sparse signal recovery in dynamic sensing scenarios. Specifically, we study the recovery of a sparse time-varying signal from linear measurements of a single static sensor that are taken at two different points in time. This setup can be modelled as observing a single signal using two different sensors – a real one and a ...
Dalitz, Robert +2 more
openaire +2 more sources
We consider the problem of sparse signal recovery in dynamic sensing scenarios. Specifically, we study the recovery of a sparse time-varying signal from linear measurements of a single static sensor that are taken at two different points in time. This setup can be modelled as observing a single signal using two different sensors – a real one and a ...
Dalitz, Robert +2 more
openaire +2 more sources