Results 241 to 250 of about 187,054 (294)

Insights into the High-Pressure Behavior of AWO₄‑Type Orthotungstates. [PDF]

J Phys Chem C Nanomater Interfaces
Muñoz A, Radescu S, Mujica A, Errandonea D.   +3 more
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May-Thurner Syndrome and Its Possible Influence on Pulmonary Embolism Severity: A Case Report. [PDF]

Cureus
Nabhan E, Naoum Nehme E, Vana AP, Poulos N.   +3 more
europepmc   +1 more source

Identifying Gastrointestinal Pathologies Using Point-of-Care Ultrasound. [PDF]

Diagnostics (Basel)
Theophanous RG, Abramson L, Bronshteyn YS.   +2 more
europepmc   +1 more source

Mechanistic investigation into the performance of stone columns constructed with riverbed aggregates in varied soil conditions. [PDF]

Sci Rep
Almutairi A.
europepmc   +1 more source

Multiple Blue-Coloured Nodules on the Whole Body.

Indian J Dermatol
Lee KH, Jeong JH, Park CJ, Kim YS.
europepmc   +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), 2011
In 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.
Davis Kirachaiwanich, Qilian Liang
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To compress or not to compress?

Proceedings of GLOBECOM'96. 1996 IEEE Global Telecommunications Conference, 2002
For practical communications which transmit finite blocks of source data over noisy channels, we question the common practice to compress (C) the source and then to add redundancy for error control. Rather we exploit the redundancy of the non-compressed source (NC) at the channel decoder by source-controlled channel-decoding. For a simple binary Markov
G. Buch, F. Burkert, J. Hagenauer, B. Kukla   +3 more
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Compressed sensing of compressible signals

2017 IEEE International Symposium on Information Theory (ISIT), 2017
A 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, Shirin Jalali, Arian Maleki, Urbashi Mitra   +3 more
openaire   +1 more source