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ICASSP
2008
IEEE
14 years 4 months ago
Subspace compressive detection for sparse signals
The emerging theory of compressed sensing (CS) provides a universal signal detection approach for sparse signals at sub-Nyquist sampling rates. A small number of random projection...
Zhongmin Wang, Gonzalo R. Arce, Brian M. Sadler
PKDD
2007
Springer
143views Data Mining» more  PKDD 2007»
14 years 4 months ago
Using the Web to Reduce Data Sparseness in Pattern-Based Information Extraction
Textual patterns have been used effectively to extract information from large text collections. However they rely heavily on textual redundancy in the sense that facts have to be m...
Sebastian Blohm, Philipp Cimiano
ICPR
2010
IEEE
14 years 6 days ago
Compressing Sparse Feature Vectors Using Random Ortho-Projections
In this paper we investigate the usage of random ortho-projections in the compression of sparse feature vectors. The study is carried out by evaluating the compressed features in ...
Esa Rahtu, Mikko Salo, Janne Heikkilä
CORR
2011
Springer
191views Education» more  CORR 2011»
13 years 5 months ago
A Message-Passing Receiver for BICM-OFDM over Unknown Clustered-Sparse Channels
We propose a factor-graph-based approach to joint channel-estimationand-decoding of bit-interleaved coded orthogonal frequency division multiplexing (BICM-OFDM). In contrast to ex...
Philip Schniter
ICASSP
2011
IEEE
13 years 1 months ago
Source localization using time difference of arrival within a sparse representation framework
The problem addressed is source localization via time-differenceof-arrival estimation in a multipath channel. Solving this localization problem typically implies cross-correlating...
Ciprian R. Comsa, Alexander M. Haimovich, Stuart C...