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ICASSP
2008
IEEE
14 years 13 days 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
CHI
2007
ACM
13 years 10 months ago
A familiar face(book): profile elements as signals in an online social network
Using data from a popular online social network site, this paper explores the relationship between profile structure (namely, which fields are completed) and number of friends, gi...
Cliff Lampe, Nicole Ellison, Charles Steinfield
CN
2010
121views more  CN 2010»
13 years 2 months ago
Probabilistic flooding for efficient information dissemination in random graph topologies
Probabilistic flooding has been frequently considered as a suitable dissemination information approach for limiting the large message overhead associated with traditional (full) f...
Konstantinos Oikonomou, Dimitrios Kogias, Ioannis ...
IEEECGIV
2009
IEEE
14 years 19 days ago
Two Dimensional Compressive Classifier for Sparse Images
The theory of compressive sampling involves making random linear projections of a signal. Provided signal is sparse in some basis, small number of such measurements preserves the ...
Armin Eftekhari, Hamid Abrishami Moghaddam, Massou...
CORR
2010
Springer
116views Education» more  CORR 2010»
13 years 6 months ago
Restricted Isometries for Partial Random Circulant Matrices
In the theory of compressed sensing, restricted isometry analysis has become a standard tool for studying how efficiently a measurement matrix acquires information about sparse an...
Holger Rauhut, Justin K. Romberg, Joel A. Tropp