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CORR
2007
Springer
112views Education» more  CORR 2007»
13 years 7 months ago
Learning from compressed observations
— The problem of statistical learning is to construct a predictor of a random variable Y as a function of a related random variable X on the basis of an i.i.d. training sample fr...
Maxim Raginsky
TSP
2010
13 years 2 months ago
Sequential and cooperative sensing for multi-channel cognitive radios
Effective spectrum sensing is a critical prerequisite for multi-channel cognitive radio (CR) networks, where multiple spectrum bands are sensed to identify transmission opportuniti...
Seung-Jun Kim, Georgios B. Giannakis
ICIP
2009
IEEE
13 years 5 months ago
A compressive-sensing based watermarking scheme for sparse image tampering identification
In this paper we describe a robust watermarking scheme for image tampering identification and localization. A compact representation of the image is first produced by assembling a...
Giuseppe Valenzise, Marco Tagliasacchi, Stefano Tu...
CORR
2010
Springer
130views Education» more  CORR 2010»
13 years 7 months ago
Phase Transitions for Greedy Sparse Approximation Algorithms
A major enterprise in compressed sensing and sparse approximation is the design and analysis of computationally tractable algorithms for recovering sparse, exact or approximate, s...
Jeffrey D. Blanchard, Coralia Cartis, Jared Tanner...
ICASSP
2010
IEEE
13 years 7 months ago
Empirical quantization for sparse sampling systems
We propose a quantization design technique (estimator) suitable for new compressed sensing sampling systems whose ultimate goal is classification or detection. The design is base...
Michael A. Lexa