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ICASSP
2011
IEEE
12 years 9 months ago
Using the kernel trick in compressive sensing: Accurate signal recovery from fewer measurements
Compressive sensing accurately reconstructs a signal that is sparse in some basis from measurements, generally consisting of the signal’s inner products with Gaussian random vec...
Hanchao Qi, Shannon Hughes
CORR
2010
Springer
130views Education» more  CORR 2010»
13 years 5 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...
CDC
2010
IEEE
154views Control Systems» more  CDC 2010»
13 years 23 days ago
Concentration of measure inequalities for compressive Toeplitz matrices with applications to detection and system identification
In this paper, we derive concentration of measure inequalities for compressive Toeplitz matrices (having fewer rows than columns) with entries drawn from an independent and identic...
Borhan Molazem Sanandaji, Tyrone L. Vincent, Micha...
ICCAD
2006
IEEE
129views Hardware» more  ICCAD 2006»
14 years 2 months ago
Energy budgeting for battery-powered sensors with a known task schedule
Battery-powered wireless sensors are severely constrained by the amount of the available energy. A method for computing the energy budget per sensing task can be a valuable design...
Daler N. Rakhmatov
CORR
2011
Springer
200views Education» more  CORR 2011»
13 years 23 days ago
Optimal Channel Training in Uplink Network MIMO Systems
We consider a multi-cell frequency-selective fading uplink channel (network MIMO) from K singleantenna user terminals (UTs) to B cooperative base stations (BSs) with M antennas ea...
Jakob Hoydis, Mari Kobayashi, Mérouane Debb...