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TSP
2010
15 years 2 months ago
Methods for sparse signal recovery using Kalman filtering with embedded pseudo-measurement norms and quasi-norms
We present two simple methods for recovering sparse signals from a series of noisy observations. The theory of compressed sensing (CS) requires solving a convex constrained minimiz...
Avishy Carmi, Pini Gurfil, Dimitri Kanevsky
CGO
2008
IEEE
16 years 2 months ago
Latency-tolerant software pipelining in a production compiler
In this paper we investigate the benefit of scheduling non-critical loads for a higher latency during software pipelining. "Noncritical" denotes those loads that have s...
Sebastian Winkel, Rakesh Krishnaiyer, Robyn Sampso...
ICA
2007
Springer
16 years 1 months ago
Sparse Component Analysis in Presence of Noise Using an Iterative EM-MAP Algorithm
Abstract. In this paper, a new algorithm for source recovery in underdetermined Sparse Component Analysis (SCA) or atomic decomposition on over-complete dictionaries is presented i...
Hadi Zayyani, Massoud Babaie-Zadeh, G. Hosein Mohi...
ECRTS
1999
IEEE
15 years 12 months ago
QoS guarantee using probabilistic deadlines
This paper presents a probabilistic approach to guarantee the performance of a real-time system. While traditional real-time system analysis tends to guarantee that each task inst...
Luca Abeni, Giorgio C. Buttazzo
AUTOMATICA
2005
155views more  AUTOMATICA 2005»
15 years 7 months ago
Identification of MIMO Hammerstein models using least squares support vector machines
This paper studies a method for the identification of Hammerstein models based on Least Squares Support Vector Machines (LS-SVMs). The technique allows for the determination of th...
Ivan Goethals, Kristiaan Pelckmans, Johan A. K. Su...