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Given a corrupted low-rank matrix, robust principal component analysis performs a low-rank-plus-sparse matrix decomposition by solving a convex program. In this paper we first develop an efficient rank-revealing decomposition algorithm aided by randomization, which provides information about the singular subspaces and singular values of a given data matrix. The proposed factorization termed randomized...
This paper deals with the optimization of sensing matrices and sparsifying dictionaries for compressed sensing systems. A gradient-based method with a new measurement strategy denoted as real mutual coherence is proposed. Further more, the sensing matrix is optimized by minimizing an objective function in which the target Gram is selected as Ψ Ψ, this choice has advantages to reconstruct real images...
In this paper we propose novel randomized subspace methods to detect anomalies in Internet Protocol networks. Given a data matrix containing information about network traffic, the proposed approaches perform a normal-plus-anomalous matrix decomposition aided by the randomized sampling scheme and subsequently detect traffic anomalies in the anomalous subspace using a statistical test. Simulation results...
In this paper we propose a new robust principal component analysis method to separate the background and foreground scenes in video surveillance. Our approach uses a random projection method called Bilateral Random Projections (BRP) in conjunction with a switching between random projection matrices and a singular value estimation technique to separate the background and moving objects. The proposed...
A novel algorithm to design Root-Check LDPC codes based on Progressive Edge Growth (PEG) techniques for block-fading channels is proposed. The performance of the new codes is investigated in terms of the Frame Error Rate (FER) and the Bit Error Rate (BER). Numerical results show that the codes constructed by the proposed algorithm outperform codes constructed by the existing methods by 0.5dB.
In this work, we present a novel adaptive filtering scheme that builds on and advances the method of joint iterative optimization (JIO) of reduced-rank filters proposed in [1]. The scheme applies the theory of error bounded set-membership filtering to both the adaptation of the bank of full-rank filters that form the projection matrix, and the reduced-rank adaptive filter that operates in the lower...
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