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Due to rich characteristics and functionalities, PDF format has become the de facto standard for the electronic document exchange. As vulnerabilities in the major PDF viewers have been disclosed, a number of methods have been proposed to tame the increasing PDF threats. However, one recent evasion exploit is found to evade most of detections and renders all of the major static methods void. Moreover,...
This paper deals with designing incoherent sparsifying dictionaries. A new framework is proposed, in which the sparse representation error and mutual coherence are embedded. An alternating minimization method is developed for solving the optimal dictionary problem. One of the significant features of the proposed approach is that the dictionary is directly updated with each atom being normalized. A...
The rotation of rotor blades of a helicopter induces a Doppler modulation around the main Doppler shift. Such a non-stationary modulation, commonly called micro-Doppler signature, can be used to perform classification of the target. In this paper a model-based automatic helicopter classification algorithm is presented. A sparse signal model for radar return from a helicopter is developed and by means...
Sparse signals can be sensed with a reduced number of projections and then reconstructed if compressive sensing is employed. Traditionally, the projection matrix is chosen as a random matrix, but a projection sensing matrix that is optimally designed for a certain class of signals can further improve the reconstruction accuracy. This paper considers the problem of designing the projection matrix Φ...
The compressive sensing (CS) theory has shown that sparse signals can be reconstructed exactly from much fewer measurements than traditionally believed. What's more, using ℓp-norm minimization with p < 1 can do so with much fewer measurements than with p=1. In this paper, a novel algorithm is proposed for computing local minima of the nonconvex problem in the block-sparse system. A series of experiments...
This paper deals with dictionary learning and optimal sensing matrix design for compressed sensing (CS) systems. An improved version of the method of optimal directions (MOD) is proposed, which can overcome the problem with matrix inversion. The optimal sensing matrix design problem is defined as to find those sensing matrices that minimize a Frobenius norm-based difference between the Gram of the...
Neonatal brain MR image segmentation is challenging due to the poor image quality. In this paper, we propose a novel patch-driven level sets method for segmentation of neonatal brain images by taking advantage of sparse representation techniques. Specifically, we first build a subject-specific atlas from a library of aligned, manually segmented images by using sparse representation in a patch-based...
In this paper, a new method is proposed to optimize the projection matrix, which, unlike the existing approaches, attempts to choose the projection matrix such that the sensing matrix is as close to a tight frame/equiangular tight frame as possible. In the proposed method, there are two important procedures that are used to adjust the Gram matrix of the sensing matrix. The simulation results are presented...
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