Saeed Vahidian

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This paper introduces a framework for superresolution of scalable video based on compressive sensing and sparse representation of residual frames in reconnaissance and surveillance applications. We exploit efficient compressive sampling and sparse reconstruction algorithms to super-resolve the video sequence with respect to different compression rates. We(More)
In this paper, we investigate the performance of a dual-hop block fading cognitive radio network with underlay spectrum sharing over independent but not necessarily identically distributed (i.n.i.d.) Nakagami-m fading channels. The primary network consists of a source and a destination. Depending on whether the secondary network which consists of two source(More)
In this research work, a novel framework is proposed as an efficient successor to traditional imaging methods for breast cancer detection in order to decrease the computational complexity. In this framework, the breast is devided into segments in an iterative process and in each iteration, the one having the most probability of containing tumor with lowest(More)
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