Browsing by Subject "Convex optimization problems"

Now showing 1 - 3 of 3

Open Access
Denoising images corrupted by impulsive noise using projections onto the epigraph set of the total variation function (PES-TV)
(Springer U K, 2015) Tofighi M.; Kose, K.; Çetin, A. Enis
In this article, a novel algorithm for denoising images corrupted by impulsive noise is presented. Impulsive noise generates pixels whose gray level values are not consistent with the neighboring pixels. The proposed denoising algorithm is a two-step procedure. In the first step, image denoising is formulated as a convex optimization problem, whose constraints are defined as limitations on local variations between neighboring pixels. We use Projections onto the Epigraph Set of the TV function (PES-TV) to solve this problem. Unlike other approaches in the literature, the PES-TV method does not require any prior information about the noise variance. It is only capable of utilizing local relations among pixels and does not fully take advantage of correlations between spatially distant areas of an image with similar appearance. In the second step, a Wiener filtering approach is cascaded to the PES-TV-based method to take advantage of global correlations in an image. In this step, the image is first divided into blocks and those with similar content are jointly denoised using a 3D Wiener filter. The denoising performance of the proposed two-step method was compared against three state-of-the-art denoising methods under various impulsive noise models.
Open Access
A heterogeneous memory organization with minimum energy consumption in 3D chip-multiprocessors
(IEEE, 2016-05) Asad, Arghavan; Onsori, Salman; Fathy, M.; Jahed-Motlagh, M. R.; Raahemifar, K.
Main memories play an important role in overall energy consumption of embedded systems. Using conventional memory technologies in future designs in nanoscale era cause a drastic increase in leakage power consumption and temperature-related problems. Emerging non-volatile memory (NVM) technologies offer many desirable characteristics such as near-zero leakage power, high density and non-volatility. They can significantly mitigate the issue of memory leakage power in future embedded chip-multiprocessor (eCMP) systems. However, they suffer from challenges such as limited write endurance and high write energy consumption which restrict them for adoption in modern memory systems. In this article, we propose a stacked hybrid memory system for 3D chip-multiprocessors to take advantages of both traditional and non-volatile memory technologies. For reaching this target, we present a convex optimization-based model that minimizes the system energy consumption while satisfy endurance constraint in order to design a reliable memory system. Experimental results show that the proposed method improves energy-delay product (EDP) and performance by about 44.8% and 13.8% on average respectively compared with the traditional memory design where single technology is used. © 2016 IEEE.
Open Access
Ultra-wideband orthogonal pulse shape set design by using Hermite-Gaussian functions
(IEEE, 2012) Alp, Yaşar Kemal; Dedeoǧlu, Mehmet; Arıkan, Orhan
Ultra-Wideband (UWB) communication systems have been developed for short distance, high data rate communications. To avoid interfering with the existing systems in the same environment, very short duration pulses used by these systems should satisfy a predefined spectral mask. Data rate of UWB systems can be increased by using multiple pulse shapes simultaneously. Orthogonality of the simultaneously used pulse shapes simplifies the receiver design. In this work, design of orthogonal pulse shapes which satisfy the spectral mask is modelled as an optimization problem. First, it is converted to a convex optimization problem by constraining the pulse shapes to lie in a subspace spanned by the Hermite-Gaussian (HG) functions. Then the optimal solution is obtained. It is shown that a larger pulse shape set can be designed compared to the existing approaches, and hence, a higher data rate can be achieved. © 2012 IEEE.