The aim of this study is to focus on spectrum sensing in cognitive radio which is a recently introduced technology in order to increase the spectrum efficiency. We studied the Singular value decomposition based signal detector and its advantages over the energy based signal detection. Soft thresholding technique for spectrum sensing is optimized using SFLA (Shuffled Frog Leaping) and Ant Bee Co...

Convex optimization models find interesting applications, especially in signal/image processing and compressive sensing. We study some augmented convex models, which are perturbed by strongly convex functions, and propose a dual gradient algorithm. The proposed algorithm includes the linearized Bregman algorithm and the singular value thresholding algorithm as special cases. Based on fundamenta...

In this work a new version of block-matching and 3D filtering (BM3D) denoising approach introduced by Dabov et al. for denoising the image corrupted by additive white Gassian noise is proposed. The BM3D performs collaborative filtering to the 3D image groups composed by similar image blocks with the fixed hard-thresholding operator. The proposed version of BM3D adopts adaptive block-matching th...

in this paper, we use modified laplace decomposition method to solving initial value problems (ivp) of the second order ordinary differential equations. theproposed method can be applied to linear and nonlinearproblems

Image denoising is an important pre-processing step for accurately quantifying functional morphology and measuring activities of the tissues using PET images. Unlike structural imaging modalities, PET images have two difficulties: (1) the Gaussian noise model does not necessarily fit into PET imaging because the exact nature of noise propagation in PET imaging is not well known, and (2) PET ima...

Over the past decade, various matrix completion algorithms have been developed. Thresholded singular value decomposition (SVD) is a popular technique in implementing many of them. A sizable number of studies have shown its theoretical and empirical excellence, but choosing the right threshold level still remains as a key empirical difficulty. This paper proposes a novel matrix completion algori...

Patch-based sparse representation and low-rank approximation for image processing attract much attention in recent years. The minimization of the matrix rank coupled with the Frobenius norm data fidelity can be solved by the hard thresholding filter with principle component analysis (PCA) or singular value decomposition (SVD). Based on this idea, we propose a patch-based low-rank minimization m...

Latent semantic indexing (LSI) is commonly used to match queries to documents in information retrieval (IR) applications. It has been shown to improve the retrieval performance, as it can deal with synonymy and polysemy problems. This paper proposes a hybrid approach which can improve result accuracy significantly. Evaluation of the approach based on using the Haar wavelet transform (HWT) as a ...