Adaptive Non-uniform Compressive Sampling for Time-varying Signals

In this paper, adaptive non-uniform compressive sampling (ANCS) of time-varying signals, which are sparse in a proper basis, is introduced. ANCS employs the measurements of previous time steps to distribute the sensing energy among coefficients more intelligently. To this aim, a Bayesian inference method is proposed that does not require any prior knowledge of importance levels of coefficients or sparsity of the signal. Our numerical simulations show that ANCS is able to achieve the desired non-uniform recovery of the signal. Moreover, if the signal is sparse in canonical basis, ANCS can reduce the number of required measurements significantly.Comment: 6 pages, 8 figures, Conference on Information Sciences and Systems (CISS 2017) Baltimore, Marylan

A Study on Clustering for Clustering Based Image De-Noising

In this paper, the problem of de-noising of an image contaminated with Additive White Gaussian Noise (AWGN) is studied. This subject is an open problem in signal processing for more than 50 years. Local methods suggested in recent years, have obtained better results than global methods. However by more intelligent training in such a way that first, important data is more effective for training, second, clustering in such way that training blocks lie in low-rank subspaces, we can design a dictionary applicable for image de-noising and obtain results near the state of the art local methods. In the present paper, we suggest a method based on global clustering of image constructing blocks. As the type of clustering plays an important role in clustering-based de-noising methods, we address two questions about the clustering. The first, which parts of the data should be considered for clustering? and the second, what data clustering method is suitable for de-noising.? Then clustering is exploited to learn an over complete dictionary. By obtaining sparse decomposition of the noisy image blocks in terms of the dictionary atoms, the de-noised version is achieved. In addition to our framework, 7 popular dictionary learning methods are simulated and compared. The results are compared based on two major factors: (1) de-noising performance and (2) execution time. Experimental results show that our dictionary learning framework outperforms its competitors in terms of both factors.Comment: 9 pages, 8 figures, Journal of Information Systems and Telecommunications (JIST

Missing Spectrum-Data Recovery in Cognitive Radio Networks Using Piecewise Constant Nonnegative Matrix Factorization

In this paper, we propose a missing spectrum data recovery technique for cognitive radio (CR) networks using Nonnegative Matrix Factorization (NMF). It is shown that the spectrum measurements collected from secondary users (SUs) can be factorized as product of a channel gain matrix times an activation matrix. Then, an NMF method with piecewise constant activation coefficients is introduced to analyze the measurements and estimate the missing spectrum data. The proposed optimization problem is solved by a Majorization-Minimization technique. The numerical simulation verifies that the proposed technique is able to accurately estimate the missing spectrum data in the presence of noise and fading.Comment: 6 pages, 6 figures, Accepted for presentation in MILCOM'15 Conferenc

Efficiency in phenol removal from aqueous solutions of pomegranate peel ash as a natural adsorbent

Background: Phenol is an organic pollutant found in industrial effluents that is very toxic to humans and the environment. This study used pomegranate peel ash as a natural absorbent to remove phenol from aqueous solutions. Methods: In this study, pomegranate peel ash in different doses was used as a new adsorbent for the removal of phenol. The effects of contact time, pH, adsorbent dose and initial phenol concentration were recorded. Then, the adsorption data was described with Langmuir and Freundlich adsorption isotherms; Excel software was used for data analysis. Results: The highest percentage of phenol adsorption was observed at pH = 7. The optimum amount of adsorbent was 0.6 g/l, and after 120 minutes, the process reached an equilibrium state. The adsorption of phenol decreased following an increase in the pH of the solution. It was also observed that contact time significantly affected the rate of phenol adsorption. The experimental data fit much better in the Freundlich (R2 = 0.9056) model than in the Langmuir (R2 = 0.8674) model. Conclusion: Pomegranate peel ash has the potential to be utilized for the cost-effective removal of phenol from aqueous solutions. Keywords: Phenol removal, Pomegranate peel ash, Aqueous solutio