Sharif Digital Repository

This paper investigates the problem of recovering missing samples using methods based on sparse representation adapted for visually enhanced quality of reconstruction of image signals. Although, the popular Mean Square Error (MSE) criterion is convex and simple, it may not be entirely consistent with Human Visual System (HVS). Thus, instead of ℓ2-norm or MSE, a new perceptual quality measure is used as the similarity criterion between the original and the reconstructed images. The proposed criterion called Convex SIMilarity (CSIM) index is a modified version of the Structural SIMilarity (SSIM) index, which despite its predecessor, is convex and uni-modal. We derive mathematical properties... 

In this study, we address the linguistic challenges posed by Tajiki Persian, a distinct variant of the Persian language that utilizes the Cyrillic script due to historical “Russification”. This distinguishes it from other Persian dialects that adopt the Arabic script. Despite its profound linguistic and cultural significance, Tajiki Persian remains a low-resource language with scant digitized datasets for computational applications. To address this deficiency, we created a parallel corpus using Shahnameh, a seminal Persian epic poem. Employing optical character recognition, we extracted Tajiki Persian verses from primary sources and applied a heuristic method to align them with their Iranian... 

The necessity of the development of ICT in rural areas has become a major issue in the developing countries for some time now. Iran, one of the participants of this movement, plans to take some significant short-term steps in this field, steps which will revolve the situation of it's rural ICT extensively. One of the main related issues in Iran is the large number of geographical situations present in rural parts demanding distinct telecommunication solutions. Having studied thoroughly the current situation of rural telecommunication in Iran and also the available and the newly introduced technical solutions, in this article we propose a complete telecommunication plan for rural Iran that... 

This letter investigates the problem of sparse signal recovery in the presence of additive impulsive noise. The heavy-tailed impulsive noise is well modeled with stable distributions. Since there is no explicit formula for the probability density function of SαS distribution, alternative approximations are used, such as, generalized Gaussian distribution, which imposes ℓp-norm fidelity on the residual error. In this letter, we exploit a continuous mixed norm (CMN) for robust sparse recovery instead of ℓp-norm. We show that in blind conditions, i.e., in the case where the parameters of the noise distribution are unknown, incorporating CMN can lead to near-optimal recovery. We apply... 

In this paper, we study the missing sample recovery problem using methods based on sparse approximation. In this regard, we investigate the algorithms used for solving the inverse problem associated with the restoration of missed samples of image signal. This problem is also known as inpainting in the context of image processing and for this purpose, we suggest an iterative sparse recovery algorithm based on constrained l1-norm minimization with a new fidelity metric. The proposed metric called Convex SIMilarity (CSIM) index, is a simplified version of the Structural SIMilarity (SSIM) index, which is convex and error-sensitive. The optimization problem incorporating this criterion, is then... 

This paper presents an approach to improve the sliding mode controller performance. Accordingly, a fuzzy controller is designed based on a limited number of rules and independent of the sliding mode controller complexity. To enhance the performance, this controller will continuously optimize the sliding mode controller parameters including hitting control gain, boundary layer thickness, sliding surface slope and intercept. The controller is applied to a two-degree-of-freedom robot and the experimental results are compared with a QFT based and a PID controller. The results demonstrate the significant performance improvement of the proposed controller. © 2005 IEEE  

In this paper we report experimental results for the performance and rate of heat transfer in copper tubes in solar water heaters with thermosyphonic flow in continuation of experimental data reported in previous publications (Solar Energy, 2003, vol. 74, pp. 441-445, and Energy Sources, 1997, vol. 19, pp. 147-152). We also show a comparison between performances of three kinds of tubes: copper, polypropylene and steel under similar conditions. An analytical relation for calculation of rate of heat transfer in copper tubes is also presented in terms of Nusselt versus Reynolds and Prantdl numbers. A comparison of experimental data showed that performance of copper tubes is slightly better than... 

Learning a graph from data is the key to taking advantage of graph signal processing tools. Most of the conventional algorithms for graph learning require complete data statistics, which might not be available in some scenarios. In this work, we aim to learn a graph from incomplete time-series observations. From another viewpoint, we consider the problem of semi-blind recovery of time-varying graph signals where the underlying graph model is unknown. We propose an algorithm based on the method of block successive upperbound minimization (BSUM), for simultaneous inference of the signal and the graph from incomplete data. Simulation results on synthetic and real time-series demonstrate the... 

This paper investigates the problem of learning a graphical model from incomplete spatio-temporal measurements. Our purpose is to analyze a time-varying graph signal represented by an incomplete data matrix, the rows and columns of which correspond to spatial and temporal features/measurements of the signal, respectively. In contrast to the conventional approaches which utilize either a directed or an undirected graphical model for data analysis, we propose a compound multi-relational model exploiting both directed and undirected structures. Our approach is based on statistical inference in which a spatio-temporal signal is considered as a random graph process to which we can apply... 

In this paper a method for extracting keypoints from human brain MR images is proposed. These keypoints are obtained based on curved structures in the brain MR images. In this method, a keypoint is center of a circle which includes circular boundaries in the image and is selected based on gradients of the image. These keypoints and their descriptors are scale and rotation invariant. The proposed method is compared with other well-known methods with repeatability measure and ROC curves. Experimental results show that proposed method performs better than other well-known methods, specially, when deformations are remarkable  

This paper presents an approach to tackle the task of Visual Word Sense Disambiguation (Visual-WSD), which involves determining the most appropriate image to represent a given polysemous word in one of its particular senses. The proposed approach leverages the CLIP model, prompt engineering, and text-to-image models such as GLIDE and DALL-E 2 for both image retrieval and generation. To evaluate our approach, we participated in the SemEval 2023 shared task on “Visual Word Sense Disambiguation (Visual-WSD)” using a zero-shot learning setting, where we compared the accuracy of different combinations of tools, including “Simple prompt-based” methods and “Generated prompt-based” methods for...