Sharif Digital Repository

The sampling process in electrical devices includes non-linear distortion which needs to be compensated to boost up the system efficiency. In this paper, a blind method is suggested for non-linear distortion compensation. The core idea is to leverage the sparsity of the signal to cope with the ill-posedness of the distortion compensation task. The proposed scheme is an iterative method based on out of support energy minmization where the support information is not available. An adaptive thresholding operator is used to give a rough approximation of the support according to the estimated signal at each iteration. Various simulation scenarios have validated the capability of the suggested... 

The sampling process in electrical devices includes nonlinear distortion that needs to be compensated to boost up the system efficiency. In this brief, a blind method is suggested for nonlinear distortion compensation. The core idea is to leverage the sparsity of the signal to cope with the ill-posedness of the distortion compensation task. The proposed scheme is an iterative method based on out of support energy minimization, in which the support information is not available. An adaptive thresholding operator is used to give a rough approximation of the support according to the estimated signal at each iteration. Various simulation scenarios have validated the capability of the suggested... 

We study the problem of remote reconstruction of a continuous signal from its multiple corrupted versions. We are interested in the optimal number of samples and their locations for each corrupted signal to minimize the total reconstruction distortion of the remote signal. The correlation among the corrupted signals can be utilized to reduce the sampling rate. For a class of Gaussian signals, we show that in the low sampling rate region, it is optimal to use a certain nonuniform sampling scheme on all the signals. On the other hand, in the high sampling rate region, it is optimal to uniformly sample all the signals. We also show that both of these sampling strategies are optimal if we are... 

In this paper, the optimal sampling strategies (uniform or nonuniform) and distortion tradeoffs for Gaussian bandlimited periodic signals with additive white Gaussian noise are studied. Our emphasis is on characterizing the optimal sampling locations as well as the optimal pre-sampling filter to minimize the reconstruction distortion. We first show that to achieve the optimal distortion, no pre-sampling filter is necessary for any arbitrary sampling rate. Then, we provide a complete characterization of optimal distortion for low and high sampling rates (with respect to the signal bandwidth). We also provide bounds on the reconstruction distortion for rates in the intermediate region. It is... 

Multiple Description Coding (MDC) is a technique for video transmission over error prone networks where the descriptions are routed over multiple paths. Intra coding like MDC provides error resiliency but coding in this mode must be decided with care since it degrades the compression ratio. In this paper, we present our investigation results for a new intra coding approach in MDC. We have found that, in MDC streams, the best policy is to encode selective frames as I-frame instead of coding some macroblocks of frames in intra mode. In order to find the most suitable I-frame positions within a given video stream, we developed a cost function based on which intra/inter frame type is decided.... 

In this paper, the optimal sampling strategies (uniform or nonuniform) and distortion tradeoffs for Gaussian bandlimited periodic signals with additive white Gaussian noise are studied. Our emphasis is on characterizing the optimal sampling locations as well as the optimal presampling filter to minimize the reconstruction distortion. We first show that to achieve the optimal distortion, no presampling filter is necessary for any arbitrary sampling rate. Then, we provide a complete characterization of optimal distortion for low and high sampling rates (with respect to the signal bandwidth). We also provide bounds on the reconstruction distortion for rates in the intermediate region. It is... 

Similarity measure is a main core of image registration algorithms. Spatially varying intensity distortion is an important challenge, which affects the performance of similarity measures. Correlation among the pixels is the main characteristic of this distortion. Similarity measures such as sum-of-squared-differences (SSD) and mutual information ignore this correlation; hence, perfect registration cannot be achieved in the presence of this distortion. In this paper, we model this correlation with the aid of the low rank matrix theory. Based on this model, we compensate this distortion analytically and introduce rank-regularized SSD (RRSSD). This new similarity measure is a modified SSD based...