Mohammed Al-Qizwini

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In this paper we consider the problem of recovering an N-dimensional data from a subset of its observed entries. We provide a generalization for the smooth Shcatten-p rank approximation function in [1] to the N-dimensional space. In addition, we derive an optimization algorithm using the Augmented Lagrangian Multiplier in the N-dimensional space to solve(More)
Partial Differential Equation (PDE) based diffusion has been utilized for image denoising for more than two decades. It is known that the process of diffusion preserves the edges and object boundaries making it a suitable preprocessing step for edge detection. Synergetic to these efforts, in this work, we apply diffusion to network graphs leading to an(More)
In this paper, we consider the Direct Perception approach for autonomous driving. Previous efforts in this field focused more on feature extraction of the road markings and other vehicles in the scene rather than on the autonomous driving algorithm and its performance under realistic assumptions. Our main contribution in this paper is introducing a new,(More)
In this paper we consider the problem of recovering an N-dimensional data from a subset of its observed entries. We provide a generalization for the smooth Shcatten-p rank approximation function in [1] to the N-dimensional space. In addition, we derive an optimization algorithm using the Augmented Lagrangian Multiplier in the N-dimensional space to solve(More)
This paper addresses the problem of identifying a very small subset of data points that belong to a significantly larger massive dataset (i.e., Big Data). The small number of selected data points must adequately represent and faithfully characterize the massive Big Data. Such identification process is known as representative selection [19]. We propose a(More)
In this paper, we consider the robust tensor recovery problem in which we recover the low rank and sparse tensors from an observed data that is formed by the superposition of the two tensors. Our main contribution in this paper is deriving the truncated and smoothed schatten-p function to solve the robust tensor recovery problem using the Augmented(More)
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