Radar forward-looking imaging is gaining significance in various applications like battlefield reconnaissance, target surveillance, and precision guidance. Although synthetic aperture radar techniques provide high azimuth resolution but faced limitations area due to the poor Doppler “left-right” ambiguity problem. Recently, generative adversarial networks have been extensively used for image mo...

Artificial Neural Network (ANN) learns from inputs and outputs. The values of the weights biases in ANN are updated according to Researchers have proposed algorithms train Multi-Layer Perceptron (MLP). However, classical techniques often face problems solving this optimization problem. They tend need large amounts computing time, memory. More importantly, they get stuck within local optimum pro...

A practical joint source/channel coding algorithm is proposed for the transmission of multiple images and videos to reduce the overall reconstructed source distortion at the receiver within a given total bit rate. It is demonstrated that by joint coding of multiple sources with such an objective, both improved distortion performance as well as reduced quality variation can be achieved at the sa...

Geometric data acquired via a scanning process can suffer from holes due to errors in the acquisition process, noise, or challenges in merging multiple inputs together into a unified map. We present a straight forward algorithm to fill holes in incomplete evidence grids representing acquired geometric data. We also present our methods to apply learning in order to statistically evaluate the pro...

In this paper, a perturbation expansion technique is introduced to decompose the tracking error of a general adaptive tracking algorithm in a linear regression model. This method allow to obtain tracking error bound but also tight approximate expressions for the moments of the tracking error. These expressions allow to evaluate, both qualitatively and quantitatively, the impact of several facto...