Ibrahim Hossain

TL;DR: This study comprehensively investigates and quantifies the impact of CNN architecture on the performance of learned policy for an autonomous vehicle and obtained results indicate that deeper networks show a better performance than less deep networks during autonomous driving.

TL;DR: A comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM is provided in this article .

TL;DR: This proposed weighted informative inverse active class selection method has been applied on BCI competition IV motor imagery (MI) binary class data set 2B and shows better or similar performance on most of the subjects with less amount of training samples.

TL;DR: This paper comprehensively investigates the applicability of the deep transfer learning for the specific task of end to end learning of autonomous navigation and results indicate that the transfer learning-based models show a promising performance for accurately estimating the angular velocity purely using visual information.