no code implementations • 26 Feb 2021 • Issam Hammad, Ryan Simpson, Hippolyte Djonon Tsague, Sarah Hall
The proposed CNN model achieves this target by automatically identifying at least a portion of each flaw where further manual analysis is performed to identify the width, the length, and the type of the flaw.
no code implementations • 26 Dec 2019 • Issam Hammad, Kamal El-Sankary, Jason Gu
The paper demonstrates that using approximate multipliers for CNN training can significantly enhance the performance in terms of speed, power, and area at the cost of a small negative impact on the achieved accuracy.
no code implementations • 26 Dec 2019 • Issam Hammad, Kamal El-Sankary, Jason Gu
A comparison of the performance of various machine learning models to predict the direction of a wall following robot is presented in this paper.