Department Publications

This paper explores the use cases and performance of generative artificial intelligence (AI) tools in the context of Electrical Engineering education. A summary of commonly used generative AI tools in Engineering education is provided along with a review of their use cases from recent literature. The performances of the generative AI tools GPT-3.5, GPT-4 and Google Bard are examined using custom-made question sets in two representative courses: Digital Logic Design and Digital Signal Processing. The paper highlights that GPT4 achieves over 50% accuracy in both courses and generative AI tools are more effective at solving standalone concept questions than analytical or numerical questions.

Image generation from a single image using generative adversarial networks is quite interesting due to the realism of generated images. However, recent approaches need improvement for such realistic and diverse image generation, when the global context of the image is important such as in face, animal, and architectural image generation. This is mainly due to the use of fewer convolutional layers for capturing the patch statistics and, thereby, not being able to capture global statistics well. The challenge, then, is to preserve the global structure, while retaining the diversity and quality of image generation. We solve this problem by using attention blocks at selected scales and feeding a random Gaussian blurred image to the discriminator for training. We use adversarial feedback to make the quality of the generation better. Our results are visually better than the state-of-the-art, particularly, in generating images that …