Department Publications

This paper addresses the problem of high energy consumption in the standard take-off mode of quad-plane unmanned aerial vehicles (UAV). In the standard mode, the quad-plane takes-off vertically using the quad mode, and when the predefined altitude is reached the plane mode kicks in. However, the" bird" mode, where both quad and plane modes kick in together, the UAV could achieve altitude and airspeed while consuming lower energy. This paper presents the energy consumption and comparison in standard and bird take-off modes with actual experimental results obtained through flight tests of a small 1.35 kg quad-plane. The experiments showed that as much as 22% energy can be saved if the bird take-off mode is used.

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 mainly capturing the patch statistics and, thereby, not being able to capture global statistics very well. We solve this problem by using attention blocks at selected scales and feeding a random Gaussian blurred image to the discriminator for training. Our results are visually better than the state-of-the-art particularly in generating images that require global context. The diversity of our image generation, measured using the average standard deviation of pixels, is also better.