A Scalable Low-Cost-UAV Traffic Network (uNet)

This article proposes a new Unmanned Aerial Vehicle (UAV) operation paradigm to enable a large number of relatively low-cost UAVs to fly beyond-line-of-sight without costly sensing and communication systems or substantial human intervention in individual UAV control. Under current free-flight-like paradigm, wherein a UAV can travel along any route as long as it avoids restricted airspace and altitudes. However, this requires expensive on-board sensing and communication as well as substantial human effort in order to ensure avoidance of obstacles and collisions. The increased cost serves as an impediment to the emergence and development of broader UAV applications. The main contribution of this work is to propose the use of pre-established route network for UAV traffic management, which allows: (i) pre- mapping of obstacles along the route network to reduce the onboard sensing requirements and the associated costs for avoiding such obstacles; and (ii) use of well-developed routing algorithms to select UAV schedules that avoid conflicts. Available GPS-based navigation can be used to fly the UAV along the selected route and time schedule with relatively low added cost, which therefore, reduces the barrier to entry into new UAV-applications market. Finally, this article proposes a new decoupling scheme for conflict-free transitions between edges of the route network at each node of the route network to reduce potential conflicts between UAVs and ensuing delays. A simulation example is used to illustrate the proposed uNet approach.Comment: To be submitted to journal, 21 pages, 9 figure

A Distance Map Regularized CNN for Cardiac Cine MR Image Segmentation

Cardiac image segmentation is a critical process for generating personalized models of the heart and for quantifying cardiac performance parameters. Several convolutional neural network (CNN) architectures have been proposed to segment the heart chambers from cardiac cine MR images. Here we propose a multi-task learning (MTL)-based regularization framework for cardiac MR image segmentation. The network is trained to perform the main task of semantic segmentation, along with a simultaneous, auxiliary task of pixel-wise distance map regression. The proposed distance map regularizer is a decoder network added to the bottleneck layer of an existing CNN architecture, facilitating the network to learn robust global features. The regularizer block is removed after training, so that the original number of network parameters does not change. We show that the proposed regularization method improves both binary and multi-class segmentation performance over the corresponding state-of-the-art CNN architectures on two publicly available cardiac cine MRI datasets, obtaining average dice coefficient of 0.84$\pm$0.03 and 0.91$\pm$0.04, respectively. Furthermore, we also demonstrate improved generalization performance of the distance map regularized network on cross-dataset segmentation, showing as much as 42% improvement in myocardium Dice coefficient from 0.56$\pm$0.28 to 0.80$\pm$0.14.Comment: 11 pages manuscript, 5 pages supplementary material