Enabling Leg-Based Guidance on Top of Waypoint-Based Autopilots for UAS

This paper presents a methodology to extend the guidance functionalities of Commercial Off-The-Shelf autopilots currently available for Unmanned Aircraft Systems (UAS). Providing that most autopilots only support elemental waypoint-based guidance, this technique allows the aircraft to follow leg-based flight plans without needing to modify the internal control algorithms of the autopilot. It is discussed how to provide Direct to Fix, Track to Fix and Hold to Fix path terminators (along with Fly-Over and Fly-By waypoints) to basic autopilots able to natively execute only a limited set of legs. Preliminary results show the feasibility of the proposal with flight simulations that used a flexible and reconfigurable UAS architecture specifically designed to avoid dependencies with a single or particular autopilot solution.

Distributed parallelization of greedy Mobile Network Optimization algorithms

The Mobile Network Optimization (MNO) technologies have advanced at a tremendous pace in recent years. And the Dynamic Network Optimization (DNO) concept emerged years ago, aimed to continuously optimize the network in response to variations in network traffic and conditions. Yet, DNO development is still at its infancy, mainly hindered by a significant bottleneck of the lengthy optimization runtime. This paper identifies parallelism in greedy MNO algorithms and presents an advanced distributed parallel solution. The solution is designed, implemented and applied to real-life projects whose results yield a significant, highly scalable and nearly linear speedup up to 6.9 and 14.5 on distributed 8-core and 16-core systems respectively. Meanwhile, optimization outputs exhibit self-consistency and high precision compared to their sequential counterpart. This is a milestone in realizing the DNO. Further, the techniques may be applied to similar greedy optimization algorithm based applications.

Performance Evaluation of a Statistical and a Neural Network Model for Nonrigid Shape-Based Registration

Shape-based registration methods frequently encounters in the domains of computer vision, image processing and medical imaging. The registration problem is to find an optimal transformation/mapping between sets of rigid or nonrigid objects and to automatically solve for correspondences. In this paper we present a comparison of two different probabilistic methods, the entropy and the growing neural gas network (GNG), as general feature-based registration algorithms. Using entropy shape modelling is performed by connecting the point sets with the highest probability of curvature information, while with GNG the points sets are connected using nearest-neighbour relationships derived from competitive hebbian learning. In order to compare performances we use different levels of shape deformation starting with a simple shape 2D MRI brain ventricles and moving to more complicated shapes like hands. Results both quantitatively and qualitatively are given for both sets.