29,721 research outputs found
Acceleration and visualization of Dynamic Network Optimization
With the emerging prevalence of smart phones and 4G LTE networks, the demand for faster-better-cheaper mobile services anytime and anywhere is ever growing. The Dynamic Network Optimization (DNO) concept emerged as a solution that optimally and continuously tunes the network settings, in response to varying network conditions and subscriber needs. Yet, the DNO realization is still at infancy, largely hindered by the bottleneck of the lengthy optimization runtime. This paper presents the design and prototype of a novel cloud based parallel solution that further enhances the scalability of our prior work on various parallel solutions that accelerate network optimization algorithms. The solution aims to satisfy the high performance required by DNO, preliminarily on a sub-hourly basis. The paper subsequently visualizes a design and a full cycle of a DNO system. A set of potential solutions to large network and real-time DNO are also proposed. Overall, this work creates a breakthrough towards the realization of DNO
Flight Dynamics-based Recovery of a UAV Trajectory using Ground Cameras
We propose a new method to estimate the 6-dof trajectory of a flying object
such as a quadrotor UAV within a 3D airspace monitored using multiple fixed
ground cameras. It is based on a new structure from motion formulation for the
3D reconstruction of a single moving point with known motion dynamics. Our main
contribution is a new bundle adjustment procedure which in addition to
optimizing the camera poses, regularizes the point trajectory using a prior
based on motion dynamics (or specifically flight dynamics). Furthermore, we can
infer the underlying control input sent to the UAV's autopilot that determined
its flight trajectory.
Our method requires neither perfect single-view tracking nor appearance
matching across views. For robustness, we allow the tracker to generate
multiple detections per frame in each video. The true detections and the data
association across videos is estimated using robust multi-view triangulation
and subsequently refined during our bundle adjustment procedure. Quantitative
evaluation on simulated data and experiments on real videos from indoor and
outdoor scenes demonstrates the effectiveness of our method
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