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Asfandyar Qureshi- Power-Demand Routing in Massive Geo-Distributed Systems
Abstract for 'Power-Demand Routing in Massive Geo-Distributed Systems' : There is an increasing trend toward massive, geographically distributed systems. The largest Internet companies operate hundreds of thousands of servers in multiple geographic locations, and are growing at a fast clip. A single system's servers and data centers can consume many megawatts of electricity, as much as tens of thousands of US homes. Two important concerns have arisen: rising electric bills; and growing carbon footprints.
Alexander Shkolnik- Sample-Based Motion Planning for High-Dimensional and Differentially-Constrained Systems
State of the art sample-based path planning algorithms, such as the Rapidly-exploring Randomized Tree (RRT), have proven to be effective in path planning for systems subject to complex kinematic and geometric constraints. The performance of these algorithms, however, degrade as the dimension of the system increases. Furthermore, sample-based planners rely on distance metrics which do not work well when the system has differential constraints. Such constraints are particularly challenging in systems with non-holonomic and underactuated dynamics. This thesis develops two intelligent sampling strategies to help guide the search process. To reduce sensitivity to dimension, sampling can be done in a low-dimensional task space rather than in the high-dimensional state space.
Albert Huang- Lane Estimation for Autonomous Vehicles using Vision and LIDAR
Autonomous ground vehicles, or self-driving cars, require a high level of situational awareness in order to operate safely and efficiently in real-world conditions. A system that is able to quickly and reliably estimate the roadway and its lanes based upon local sensor data would be a valuable asset both to fully autonomous vehicles as well as driver assistance systems. To be most useful, it must accommodate a variety of roadways, environments with a range of weather and lighting conditions, and highly dynamic scenes with other vehicles and moving objects. Lane estimation can be modeled as a curve estimation problem, where sensor data provides partial and noisy observations of curves.