@conference{feldman2012single,

title = {The single pixel GPS: Learning big data signals from tiny coresets},

author = {Dan Feldman and Cynthia Sung and Daniela Rus},

doi = {10.1145/2424321.2424325},

year  = {2012},

date = {2012-11-06},

booktitle = {20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS 2012)},

pages = {23-32},

publisher = {ACM},

abstract = {We present algorithms for simplifying and clustering patterns from sensors such as GPS, LiDAR, and other devices that can produce high-dimensional signals. The algorithms are suitable for handling very large (e.g. terabytes) streaming data and can be run in parallel on networks or clouds. Applications include compression, denoising, activity recognition, road matching, and map generation.



We encode these problems as (k, m)-segment mean problems. Formally, we provide (1 + ε)-approximations to the k-segment and (k, m)-segment mean of a d-dimensional discrete-time signal. The k-segment mean is a k-piecewise linear function that minimizes the regression distance to the signal. The (k,m)-segment mean has an additional constraint that the projection of the k segments on Rd consists of only m ≤ k segments. Existing algorithms for these problems take O(kn2) and nO(mk) time respectively and O(kn2) space, where n is the length of the signal.



Our main tool is a new coreset for discrete-time signals. The coreset is a smart compression of the input signal that allows computation of a (1 + ε)-approximation to the k-segment or (k,m)-segment mean in O(n log n) time for arbitrary constants ε,k, and m. We use coresets to obtain a parallel algorithm that scans the signal in one pass, using space and update time per point that is polynomial in log n. We provide empirical evaluations of the quality of our coreset and experimental results that show how our coreset boosts both inefficient optimal algorithms and existing heuristics. We demonstrate our results for extracting signals from GPS traces. However, the results are more general and applicable to other types of sensors.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{sung2012trajectory,

title = {Trajectory clustering for motion prediction},

author = {Cynthia Sung and Dan Feldman and Daniela Rus},

doi = {10.1109/IROS.2012.6386017},

year  = {2012},

date = {2012-10-07},

booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},

pages = {1547-1552},

publisher = {IEEE},

abstract = {We investigate a data-driven approach to robotic path planning and analyze its performance in the context of interception tasks. Trajectories of moving objects often contain repeated patterns of motion, and learning those patterns can yield interception paths that succeed more often. We therefore propose an original trajectory clustering algorithm for extracting motion patterns from trajectory data and demonstrate its effectiveness over the more common clustering approach of using k-means. We use the results to build a Hidden Markov Model of a target's motion and predict movement. Our simulations show that these predictions lead to more effective interception. The results of this work have potential applications in coordination of multi-robot systems, tracking and surveillance tasks, and dynamic obstacle avoidance.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}