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In this short paper, we report on an approach to datamine the brain from a novel perspective, namely traffic analysis. Our datamining approach considers the brain regions and the tracts that connect them as a road network, and the signals traveling between them as vehicles. We analyze travel patterns by a process called traffic assignment. The results are unexpected in the sense that the movement...
In this paper, we present a platform for aiding in the development and evaluation of novel ITS passive safety applications. Such applications work by having vehicles detect certain events that may be dangerous to other vehicles and disseminating reports about these events using wireless communication. A vehicle receiving the report about the event can then be warned. However, a large number of false...
In this paper, we compare two methods of estimating relevance for the emergency electronic brake light application: One method uses an analytically derived formula based on the minimum safety gap that is required to avoid a collision, whereas the other method uses a machine learning approach. The application works by disseminating reports about vehicles that perform emergency deceleration in an effort...
In this paper, we compare two methods of estimating relevance for the emergency electronic brake light application. One uses an analytically derived formula based on the minimal safety gap required to avoid a collision. The other method uses a machine learning approach. The application works by disseminating reports about vehicles that are performing emergency deceleration in effort to warn drivers...
Vehicular ad-hoc networks (VANETs) is a promising approach to the dissemination of spatio-temporal information such as the current traffic condition of a road segment or the availability of a parking space. Due to the constraint of the communication bandwidth, only a limited number of information items may be transmitted upon a vehicle-to-vehicle communication opportunity. Ranking becomes critical...
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