Browsing by Author "Holec, Eric"
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Item Monitoring the Use of HOV and HOT Lanes(Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2013-01) Holec, Eric; Somasundaram, Guruprasad; Papanikolopoulos, Nikolaos; Morellas, VassiliosThis report presents the formulation and implementation of an automated computer vision and machine learning based system for estimation of the occupancy of passenger vehicles in high-occupancy vehicles and highoccupancy toll (HOV/HOT) lanes. We employ a multi-modal approach involving near-infrared images and highresolution color video images in conjunction with strong maximum margin based classifiers such as support vector machines. We attempt to maximize the information that can be extracted from these two types of images by computing different features. Then, we build classifiers for each type of feature which are compared to determine the best feature for each imaging method. Based on the performance of the classifiers we critique the efficacy of the individual approaches as the costs involved are significantly different.Item Using linear programming to minimize interference in wireless sensor networks(2013-09) Holec, EricInterference in wireless sensor networks can have a significant impact on power consumption and throughput. In this paper, we address the problem of finding a network topology that minimizes the maximum interference experienced by any sensor in the network. In the standard interference model, each sensor interferes with every other sensor within its communication range. We approach the problem of minimizing interference by creating a linear relaxation to a similar problem with a different interference model. Using randomized rounding, this relaxation gives an O(OPT*log n) approximation to this new problem. We then show that this solution is an O(OPT^2*log n) approximation to minimize the maximum interference using the standard interference model. If OPT=O(log n) (as is the case in most networks), this is an improvement over existing best known O(OPT*sqrt(n)) approximation. Additionally, we perform several experiments using simulated sensor networks where our algorithm often significantly outperforms its theoretical bounds.