Team for Research in
Ubiquitous Secure Technology

2007 TRUST Research Experiences for Undergraduates

Program Overview

The Team for Research in Ubiquitous Secure Technology sponsored two undergraduate students to participate in the summer 2007 TRUST-REU program. Below are descriptions of the 2007 TRUST-REU research projects and links to each student's or team's research report, poster, and final presentation.

Research Projects

Ashira Khera
San Jose State University
Dynamic Graph Analysis and Optimal Control -- Dynamic Programming Approaches to Safety

In this report, we shall first introduce some background concept on Dynamic Programming, its application to minimum path problems over static graphs, its comparison with greedy algorithms, and its application to Security problems. We then describe some concepts from dynamical systems theory, namely the use of deterministic dynamical models to describe time-varying systems. We will look at a case study, that of room temperature regulation. We then introduce a setup for the problem of safety verfication, and propose two approaches, both based on Dynamic Programming, to solve this problem. One approach is based on a multiplicative cost function the other on an additive one. We compare the two techniques. We shall then extend to models under study to the probabilistic case, and look at the safety problem from a new perspective. The application of the above Dynamic Programming approaches will again help in tackling this study. Simulations will be performed, which will compare the outcomes of the two techniques. Amalia Viti
Columbia University
Design of a Distributed Tracking System for Camera Networks

In this project we investigate a collaborative signal processing scheme for physical movement monitoring with motion sensors. The signal processing consists of preprocessing, feature extraction and classification. We define a measure on feature significance as well as features' correlations. Because these nodes have a limited amount of battery power, we extract features to characterize the data. We develop a metric to rank each feature by its significance with respect to a particular movement class. We validate our metric by analyzing the accuracy of classification when using a subset of features selected by our metric. Using the characteristics of the best features we find from the rankings, we develop new features to improve the system accuracy and then assess their effectiveness.