<p> According to the World Health Organization, long-term exposures to heavy metal toxicants such as arsenic (As) and lead (Pb), even at the parts per billion (ppb, μg/L) level, can cause severe health problems such as cancer, skin lesions, and cardiovascular diseases. Therefore, an accurate and rapid heavy metal toxicant monitoring technique is needed. This research investigated the proof-of-the concept of a portable sensor for detecting As and Pb in water. The sensor system utilized a Quartz Crystal Microbalance - QCM (openQCM w-i2) system interfaced with a computer as the sensing platform. It was further integrated with a peristaltic pump and required tubing to create the integrated sensing system. It used a 10 MHz AT-cut quartz crystal gold electrode as the sensing substrate. For the determination of As in water, dithiothreitol (DTT) was used as the ligand to be deposited on the gold electrode using the Self-assembly-monolayer method (SAM). For the determination of Pb, a combination of ligands (Chitosan, Glutaraldehyde, and lead ionophore II - CGL) was used and deposited on the gold electrode using the spin-coating method. The system was tested for As in water with specific concentrations (0, 50, 100, and 200 ppb) under laboratory conditions. Similarly, the system was tested for Pb in water with different concentrations (0, 10, 25, 50, and 100 ppb) under laboratory conditions. The resulted change of frequency (with respect to time, in seconds) of the QCM system to different concentrations of the individual analyte was recorded. Subsequently, the recorded data were analyzed to determine the correlation model and coefficient of determination, R2. The maximum R2 values for detecting As and Pb were 0.963 and 0.991, respectively. Thus, this proof-of-the-concept study using the developed QCM-based sensing system for detecting As and Pb in water was successful.</p>

This thesis presents the development of a surveillance testbed using wireless sensors and video cameras for vehicle detection and tracking. The experimental study includes testbed design and discusses some of the implementation issues in using wireless sensors and video cameras for a practical application. A group of sensor devices equipped with light sensors are used to detect and localize the position of moving vehicle. Background subtraction method is used to detect the moving vehicle from the video sequences. Vehicle centroid is calculated in each frame. A non-linear minimization method is used to estimate the perspective transformation which project 3D points to 2D image points. Vehicle location estimates from three cameras are fused to form a single trajectory representing the vehicle motion. Experimental results using both sensors and cameras are presented. Average error between vehicle location estimates from the cameras and the wireless sensors is around 0.5ft.