USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people.

Hall-effect thruster-cathode coupling : the effect of cathode position and magnetic field topology

Hall-effect thruster (HET) cathodes are responsible for the generation of the free electrons necessary to initiate and sustain the main plasma discharge and to neutralize the ion beam. The position of the cathode relative to the thruster strongly affects the efficiency of thrust generation. However, the mechanisms by which the position affects the efficiency are not well understood. This dissertation explores the effect of cathode position on HET efficiency. Magnetic field topology is shown to play an important role in the coupling between the cathode plasma and the main discharge plasma. The position of the cathode within the magnetic field affects the ion beam and the plasma properties of the near-field plume, which explains the changes in efficiency of the thruster. Several experiments were conducted which explored the changes of efficiency arising from changes in cathode coupling. In each experiment, the thrust, discharge current, and cathode coupling voltage were monitored while changes in the independent variables of cathode position, cathode mass flow and magnetic field topology were made. From the telemetry data, the efficiency of the HET thrust generation was calculated. Furthermore, several ion beam and plasma properties were measured including ion energy distribution, beam current density profile, near-field plasma potential, electron temperature, and electron density. The ion beam data show how the independent variables affected the quality of ion beam and therefore the efficiency of thrust generation. The measurements of near-field plasma properties partially explain how the changes in ion beam quality arise. The results of the experiments show that cathode position, mass flow, and field topology affect several aspects of the HET operation, especially beam divergence and voltage utilization efficiencies. Furthermore, the experiments show that magnetic field topology is important in the cathode coupling process. In particular, the magnetic field separatrix plays a critical role in impeding the coupling between cathode and HET. Suggested changes to HET thruster designs are provided including ways to improve the position of the separatrix to accommodate the cathode.