Photolithography based patterning of bacteriorhodopsin films

The patterning of photoactive purple membrane (PM) films onto electronic substrates to create a biologically based light detection device was investigated. This research is part of a larger collaborative effort to develop a miniaturized toxin detection platform. This platform will utilize PM films containing the photoactive protein bacteriorhodopsin to convert light energy to electrical energy. Following an effort to pattern PM films using focused ion beam machining, the photolithography based bacteriorhodopsin patterning technique (PBBPT) was developed. This technique utilizes conventional photolithography techniques to pattern oriented PM films onto flat substrates. After the basic patterning process was developed, studies were conducted that confirmed the photoelectric functionality of the PM films after patterning. Several process variables were studied and optimized in order to increase the pattern quality of the PM films. Optical microscopy, scanning electron microscopy, and interferometric microscopy were used to evaluate the PM films produced by the patterning technique. Patterned PM films with lateral dimensions of 15 μm have been demonstrated using this technique. Unlike other patterning techniques, the PBBPT uses standard photolithographic processes that make its integration with conventional semiconductor fabrication feasible. The final effort of this research involved integrating PM films patterned using the PBBPT with PMOS transistors. An indirect integration of PM films with PMOS transistors was successfully demonstrated. This indirect integration used the voltage produced by a patterned PM film under light exposure to modulate the gate of a PMOS transistor, activating the transistor. Following this success, a study investigating how this PM based light detection system responded to variations in light intensity supplied to the PM film. This work provides a successful proof of concept for a portion of the toxin detection platform currently under development.

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.

Detection of complex point targets with distributed assets in a MIMO radar system

The report explores the problem of detecting complex point target models in a MIMO radar system. A complex point target is a mathematical and statistical model for a radar target that is not resolved in space, but exhibits varying complex reflectivity across the different bistatic view angles. The complex reflectivity can be modeled as a complex stochastic process whose index set is the set of all the bistatic view angles, and the parameters of the stochastic process follow from an analysis of a target model comprising a number of ideal point scatterers randomly located within some radius of the targets center of mass. The proposed complex point targets may be applicable to statistical inference in multistatic or MIMO radar system. Six different target models are summarized here – three 2-dimensional (Gaussian, Uniform Square, and Uniform Circle) and three 3-dimensional (Gaussian, Uniform Cube, and Uniform Sphere). They are assumed to have different distributions on the location of the point scatterers within the target. We develop data models for the received signals from such targets in the MIMO radar system with distributed assets and partially correlated signals, and consider the resulting detection problem which reduces to the familiar Gauss-Gauss detection problem. We illustrate that the target parameter and transmit signal have an influence on the detector performance through target extent and the SNR respectively. A series of the receiver operator characteristic (ROC) curves are generated to notice the impact on the detector for varying SNR. Kullback–Leibler (KL) divergence is applied to obtain the approximate mean difference between density functions the scatterers assume inside the target models to show the change in the performance of the detector with target extent of the point scatterers.