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Recent advancements in control systems, such as the ones used in missile technology in the military or autonomous vehicle development have motivated this study in an attempt to explore various control algorithms and their implementation relevant those applications. Both missile interceptor and autonomous vehicle technology require precise and responsive control system to accurately determine the projectile path of pursuer to strike a moving target or reach a static finish line.The objective of this study is to investigate the performance of several control techniques for a mobile robot to autonomously track and pursue a moving object. Computer model is developed to numerically predict the path taken by the pursuer as it tracks an object moving in regular or random manner. In the computer simulation, the robot's path is calculated using three different techniques: reactive controller, linear estimation, and artificial neural network. Fitness of each method may be determined by evaluating the controller against several factors, such as interception time, steady-state positional error, steady-state time (settling time) and algorithm complexity, listed in decreasing order of importance. A working experimental model is developed to validate the controller selection determined from the computer model simulation. In the experimental setting, the primary inputs to the robot are visual images from cameras. The experiments are carried out with the robot receiving visual inputs from two different perspectives, overhead and frontal vision. Robust image processing technique becomes a topic of significant importance for the system. To manipulate visual images in real-time from raw inputs to comprehensible data, while maintaining fast computational time is a challenge that is addressed in this study. The results from computer simulations show that artificial neural network is a more powerful control algorithm, capable of estimating the object's path more accurately than the other two controllers, resulting in smaller steady-state positional error. The experimental results confirm this conclusion as artificial neural network outperforms the reactive and linear controller by intercepting the object more quickly, i.e. shorter interception time. / text

Breast transillumination has received renewed interest as a noninvasive diagnostic method. However, it is still considered an experimental technique. Many aspects of the physics involved in transillumination have not yet been studied. In this project transillumination was simulated by experiments with a simple breast phantom of realistic dimensions. The light source used was a HeNe laser. From the experiments we found how the contrast of images of a spherical inhomogeneity (simulating a lesion e.g. tumor or cyst) depended on its size and position. The contrast was also measured as a function of the scattering and absorption coefficients of the inhomogeneity. The contrast of the image is shown to fit very well to the curve A(1-exp(-d(Σ^inh_eff – Σ_eff))), where a simple analytical model is developed, where A is identified to (1+B)^-1, B being a build-up factor depending on the surrounding medium and on the position of the inhomogeneity in the medium and d is identified as an effective optical diameter of the inhomogeneity. A three dimensional numerical diffusion program was developed to model the transillumination situation. It was found that diffusion theory gave accurate predictions of the contrast within the constraints of the mathematical model. The three dimensional diffusion approximation and its limitations are discussed in some detail. / Thesis / Master of Science (MSc)

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A computer simulation model was developed to represent a logging contractor's harvesting and trucking system of wood delivery from the contractor's in-woods landing to the receiving mill. The Log Trucking System Simulation model (LTSS) focuses on the impacts to logging contractors as changes in truck turn times cause an imbalance between harvesting and trucking systems. The model was designed to serve as a practical tool that can illustrate the magnitude of cost and productivity changes as the delivery capacity of the contractor's trucking system changes. The model was used to perform incremental analyses using an example contractor's costs and production rates to illustrate the nature of impacts associated with changes in the contractor's trucking system. These analyses indicated that the primary impact of increased turn times occurs when increased delivery time decreases the number of loads per day the contractor's trucking system can deliver. When increased delivery times cause the trucking system to limit harvesting production, total costs per delivered ton increase. In cases where trucking significantly limits system production, total costs per delivered ton would decrease if additional trucks were added. The model allows the user to simulate a harvest with up to eight products trucked to different receiving mills. The LTSS model can be utilized without extensive data input requirements and serves as a user friendly tool for predicting cost and productivity changes in a logging contractor's harvesting and trucking system based on changes in truck delivery times. / Master of Science

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