This thesis describes the detailed modelling procedures for d-q axis representation of multi-machine electromechanical systems. It proposes two methods of connecting electrical machine models which have been formulated in various reference frames, to transmission network models. Investigation of these two methods was accomplished, and the effectiveness and merits of each are fully discussed. Conclusions are drawn which form the bases for recommendation of a connection method for developing a generalised multi-machine power system model. In addition two computer programs are written to provide means of simulating both large and limited capacity power systems on personal computers.

Recognising faces is a task taken for granted by most people, yet it probably represents one of the most complicated visual tasks we routinely perform. Progress in machine vision over recent years has been considerable, but has generally concentrated on areas inappropriate to face recognition. Faces are soft and round, lacking the clear edges and strong geometric properties usually required for machine vision. Instead, subtle changes in shading and texture indicate the transition from one <i>feature</i> to another. To compound the problem, faces are generally very similar, and the small differences that do exist are significant. We describe a machine vision system, called FindFace, that makes use of the underlying similarity of faces to locate specific features, such as the eyes and the mouth. Statistics gathered from 1000 faces are used both to predict the location of features, and evaluate locations generated by numerous independent feature locating routines, called <i>experts</i>. Once an initial location is determined, predictions about the positions of other features can be investigated. This can lead to a rapid increase in confidence as other features are identified in their predicted position, or alternativley to the initial location being quickly rejected. Individual experts can be simple, as a supervisory control system evaluates their performance using the face statistics, and can distinguish good results from bad. The control system can utilise multiple experts for individual features, selecting the most appropriate dynamically based on their previous success rate. The interface between experts and the control system is simple, making the addition of new experts easy. The combination of detailed statistics with many feature experts results in a system that is unhindered by failure to locate specific features, and that continues serching for features until the best solution is obtained with the experts available.

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In today's high-tech industrialized world, we are always looking for faster, and more reliable ways to produce goods. Robotics offers us a possible replacement for the human worker, but can a robot reliably perform the same tasks as a human arm, for example?The complex problem of teaching a robot to move it's hand in some well defined path can be broken down into a variety of algorithms. These path control algorithms generally compute some path description equation, which is used to generate path points either in terms of the Cartesian coordinates of the robot's work cell or the robot's joint variables. Common functions used in the path generation process include cubic spline functions and linear functions.This research project tests a variety of algorithms on a relatively simple robot in order to perform the task of drawing shapes (lines, squares, circles) on planes (horizontal and vertical) in the workcell. By studying the paths drawn we can determine the effect of each algorithm on the path control process, as well as the effect of plane positioning, robot structure, and the robot's controller. / Department of Computer Science

In the 21st century, we are moving ahead in making robots a ubiquitous part of our everyday life. The need for a robot to interact with the environment has become a necessity. The interaction with the world requires a sense of it's pose. Humans clearly are very good in having a sense of their location in the world around them. The same task for robots is very difficult due to the uncertainties in the movement, limitation in sensing of the environment and complexities in the environment itself. When we close our eyes and walk we have a good estimate of our location but the same can't be said for robots. Without the help of external sensors the problem of localization becomes difficult. Humans use their vestibular system to generate cues about their movement and update their position. The same can be done for robots by using acceleration, velocity or odometry as cues to a motion model. The motion model can be represented as a distribution to account for uncertainties in the environment. The parameters to the model are typically static in the current implementation throughout the experiment. Previous work has shown that by having an online calibration method for the model has improved localization. The previous work provided a framework to build adaptive motion model and targeted land based robot and sensors. The work presented here builds on the same framework to adapt motion models for Autonomous Underwater Vehicle. We present detailed results of the framework in a simulator. The work also proposes a method for motion estimation using side sonar images. This is used as a feedback to the motion model. We validate the motion estimation approach with real world datasets.

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