Image segmentation is an important step in image processing, with many applications such as pattern recognition, object detection, and medical image analysis. It is a technique that separates objects of interests from the background in an image. Geometric active contour is a recent image segmentation method that overcomes previous problems with snakes. It is an attractive method for medical image segmentation as it is able to capture the object of interest in one continuous curve. The theory and implementation details of geometric active contours are discussed in this work. The robustness of the algorithm is tested through a series of tests, involving both synthetic images and medical images. Curve leaking past boundaries is a common problem in cases of non-ideal edges. Noise is also problematic for the advancement of the curve. Smoothing and parameters selection are discussed as ways to help solve these problems. This work also explores the incorporation of the multi-resolution method of Gaussian pyramids into the algorithm. Multi-resolution methods, used extensively in the areas of denoising and edge-selection, can help capture the spatial structure of an image. Results show that similar to the multi-resolution methods applied to parametric active contours, the multi-resolution can greatly increase the computation without sacrificing performance. In fact, results show that with successive smoothing and sub-sampling, performance often improves. Although smoothing and parameter adjustment help improve the performance of geometric active contours, the edge-based approach is still localized and the improvement is limited. Region-based approaches are recommended for further work on active contours.

During the past decade, a new class of knowledge representation has emerged known as structured distributed representation (SDR). A number of schemes for encoding and manipulating such representations have been developed; e. g. Pollack's Recursive Auto-Associative Memory (RAAM), Kanerva's Binary Spatter Code (BSC), Gayler's MAP encoding, and Plate's Holographically Reduced Representations (HRR). All such schemes encode structural information throughout the elements of high dimensional vectors, and are manipulated with rudimentary algebraic operations. <br /><br /> Most SDRs are very compact; components and compositions of components are all represented as fixed-width vectors. However, such compact compositions are unavoidably noisy. As a result, resolving constituent components requires a cleanup memory. In its simplest form, cleanup is performed with a list of vectors that are sequentially compared using a similarity metric. The closest match is deemed the cleaned codevector. <br /><br /> While SDR schemes were originally designed to perform cognitive tasks, none of them have been demonstrated in a neurobiologically plausible substrate. Potentially, mathematically proven properties of these systems may not be neurally realistic. Using Eliasmith and Anderson's (2003) Neural Engineering Framework, I construct various spiking neural networks to simulate a general cleanup memory that is suitable for many schemes. <br /><br /> Importantly, previous work has not taken advantage of parallelization or the high-dimensional properties of neural networks. Nor have they considered the effect of noise within these systems. As well, additional improvements to the cleanup operation may be possible by more efficiently structuring the memory itself. In this thesis I address these lacuna, provide an analysis of systems accuracy, capacity, scalability, and robustness to noise, and explore ways to improve the search efficiency.

Modelling mechanical systems using symbolic equations can provide many advantages over the more widely-used numerical methods of modelling these systems. The use of symbolic equations produces more efficient models, which can be used for many purposes such as real-time simulation and control. However, the number, complexity, and computational efficiency of these equations is highly dependent on which coordinate set was used to model the system. One method of modelling a mechanism's topology and formulating its symbolic equations is to model the system using a graph-theoretical approach. This approach models mechanisms using a linear graph, from which spanning trees can be used to define a mechanism's coordinate set. This report develops two tree selection algorithms capable of estimating the tree set, and hence coordinate set, that produces models having the fastest forward dynamic simulation times. The first tree selection algorithm is a heuristic-based algorithm that tries to find the coordinate set containing the minimal possible number of modelling variables. Most of this algorithm's heuristics are based on tree selection criteria found in the literature and on observations of a series of benchmark problems. It uses the topology information provided by a system's graph to find the coordinates set for the given system that produce very low simulation times of the system. The second tree selection algorithm developed in this report also uses graph theory. It bases most of its heuristics on observations of one of the methods developed to obtain a mechanical system's symbolic equations using graph theory. This second algorithm also makes use of, and improves upon, a few of the heuristics developed in the first tree selection algorithm. A series of examples for both algorithms will demonstrate the computational efficiency obtained by using the modelling variables found by the automated tree selection algorithms that are proposed in this report.

Multiple criteria decision analysis (MCDA) techniques are developed to address challenging classification problems arising in engineering management and elsewhere. MCDA consists of a set of principles and tools to assist a decision maker (DM) to solve a decision problem with a finite set of alternatives compared according to two or more criteria, which are usually conflicting. The three types of classification problems to which original research contributions are made are <ol> <li>Screening: Reduce a large set of alternatives to a smaller set that most likely contains the best choice. </li> <li>Sorting: Arrange the alternatives into a few groups in preference order, so that the DM can manage them more effectively. </li> <li>Nominal classification: Assign alternatives to nominal groups structured by the DM, so that the number of groups, and the characteristics of each group, seem appropriate to the DM. </ol> Research on screening is divided into two parts: the design of a sequential screening procedure that is then applied to water resource planning in the Region of Waterloo, Ontario, Canada; and the development of a case-based distance method for screening that is then demonstrated using a numerical example. <br /><br /> Sorting problems are studied extensively under three headings. Case-based distance sorting is carried out with Model I, which is optimized for use with cardinal criteria only, and Model II, which is designed for both cardinal and ordinal criteria; both sorting approaches are applied to a case study in Canadian municipal water usage analysis. Sorting in inventory management is studied using a case-based distance method designed for multiple criteria ABC analysis, and then applied to a case study involving hospital inventory management. Finally sorting is applied to bilateral negotiation using a case-based distance model to assist negotiators that is then demonstrated on a negotiation regarding the supply of bicycle components. <br /><br /> A new kind of decision analysis problem, called multiple criteria nominal classification (MCNC), is addressed. Traditional classification methods in MCDA focus on sorting alternatives into groups ordered by preference. MCNC is the classification of alternatives into nominal groups, structured by the DM, who specifies multiple characteristics for each group. The features, definitions and structures of MCNC are presented, emphasizing criterion and alternative flexibility. An analysis procedure is proposed to solve MCNC problems systematically and applied to a water resources planning problem.

Offshore shipbuilding industry is known for its highly customized products, which are in most cases tailor-made for specific missions. Being a traditionally conservative industry, it follows conventional design practices. Because of the urge to quickly respond to changing market situations and the need for a structured method to reuse design knowledge across different projects, modern design methodologies like modularization are of considerable interest in this industry. To implement modular thinking, it has to overcome many challenges such as non-functional engineering description of design, scaling, clustering, logistical issues, structural complexities etc. Different modular methodologies can be adopted to establish a modular product platform. Modular Function Deployment (MFDTM) is a popular theory of modularization with five distinct steps to identify the objects that should be modularized and optimize them for the whole manufacturing system. Application of MFDTM method reveals that among various spaces inside the accommodation of an OSV, crew single, crew double and officer&#146;s cabins are the most appropriate ones to be modularized.Based on the output of MFDTM method and the vessel database from System Based Ship Design (SBSD) approach for OSVs, a decision support framework has been developed to assist the naval architect of the vessel to design the accommodation block. Standardized templates are used for arrangement of the modules. The spaces in the accommodation are categorized into three module classes. The DSF can calculate the optimum size, type and location of the modules according to design requirements and system constraints. The vessel database is used here to determine the required area for the spaces that are not standardized. The DSF is demonstrated by an illustrative example and later by a simplified real case study. A mock up of a user interface has also been developed to give an idea of user interaction with a computer tool based on the DSF. For the real case study, a small program has been developed containing some 1200+ lines of code, which can process simplified accommodation design cases.To build an effective DSS for designing the accommodation block of OSVs, standardization of spaces by modularization must be done with enough room for flexibility of design by designer intervention. This DSF exhibits the underlying logics and structure of such DSS, which can be very useful to the designers upon further development.

This thesis seeks to understand the past and present state of engineering education and to plot a course for its future evolution. This research is limited to engineering education as it has taken place in North American universities during the last half of the 20th century. Within this context, broad trends are described. The description is supplemented with a case study of a unique and innovative engineering programme. The trends and case study form the foundation of a synthesis, and alternative vision, for higher education and engineering education. The intended audience of this thesis includes those who teach, design curriculum, or administer engineering education programmes. The description of the current state of engineering education contains analyses of the state and of the gaps within it. Both of these analyses are based almost exclusively on publicly available documentation. The present state of engineering is drawn from accreditation criteria. Critiques of the current state and suggestions for future change are drawn from reports commissioned by groups affiliated with professional engineering. The discussions identify recurring themes and patterns. Unlike the analysis of the literature, the case study merges interview evidence and personal experience with the available documentation. The synthesis and visions continue the trend away from formal sources towards experiences and beliefs. Engineering education research is in its infancy and shows few signs of maturing. There is no documented, common framing of engineering education nor have there been any efforts in this regard. Few sources address broad issues and those that do lack theoretical rigour. The visions for engineering education are simple amalgams of visions for the profession and for general higher education. The Department of Systems Design Engineering has enjoyed great past successes because of its unique vision that combines the theories of systems, complexity, and design with the discipline of engineering. Its recent decay can be traced to its faculty having collectively lost this vision. The original vision for Systems Design Engineering holds promise as a means to reinvent and reinvigorate both the engineering profession and engineering education. For this renaissance to be successful a theoretically rigorous research programme assessing the past, present, and future of engineering and engineering education must be developed.

Haptic applications often employ devices with many degrees of freedom in order to allow the user to have natural movement during human-machine interaction. From the development point of view, the complexity in mechanical dynamics imposes a lot of challenges in modelling the behaviour of the device. Traditional system identification methods for nonlinear systems are often computationally expensive. Moreover, current research on using neural network approaches disconnect the physical device dynamics with the identification process. This thesis proposes a different approach to system identification of complex haptic devices when analytical models are formulated. It organizes the unknowns to be identified based on the governing dynamic equations of the device and reduces the cost of computation. All the experimental work is done with the Freedom 6S, a haptic device with input and feedback in positions and velocities for all 6 degrees of freedom . <br /><br /> Once a symbolic model is developed, a subset of the overall dynamic equations describing selected joint(s) of the haptic robot can be obtained. The advantage of being able to describe the selected joint(s) is that when other non-selected joints are physically fixed or locked up, it mathematically simplifies the subset dynamic equation. Hence, a reduced set of unknowns (e. g. mass, centroid location, inertia, friction, etc) resulting from the simplified subset equation describes the dynamic of the selected joint(s) at a given mechanical orientation of the robot. By studying the subset equations describing the joints, a locking sequence of joints can be determined to minimize the number of unknowns to be determined at a time. All the unknowns of the system can be systematically determined by locking selected joint(s) of the device following this locking sequence. Two system identification methods are proposed: Method of Isolated Joint and Method of Coupling Joints. Simulation results confirm that the latter approach is able to successfully identify the system unknowns of Freedom 6S. Both open-loop experimental tests and close-loop verification comparison between the measured and simulated results are presented. <br /><br /> Once the haptic device is modelled, fuzzy logic is used to address chattering phenomenon common to strong virtual effects. In this work, a virtual wall is used to demonstrate this approach. The fuzzy controller design is discussed and experimental comparison between the performance of using a proportional-derivative gain controller and the designed fuzzy controller is presented. The fuzzy controller is able to outperform the traditional controller, eliminating the need for hardware upgrades for improved haptic performance. Summary of results and conclusions are included along with suggested future work to be done.

The goal of this project is to apply system identification techniques to model people's perception of emotion in music as a function of time. Emotional appraisals of six selections of classical music are measured from volunteers who continuously quantify emotion using the dimensions valence and arousal. Also, features that communicate emotion are extracted from the music as a function of time. By treating the features as inputs to a system and the emotional appraisals as outputs of that system, linear models of the emotional appraisals are created. The models are validated by predicting a listener's emotional appraisals of a musical selection (song) unfamiliar to the system. The results of this project show that system identification provides a means to improve previous models for individual songs by allowing them to generalize emotional appraisals for a genre of music. The average <i>R</i>² statistic of the best model structure in this project is 7. 7% for valence and 75. 1% for arousal, which is comparable to the <i>R</i>² statistics for models of individual songs.

The main goal of this thesis was to discover the relationships between MU characteristics and MUP features. To reach this goal, several features explaining the anatomical structure of the muscle were introduced. Additionally, features representing specific properties of the EMG signal detected from that muscle, were defined. Since information regarding the underlying anatomy was not available from real data, a physiologically based muscle model was used to extract the required features. This muscle model stands out from others, by providing similar acquisition schemes as the ones utilized by physicians in real clinical settings and by modelling the interactions among different volume conductor factors and the collection of MUs in the muscle in a realistic way. Having the features ready, several relationship discovery techniques were used, to reveal relationships between MU features and MUP features. To interpret the results obtained from the correlation analysis and pattern discovery techniques properly, several algorithms and new statistics were defined. The results obtained from correlation analysis and pattern discovery technique were similar to each other, and suggested that to maximize the inter-relationships between MUP features and MU features, MUPs could be filtered based on their slope values, specifically MUPs with slopes lower than 0. 6 v/s could be excluded. Additionally PDT results showed that high slope MUPs were not as informative about the underlying MU and could be excluded to maximize the relationships between MUP features and MU characteristics. Certain MUP features were determined to be highly related to certain MU characteristics. MUP <em>area</em> and <em>duration</em> were shown to be the best representative feature for the MU size and <em>average fiber density</em>, respectively. For the distribution of fiber diameter in the MU, <em>duration</em> and <em>number of turns</em> were determined to reflect <em>mean fiber diameter</em> and <em>stdv of fiber diameter</em> the best, correspondingly.

Image segmentation is an important step in image processing, with many applications such as pattern recognition, object detection, and medical image analysis. It is a technique that separates objects of interests from the background in an image. Geometric active contour is a recent image segmentation method that overcomes previous problems with snakes. It is an attractive method for medical image segmentation as it is able to capture the object of interest in one continuous curve. The theory and implementation details of geometric active contours are discussed in this work. The robustness of the algorithm is tested through a series of tests, involving both synthetic images and medical images. Curve leaking past boundaries is a common problem in cases of non-ideal edges. Noise is also problematic for the advancement of the curve. Smoothing and parameters selection are discussed as ways to help solve these problems. This work also explores the incorporation of the multi-resolution method of Gaussian pyramids into the algorithm. Multi-resolution methods, used extensively in the areas of denoising and edge-selection, can help capture the spatial structure of an image. Results show that similar to the multi-resolution methods applied to parametric active contours, the multi-resolution can greatly increase the computation without sacrificing performance. In fact, results show that with successive smoothing and sub-sampling, performance often improves. Although smoothing and parameter adjustment help improve the performance of geometric active contours, the edge-based approach is still localized and the improvement is limited. Region-based approaches are recommended for further work on active contours.