One of the most significant developments in cockpit technology over the past several years is the emergence of a new cockpit architecture that uses cursor control devices and keyboards for interaction with individual and shared displays. This architecture has allowed for the design of cockpit interfaces with many advantages compared to traditional designs. However, there are a number of challenges associated with these new cockpits that should be addressed so that pilots will be able to take full advantage of the performance improvements available from the new designs. This thesis describes three of the major challenges associated with the new architecture: supporting awareness, assisting interruption recovery, and mitigating interaction conflicts. It also describes the analysis process used to identify these challenges and proposes an interface augmentation with the potential to address them. The proposed design uses visualizations of the history of operator interactions with the interface to provide cues to the pilots about where each of them has been (and is currently) interacting. This interaction data includes both visual (sourced from a gaze tracking system) and input (from the keyboard or cursor control device) information, and was communicated on the interface using dynamic borders around the relevant areas of the interface. This augmentation aimed to address the three identified challenges by providing pilots with: improved awareness of each other’s actions, visual cues of where they were working prior to an interruption and what has changed since, and clear indications of where each is working to allow them to avoid conflicts. A two-stage evaluation process was used to determine the utility of the interface concept in a cockpit context by developing a non-interactive video prototype and showing it to pilots. The results of the evaluation indicated that the design has sufficient potential to warrant further study, as evaluation in higher fidelity environments would help provide further evidence of its potential utility for live cockpit operations. Therefore, future work should include the development and evaluation of a fully interactive prototype for live cockpit operations, as well as further examination of the design concept’s potential for use as a training tool.

Interruptions have negative effects on the task performance in modern work environments. These negative effects are not affordable in tasks in which decisions are time-critical and have a life-critical nature. Human-supervisory control (HSC) tasks in time-critical settings such as mission command and control and emergency response are especially vulnerable to the negative effects of interruptions since supervisors in these settings are prone to frequent interruptions which are valuable source of information and hence cannot be ignored and consequences of a wrong decision in these settings is very costly because of their life-critical nature. To address this issue, this thesis investigates an activity-centric design approach that aims to help team supervisors in a complex mission control operation to remain aware of the activities that most likely would affect their decisions, while minimizing disruption. An interruption recovery assistant (IRA) tool was designed to promote activity and situation awareness of a team of UAV operators in a representative task. Initial pilot studies showed a positive trend in effectiveness of the IRA tool on recovery time and decision accuracy. This thesis explores alternative design approaches to validate the effectiveness of an interruption recovery tool that enable mission commanders rapidly and effectively regain the situational awareness after an interruption occurs in the mission environment. This thesis overview these design approaches and present results from a series of formative evaluations of our prototype designs. These evaluations were conducted in an experimental platform designed to emulate futuristic semi-autonomous UAV team mission operations.

Symmetry has been a very useful tool to researchers in various scientific fields. At its most basic, symmetry refers to the invariance of an object to some transformation, or set of transformations. Usually one searches for, and uses information concerning an existing symmetry within given data, structure or concept to somehow improve algorithm performance or compress the search space. This thesis examines the effects of imposing or inducing symmetry on a search space. That is, the question being asked is whether only existing symmetries can be useful, or whether changing reference to an intuition-based definition of symmetry over the evaluation function can also be of use. Within the context of optimization, symmetry induction as defined in this thesis will have the effect of equating the evaluation of a set of given objects. Group theory is employed to explore possible symmetrical structures inherent in a search space. Additionally, conditions when the search space can have a symmetry induced on it are examined. The idea of a neighborhood structure then leads to the idea of opposition-based computing which aims to induce a symmetry of the evaluation function. In this context, the search space can be seen as having a symmetry imposed on it. To be useful, it is shown that an opposite map must be defined such that it equates elements of the search space which have a relatively large difference in their respective evaluations. Using this idea a general framework for employing opposition-based ideas is proposed. To show the efficacy of these ideas, the framework is applied to popular computational intelligence algorithms within the areas of Monte Carlo optimization, estimation of distribution and neural network learning. The first example application focuses on simulated annealing, a popular Monte Carlo optimization algorithm. At a given iteration, symmetry is induced on the system by considering opposite neighbors. Using this technique, a temporary symmetry over the neighborhood region is induced. This simple algorithm is benchmarked using common real optimization problems and compared against traditional simulated annealing as well as a randomized version. The results highlight improvements in accuracy, reliability and convergence rate. An application to image thresholding further confirms the results. Another example application, population-based incremental learning, is rooted in estimation of distribution algorithms. A major problem with these techniques is a rapid loss of diversity within the samples after a relatively low number of iterations. The opposite sample is introduced as a remedy to this problem. After proving an increased diversity, a new probability update procedure is designed. This opposition-based version of the algorithm is benchmarked using common binary optimization problems which have characteristics of deceptivity and attractive basins characteristic of difficult real world problems. Experiments reveal improvements in diversity, accuracy, reliability and convergence rate over the traditional approach. Ten instances of the traveling salesman problem and six image thresholding problems are used to further highlight the improvements. Finally, gradient-based learning for feedforward neural networks is improved using opposition-based ideas. The opposite transfer function is presented as a simple adaptive neuron which easily allows for efficiently jumping in weight space. It is shown that each possible opposite network represents a unique input-output mapping, each having an associated effect on the numerical conditioning of the network. Experiments confirm the potential of opposite networks during pre- and early training stages. A heuristic for efficiently selecting one opposite network per epoch is presented. Benchmarking focuses on common classification problems and reveals improvements in accuracy, reliability, convergence rate and generalization ability over common backpropagation variants. To further show the potential, the heuristic is applied to resilient propagation where similar improvements are also found.

In magnetic resonance imaging (MRI), sampling methods that lead to incomplete data coverage of k-space are used to accelerate imaging and reduce overall scan time. Non-Cartesian sampling trajectories such as radial, spiral, and random trajectories are employed to facilitate advanced imaging techniques, such as compressed sensing, or to provide more efficient coverage of k-space for a shorter scan period. When k-space is undersampled or unevenly sampled, traditional methods of transforming Fourier data to obtain the desired image, such as the FFT, may no longer be applicable. The Fourier reconstruction of optical interferometer data (FROID) algorithm is a novel reconstruction method developed by A. R. Hajian that has been successful in the field of optical interferometry in reconstructing images from sparsely and unevenly sampled data. It is applicable to cases where the collected data is a Fourier representation of the desired image or spectrum. The framework presented allows for a priori information, such as the positions of the sampled points, to be incorporated into the reconstruction of images. Initially, FROID assumes a guess of the real-valued spectrum or image in the form of an interpolated function and calculates the corresponding integral Fourier transform. Amplitudes are then sampled in the Fourier space at locations corresponding to the acquired measurements to form a model dataset. The guess spectrum or image is then adjusted such that the model dataset in the Fourier space is least squares fitted to measured values. In this thesis, FROID has been adapted and implemented for use in MRI where k-space is the Fourier transform of the desired image. By forming a continuous mapping of the image and modelling data in the Fourier space, a comparison and optimization with respect to data acquired in k-space that is either undersampled or irregularly sampled can be performed as long as the sampling positions are known. To apply FROID to the reconstruction of magnetic resonance images, an appropriate objective function that expresses the desired least squares fit criteria was defined and the model for interpolating Fourier data was extended to include complex values of an image. When an image with two Gaussian functions was tested, FROID was able to reconstruct images from data randomly sampled in k-space and was not restricted to data sampled evenly on a Cartesian grid. An MR image of a bone with complex values was also reconstructed using FROID and the magnitude image was compared to that reconstructed by the FFT. It was found that FROID outperformed the FFT in certain cases even when data were rectilinearly sampled.

The accurate detection of static object boundaries such as contours or surfaces and dynamic tunnels of moving objects via deformable models is an ongoing research topic in computer vision. Most deformable models attempt to converge towards a desired solution by minimizing the sum of internal (prior) and external (measurement) energy terms. Such an approach is elegant, but frequently mis-converges in the presence of noise or complex boundaries and typically requires careful semi-dependent parameter tuning and initialization. Furthermore, current deformable model based approaches are computationally demanding which precludes real-time use. To address these limitations, a decoupled deformable model (DDM) is developed which optimizes the two energy terms separately. Essentially, the DDM consists of a measurement update step, employing a Hidden Markov Model (HMM) and Maximum Likelihood (ML) estimator, followed by a separate prior step, which modifies the updated deformable model based on the relative strengths of the measurement uncertainty and the non-stationary prior. The non-stationary prior is generated by using a curvature guided importance sampling method to capture high curvature regions. By separating the measurement and prior steps, the algorithm is less likely to mis-converge; furthermore, the use of a non-iterative ML estimator allows the method to converge more rapidly than energy-based iterative solvers. The full functionality of the DDM is developed in three phases. First, a DDM in 2D called the decoupled active contour (DAC) is developed to accurately identify the boundary of a 2D object in the presence of noise and background clutter. To carry out this task, the DAC employs the Viterbi algorithm as a truncated ML estimator, curvature guided importance sampling as a non-stationary prior generator, and a linear Bayesian estimator to fuse the non-stationary prior with the measurements. Experimental results clearly demonstrate that the DAC is robust to noise, can capture regions of very high curvature, and exhibits limited dependence on contour initialization or parameter settings. Compared to three other published methods and across many images, the DAC is found to be faster and to offer consistently accurate boundary identification. Second, a fast decoupled active contour (FDAC) is proposed to accelerate the convergence rate and the scalability of the DAC without sacrificing the accuracy by employing computationally efficient and scalable techniques to solve the three primary steps of DAC. The computational advantage of the FDAC is demonstrated both experimentally and analytically compared to three computationally efficient methods using illustrative examples. Finally, an extension of the FDAC from 2D to 3D called a decoupled active surface (DAS) is developed to precisely identify the surface of a volumetric 3D image and the tunnel of a moving 2D object. To achieve the objectives of the DAS, the concepts of the FDAC are extended to 3D by using a specialized 3D deformable model representation scheme and a computationally and storage efficient estimation scheme. The performance of the DAS is demonstrated using several natural and synthetic volumetric images and a sequence of moving objects.

Systems methodologies are employed to investigate strategic decision problems regarding the development of the oil sands in Canada. Many countries believe energy to be one of their most important national security factors in today’s competitive global era. Canada is no exception. Energy is an issue in Canadians’ growing concerns related to the conflicting priorities of its economy, environment, and society. Various studies have tried to map out Canada’s establishment as an energy superpower. In particular, the massive resources in Canada must be considered as competitive advantages, and oil sands (tar sands) constitute one of the most crucial elements in terms of non-renewable energy. This thesis describes Canada’s oil sands – their characteristics, cost and market analysis, as well as social, economic, and environmental impacts – in order to clarify conflicts that have arisen in recent years. In addition, the importance, potential, and constraints of the oil sands are examined as leading drivers to the country becoming an energy superpower and are compared with the Canadian Academy of Engineering (CAE)’s studies and recommendations. Multiple-criteria decision analyses based on the ProGrid methodology are carried out at different levels to clarify the structure and current position of Canada’s energy system. An Evaluation Matrix, including multiple criteria, is built, and language ladders with different weights are established to allow various groups of experts to evaluate available options. Based on their evaluations, the strong and weak points of the oil sands are analyzed. At a more detailed level, alternative solutions for water quantity and quality problems in Canada’s oil sands are prioritized with respect to specific criteria, using the ProGrid methodology. The strategic issues in Canada’s oil sands are addressed at different levels, and priorities for decision-making are determined and discussed to guide Canada in becoming an energy superpower.

Humidity sensors are one of the most widely used sensors in commercial and industrial applications for environmental monitoring and controlling. Although related technology have been studied intensively, humidity sensing in harsh environments still remains a challenge. The inability of current humidity sensors to operate in high temperature environments is generally due to the degradation of the sensing films caused by high temperature, high humidity level, and/or contamination. Our goal is the design and fabrication of a humidity sensor that is capable of working under high temperatures and in a condensing environment. The targeted application of this sensor is in the polymer electrolyte membrane (PEM) fuel cell, where humidity control is crucial for performance optimization. In this work, ordered macroporous silicon is thoroughly studied as a humidity sensing layer. In addition to the advantages of traditional porous silicon for gas sensing (high resistance to high temperature and good compatibility with current IC fabrication process), the ordered macroporous silicon used in these experiment has uniform pore size, pore shape and distribution. All the vertical aligned pores can be opened to the environment at both ends, which can significantly increase the efficiency of gas diffusion and adsorption. Moreover, this special structure opens the door to uniform surface modifications for sensing enhancement. Both ordered macroporous silicon based heterostructure and self-supporting membrane are fabricated and investigated as a humidity sensor. Heterostructure sensors with different thin film surface coatings including bare Si, thermally grown SiO2, atom layer deposited ZnO, HfO2, and Ta2O5 are characterized. Post micro-fabrication is achieved on this ordered porous structure without affecting the material and its sensing properties. It has been proven that the ordered macroporous silicon with Ta2O5 surface coating shows the best sensing property due to its ultra-hydrophilic surface. The sensor shows high sensitivity, fast response times, small hysteresis, and extraordinary stability and repeatability under high temperatures and in condensing environment. It demonstrates great potential and advantages over existing commercial humidity sensors in the fuel cell application field. In addition to ordered macroporous silicon, well aligned 1D ZnO nanorods/nanowires -another widely used nanostructure in gas sensing- is also investigated as humidity sensing materials. Both vertically and laterally aligned nanorods/nanowires are fabricated and tested against humidity changes. The sensors shows increasing resistance to increasing relative humidity, which is contrary to most published works so far. Possible mechanisms have been proposed in this thesis and future work has been suggested for further study. To the best of our knowledge, this work is the first to use ordered macroporous silicon and well aligned 1D ZnO nanorods/nanowires for humidity sensing.

The determination of the 3D motion of a camera and the 3D structure of the scene in which the camera is moving, known as the Structure from Motion (SFM) problem, is a central problem in computer vision. Specifically, the recursive (online) estimation is of major interest for robotics applications such as navigation and mapping. Many problems still hinder the deployment of SFM in real-life applications namely, (1) the robustness to noise, outliers and ambiguous motions, (2) the numerical tractability with a large number of features and (3) the cases of rapidly varying camera velocities. Towards solving those problems, this research presents the following four contributions that can be used individually, together, or combined with other approaches. A motion-only filter is devised by capitalizing on algebraic threading constraints. This filter efficiently integrates information over multiple frames achieving a performance comparable to the best state of the art filters. However, unlike other filter based approaches, it is not affected by large baselines (displacement between camera centers). An approach is introduced to incorporate, with only a small computational overhead, a large number of frame-to-frame features (i.e., features that are matched only in pairs of consecutive frames) in any analytic filter. The computational overhead grows linearly with the number of added frame-to-frame features and the experimental results show an increased accuracy and consistency. A novel filtering approach scalable to accommodate a large number of features is proposed. This approach achieves both the scalability of the state of the art filter in scalability and the accuracy of the state of the art filter in accuracy. A solution to the problem of prediction over large baselines in monocular Bayesian filters is presented. This problem is due to the fact that a simple prediction, using constant velocity models for example, is not suitable for large baselines, and the projections of the 3D points that are in the state vector can not be used in the prediction due to the need of preserving the statistical independence of the prediction and update steps.

The current and future energy situations in Canada are put into perspective, and the importance of nuclear energy and controversies surrounding it are investigated. More specifically, to demonstrate the important role nuclear energy has to play in Canada's future, a novel energy modeling tool, Canadian Energy Systems Simulator (CanESS), is employed. CanESS is a modeling platform with a huge database that assists an analyst in defining different energy scenarios by modifying the variables such as population and contributions of different energy sources to the overall production. The CanESS results clearly show that expansion of nuclear energy production is required to meet energy demand and simultaneously reduce greenhouse gas emissions. To formally study strategic issues connected to the ongoing conflict over nuclear power production in Ontario, the Graph Model for Conflict Resolution (GMCR) is utilized. This flexible systems methodology is used to study the nuclear disputes that existed in Ontario at two key points in time: the fall of 2008 and spring of 2010. The results of the 2008 analysis, especially the sensitivity analyses, show that the only decision makers (DMs) involved in the conflict who hold real power are the Federal and Ontario governments, although at the beginning of the investigation the Atomic Energy of Canada Ltd. (AECL) and the environmental groups had also been considered as participating DMs. The findings and information of the analysis in 2008, as well as an updated background for 2010, are used to perform another analysis in 2010. Meanwhile, their options or possible courses of action have also been changed. Again, at this stage the stable states of the game are found, and attitude analysis is carried out to obtain deeper insights about the dispute. The equilibria or potential resolutions of the 2008 analysis are found to be the transition states in the 2010 analysis. Specifically, it is discovered that if the Federal Government does have a negative attitude towards the Ontario Government, it is possible that the final outcome is a state that is among the least preferred states for both DMs. To formally study strategic issues connected to the ongoing conflict over nuclear power production in Ontario, the Graph Model for Conflict Resolution (GMCR) is utilized. This flexible systems methodology is used to study the nuclear disputes that existed in Ontario at two key points in time: the fall of 2008 and spring of 2010. The results of the 2008 analysis, especially the sensitivity analyses, show that the only decision makers (DMs) involved in the conflict who hold real power are the Federal and Ontario governments, although at the beginning of the investigation the Atomic Energy of Canada Ltd. (AECL) and the environmental groups had also been considered as participating DMs. The findings and information of the analysis in 2008, as well as an updated background for 2010, are used to perform another analysis in 2010. According to the results of the 2008 analysis, only the two governments are considered as the DMs in 2010. Meanwhile, their options or possible courses of action have also been changed. Again, at this stage the stable states of the game are found, and attitude analysis is carried out to obtain deeper insights about the dispute. The equilibria or potential resolutions of the 2008 analysis are found to be the transition states in the 2010 analysis. Specifically, it is discovered that if the Federal Government does have a negative attitude towards the Ontario Government, it is possible that the final outcome is a state that is among the least preferred states for both DMs.

This thesis is an exploration of how to do sustainable development for buildings, especially during the earliest stages of such development. The thesis starts by considering clear definitions of sustainability, development and sustainable development as these concepts apply to organizations in general and as they apply specifically to the charity All Our Relations (AOR) and their community of the Region of Waterloo in Ontario, Canada. Three critical challenges to the process of development are also discussed in these early chapters, namely assessment, vision and feedback. In the third chapter, these same challenges are put under the lens of sustainable development and three new, but related, challenges of connection complexity, shared futures and resilience are examined to better understand the problems and solutions that surround them. At the end of this broad introductory section, AOR’s relationships with the community are explored as part of their efforts to draft an organization-wide sustainability plan. The second part of the thesis is an attempt to apply and expand on the general ideas from the first half through a focus on buildings and specifically the building of AOR’s planned Hospice and Retreat Centre in Bloomingdale, Ontario. As part of the focus on sustainable buildings, the Leadership in Energy and Environmental Design (LEED™) system of assessing building impacts is presented and critiqued. As part of a focus on building developments the earlier challenges of assessment, vision and feedback are revisited as they apply to the concept design phase of the typical building design. The final three chapters of the thesis are a synthesis of all the previous chapters and the formal presentation of the case study concept development for the AOR building. A full summary of all previous definitions are presented and the final definition of sustainable building development is expressed as a culmination and extension of its parts: Sustainable building development is a process of creating space-for-use which recognizes both the importance of space in our lives and the impact that developing that space has on our greater goal to pursue sustainability. Potential critiques of this definition are discussed and two methods of engaging in the difficult challenges of sustainable building development are presented: the decider’s dilemma and the life-cycle-service-network model of connection complexity. Finally, the case study use of LEED as a guide for doing sustainable development in buildings is contrasted against the author’s proposed approaches. Through a series of qualitative and quantitative observations based on the results from the case study design, LEED is revealed as being effective mostly as an early guide, but lacking in the rigor and complexity needed to address properly the challenges of building sustainability.