The Nursing Coordinators (NCs) at the University of Ottawa Heart Institute (UOHI) fields phone calls from patients who have been discharged and are undergoing home care procedures at a daily basis. The project described in this thesis aims to provide tools for the Personal Digital Assistant (PDA) that the NCs can use during the phone calls. The Cognitive Work Analysis (CWA) and Ecological Interface Design (EID) approach are used to identify the information requirements to design the system. Major challenges of the telephone consultation process that are additionally identified by literature review and interviewing the NCs included visibility of patients, individual differences, and lack of standardized procedures. A combination of decision trees and visualization techniques is proposed to aid the process. Implementation of decision trees would help unload mental workload especially accesses to "knowledge in the head" as well as facilitate expert knowledge transfer to less experienced nurses. Visualization tools display integration of multiple-cues from patients in an abstract nature and can be accessed by users at any point of decision process. <br /><br /> Preliminary experiment with static images showed that visualization tools helped the decision makers more when the judgement tasks were more complex. The effects of different types of decision support on the cardiac nurses in simulated telephone consultation processes were examined. The system improved the performance of the decision makers and induced different types of strategic behaviours: a standardized checklist, OLDCAR, induced more through assessment, the decision algorithms induced efficient and more detailed recommendation, and the semantic network symptom map induced information gathering more relevant to diagnosis. <br /><br /> The research also explored methodologies to examine multi-layered decision process, where many decision makers with varying expertise are involved in modeling the strategic behaviours. This type of process can be applicable when the primary decision makers do not monitor the work domain, but can be alerted when something goes wrong.

The main crux of this thesis was to produce a model that was capable of simulating the theoretical performance of different configurations for a spatial measurement system using radio frequency technology. It has been important to study new modalities of spatial measurement since spatial measurement systems are an enabling technology that have allowed for the creation of better medical procedures and techniques, provided valuable data for motion capture in animation and biomechanics, and have improved the quality of manufacturing processes in many industries. However, there has been room for improvement in the functional design and accuracy of spatial measurement systems that will enhance current applications and further develop new applications in medicine, research and industry. <br /><br /> In this thesis, a modelling framework for the investigation of spatial measurement based on radio frequency signals was developed. The simulation framework was designed for the purpose of investigating different position determination algorithms and sensor geomatries. A finite element model using the FEMLAB partial differential equation modelling tool was created for a time-domain model of electromagnetic wave propagation in order to simulate the radio frequency signals travelling from a transmitting source antenna to a set of receiving antenna sensors. Electronic line signals were obtained using a simple receiving infinitesimal dipole model and input into a time difference of arrival localization algorithm. The finite element model results were validated against a set of analytical solutions for the free space case. The accuracy of the localization algorithm was measured against a set of possible applications for a potential radio frequency spatial measurement system design. <br /><br /> It was concluded that the simulation framework was successful should one significant deficiency be corrected in future research endeavours. A phase error was observed in the signals extracted at the receiving antenna locations. This phase error, which can be up to 40°, was attributed to the zeroth order finite elements implemented in the finite element model. This phase error can be corrected in the future if higher order vector elements are introduced into future versions of FEMLAB or via the development of custom finite element analysis software but were not implemented in this thesis due to time constraints. Other improvements were also suggested for future work.

Optimal control methods are applied to mechanical models in order to predict the control strategies in human arm movements. Optimality criteria are used to determine unique controls for a biomechanical model of the human upper-limb with redundant actuators. The motivation for this thesis is to provide a non-task-specific method of motion prediction as a tool for movement researchers and for controlling human models within virtual prototyping environments. The current strategy is based on determining the muscle activation levels (control signals) necessary to perform a task that optimizes several physical determinants of the model such as muscular and joint stresses, as well as performance timing. Currently, the initial and final location, orientation, and velocity of the hand define the desired task. Several models of the human arm were generated using a graph-theoretical method in order to take advantage of similar system topology through the evolution of arm models. Within this framework, muscles were modelled as non-linear actuator components acting between origin and insertion points on rigid body segments. Activation levels of the muscle actuators are considered the control inputs to the arm model. Optimization of the activation levels is performed via a hybrid genetic algorithm (GA) and a sequential quadratic programming (SQP) technique, which provides a globally optimal solution without sacrificing numerical precision, unlike traditional genetic algorithms. Advantages of the underlying genetic algorithm approach are that it does not require any prior knowledge of what might be a 'good' approximation in order for the method to converge, and it enables several objectives to be included in the evaluation of the fitness function. Results indicate that this approach can predict optimal strategies when compared to benchmark minimum-time maneuvers of a robot manipulator. The formulation and integration of the aforementioned components into a working model and the simulation of reaching and lifting tasks represents the bulk of the thesis. Results are compared to motion data collected in the laboratory from a test subject performing the same tasks. Discrepancies in the results are primarily due to model fidelity. However, more complex models are not evaluated due to the additional computational time required. The theoretical approach provides an excellent foundation, but further work is required to increase the computational efficiency of the numerical implementation before proceeding to more complex models.

Controlling systems with multiple robots is quickly becoming the next large hurdle that must be overcome for groups of robots to successfully function as a team. An agent oriented approach for this problem is presented in this thesis. By using an agent oriented method, the robots can act independently yet still work together. To be able to establish communities of robots, a basic agent oriented control system for each robot must first be implemented. This thesis introduces a novel method to create Physical Robot Agents, promoting a separation of cognitive and reactive behaviours into a two layer system. These layers are further abstracted into key subsections that are required for the Physical Robot Agents to function. To test this architecture, experiments are performed with physical robots to determine the feasibility of this approach. <br /><br /> A real-time implementation of a Physical Robot Agent would greatly expand its field of use. The speed of internal communication is analyzed to validate the application of this architecture to real-time tasks. <br /><br /> It is concluded that the Physical Robot Agents are well suited for multiple robot systems and that real-time applications are feasible.

Texture analysis has been used extensively in the computer-assisted interpretation of SAR sea ice imagery. Provision of maps which distinguish relevant ice types is significant for monitoring global warming and ship navigation. Due to the abundance of SAR imagery available, there exists a need to develop an automated approach for SAR sea ice interpretation. Grey level co-occurrence probability (<i>GLCP</i>) texture features are very popular for SAR sea ice classification. Although these features are used extensively in the literature, they have a tendency to erode and misclassify texture boundaries. Proposed is an advancement to the <i>GLCP</i> method which will preserve texture boundaries during image segmentation. This method exploits the relationship a pixel has with its closest neighbors and weights the texture measurement accordingly. These texture features are referred to as <i>WGLCP</i> (weighted <i>GLCP</i>) texture features. In this research, the <i>WGLCP</i> and <i>GLCP</i> feature sets are compared in terms of boundary preservation, unsupervised segmentation ability, robustness to increasing boundary density and computation time. The <i>WGLCP</i> method outperforms the <i>GLCP</i> method in all aspects except for computation time, where it suffers. From the comparative analysis, an inconsistency with the <i>GLCP</i> correlation statistic was observed, which motivated an investigative study into using this statistic for image segmentation. As the overall goal of the thesis is to improve SAR sea ice segmentation accuracy, the concepts developed from the study are applied to the image segmentation problem. The results indicate that for images with high contrast boundaries, the <i>GLCP</i> correlation statistical feature decreases segmentation accuracy. When comparing <i>WGLCP</i> and <i>GLCP</i> features for segmentation, the <i>WGLCP</i> features provide higher segmentation accuracy.

Powered orthotic devices can be used to restore mobility to the impaired user, and may thereby assist them in daily living tasks. An investigation is performed herein to examine the feasibility of a powered lower-limb orthotic in assisting the sit-to-stand task by 50% of the required torque. Feasibility is considered via simulation. A three-link sit-to-stand model, which is driven by kinematic data, is developed. Models of a Pneumatic Muscle Actuator and a DC motor are used to determine which of the two technologies can make a more appropriate contribution to the sit-to-stand task. Simulation revealed that both the Pneumatic Muscle Actuator and the DC motor are reasonable actuator choices, and neither limited the ability to achieve 50% torque assistance. The ability to assist the task was, however, limited by the ability to derive a control signal for the actuator from the user-orthotic interface. It was concluded that the user-orthotic interface requires further investigation. It was also found that while both actuator technologies are suitable for contributing 50% of the required torque, the Pneumatic Muscle Actuator is preferable due to its ability to scale to greater torques.

This research deals with urban water reuse planning and management modeling in the context of sustainable development. Rapid urbanization and population growth have presented a great challenge to urban water resources management. As water reuse may alleviate pollution loads and enhance water supply sources, water reuse is being recognized as a sustainable urban water management strategy and is becoming increasingly attractive in urban water resources management. An efficient water reuse planning and management model is of significance in promoting water reuse practices. This thesis introduces an urban water reuse management and planning model using optimization methods with an emphasis on modeling uncertainty issues associated with water demand and water quality. The model is developed in conjunction with the overall urban water system with considerations over water supply, water demand, water distribution, water quality, and wastewater treatment and discharge. The objective of the model is to minimize the overall cost of the system subject to technological, societal and environmental constraints. Uncertainty issues associated with water demand and treatment quality are modeled by introducing stochastic programming methods, namely, two-stage stochastic recourse programming and chance-constraint programming. The model is capable of identifying and evaluating water reuse in urban water systems to optimize the allocation of urban water resources with regard to uncertainties. It thus provides essential information in planning and managing urban water reuse systems towards a more sustainable urban water resources management. An application was presented in order to demonstrate the modeling process and to analyze the impact of uncertainties.

The physiological energy requirements of prosthetic gait in lower-limb amputees have been observed to be significantly greater than those for able-bodied subjects. However, existing models of energy flow in walking have not been very successful in explaining the reasons for this additional energy cost. Existing mechanical models fail to capture all of the components of energy cost involved in human walking. In this thesis, a new model is developed that estimates the physiological cost of walking for an able-bodied individual; the same cost of walking is then computed using a variation of the model that represents a bi-lateral below-knee amputee. The results indicate a higher physiological cost for the amputee model, suggesting that the model more accurately represents the relative metabolic costs of able-bodied and amputee walking gait. The model is based on a two-dimensional multi-body mechanical model that computes the joint torques required for a specified pattern of joint kinematics. In contrast to other models, the mechanical model includes a balance controller component that dynamically maintains the stability of the model during the walking simulation. This allows for analysis of many consecutive steps, and includes in the metabolic cost estimation the energy required to maintain balance. A muscle stress based calculation is used to determine the optimal muscle force distribution required to achieve the joint torques computed by the mechanical model. This calculation is also used as a measure of the metabolic energy cost of the walking simulation. Finally, an optimization algorithm is applied to the joint kinematic patterns to find the optimal walking motion for the model. This approach allows the simulation to find the most energy efficient gait for the model, mimicking the natural human tendency to walk with the most efficient stride length and speed.

This thesis presents a model of fabric bending hysteresis. The hysteresis model is designed to reproduce the fabric bending measurements taken by the Kawabata Evaluation System (KES) and the model parameters can be derived directly from these property measurements. The advantage to using this technique is that it provides the ability to simulate a continuum of property curves. Results of the model and its components are compared and constrasted with experimental results for fabrics composed of different weaves and yarn types. An attempt to incorporate the bending model as part of a fabric drape simulation is also made.

Theoretical procedures are developed to account for the effect of emotion and perception in strategic conflict. The <em>possibility principle</em> facilitates modeling the effects of emotions on future scenarios contemplated by decision makers; <em>perceptual graph models</em> and a <em>graph model system</em> permit the decision makers (DMs) to experience and view the conflict independently; and <em>perceptual stability analysis</em>, which is based on individual- and meta-stability analysis techniques, is employed in analyzing graph model systems when the DMs have inconsistent perceptions. These developments improve the methodology of the Graph Model for Conflict Resolution by reconciling emotion, perception, and strategy to make predictions consistent with the actual unfolding of events. <br /><br /> Current research in neuroscience suggests that emotions are a necessary component of cognitive processes such as memory, attention, and reasoning. The somatic marker hypothesis, for example, holds that feelings are necessary to reasoning, especially during social interactions (Damasio, 1994, 2003). Somatic markers are memories of past emotions: we use them to predict future outcomes. To incorporate the effect of emotion in conflict, the underlying principle of Damasio?s hypothesis is used in developing the possibility principle, which significantly expands the paradigm of the Graph Model for Conflict Resolution of Fang, Hipel, and Kilgour (1993). <br /><br /> State identification is a crucial step in determining future scenarios for DMs. The possibility principle is integrated into the modeling stage of the Graph Model by refining the method of determining feasible states. The possibility principle enables analysts and DMs to include emotion in a conflict model, without sacrificing the parsimonious design of the Graph Model methodology, by focusing attention on two subsets of the set of feasible states: <em>hidden</em> and <em>potential</em> states. Hidden states are logically valid, feasible states that are invisible because of the presence of negative emotions such as anger and fear; potential states are logically valid, feasible states that are invisible because of missing positive emotions. Dissipating negative emotions will make the hidden states visible, while expressing the appropriate positive emotions will make the potential states visible. The possibility principle has been applied to a number of real world conflicts. In all cases, eliminating logically valid states not envisioned by any DM simplifies a conflict model substantially, expedites the analysis, and makes it an intuitive and a realistic description of the DMs' conceptualizations of the conflict. <br /><br /> A fundamental principle of the Graph Model methodology is that all DMs' directed graphs must have the same set of feasible states, which are integrated into a <em>standard</em> graph model. The possibility principle may modify the set of feasible states perceived by each DM according to his or her emotion, making it impossible to construct a single standard graph model. When logically valid states are no longer achievable for one or more DMs due to emotions, the apprehension of conflict becomes inconsistent, and resolution may become difficult to predict. Therefore, reconciling emotion and strategy requires that different apprehensions of the underlying decision problem be permitted, which can be accomplished using a perceptual graph model for each DM. A perceptual graph model inherits its primitive ingredients from a standard graph model, but reflects a DM's emotion and perception with no assumption of complete knowledge of other DMs' perceptions. <br /><br /> Each DM's perceptual graph model constitutes a complete standard graph model. Hence, conclusions drawn from a perceptual graph model provide a limited view of equilibria and predicted resolutions. A graph model system, which consists of a list of DMs' perceptual graph models, is defined to reconcile perceptions while facilitating conclusions that reflect each DM's viewpoint. However, since a DM may or may not be aware that other graph models differ from his or her own, different variants of graph model systems are required to describe conflicts. Each variant of graph model system corresponds to a configuration of awareness, which is a set of ordered combinations of DMs' viewpoints. <br /><br /> Perceptual stability analysis is a new procedure that applies to graph model systems. Its objective is to help an <em>outside</em> analyst predict possible resolutions, and gauge the robustness and sustainability of these predictions. Perceptual stability analysis takes a two-phase approach. In Phase 1, the stability of each state in each perceptual graph model is assessed from the point of view of the owner of the model, for each DM in the model, using standard or perceptual solution concepts, depending on the owner's awareness of others' perceptions. (In this research, only perceptual solution concepts for the 2-decision maker case are developed. ) In Phase 2, meta-stability analysis is employed to consolidate the stability assessments of a state in all perceptual graph models and across all variants of awareness. Distinctive modes of equilibria are defined, which reflect incompatibilities in DMs' perceptions and viewpoints but nonetheless provide important insights into possible resolutions of conflict. <br /><br /> The possibility principle and perceptual stability analysis are integrative techniques that can be used as a basis for empathetically studying the interaction of emotion and reasoning in the context of strategic conflict. In general, these new techniques expand current modeling and analysis capabilities, thereby facilitating realistic, descriptive models without exacting too great a cost in modeling complexity. In particular, these two theoretical advances enhance the applicability of the Graph Model for Conflict Resolution to real-world disputes by integrating emotion and perception, common ingredients in almost all conflicts. <br /><br /> To demonstrate that the new developments are practical, two illustrative applications to real-world conflicts are presented: the US-North Korea conflict and the confrontation between Russia and Chechen Rebels. In both cases, the analysis yields new strategic insights and improved advice.