Cognitive Work analysis (CWA) as an analytical approach for examining complex socio-technical systems has shown success in modeling the work of single operators. The CWA approach allows room for social and team interactions, but a more explicit analysis of team aspects can reveal more information for systems design. CWA techniques and models do not yet provide sufficient guidance on identifying shared constraints, team strategies, or social competencies of team players. In this thesis, I explore whether a team approach to CWA can yield more information than a typical CWA. Team CWA techniques and models emerge and extend from theories and models of teamwork, past attempts to model teams with CWA, and the results of two sets of observational studies. The potential benefits of using Team CWA models in domains with strong team collaboration are demonstrated through the results of a two-week observation at the Labour and Delivery Department of The Ottawa Hospital and a fifteen-week observation at the IBM Ottawa Software Group.

The main objective of this research is the development of a volumetric wheel-soil ground contact model that is suitable for mobile robotics applications with a focus on efficient simulations of planetary rover wheels operating on compliant and irregular terrains. To model the interaction between a rover wheel and soft soil for use in multibody dynamic simualtions, the terrain material is commonly represented by a soil continuum that deforms substantially when in contact with the locomotion system of the rover. Due to this extensive deformation and the large size of the contact patch, a distributed representation of the contact forces is necessary. This requires time-consuming integration processes to solve for the contact forces and moments during simulation. In this work, a novel approach is used to represent these contact reactions based on the properties of the hypervolume of penetration, which is defined by the intersection of the wheel and the terrain. This approach is based on a foundation of springs for which the normal contact force can be calculated by integrating the spring deflections over the contact patch. In the case of an elastic foundation, this integration results in a linear relationship between the normal force and the penetration volume, with the foundation stiffness as the proportionality factor. However, due to the highly nonlinear material properties of the soft terrain, a hyperelastic foundation has to be considered and the normal contact force becomes proportional to a volume with a fractional dimension --- a hypervolume. The continuous soil models commonly used in terramechanics simulations can be used in the derivation of the hypervolumetric contact forces. The result is a closed-form solution for the contact forces between a planetary rover wheel and the soft soil, where all the information provided by a distributed load is stored in the hypervolume of interpenetration. The proposed approach is applied to simulations of rigid and flexible planetary rover wheels. In both cases, the plastic behaviour of the terrain material is the main source of energy loss during the operation of planetary rovers. For the rigid wheel model, a penetration geometry is proposed to capture the nonlinear dissipative properties of the soil. The centroid of the hypervolume based on this geometry then allows for the calculation of the contact normal that defines the compaction resistance of the soil. For the flexible wheel model, the deformed state of the tire has to be determined before applying the hypervolumetric contact model. The tire deformation is represented by a distributed parameter model based on the Euler-Bernoulli beam equations. There are several geometric and soil parameters that are required to fully define the normal contact force. While the geometric parameters can be measured, the soil parameters have to be obtained experimentally. The results of a drawbar pull experiment with the Juno rover from the Canadian Space Agency were used to identify the soil parameters. These parameters were then used in a forward dynamics simulation of the rover on an irregular 3-dimensional terrain. Comparison of the simulation results with the experimental data validated the planetary rover wheel model developed in this work.

The recent breakthroughs in genomics and molecular diagnostics will not be reflected in health-care systems unless the biogenetic or other nucleic acid-based tests are transferred from the laboratory to clinical market. Developments in microfabrication techniques brought lab-on-a-chip (LOC) into being the best candidate for conducting sample preparation for such clinical devices, or point-of-care testing set-ups. Sample preparation procedure consists of several stages including cell transportation, separation, cell lysis and nucleic acid purification and detection. LOC, as a subset of Microelectromechanical systems (MEMS), refers to a tiny, compact, portable, automated and easy-to-use microchip capable of performing the sample-preparation stages together. Complexity in micro-fabrications and inconsistency of the stages oppose integration of them into one chip. Among the variety of mechanisms utilized in LOC for cell lysis, electrical methods have the highest potential to be integrated with other microchip-based mechanisms. There are, however, major limitations in electrical cell lysis methods: the difficulty and high-cost fabrication of microfluidic chips and the high voltage requirements for cell lysis. Addressing these limitations, the focus of this thesis is on realization of cell lysis microchips suitable for LOC applications. We have developed a new methodology of fabricating microfluidic chips with electrical functionality. Traditional lithography of microchannel with electrode, needed for making electro-microfluidic chips, is considerably complicated. We have combined several easy-to-implement techniques to realize electro-microchannel with laser-ablated polyimide. The current techniques for etching polyimide are by excimer lasers in bulky set-ups and with involvement of toxic gas. We present a method of ablating microfluidic channels in polyimide using a 30W CO2 laser. Although this technique has poorer resolution, this approach is more cost effective, safer and easier to handle. We have verified the performance of the fabricated electro-microfluidic chips on electroporation of mammalian cells. Electrical cell lysis mechanisms need an operational voltage that is relatively high compared to other cell manipulation techniques, especially for lysing bacteria. Microelectro-devices have dealt with this limitation mostly by reducing the inter-distance of electrodes. The technique has been realized in tiny flow-through microchips with built-in electrodes in a distance of a few micrometers which is in the scale of cell size. In addition to the low throughput of such devices, high probability of blocking cells in such tiny channels is a serious challenge. We have developed a cell lysis device featured with aligned carbon nanotube (CNT) to reduce the high voltage requirement and to improve the throughput. The vertically aligned CNT on an electrode inside a MEMS device provides highly strengthened electric field near the tip. The concept of strengthened electric field by means of CNT has been applied in field electron emission but not in cell lysis. The results show that the incorporation of CNT in lysing bacteria reduces the required operational voltage and improves throughput. This achievement is a significant progress toward integration of cell lysis in a low-voltage, high-throughput LOC. We further developed the proposed fabrication methodology of micro-electro-fluidic chips, described earlier, to perform electroporation of single mammalian cell. We have advanced the method of embedding CNT in microchannel so that on-chip fluorescent microscopy is also feasible. The results verify the enhancement of electroporation by incorporating CNT into electrical cell lysis. In addition, a novel methodology of making CNT-embedded microfluidic devices has been presented. The embedding methodology is an opening toward fabrication of a CNT-featured LOC for other applications.

Evolutionary algorithms (EAs) are well-established techniques to approach those problems which for the classical optimization methods are difficult to solve. Tackling problems with mixed-type of variables, many local optima, undifferentiable or non-analytical functions are some examples to highlight the outstanding capabilities of the evolutionary algorithms. Among the various kinds of evolutionary algorithms, differential evolution (DE) is well known for its effectiveness and robustness. Many comparative studies confirm that the DE outperforms many other optimizers. Finding more accurate solution(s), in a shorter period of time for complex black-box problems, is still the main goal of all evolutionary algorithms. The opposition concept, on the other hand, has a very old history in philosophy, set theory, politics, sociology, and physics. But, there has not been any opposition-based contribution to optimization. In this thesis, firstly, the opposition-based optimization (OBO) is constituted. Secondly, its advantages are formally supported by establishing mathematical proofs. Thirdly, the opposition-based acceleration schemes, including opposition-based population initialization and generation jumping, are proposed. Fourthly, DE is selected as a parent algorithm to verify the acceleration effects of proposed schemes. Finally, a comprehensive set of well-known complex benchmark functions is employed to experimentally compare and analyze the algorithms. Results confirm that opposition-based DE (ODE) performs better than its parent (DE), in terms of both convergence speed and solution quality. The main claim of this thesis is not defeating DE, its numerous versions, or other optimizers, but to introduce a new notion into nonlinear continuous optimization via innovative metaheuristics, namely the notion of opposition. Although, ODE has been compared with six other optimizers and outperforms them overall. Furthermore, both presented experimental and mathematical results conform with each other and demonstrate that opposite points are more beneficial than pure random points for black-box problems; this fundamental knowledge can serve to accelerate other machine learning approaches as well (such as reinforcement learning and neural networks). And perhaps in future, it could replace the pure randomness with random-opposition model when there is no a priori knowledge about the solution/problem. Although, all conducted experiments utilize DE as a parent algorithm, the proposed schemes are defined at the population level and, hence, have an inherent potential to be utilized for acceleration of other DE extensions or even other population-based algorithms, such as genetic algorithms (GAs). Like many other newly introduced concepts, ODE and the proposed opposition-based schemes still require further studies to fully unravel their benefits, weaknesses, and limitations.

Flexible approaches for eliciting preferences of decision makers involved in a conflict are developed along with applications to real-world disputes. More specifically, two multiple criteria decision making approaches are proposed for capturing the relative preferences of a decision maker participating in a conflict situation. A case study in logistics concerned with the conflict arising over the expansion of port facilities on the west coast of North America as well as a transportation negotiation dispute are used to illustrate how these approaches can be integrated with the Graph Model for Conflict Resolution, a practical conflict analysis methodology. Ascertaining the preferences of the decision makers taking part in a conflict constitutes a key element in the construction of a formal conflict model. In practice, the relative preferences, which reflect each decision maker’s objectives or goals in a given situation, are rather difficult to obtain. The first method for preference elicitation is to integrate an Analytic Hierarchy Process (AHP) preference ranking method with the Graph Model for Conflict Resolution. The AHP approach is used to elicit relative preferences of decision makers, and this preference information is then fed into a graph model for further stability analyses. The case study of the Canadian west coast port congestion conflict is investigated using this integrated model. Another approach is based on a fuzzy multiple criteria out-ranking technique called ELECTRE III. It is also employed for ranking states or possible scenarios in a conflict from most to least preferred, with ties allowed, by the decision maker according to his or her own value system. The model is applied to a transportation negotiation dispute between the two key parties consisting of shippers and carriers.

This research focuses on developing new techniques and designs for highly con- trollable microactuating systems with large force-stroke outputs. A fixed-fixed mi- crobeam is the actuating element in the introduced techniques. Either buckling of a microbridge by thermal stress, lateral deflection of a microbridge by electro- magnetic force, or combined effects of both can be employed for microactuation. The proposed method here is MicroElectroThermoMagnetic Actuation (METMA), which uses the combined techniques of electrical or electro-thermal driving of a mi- crobridge in the presence of a magnetic field. The electrically controllable magnetic field actuates and controls the electrically or electrothermally driven microstruc- tures. METMA provides control with two electrical inputs, the currents driving the microbridge and the current driving the external magnetic field. This method enables a more controllable actuating system. Different designs of microactuators have been implemented by using MEMS Pro as the design software and MUMPs as the standard MEMS fabrication technology. In these designs, a variety of out-of- plane buckling or displacement of fixed-fixed microbeams have been developed and employed as the actuating elements. This paper also introduces a novel actuating technique for larger displacements that uses a two-layer buckling microbridge actu- ated by METMA. Heat transfer principles are applied to investigate temperature distribution in a microbeam, electrothermal heating, and the resulting thermoelas- tic effects. Furthermore, a method for driving microactuators by applying powerful electrical pulses is proposed. The integrated electromagnetic and electrothermal microactuation technique is also studied. A clamped-clamped microbeam carry- ing electrical current has been modeled and simulated in ANSYS. The simulations include electrothermal, thermoelastic, electromagnetic, and electrothermomagnetic effects. The contributions are highlighted, the results are discussed, the research and design limitations are reported, and future works are proposed.

In recent years, carbon nanotubes (CNTs) have emerged as one of the best field emitters for a variety of technological applications. The field emitting cathodes have several advantages over the conventional thermionic cathodes: (i) current density from field emission would be orders of magnitude greater than in the thermionic case, (ii) a cold cathode would minimize the need for cooling, and (iii) a field emitting cathode can be miniaturized. In spite of good performance of such cathodes, the procedure to estimate the device current is not straight forward and the required insight towards design optimization is not well understood. In addition, the current in CNT-based thin film devices shows fluctuation. Such fluctuation in field emission current is not desirable for many biomedical applications such as x-ray devices. The CNTs in a thin film undergo complex dynamics during field emission, which includes processes such as (i) evolution, (ii) electromechanical interaction, (iii) thermoelectric heating, (iv) ballistic transport, and (v) electron gas flow. These processes are coupled and nonlinear. Therefore, they must be analyzed accurately from the stability and long-term performance point of view. In this research, we develop detailed physics-based models of CNTs considering the aspects mentioned above. The models are integrated in a systematic manner to calculate the device current by using the Fowler-Nordheim equation. Using the models, we were able to capture the fluctuations in the field emission current, which have been observed in actual experiments. A detailed analysis of the results reveals the deflected shapes of the CNTs in an ensemble and the extent to which the initial state of geometry and orientation angles affect the device current. In addtion, investigations on the influence of defects and impurities in CNTs on their field emission properties have been carried out. By inclusion of defects and impurities, the field emission properties of CNTs can be tailored for specific device applications in future. For stable performance of CNT-based field emission devices, such as x-ray generators, design optimization studies have been presented. It has been found that the proposed design minimizes transience in field emission current. In this thesis, it has been demonstrated that phonon-assisted control of field emission current in CNT based thin film is possible. Finally, experimental studies pertaining to crosstalk phenomenon in a multi-pixel CNT array are presented.

There has been a growth in demand for advancing algorithms in surveillance applications concerning moving vehicles where analysis of traffic has a potential application to security, traffic management (congestion and accident detection), speed measurement, car counting and statistics, as well as turning movement at intersections. This research focuses on multiple-vehicle detection, recognition, and tracking in urban environments based on video sequences obtained from a single CCD camera mounted on a pole at urban highways and crossroads. The proposed system integrates several modules including segmentation, object detection, object recognition and classification, and tracking. Background segmentation, based on Gaussian Mixture models, is used to extract moving objects from images using the respective foreground object information such as location, size, and color distribution. To recognize vehicles, a 3D polyhedral car model described by a set of parameters is built and mapped to the 2D edge information attained from the video sequence. The matching process is then used to classify the foreground object obtained into vehicles and non-vehicles. The output from the recognition model is used in tracking multiple cars based on a deterministic data association method that takes place between consecutive frame information. The multiple-vehicle surveillance system developed in this thesis, based on integrating different modules, provides a novel approach for vehicle monitoring. Furthermore, the system makes use of minimal a priori knowledge about vehicle location, size, type, numbers, and pathways. The system implemented in this work functions well under various camera perspectives, background clutter, vehicle viewpoints, road types, scale changes, image noise, image resolutions, and lighting conditions.

Noise reduction is an open problem and has received considerable attention in the literature for several decades. Over the last two decades, wavelet based methods have been applied to the problem of noise reduction and have been shown to outperform the traditional Wiener filter, Median filter, and modified Lee filter in terms of root mean squared error (MSE), peak signal noise ratio (PSNR) and other evaluation methods. In this research, two approaches for the development of high performance algorithms for de-noising are proposed, both based on soft computing tools, such as fuzzy logic, neural networks, and genetic algorithms. First, an improved additive noise reduction method for digital grey scale nature images, which uses an interval type-2 fuzzy logic system to shrink wavelet coefficients, is proposed. This method is an extension of a recently published approach for additive noise reduction using a type-1 fuzzy logic system based wavelet shrinkage. Unlike the type-1 fuzzy logic system based wavelet shrinkage method, the proposed approach employs a thresholding filter to adjust the wavelet coefficients according to the linguistic uncertainty in neighborhood values, inter-scale dependencies and intra-scale correlations of wavelet coefficients at different resolutions by exploiting the interval type-2 fuzzy set theory. Experimental results show that the proposed approach can efficiently and rapidly remove additive noise from digital grey scale images. Objective analysis and visual observations show that the proposed approach outperforms current fuzzy non-wavelet methods and fuzzy wavelet based methods, and is comparable with some recent but more complex wavelet methods, such as Hidden Markov Model based additive noise de-noising method. The main differences between the proposed approach and other wavelet shrinkage based approaches and the main improvements of the proposed approach are also illustrated in this thesis. Second, another improved method of additive noise reduction is also proposed. The method is based on fusing the results of different filters using a Fuzzy Neural Network (FNN). The proposed method combines the advantages of these filters and has outstanding ability of smoothing out additive noise while preserving details of an image (e.g. edges and lines) effectively. A Genetic Algorithm (GA) is applied to choose the optimal parameters of the FNN. The experimental results show that the proposed method is powerful for removing noise from natural images, and the MSE of this approach is less, and the PSNR of is higher, than that of any individual filters which are used for fusion. Finally, the two proposed approaches are compared with each other from different point of views, such as objective analysis in terms of mean squared error(MSE), peak signal to noise ratio (PSNR), image quality index (IQI) based on quality assessment of distorted images, and Information Theoretic Criterion (ITC) based on a human vision model, computational cost, universality, and human observation. The results show that the proposed FNN based algorithm optimized by GA has the best performance among all testing approaches. Important considerations for these proposed approaches and future work are discussed.

Exploring a set of unlabeled data to extract the similar clusters, known as data clustering, is an appealing problem in machine learning. In other words, data clustering organizes the underlying data into different groups using a notion of similarity between patterns. A new approach to solve the data clustering problem based on multiple cooperative swarms is introduced. The proposed approach is inspired by the social swarming behavior of biological bird flocks which search for food situated in several places. The proposed approach is composed of two main phases, namely, initialization and exploitation. In the initialization phase, the aim is to distribute the search space among several swarms. That is, a part of the search space is assigned to each swarm in this phase. In the exploitation phase, each swarm searches for the center of its associated cluster while cooperating with other swarms. The search proceeds to converge to a near-optimal solution. As compared to the single swarm clustering approach, the proposed multiple cooperative swarms provide better solutions in terms of fitness function measure for the cluster centers, as the dimensionality of data and number of clusters increase. The multiple cooperative swarms clustering approach assumes that the number of clusters is known a priori. The notion of stability analysis is proposed to extract the number of clusters for the underlying data using multiple cooperative swarms. The mathematical explanations demonstrating why the proposed approach leads to more stable and robust results than those of the single swarm clustering are also provided. Application of the proposed multiple cooperative swarms clustering is considered for one of the most challenging problems in speech recognition: phoneme recognition. The proposed approach is used to decompose the recognition task into a number of subtasks or modules. Each module involves a set of similar phonemes known as a phoneme family. Basically, the goal is to obtain the best solution for phoneme families using the proposed multiple cooperative swarms clustering. The experiments using the standard TIMIT corpus indicate that using the proposed clustering approach boosts the accuracy of the modular approach for phoneme recognition considerably.