Restenosis that occurs after stent implantation is a great threat to the quality of life of patients with coronary artery diseases. Studies on thrombosis have revealed that platelet is a primary component of blood clots, and leukocytes and other plasma proteins also contribute to thrombus formation. However, the mechanism of material-induced activation, especially with metallic materials, is currently not well understood and the effect of blood flow on material-induced blood cell activation has not been well characterized. In vitro static and flow experiments were performed to assess the effect of flow on blood cell activation. Blood was taken from healthy donors. Two common stent metals, ST316L and TiAl6V4, were used as test materials. In static experiments, blood was incubated with metal disks at 37 ℃ for two hours. In flow experiments, blood was circulated in flow chambers preloaded with or without metal wires at shear rates of 100 s-1, 500 s-1, and 1500 s-1. Platelet and leukocyte activation, leukocyte-platelet aggregation, and tissue factor expression on monocytes were measured by using a three-color FACSCalibur flow cytometer. Fibrin deposition and blood cell adhesion on metal surfaces were evaluated by scanning electron microscopy (SEM). The results indicate that platelet and leukocyte activation under static condition was low. TiAl6V4 metal disk induced relative lower platelet and leukocyte activation, albeit no significant difference was found between control groups and the two metals tested. Shear stress significantly enhanced platelet activation as measured by an increase in platelet microparticle formation and platelet receptor CD61 expression. CD11b up-regulation, leukocyte-platelet aggregates, and tissue factor (TF) expression increased at 100 s-1. However as shear rate increased, lower leukocyte activation was observed. TiAl6V4-induced leukocyte activation was generally lower than that of ST316L. Significant fibrin deposition and blood cell adhesion were observed on metal surfaces under static condition and with shear rate of 500 s-1. Adhesion significantly decreased with increasing shear rate to 1500 s-1. The flow cytometry analysis results and SEM images demonstrated that materials with different surface properties can induce different blood cell activation and adhesion after incubation with blood. While shear may have an effect on blood cell activation, the effect of biomaterials on cell activation under physiological shear conditions remains unclear. Further studies are required to determine if an increase in biomaterial surface area will provide some answers into the mechanisms of material-induced blood activation in the presence of flow.

The deployment of high-fidelity information systems in command and control environments is common, however it is not yet well understood what impacts these systems have on decision-making processes, or whether the implementation of these systems is always a positive change. Research in military domains has suggested that these types of systems can create substantial increases in micromanagement, but these changes have not been empirically investigated. In this thesis, the effect of high-fidelity information on command environments is experimentally evaluated. A baseline set of data is collected within a real-world command center that uses only low-fidelity information. Then, a laboratory-based controlled technology experiment is used to gather information about how the command processes change as information fidelity is increased. Finally, the same system is implemented within the functioning command center and a preliminary comparison is carried out against the original baseline data. The experimental study suggests that an increase in micromanagement may occur with an increase in information fidelity, while increases in situation awareness and performance improvements during times of both extremely low and high workload are seen. The preliminary ecological validation study shows support for these effects.

Vision and audition are the two best understood modalities which humans use to interact with the outside world. These modalities can provide highly precise spatial and temporal information. Thus, the field of human-computer interface design has focused much of their study and design on these modalities. On the other hand, the sense of touch has been largely ignored despite the fact that it is an essential part of human ability to interact with the environment. We are interested to identify key findings on how to use tactile technology effectively to design and fabricate a tactile interface. We intend to design a wearable tactile interface which can assist Unmanned Aerial Vehicles (UAV) operators in supervisory control and monitoring tasks. Tactile displays are usually comprised of vibratory stimulators which are arranged in specific formation based on the application of the display. Quantitative properties of a vibrating tactor which was used as the vibratory stimulator in our tactile interface were investigated and evaluated in this study. We executed a series of experiments to investigate the intensity of vibrations that the vibrating tactor can generate when it is being activated through different electrical signals. Driving signals were different in terms of waveform, frequency and amplitude. By applying the outcomes of our experiments, and using the available guidelines for the design of tactile displays, we proposed some methods for displaying flight dynamics (Roll, Pitch and Yaw) of a UAV through a tactile display which is structured in form of a vest. Due to the relative infancy of this branch of information presentation, and also the lack of thorough discussion within the scientific community we need to execute further experiments to evaluate the performance of the suggested tactile display.

Restenosis that occurs after stent implantation is a great threat to the quality of life of patients with coronary artery diseases. Studies on thrombosis have revealed that platelet is a primary component of blood clots, and leukocytes and other plasma proteins also contribute to thrombus formation. However, the mechanism of material-induced activation, especially with metallic materials, is currently not well understood and the effect of blood flow on material-induced blood cell activation has not been well characterized. In vitro static and flow experiments were performed to assess the effect of flow on blood cell activation. Blood was taken from healthy donors. Two common stent metals, ST316L and TiAl6V4, were used as test materials. In static experiments, blood was incubated with metal disks at 37 ℃ for two hours. In flow experiments, blood was circulated in flow chambers preloaded with or without metal wires at shear rates of 100 s-1, 500 s-1, and 1500 s-1. Platelet and leukocyte activation, leukocyte-platelet aggregation, and tissue factor expression on monocytes were measured by using a three-color FACSCalibur flow cytometer. Fibrin deposition and blood cell adhesion on metal surfaces were evaluated by scanning electron microscopy (SEM). The results indicate that platelet and leukocyte activation under static condition was low. TiAl6V4 metal disk induced relative lower platelet and leukocyte activation, albeit no significant difference was found between control groups and the two metals tested. Shear stress significantly enhanced platelet activation as measured by an increase in platelet microparticle formation and platelet receptor CD61 expression. CD11b up-regulation, leukocyte-platelet aggregates, and tissue factor (TF) expression increased at 100 s-1. However as shear rate increased, lower leukocyte activation was observed. TiAl6V4-induced leukocyte activation was generally lower than that of ST316L. Significant fibrin deposition and blood cell adhesion were observed on metal surfaces under static condition and with shear rate of 500 s-1. Adhesion significantly decreased with increasing shear rate to 1500 s-1. The flow cytometry analysis results and SEM images demonstrated that materials with different surface properties can induce different blood cell activation and adhesion after incubation with blood. While shear may have an effect on blood cell activation, the effect of biomaterials on cell activation under physiological shear conditions remains unclear. Further studies are required to determine if an increase in biomaterial surface area will provide some answers into the mechanisms of material-induced blood activation in the presence of flow.

The deployment of high-fidelity information systems in command and control environments is common, however it is not yet well understood what impacts these systems have on decision-making processes, or whether the implementation of these systems is always a positive change. Research in military domains has suggested that these types of systems can create substantial increases in micromanagement, but these changes have not been empirically investigated. In this thesis, the effect of high-fidelity information on command environments is experimentally evaluated. A baseline set of data is collected within a real-world command center that uses only low-fidelity information. Then, a laboratory-based controlled technology experiment is used to gather information about how the command processes change as information fidelity is increased. Finally, the same system is implemented within the functioning command center and a preliminary comparison is carried out against the original baseline data. The experimental study suggests that an increase in micromanagement may occur with an increase in information fidelity, while increases in situation awareness and performance improvements during times of both extremely low and high workload are seen. The preliminary ecological validation study shows support for these effects.

Optimization problems in two different application fields are investigated: the first one is the popular portfolio optimization problem and the second one is the newly developed online display advertising problem. The portfolio optimization problem has two main concerns: an appropriate statistical input data, which is improved with the use of factor model and, the inclusion of the transaction cost function into the original objective function. Two methods are applied to solve the optimization problem, namely,the conditional value at risk (CVaR) method and the reliability based (RB) method. Asset allocation problem in finance continues to be of practical interest because decisions as to where to invest must be made to maximize the total return and minimizing the risk of not attaining the target return. However, the commonly used Markowitz method, also known as the mean-variance approach, uses historic stock prices data and has been facing problems of parameter estimation and short sample errors. An alternative method that attempts to overcome this problem is the use of factor models. This thesis will explain this model in addition to explaining the basic portfolio optimization problem. Conditional value at risk and the reliability based optimization method are applied to solve the portfolio optimization problem with the consideration of transaction costs in the objective function.They are applied and evaluated by simulation in terms of their convergence, efficiency and results. The online display advertising problem extends a normal deterministic revenue optimization model to a stochastic allocation model. The incorporation of randomness makes it more realistic for the estimation of demand, supply and market price. Revenues are considered as a combination of gains from guaranteed contracts and unguaranteed spot market. The objective is not only to maximize the revenue but also to consider the quality of ads, so that the whole market obtains long-term benefits and stability. The thesis accomplishes in solving the online display advertising allocation problem in a stochastic case with the measure of conditional value at risk algorithm.

Optimization problems in two different application fields are investigated: the first one is the popular portfolio optimization problem and the second one is the newly developed online display advertising problem. The portfolio optimization problem has two main concerns: an appropriate statistical input data, which is improved with the use of factor model and, the inclusion of the transaction cost function into the original objective function. Two methods are applied to solve the optimization problem, namely,the conditional value at risk (CVaR) method and the reliability based (RB) method. Asset allocation problem in finance continues to be of practical interest because decisions as to where to invest must be made to maximize the total return and minimizing the risk of not attaining the target return. However, the commonly used Markowitz method, also known as the mean-variance approach, uses historic stock prices data and has been facing problems of parameter estimation and short sample errors. An alternative method that attempts to overcome this problem is the use of factor models. This thesis will explain this model in addition to explaining the basic portfolio optimization problem. Conditional value at risk and the reliability based optimization method are applied to solve the portfolio optimization problem with the consideration of transaction costs in the objective function.They are applied and evaluated by simulation in terms of their convergence, efficiency and results. The online display advertising problem extends a normal deterministic revenue optimization model to a stochastic allocation model. The incorporation of randomness makes it more realistic for the estimation of demand, supply and market price. Revenues are considered as a combination of gains from guaranteed contracts and unguaranteed spot market. The objective is not only to maximize the revenue but also to consider the quality of ads, so that the whole market obtains long-term benefits and stability. The thesis accomplishes in solving the online display advertising allocation problem in a stochastic case with the measure of conditional value at risk algorithm.

Thesis (M.S.)--Montana State University--Bozeman, 2006. / Typescript. Chairperson, Graduate Committee: Durwood K. Sobek II. Includes bibliographical references (leaves 66-68).

In many image-processing applications, one object of interest must be segmented. The techniques used for segmentation vary depending on the particular situation and the specifications of the problem at hand. In methods that rely on a learning process, the lack of a sufficient number of training samples is usually an obstacle, especially when the samples need to be manually prepared by an expert. The performance of some other methods may suffer from frequent user interactions to determine the critical segmentation parameters. Also, none of the existing approaches use online (permanent) feedback, from the user, in order to evaluate the generated results. Considering the above factors, a new multi-stage image segmentation system, based on Reinforcement Learning (RL) is introduced as the main contribution of this research. In this system, the RL agent takes specific actions, such as changing the tasks parameters, to modify the quality of the segmented image. The approach starts with a limited number of training samples and improves its performance in the course of time. In this system, the expert knowledge is continuously incorporated to increase the segmentation capabilities of the method. Learning occurs based on interactions with an offline simulation environment, and later online through interactions with the user. The offline mode is performed using a limited number of manually segmented samples, to provide the segmentation agent with basic information about the application domain. After this mode, the agent can choose the appropriate parameter values for different processing tasks, based on its accumulated knowledge. The online mode, consequently, guarantees that the system is continuously training and can increase its accuracy, the more the user works with it. During this mode, the agent captures the user preferences and learns how it must change the segmentation parameters, so that the best result is achieved. By using these two learning modes, the RL agent allows us to optimally recognize the decisive parameters for the entire segmentation process.

Vehicle tracking in environments containing occlusion and clutter is an active research area. The problem of tracking vehicles through such environments presents a variety of challenges. These challenges include vehicle track initialization, tracking an unknown number of targets and the variations in real-world lighting, scene conditions and camera vantage. Scene clutter and target occlusion present additional challenges. A stochastic framework is proposed which allows for vehicles tracks to be identified from a sequence of images. The work focuses on the identification of vehicle tracks present in transportation scenes, namely, vehicle movements at intersections. The framework combines background subtraction and motion history based approaches to deal with the segmentation problem. The tracking problem is solved using a Monte Carlo Markov Chain Data Association (MCMCDA) method. The method includes a novel concept of including the notion of discrete, independent regions in the MCMC scoring function. Results are presented which show that the framework is capable of tracking vehicles in scenes containing multiple vehicles that occlude one another, and that are occluded by foreground scene objects.