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Optimizing connected automated vehicle movements through roadway intersections is a challenging problem. Traditional traffic control strategies, such as traffic signals are not optimal, especially for heavy traffic. Alternatively, centralized automated vehicle control strategies are costly and not scalable given that the ability of a central controller to track and schedule the movement of hundreds of vehicles in real-time is highly questionable. In this research, a series of fully distributed heuristic algorithms are proposed where vehicles in the vicinity of an intersection continuously cooperate with each other to develop a schedule that allows them to safely proceed through the intersection while incurring minimum delays. An algorithm is proposed for the case of an isolated intersection then a number of algorithms are proposed for a network of intersections where neighboring intersections communicate directly or indirectly to help the distributed control at each intersection makes a better estimation of traffic in the whole network. An algorithm based on the Godunov scheme outperformed optimized signalized control. The simulated experiments show significant reductions in the average delay. The base algorithm is successfully added to the INTEGRATION micro-simulation model and the results demonstrate improvements in delay, fuel consumption, and emissions when compared to roundabout, signalized, and stop sign controlled intersections. The study also shows the capability of the proposed technique to favor emergency vehicles, producing significant increases in mobility with minimum delays to the other vehicles in the network. / Ph. D. / Intelligent self-driving cars are getting much closer to reality than fiction. Technological advances make it feasible to produce such vehicles at low affordable cost. This type of vehicles is also promising to significantly reduce car accidents saving people lives and health. Moreover, the congested roads in cities and metropolitan areas especially at rush hours can benefit from this technology to avoid or at least to reduce the delays experienced by car passengers during their trips. One major challenge facing the operation of an intelligent self-driving car is how to pass an intersection as fast as possible without any collision with cars approaching from other directions of the intersection. The use of current traffic lights or stop signs is not the best choice to make the best use of the capabilities of future cars. In this dissertation, the aim is to study and propose ways to make sure the future intersections are ready for such self-driving intelligent cars. Assuming that an intersection has no type of traditional controls such as traffic lights or stop signs, this research effort shows how vehicles can pass safely with minimum waiting. The proposed techniques focus on providing lowcost solutions that do not require installation of expensive devices at intersections that makes it difficult to be approved by authorities. The proposed techniques can be applied to intersections of various sizes. The algorithms in this dissertation carefully design a way for vehicles in a network of intersections to communicate and cooperate while passing an intersection. The algorithms are extensively compared to the case of using traffic lights, stop signs, and roundabouts. Results show significant improvement in delay reduction and fuel consumption when the proposed techniques are used.

Fringe-pattern projection (FPP) techniques are commonly used for surface-shape measurement in a wide range of applications including object and scene modeling, part inspection, and reverse engineering. Periodic intensity fringe patterns with a specific amplitude are projected by the projector onto an object and a camera captures images of the fringe patterns, which appear distorted by the object surface from the perspective of the camera. The images are then used to compute the height or depth of the object at each pixel. One of the problems with FPP is that camera sensor saturation may occur if there is a large change in ambient lighting or a large range in surface reflectivity when measuring object surfaces. Camera sensor saturation occurs when the reflected intensity exceeds the maximum quantization level of the camera. A low SNR occurs when there is a low intensity modulation of the fringe pattern compared to the amount of noise in the image. Camera sensor saturation and low SNR can result in significant measurement error. Careful selection of the camera aperture or exposure time can reduce the error due to camera sensor saturation or low SNR. However, this is difficult to perform automatically, which may be necessary when measuring objects in uncontrolled environments where the lighting may change and objects have different surface reflectivity. This research presents three methods to avoid camera sensor saturation when measuring surfaces subject to changes in ambient lighting and objects with a large range in reflectivity. All these methods use the same novel approach of lowering the maximum input gray level (MIGL) to the projector for saturation avoidance. This approach avoids saturation by lowering the reflected intensity so that formerly saturated intensities can be captured by the camera. The first method of saturation avoidance seeks a trade-off between robustness to intensity saturation and low SNR. Measurements of a flat white plate at different MIGL resulted in a trade-off MIGL that yielded the highest accuracy for a single adjustment of MIGL that is uniform within and across the projected images. The second method used several sets of images, taken at constant steps of MIGL, and combined the images pixel-by-pixel into a single set of composite images, by selecting the highest unsaturated intensities at each pixel. White plate measurements using this method had comparable accuracy to the first method but required more images to form the composite image. Measurement of a checkerboard showed a higher accuracy than the first method since the second method maintains a higher SNR when the object has a large range of reflectivity. The last method also used composite images where the step size was determined dynamically, based on the estimated percentage of pixels that would become unsaturated at the next step. In measurements of a flat white plate and a checkerboard the dynamic step size was found to add flexibility to the measurement system compared to the constant steps using the second method. Using dynamic steps, the measurement system was able to measure objects with either a low or high range of reflectivity with high accuracy and without manually adjusting the step size. This permits fully automated measurement of unknown objects with variable reflectivity in unstructured environments with changing lighting conditions. The methods can be used for measurement in uncontrolled environments, for specular surfaces, and those with a large range of reflectivity or luminance. This would allow a wider range of measurement applications using FPP techniques.

The diploma thesis deals with the topic of the optimization calculation of the mass flow of injection from the condensing pumps of the second degree to the condensate of heating steam, flowing from the system of separation and reheating, with the main goal to achieve the maximum power of the block of the power plant. There is calculation of the secondary circuit of the nuclear power plant in Dukovany before and after the optimization. In the next part, there is calculated growth of electric power of the block, results are drawn in diagrams and technical solution is designed.

This thesis deals with the solution of the crossroads of the 1st Máje and Palackého in Moravské Budějovice. Part of the work is security inspection, calculation of uncontrolled intersection capacity, traffic survey. The findings are applied to possible solutions of crossing of the 1st Máje and Palackého in Moravské Budějovice.

<p>  </p> <p>Falls mostly occur when people are walking. Investigations of control of gait stability have focused primarily on the single stance phase. My dissertation focused on the double support phase of gait because (1) responses to perturbations occur during the double support (2) the portion of the gait cycle spent in double support is increased with old age, and, more importantly, (3) since both feet can push off the ground simultaneously, there are more kinetic degrees of freedom (DoF) and therefore greater control authority over body motion during this phase. However, how these kinetic DoFs are coordinated during the double support phase is not fully understood. Thus, the goal of this dissertation was to identify the inter-leg coordination to stabilize whole-body motion and quantify how the inter-leg coordination is affected by intrinsic and extrinsic factors. Specifically, Study 1 focused on healthy aging (an intrinsic factor) and varying task demands (an extrinsic factor that changed while curb ascent versus curb descent). Study 2 investigated another extrinsic factor of future uncertain environmental demands (fixed versus uncertain foot targeting demand for the step after descending a curb). Using the uncontrolled manifold analysis, I identified ground reaction variable (GRV) synergies, i.e., synergistic covariations between the ground reaction forces and moments under the two feet that stabilize whole-body linear and angular motions. Furthermore, I found that GRV synergies were modulated by extrinsic factors: GRV synergies were sensitive to current fixed environmental demands (Study 1), whereas they were robust to future environmental demands on foot placement (Study 2). Lastly, I found that GRV synergies were not changed by the intrinsic factor of age, despite the physiological declines with aging (Study 1). The absence of an age effect on GRV synergies indicates that older adults have the preserved ability to exploit the control authority during the double support phase to maintain stability while negotiating a curb. The work extends the current body of literature on gait stability mechanisms and improves our understanding of changes in stability control as a function of different environmental demands.</p>

English language learning materials take a central role in Swedish secondary schools. The curriculum in Sweden advocates for communicative language teaching where students are expected to produce language in various contexts without restriction. There is much prior research on different textbooks used in Sweden. However, there has been limited analysis of how well a textbook aligns with the curriculum in terms of compliance with CLT expectations particularly for productive skills. As CLT preaches free production, it is important for teachers to know which books to choose for their courses to successfully incorporate the material to improve their students’ productive skills in various contexts. This study analysed Focus on English 8 which consists of a textbook and a workbook to shed some light on to what degree this textbook follows the Swedish curriculum with a focus on CLT aspects. This was done by using a mixed methods content analysis by conducting both a quantitative and qualitative analysis. The quantitative analysis was done by counting the amount of controlled to uncontrolled exercises in the textbook and workbook. In contrast, the qualitative analysis focused on the scaffolding present in the production exercises. Results show that while the book adheres to the curriculum, the textbook and workbook by themselves do not sufficiently facilitate free production. Thus, several steps outside the teaching materials need to be taken to encourage students to freely produce the language.

In this dissertation, new algorithms that address three traffic problems of major importance are developed. First automatic identification and prediction algorithms are developed to identify and predict the occurrence of traffic congestion. The identification algorithms concoct a model to identify speed thresholds by exploiting historical spatiotemporal speed matrices. We employ the speed model to define a cutoff speed separating free-flow from congested traffic. We further enhance our algorithm by utilizing weather and visibility data. To our knowledge, we are the first to include weather and visibility variables in formulating an automatic congestion identification model. We also approach the congestion prediction problem by adopting an algorithm which employs Adaptive Boosting machine learning classifiers again something novel that has not been done previously. The algorithm is promising where it resulted in a true positive rate slightly higher than 0.99 and false positive rate less than 0.001. We next address the issue of travel time modeling. We propose algorithms to model travel time using various machine learning and statistical learning techniques. We obtain travel time models by employing the historical spatiotemporal speed matrices in conjunction with our algorithms. The algorithms yield pertinent information regarding travel time reliability and prediction of travel times. Our proposed algorithms give better predictions compared to the state of practice algorithms. Finally we consider driver safety at signalized intersections and uncontrolled intersections in a connected vehicles environment. For signalized intersections, we exploit datasets collected from four controlled experiments to model the stop-run behavior of the driver at the onset of the yellow indicator for various roadway surface conditions and multiple vehicle types. We further propose a new variable (predictor) related to driver aggressiveness which we estimate by monitoring how drivers respond to yellow indications. The performance of the stop-run models shows improvements after adding the new aggressiveness predictor. The proposed models are practical and easy to implement in advanced driver assistance systems. For uncontrolled intersections, we present a game theory based algorithm that models the intersection as a chicken game to solve the conflicts between vehicles crossing the intersection. The simulation results show a 49% saving in travel time on average relative to a stop control when the vehicles obey the Nash equilibrium of the game. / Ph. D.

BACKGROUND: Clinical evidence shows that diabetes may provoke uncontrolled blood pressure (BP) in hypertensive patients. However, racial differences in the associations of diabetes with uncontrolled BP outcomes among diagnosed hypertensive patients have not been evaluated. METHODS: A total of 6,134 diagnosed hypertensive subjects aged ≥ 20 years were collected from the National Health and Nutrition Examination Survey 1999-2008 with a stratified multistage design. Odds ratios (ORs) and relative ORs of uncontrolled BP and effect differences in continuous BP for diabetes over race/ethnicity were derived using weighted logistic regression and linear regression models. RESULTS: Compared with participants who did not have diabetes, non-Hispanic black participants with diabetes had a 138% higher chance of having uncontrolled BP, Mexican participants with diabetes had a 60% higher chance of having uncontrolled BP, and non-Hispanic white participants with diabetes had a 161% higher chances of having uncontrolled BP. The association of diabetes with uncontrolled BP was lower in Mexican Americans than in non-Hispanic blacks and whites (Mexican Americans vs. non-Hispanic blacks: relative OR = 0.55, 95% confidence interval (CI) = 0.37-0.82; Mexican Americans vs. non-Hispanic whites: relative OR = 0.53, 95% CI = 0.35-0.80) and the association of diabetes with isolated uncontrolled systolic BP was lower in Mexican Americans than in non-Hispanic whites (Mexican Americans vs. non-Hispanic whites: relative OR = 0.62, 95% CI = 0.40-0.96). Mexican Americans have a stronger association of diabetes with decreased systolic BP and diastolic BP than non-Hispanic whites, and a stronger association of diabetes with decreased diastolic BP than non-Hispanic blacks. CONCLUSIONS: The association of diabetes with uncontrolled BP outcomes is lower despite higher prevalence of diabetes in Mexican Americans than in non-Hispanic whites. The stronger association of diabetes with BP outcomes in whites should be of clinical concern, considering they account for the majority of the hypertensive population in the United States.

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