The thesis develops a driving assistant system that can locate the positions of the lane boundaries and detects the existence of the front-vehicle. It can also provide warning mechanism so as to avoid the danger as possible collides with previous vehicle. In lane recognition, we utilized the largest gradient of luminance value to detect possible road surface marking, then cooperated with the marking static and dynamic behavior of road surface characteristic to set up road surface marking and detect system. On the other hand, we considered vehicle detection leach the vehicle bottom shade characteristic from dynamic area threshold processing, and then judge and label where the vehicle exits. By the principle of the optics image formation, we estimated the relative distance from the previous vehicle. In this thesis, we proposed an easy and fast measure for previous vehicle of 96% correct rate in different environment. Running on typical 1.7Ghz processor system results up to 80 frames per second.

The present study investigated the relationship between religiousness, conservatism and traffic behaviour. It was shown that, intrinsic religious orientation significantly predicted ordinary violations of both the drivers and the pedestrians. Religiousness seems to have a positive effect by orienting the individual to obey the rules and to refrain from risk taking behaviour. Moreover, components of conservatism (conservation of values and resistance to change) were found to affect the aggressive violations and the positive behaviours of the drivers. While conservation of values decreased aggressive violations of the drivers, it increased the positive behaviours. On the contrary, resistance to change decreased positive behaviours and increased aggressive violations. These contrary results were accounted for by using Wilson&rsquo / s (1973) explanations of these dimensions. To conclude, variables distal to traffic context were shown to influence traffic behaviour differentially.

With the developments in space technology, the capabilities of spacecrafts have been increased considerably which in turn have entailed the development of more efficient spacecrafts in terms of cost, mass, size and power. One way to achieve such a development is the replacement of body mounted appendages with the deployable ones, which greatly reduces the size, mass and cost of the spacecraft especially when large appendages are considered. In order to obtain these deployable structures, deployment mechanisms and deployment mechanism drivers are used. A deployment mechanism is a combination of electrical and/or mechanical structures which hold the appendages in the stowed position before launch and deploys them after the launch with the power and commands supplied by the deployment mechanism driver. This necessary power of the deployment mechanism driver is produced by the Power Stage of the deployment mechanism driver and the necessary commands required by the deployment mechanism are supplied by the Control Stage of the deployment mechanism driver. In this thesis, the power stage of a deployment mechanism driver will be designed and implemented taking into account of the requirements for Low Earth Orbit Satellites such as temperature tolerance, reliability and radiation limits. In order to acquire a cost, mass and size efficient Power Stage, different deployment mechanism topologies will be studied and the most convenient one among these topologies will be chosen as the deployment mechanism driver load and the design will be performed accordingly.

This thesis analyzes aggregated county-level data of fatal alcohol related traffic crashes where a driver was killed in the state of Texas during 1996 to 2005. Alcohol has constantly threatened drivers and passengers alike and continues to be a major cause of fatal crashes in Texas. Specifically, this paper targets those drivers that were killed while driving under the influence (0.01 BAC). With an increase in manageable data and the ease of availability of aggregated crash records, accident analysis can provide a closer look into trends such as spatial-temporal patterns, clustering and correlations to various factors. Furthermore, Geographic Information Systems (GIS) have enabled researchers to more efficiently interpret and study a large amount of datasets using techniques that were previously difficult or inaccessible in applications related to traffic safety and transportation. Loose-coupling of GIS with other spatial analysis programs and/or statistical software packages can now provide important results that in turn relate vital information which can be used towards understanding and potentially alleviating problems in the transportation domain. The following sections concluded that aggregated datasets at the county level are currently incomplete and do not provide the level of detail necessary to formulate a solid conclusion regarding relationships between the chosen factors and the crash dataset. Though this research was successful in mapping spatial variations and clusters, linking variables such as age, gender, location and population to the aggregated crash dataset requires more detailed information about the crash than was available. However, the objectives were successful in representing spatial-temporal patterns across the study period for all designated variables. This was an important step and solid contribution towards the representation of large datasets and their impact on policy, traffic safety, and transportation geography.

In the thesis, an LED driver circuit that is applied in low power lighting LED with constant output current and Power Factor Correction (PFC) is presented. The insulated Flyback converter is used for the LED driver. Power Factor Correction is realized with both the method of Voltage Follower Approach Control under Discontinuous Conduction Mode and the method of Boundary Conduction Control under Boundary Conduction Mode. Compared with Voltage Follower Approach Control, Boundary Conduction Control needs only output current feedback. Moreover, it possesses lesser magnetize inductance current, lesser electrical stress of elements, more flexible choice of elements specification, smaller output current ripples, and higher power factor under light load. The circuit design is expounded, and verified by IsSpice simulation and experiment result.

Thesis (M.A.)--University of West Florida, 2009. / Submitted to the Dept. Of Psychology. Title from title page of source document. Document formatted into pages; contains 77 pages. Includes bibliographical references.

Transportation sector has been a major contributing factor to the overall emissions of most pollutants and thus their impacts on the environment. Among all transportation activities, on-road travel accounts for most part of the Greenhouse gas (GHG) emissions and fuel use. It also has a very un-desirable impact on the transportation network conditions increasing the traffic congestion levels. The main aim of transportation planning agencies is to implement the policy changes that will reduce automobile dependency and increase transit and non-motorized modes usage. However, planning agencies can come up with proactive economic, land-use and transportation policies provided they have a model which is sensitive to all the above mentioned factors to predict the vehicle fleet composition and usage of households. Moreover, the type of vehicle that a household gets (vehicle type choice) and the annual mileage (usage) associated with that vehicle is very closely related to the person in the household who uses that vehicle the most (allocation to primary driver). So, it is no longer possible to view all these decisions separately. Instead, we need to model all these decisions- vehicle type choice, usage, and allocation to primary driver simultaneously at a household level. In this study, we estimate and apply a joint household-level model of the number of vehicles owned by the household, the vehicle type choice of each vehicle, the annual mileage on each vehicle, as well as the individual assigned as the primary driver for each vehicle. A version of the proposed model system currently serves as the engine for a household vehicle composition and evolution simulator, which itself has been embedded within the larger SimAGENT (for Simulator of Activities, Greenhouse emissions, Networks, and Travel) activity-based travel and emissions forecasting system for the Southern California Association of Governments (SCAG) planning region. / text

A traffic network can be disrupted by work zones and incidents. Calculating diversion rate is a core issue for estimating demand changes, which is needed to select a suitable work zone configuration and work schedule. An urban network can provide multiple alternative routes, so traffic assignment is the best tool to analyze diversion rates on network level and the local level. Compared with the results from static traffic assignment, dynamic traffic assignment predicts a higher network diversion rate in the morning peak period and off-peak period, a lower local diversion rate in the morning peak period. Additionally, travelers may benefit from knowing real-time traffic condition to avoid the traffic incident areas. Deploying variable message signs (VMSs) is one possible solution. One key issue is optimizing locations of VMSs. A planning model is created to solve the problem. The objective is minimize total system travel time. The link transmission model is used to evaluate the performance of the network, and bounded rational behavior is used to represent drivers' response to VMSs. A self-adapting genetic algorithm (GA) is formulated to solve the problem. This model selects the best locations to provide VMSs, typically places are that allow travelers to switch to alternative routes. Results show that adding more VMSs beyond a certain threshold level does not further reduce travel time. / text

Somatic copy number alterations (SCNAs) are a class of alterations that lead to deviations from diploidy in developing and established tumors. A feature that distinguishes SCNAs from other alterations is their genomic footprint. The large genomic footprint of SCNAs in a typical cancer's genome presents both a challenge and an opportunity to find targetable vulnerabilities in cancer. Because a single event affects many genes, it is often challenging to identify the tumorigenic targets of SCNAs. Conversely, events that affect multiple genes may provide specific vulnerabilities through "bystander" genes, in addition to vulnerabilities directly associated with the targets. We approached the goal of understanding how the structure of SCNAs may lead to dependency in two ways. To improve our understanding of how SCNAs promote tumor progression we analyzed the SCNAs in 4934 primary tumors in 11 common cancers collected by the Cancer Genome Atlas (TCGA). The scale of this dataset provided insights into the structure and patterns of SCNA, including purity and ploidy rates across disease, mechanistic forces shaping patterns of SCNA, regions undergoing significantly recurrent SCNAs, and correlations between SCNAs in regions implicated in cancer formation. In a complementary approach, we integrating SCNA data and pooled RNAi screening data involving 11,000 genes across 86 cell lines to find non-driver genes whose partial loss led to increased sensitivity to RNAi suppression. We identified a new set of cancer specific vulnerabilities predicted by loss of non-driver genes, with the most significant gene being PSMC2, an obligate member of the 26S proteasome. Biochemically, we found that PSMC2 is in excess of cellular requirement in diploid cells, but becomes the stoichiometric limiting factor in proteasome formation after partial loss of this gene. In summary, my work improved our understanding of the structure and patterns of SCNA, both informing how cancers develop and predicting novel cancer vulnerabilities. Our characterization of the SCNAs present across 5000 tumors uncovered novel structure in SCNAs and significant regions likely to contain driver genes. Through integrating SCNA data with the results of a functional genetic screen, we also uncovered a new set of vulnerabilities caused by unintended loss of non-driver genes.

The concept driver distraction is diffuse and no clear definition exists, which causes troubles when it comes to driver attention monitoring. This thesis takes an approach where eyetracking data from experienced drivers along with radar data has been used and analysed in an attempt to set up adaptive rules of how and how often the driver needs to attend to different objects in its surroundings, which circumvents the issue of not having a clear definition of driver distraction. In order to do this, a target tracking algorithm has been implemented that refines the output from the radar, subsequently used together with the eye-tracking data to in a statistical manner, in the long term, try to answer the question for how long is the driver allowed to look away in different driving scenarios? The thesis presents a proof of concept of this approach, and the results look promising.