Throughout recent years, legislations concerning emission levels for vehicles have become more restrictive and will be even more restrictive in the future. In the recent European environmental standards, EURO 4 (2006) and EURO 5 (2008), further requirements have been added on top of low emission demands. All heavy duty trucks have to be equipped with an OBD-system. Scania CV AB has today an existing OBD-system that consists of several tests. Typically, a test is designed to check if a signal is inside specified limits or thresholds. To improve the system, Scania CV AB and Vehicular Systems at Linköping University have developed a method to design diagnosis systems in an automatic way, implemented in a toolbox called DSAME. In this thesis, an automatic designed OBD-system has been created with DSAME and the corresponding parts in a manually designed OBD-system have been identified. The two systems have been compared. The result shows that both systems are equally at detecting faults but the automatic designed OBD-system is a lot better to isolate the faults than the existing OBD-system.

Weather data are huge. Traditional visualization techniques are limited to show temperature trends. Pixel-based approaches could be used to visualize the huge amount of weather data and in process show the temperature trends. A prototype using this approach is built to make temperature data more understandable in changing trends. It is implemented using a 2D representation and many popular interaction techniques. It is a lightweight and reusable tool to visualize temperatures.

As a technique to solve new problems based on previous successful cases, CBR represents significant prospects for improving the accuracy and effectiveness of unstructured decision-making problems. Similar problems have similar solutions is the main assumption. Utility oriented similarity modeling is gradually becoming an important direction for Case-based reasoning research. In this thesis, we propose a new way to represent the utility of case by using fuzzy rules. Our method could be considered as a new way to estimate case utility based on fuzzy rule based reasoning. We use modified WANG’s algorithm to generate a fuzzy if-then rule from a case pair instead of a single case. The fuzzy if-then rules have been identified as a powerful means to capture domain information for case utility approximation than traditional similarity measures based on feature weighting. The reason why we choose the WANG algorithm as the foundation is that it is a simpler and faster algorithm to generate if-then rules from examples. The generated fuzzy rules are utilized as a case matching mechanism to estimate the utility of the cases for a given problem. The given problem will be formed with each case in the case library into pairs which are treated as the inputs of fuzzy rules to determine whether or to which extent a known case is useful to the problem. One case has an estimated utility score to the given problem to help our system to make decision. The experiments on several data sets have showed the superiority of our method over traditional schemes, as well as the feasibility of learning fuzzy if-then rules from a small number of cases while still having good performances.

This study investigates variability in the importance of investment decision criteria used by venture capitalists in assessing new technology-based ventures and relates the criteria to the subsequent performance of the investment in the new venture. Variability was measured using interval and ordinal scale approaches for both criteria ratings and rankings. The analyses found that the criteria used by venture capitalists form a general hierarchy that is consistently ranked across ventures. However, there are some criteria that do not form part of this hierarchy and whose importance varies depending on the specific venture being evaluated. The criteria that are consistently considered important by venture capitalists can be thought of as necessary conditions for investment. The hypotheses concerning the relationship between the criteria and subsequent deal performance are that:· deal performance can be assessed by venture capitalists earlier for Internet-related ventures than for other-technology based ventures (H1);· Internet-related ventures have more extreme levels of deal performance (H2);· a small number of criteria will distinguish between successful and unsuccessful deal performance (H3);· criteria that do distinguish have above average variability (H4); and· criteria related to first-mover advantage distinguish between successful and unsuccessful deals (H5). The study was conducted in two parts. The original study (n=100) conducted by Bachher (2000) gathered information about the importance of the investment criteria using a web-based survey. The follow-up study (n=40) gathered information about the success of the investments by surveying the original participants and gathering information from the Internet. Limitations of the study include a nonrandom sample, a small sample size for the follow-up survey and the very small number (n=5) of unsuccessful investments identified. Evidence for hypotheses H1 and H2 was in the predicted direction but failed to achieve statistical significance. The evidence is supportive of H3. Evidence for H4 and H5 was not found. Additional analysis of the results suggests that venture capitalists whose investments were ultimately unsuccessful placed less importance on technology-related criteria than did venture capitalists investing in the other ventures. This finding implies that venture capitalists need to perform detailed assessments of the technology of new ventures.

Statistical software packages have been used for decades to perform statistical analyses. Recently, the emergence of the Internet has expanded the potential for these packages. However, none of the existing packages have fully realized the collaborative potential of the Internet. This medium, which is beginning to gain acceptance as a software development platform, allows people who might otherwise be separated by organizational or geographic barriers to come together and tackle complex issues using commonly available data sets, analysis tools and communications tools. Interestingly, there has been little work towards solving this problem in a generally applicable way. Rather, systems in this area have tended to focus on particular data sets, industries, or user groups. The Web-based statistical analysis model described in this thesis fills this gap. It includes a statistical analysis engine, data set management tools, an analysis storage framework and a communication component to facilitate information dissemination. Furthermore, its focus on enabling users with little statistical training to perform basic data analysis means that users of all skill levels will be able to take advantage of its capabilities. The value of the system is shown both through a rigorous analysis of the system’s structure and through a detailed case study conducted with the tobacco control community.

In Corpus Based Knowledge Representation [CBKR], limited association capability, that is, no criteria in place to extract substantial associations in the corpus, and lack of support for hypothesis testing and prediction in context, restricted the application of the methodology by information specialists and data analysts. In this thesis, the researcher proposed a framework called CBKR+ to increase the expressiveness of CBKR by identifying and incorporating association criteria to allow the support of new forms of analyses related to hypothesis testing and prediction in context. <br /><br /> As contributions of the CBKR+ framework, the researcher (1) defined a new domain categorization model called Basis for Categorization model, (2) incorporated the Basis for Categorization model to (a) facilitate a first level categorization of the schema components in the corpus, and (b) define the Set of Criteria for Association to cover all types of associations and association agents, (3) defined analysis mechanisms to identify and extract further associations in the corpus in the form of the Set of Criteria for Association, and (4) improved the expressiveness of the representation, and made it suitable for hypothesis testing and prediction in context using the above. <br /><br /> The application of the framework was demonstrated, first, by using it on examples from the CBKR methodology, and second, by applying it on 12 domain representations acquired from multiple sources from the physical-world domain of Criminology. The researcher arrived at the conclusion that the proposed CBKR+ framework provided an organized approach that was more expressive, and supported deeper analyses through more diagnostic and probability-based forms of queries.

It is well known that biological structures such as human brains, although may contain the same global structures, differ in shape, orientation, and fine structures across individuals and at different times. Such variabilities during registration are usually represented by nonrigid transformations. This research seeks to address this issue by developing physically based models in which transformations are constructed to obey certain physical laws. <br /><br /> In this thesis, a novel registration technique is presented based on the physical behavior of particles. Regarding the image as a particle system without mutual interaction, we simulate the registration process by a set of free particles moving toward the target positions under applied forces. The resulting partial differential equations are a nonlinear hyperbolic system whose solution describes the spatial transformation between the images to be registered. They can be numerically solved using finite difference methods. <br /><br /> This technique extends existing physically based models by completely excluding mutual interaction and highly localizing image deformations. We demonstrate its performance on a variety of images including two-dimensional and three-dimensional, synthetic and clinical data. Deformable images are achieved with sharper edges and clearer texture at less computational cost.

The epidemiology of malaria considers a complex set of local interactions amongst host, vector, and environment. A history of reemergence, epidemic transition, and ensuing endemic transmission in Iquitos, Peru reveals an interesting case used to model and explore such interactions. In this region of the Peruvian Amazon, climate change, development initiatives and landscape fragmentation are amongst a unique set of local spatial variables underlying the endemicity of malaria. Traditional population-based approaches lack the ability to resolve the spatial influences of these variables. Presented is a framework for spatially explicit, agent-based modeling of malaria transmission dynamics in Iquitos and surrounding areas. The use of an agent-based model presents a new opportunity to spatially define causal factors and influences of transmission between mosquito vectors and human hosts. In addition to spatial considerations, the ability to model individual decisions of humans can define socio-economic and human-environment interactions related to malaria transmission. Three interacting sub-models representing human decisions, vector dynamics, and environmental factors comprise the model. Feedbacks between the interacting sub-models define individual decisions and ultimately the flexibility that will allow the model to function in a diagnostic capacity. Sensitivity analysis and simulated interactions are used to discuss this diagnostic capability and to build understanding of the physical systems driving local transmission of malaria.

With the rapid growth of the digital imaging, image processing techniques are widely involved in many industrial and medical applications. Image thresholding plays an essential role in image processing and computer vision applications. It has a vast domain of usage. Areas such document image analysis, scene or map processing, satellite imaging and material inspection in quality control tasks are examples of applications that employ image thresholding or segmentation to extract useful information from images. Medical image processing is another area that has extensively used image thresholding to help the experts to better interpret digital images for a more accurate diagnosis or to plan treatment procedures. Opposition-based computing, on the other hand, is a recently introduced model that can be employed to improve the performance of existing techniques. In this thesis, the idea of oppositional thresholding is explored to introduce new and better thresholding techniques. A recent method, called Opposite Fuzzy Thresholding (OFT), has involved fuzzy sets with opposition idea, and based on some preliminary experiments seems to be reasonably successful in thresholding some medical images. In this thesis, a Weighted Opposite Fuzzy Thresholding method (WOFT) will be presented that produces more accurate and reliable results compared to the parent algorithm. This claim has been verified with some experimental trials using both synthetic and real world images. Experimental evaluations were conducted on two sets of synthetic and medical images to validate the robustness of the proposed method in improving the accuracy of the thresholding process when fuzzy and oppositional ideas are combined.

The Saskatchewan farm sector is a dynamic system that is faced with the reality of farm consolidation and other structural adjustments. While structural adjustment may result in increased productivity at the farm-level, the declining farm population has a direct impact on rural regions. Given the economic difficulties now inherent in many rural regions, there has never been a more important time to improve our understanding of the structural dynamics of the farm sector. <p> By utilizing agent-based methods, competition that exists between farm households in land markets is modelled in a dynamic framework. By modeling land markets in this manner, structural adjustments that occur due to the re-allocation of land among farm household becomes endogenous to the model. The farming simulation was validated by evaluating its ability to replicate actual structural shifts that occurred during the period of 1960-2000. The results obtained from the simulation were found to mirror historic shifts, which gives the author confidence that the parsimonious assumptions made are robust, yet still characteristic of farm level behaviour in the region. Other scenarios were simulated in order to estimate a counterfactual structural evolution of the modelled region, in the absence of government stabilization and support programs. Significant deviations are observed between the base and zero transfer scenarios with regards to the consolidation of farm assets among a declining number of farm households. Most significantly, the decline in farm numbers accelerated significantly in the late 1980s in the zero transfer scenario compared to the base simulations. <p>The application of an agent-based framework allowed for the study of regional structure with an emphasis on the behaviour and actions of the primary decisions makers within the system. While structural change is driven by a number of factors, the ability of a farm household to fully employ their labour resource was an important factor in the simulations. This contrasts with the finding that productive efficiency, and purchasing and market power at the farm level is not a necessary condition for the observed consolidation of farm assets.