The idea behind this report is based on the fact that it is not only the number of users in the traffic network that is increasing, the number of connected devices such as probe vehicles and mobile sources has increased dramatically in the last decade. These connected devices provide large-scale mobility data and new opportunities to analyze the current traffic situation as they traverse through the network and continuously send out different types of information like Global Positioning System (GPS) data and Mobile Network Data (MND). Travel demand is often described in terms of an Origin Destination (OD) matrix which represents the number of trips from an origin zone to a destination zone in a geographic area. The aim of this master thesis is to develop and evaluate a data-driven method for estimation of dynamic OD matrices using unsupervised learning, sensor fusion and large-scale mobility data. Traditionally, OD matrices are estimated based on travel surveys and link counts. The problem is that these sources of information do not provide the quality required for online control of the traffic network. A method consisting of an offline process and an online process has therefore been developed. The offline process utilizes historical large-scale mobility data to improve an inaccurate prior OD matrix. The online process utilizes the results and tuning parameters from the offline estimation in combination with real-time observations to describe the current traffic situation. A simulation study on a toy network with synthetic data was used to evaluate the data-driven estimation method. Observations based on GPS data, MND and link counts were simulated via a traffic simulation tool. The results showed that the sensor fusion algorithms Kalman filter and Kalman filter smoothing can be used when estimating dynamic OD matrices. The results also showed that the quality of the data sources used for the estimation is of high importance. Aggregating large-scale mobility data as GPS data and MND by using the unsupervised learning method Principal Component Analysis (PCA) improves the quality of the large-scale mobility data and so the estimation results. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

>Magister Scientiae - MSc / The Cape Floristic Region (CFR) in South Africa, is one of the world's five Mediterranean hotspots, and is also one of the 34 global biodiversity hotspots. It has rich biological diversity, high level of species endemism in flora and fauna and an unusual high level of human induced threats. The Sandveld forms part of the CFR and is also highly threatened by intensive agriculture (potato, rooibos and wheat farming), proliferation of tourism facilities, coastal development, and alien invasions. These biodiversity threats have led to habitat loss and are threatening the long-term security of surface and ground water resources. In order to understand trends in such biodiversity loss and improve in the management of these ecosystems, earth-orbiting observation satellite data were used. This research assessed landuse changes and trends in vegetation cover in the Sandveld, using remote sensing images. Landsat TM satellite images of 1990, 2004 and 2007 were classified using the maximum likelihood classifier into seven landuse classes, namely water, agriculture, fire patches, natural vegetation, wetlands, disturbed veld, and open sands. Change detection using remote sensing algorithms and landscape metrics was performed on these multi-temporal landuse maps using the Land Change ModelIer and Patch Analyst respectively. Markov stochastic modelling techniques were used to predict future scenarios in landuse change based on the classified images and their transitional probabilities. MODIS NDVI multi-temporal datasets with a 16day temporal resolution were used to assess seasonal and annual trends in vegetation cover using time series analysis (PCA and time profiling).Results indicated that natural vegetation decreased from 46% to 31% of the total landscape between 1990 and 2007 and these biodiversity losses were attributed to an increasing agriculture footprint. Predicted future scenario based on transitional probabilities revealed a continual loss in natural habitat and increase in the agricultural footprint. Time series analysis results (principal components and temporal profiles) suggested that the landscape has a high degree of overall dynamic change with pronounced inter and intra-annual changes and there was an overall increase in greenness associated with increase in agricultural activity. The study concluded that without future conservation interventions natural habitats would continue to disappear, a condition that will impact heavily on biodiversity and significant water dependent ecosystems such as wetlands. This has significant implications for the long-term provision of water from ground water reserves and for the overall sustainability of current agricultural practices.

Le cancer de la prostate est le cancer le plus fréquemment diagnostiqué chez les hommes canadiens. Aucun facteur de risque modifiable n’a été identifié, mais l’alimentation pourrait être impliquée. Les profils alimentaires, décrivant l’ensemble de l’apport alimentaire, constituent une approche de recherche prometteuse. L’objectif général de cette thèse était d’évaluer le rôle des profils alimentaires et du niveau de transformation des aliments sur le risque de cancer de la prostate. Les données colligées dans une vaste étude cas-témoins populationnelle menée chez les résidents montréalais ont été utilisées. Les 1919 cas incidents histologiquement confirmés étaient âgés de 75 ans ou moins et avaient été diagnostiqués entre 2005 et 2009. Les 1991 témoins ont été sélectionnés aléatoirement à partir de la liste électorale, puis appariés aux cas selon l’âge (± 5 ans). Les informations concernant l’alimentation ont été recueillies avec un questionnaire de fréquence alimentaire documentant la consommation deux ans avant le diagnostic ou l’entrevue. Le premier objectif visait à identifier des profils alimentaires parmi les témoins francophones ainsi que les caractéristiques associées à ces profils. Une analyse en composantes principales a permis d’identifier les profils alimentaires Santé, Occidental modifié - Salé et Occidental modifié - Sucré. Le profil Santé a été associé à des niveaux plus élevés de revenu et d’éducation, à un niveau modéré d’activité physique et à un faible niveau de tabagisme. Le profil Occidental modifié - Salé a été associé avec des ethnicités française, européenne (autre que française) ou latine, avec le fait d’être marié ou en union libre, et était inversement associé avec l’âge. Le profil Occidental modifié - Sucré était plus commun chez les hommes d’origine française et chez les consommateurs de suppléments de vitamines et minéraux. Le deuxième objectif visait à évaluer les associations entre les profils alimentaires et le cancer de la prostate. Les rapports de cotes (RC) et intervalles de confiance (IC) à 95% ont été obtenus par régression logistique non conditionnelle ajustée pour les facteurs de confusion. Le profil Santé était inversement associé au risque de cancer de la prostate (RC= 0,76 [IC 95% = 0,61-0,93], en comparant le quartile supérieur au quartile inférieur). Le profil Occidental - Sucré et Boissons était associé à une augmentation du risque de cancer de la prostate (RC= 1,35 [IC 95% =1,10-1,66], quartile supérieur vs inférieur). Ces résultats sont novateurs. Aucune association n’a été observée avec le profil Occidental - Salé et Alcool. Le troisième objectif visait à évaluer l’association entre le niveau de transformation des aliments et le cancer de la prostate. Les aliments transformés étaient associés à une augmentation du risque (RC= 1,32 [IC 95% =1,07-1,62], quartile supérieur vs inférieur) et l’association était légèrement plus prononcée pour les cancers agressifs. En conclusion, ces résultats suggèrent que les profils alimentaires et le niveau de transformation des aliments jouent un rôle dans le développement du cancer de la prostate. Il s’agit d’informations importantes pour soutenir la promotion de saines habitudes de vie et la prévention du cancer de la prostate. / Prostate cancer is the most commonly diagnosed cancer among men in Canada. No modifiable risk factor has been identified, but diet is suspected to play a role. Dietary patterns, which describe the overall dietary intake rather than the consumption of specific foods or nutrients, represent a promising research approach. The general objective of this thesis was to assess the role of dietary patterns and the level of food processing on the risk of prostate cancer. Data collected in a large population-based case-control study conducted among Montreal residents were used. The 1919 histologically confirmed incident cases were 75 years of age or younger and had been diagnosed between 2005 and 2009. Concurrently, the 1991 controls were randomly selected from the electoral list and frequency-matched to cases by age (± 5 years). Food consumption was assessed using a food frequency questionnaire focusing on the period two years before diagnosis or interview. The first objective was to identify dietary patterns among the French-speaking controlsas well as the characteristics associated with these patterns. Principal component analysis led to the identification of three dietary patterns: Healthy, Western modified - Salty and Western modified - Sweet. The Healthy pattern was associated with higher income, education, moderate levels of recreational physical activity and lower levels of smoking. The Western modified – Salty pattern was positively associated with French, other European (other than French), and Latino ancestries, and with married and common-law relationships, whereas it was inversely associated with age. Finally, the Modified Western – Sweet pattern was more common among men of French ancestry and users of vitamin/mineral supplements. The second objective was to assess associations between the different dietary patterns and prostate cancer. Odds ratios (OR) and 95% confidence interval (95% CI) were obtained by unconditional logistic regression adjusting for confounders. The Healthy dietary pattern was inversely associated with prostate cancer (OR = 0,76 [95% CI = 0,61-0,93], highest vs lowest quartile), whereas the Western - Sweet and beverages pattern increased the risk of this cancer (OR = 1,35 [95% CI = 1,10-1,66], highest vs lowest quartile). Both results are novel. The Western - Salty and alcohol pattern was not associated with prostate cancer risk. The third objective was to assess the association between the level of food processing and prostate cancer. The level of food processing in the diet was assigned using the NOVA food classification. Processed foods were associated with an increased risk (OR = 1,32 [95% CI] = 1,07-1,62], highest vs lowest quartile) of prostate cancer, and the association was slightly more pronounced for high-grade prostate cancers. In conclusion, these results suggest that dietary patterns and the level of food processing play a role on the risk of developing prostate cancer. This information is important for promoting a healthy lifestyle and for prostate cancer prevention.

This thesis issues with possible methods for face detection and localization according to the state of the art. It describes various approaches and it is aimed at localization by neural networks and at necessary operations that have to be done before localization and after that for correct results representation. This project contains implementation of few approaches to neural netwok based face localization with emphasis on eigenfaces based face localization as well as implementation of simple classifier using distance of reconstructed face to the original one. Detailed description of implemented system, achieved results and dependecy of system performance on it's inner settings is also provided.

No description available.

Observers can identify gender based on individual gait styles visually. Existing research showed that gender differences in gait kinematics mainly occur in the frontal and transverse planes and are influenced by various factors. This study adds to the existing work by analysing the kinematic features that distinguish gait styles influenced by gender and gait speeds. 29 females and 33 males without gait disorders took part in this study. A wireless IMU-based sensor system was used to collect 3D kinematic data at 60 Hz on a 15 m walkway at three different gait speeds. Statistical analysis was based on discrete parameters, principal component analysis (PCA), and support vector machines (SVM). Dissimilarities due to different gait speeds were analysed in transverse and frontal planes for the upper body and in the sagittal plane for the upper and lower body (p < 0.001 and Cohen’s d > 0.8). In joint angles (knees; transversal plane), segment orientation angles (upper body; frontal plane) and segment position (upper body; sagittal and frontal plane), statistically significant differences (p < 0.001 and Cohen’s d > 0.8) were observed for gender.Good classification accuracies for joint angles, segment orientation and segment positions of 97-100 % between gait speed and 77-87 % between gender groups were found. In this study, gender had less influence on gait kinematics than gait speed.:1. Introduction 2. Methods 3. Results 4. Discussion

Under high irradiation, a fourth state of matter named plasma can be obtained. Plasmas emit electromagnetic radiation that can be recorded in the form of spectra for spectroscopic elemental analysis. With the advent of lasers in the 1960s, spectroscopists realized that lasers could be used simultaneously as a source of energy and excitation to create plasmas. The use of a laser to ignite a plasma subsequently led to laser-induced breakdown spectroscopy (LIBS), an optical emission spectroscopy capable of analyzing samples in various states (solids, liquids, gases) with minimal sample preparation, rapid feedback, and endowed with in situ capability. In this dissertation, studies of LIBS for multi-elemental analysis and geological applications are reported. LIBS was applied to cosmetic powders for elemental analysis, screening and classification based on the raw material used. Principal component analysis (PCA) and internal standardization were used. The intensity ratios of Mg/Si and Fe/Si observed in talcum powder show that these two ratios could be used as indicators of the potential presence of asbestos. The feasibility of LIBS for the analysis of gasification slags was investigated and results compared with those of inductively-coupled plasma−optical emission spectrometry (ICP-OES). The limits of detection for Al, Ca, Fe, Si and V were determined. The matrix effect was studied using an internal standard and PLS-R. Apart from V, prediction results were closed to those of ICP-OES with accuracy within 10%. Elemental characterization of outcrop geological samples from the Marcellus Shale Formation was also carried out. The matrix effect was substantially reduced. The limits of detection obtained for Si, Al, Ti, Mg, Ca and C were determined. The relative errors of LIBS measurements are in the range of 1.7 to 12.6%. Gate delay and laser pulse energy, have been investigated in view of quantitative analysis of variation of trace elements in a high-pressure environment. Optimization of these parameters permits obtaining underwater plasma emission of calcium with quantitative results on the order of 30 ppm within a certain limit of increased pressure. Monitoring the variation of the trace elements can predict changes in the chemical composition in carbon sequestration reservoir.

The goal of the present dissertation is to develop and apply novel and systematic data-driven optimization approaches that can efficiently optimize Additive Manufacturing (AM) systems with respect to targeted properties of final parts. The proposed approaches are capable of achieving sets of process parameters that result in the satisfactory level of part quality in an accelerated manner. First, an Accelerated Process Optimization (APO) methodology is developed to optimize an individual scalar property of parts. The APO leverages data from similar—but non-identical—prior studies to accelerate sequential experimentation for optimizing the AM system in the current study. Using Bayesian updating, the APO characterizes and updates the difference between prior and current experimental studies. The APO accounts for the differences in experimental conditions and utilizes prior data to facilitate the optimization procedure in the current study. The efficiency and robustness of the APO is tested against an extensive simulation studies and a real-world case study for optimizing relative density of stainless steel parts fabricated by a Selective Laser Melting (SLM) system. Then, we extend the idea behind the APO in order to handle multi-objective process optimization problems in which some of the characteristics of the AMabricated parts are uncorrelated. The proposed Multi-objective Process Optimization (m-APO) breaks down the master multi-objective optimization problem into a series of convex combinations of single-objective sub-problems. The m-APO maps and scales experimental data from previous sub-problems to guide remaining sub-problems that improve the solutions while reducing the number of experiments required. The robustness and efficiency of the m-APO is verified by conducting a series of challenging simulation studies and a real-world case study to minimize geometric inaccuracy of parts fabricated by a Fused Filament Fabrication () system. At the end, we apply the proposed m-APO to maximize the mechanical properties of AMabricated parts that show conflicting behavior in the optimal window, namely relative density and elongation-toailure. Numerical studies show that the m-APO can achieve the best trade-off among conflicting mechanical properties while significantly reducing the number of experimental runs compared with existing methods.

Manual inspection is a prevailing practice for quality assessment of poultry meat, but it is labor-intensive, tedious, and subjective. This thesis aims to assess the efficacy of an emerging structured illumination reflectance imaging (SIRI) technique with machine learning approaches for assessing WS and microbial spoilage in broiler breast meat. Broiler breast meat samples were imaged by an in house-assembled SIRI platform under sinusoidal illumination. In first experiment, handcrafted texture features were extracted from direct component (DC, corresponding to conventional uniform illumination) and amplitude component (AC, unique to the use of sinusoidal illumination) images retrieved from raw SIRI pattern images build linear discriminant analysis (LDA) models for classifying normal and defective samples. A further validation experiment was performed using deep learning as a feature extractor followed by LDA. The third experiment was on microbial spoilage assessment of broiler meat, deep learning models were used to extract features from DC and AC images builds on classifiers. Overall, this research has demonstrated consistent improvements of AC over DC images in assessing WS and spoilage of broiler meat and that SIRI is a promising tool for poultry meat quality detection.

Road traffic signs provide regulatory, warning, guidance, and other important information to road users to prevent hazards and road accidents. Therefore, the traffic signs must be detectable, legible, and visible both in day and nighttime to fulfill their purpose. The nighttime visibility is critical to safe driving on the roads at night. The state of the art gives clear evidence that the retroreflectivity improves the nighttime visibility (detectability and legibility) of the road traffic signs and that the nighttime visibility can be improved by using an adequate level of retroreflectivity. Furthermore, nighttime visibility can be affected by human, sign, vehicle, environmental, and design factors.  The retroreflectivity and colors of the road signs deteriorate over time and thus the visibility worsens, therefore, maintaining the road signs is one of the important issues to improve the safety on the roads.  Thus, it is important to judge whether the retroreflectivity and colors of the road sign are within the accepted levels for visibility and the status of the signs are accepted or not and need to be replaced.  This thesis aims to use machine learning algorithms to predict the status of road signs in Sweden. To achieve this aim, three classifiers were invoked: Artificial Neural Network (ANN), Support Vector Machines (SVM), and Random Forest (RF). The data which was collected in Sweden by The Road and Transport Research Institute (VTI) was used to build the prediction models. High accuracy was achieved using the three algorithms (ANN, SVM, and RF) of 0.84.3, 0.93, and 0.98, respectively. Scaling the data was found to improve the accuracy of the prediction for all three models and better accuracy is achieved when the data was scaled using standardization compared with normalization. Additionally using principal component analysis (PCA) has a different impact on the accuracy of the prediction for each algorithm. Another aim was to build prediction models to predict the retroreflectivity performance of the in-use road signs without the need to use instruments to measure the retroreflectivity or color. Experiments using linear and logarithmic regression models were conducted in this thesis to predict the retroreflectivity performance. Two datasets were used, VTI data and another data which was collected in Denmark by voluntary Nordic research cooperation (NMF group). The age of the road traffic sign, the chromaticity coordinate X for colors, and the class of retroreflectivity were found significant to the retroreflectivity in both datasets.  The logarithmic regression models were able to predict the retroreflectivity with higher accuracy than linear models. Two suggested logarithmic regression models provided high accuracy for predicting the retroreflectivity (R2 of 0.50 on VTI data and 0.95 on NMF data) by using color, age, class, GPS position, and direction as predictors. Nearly the same accuracy (R2 of 0.57 on VTI data and 0.95 on NMF data) was achieved by using all parameters in the data as predictors (including chromaticity coordinates X, Y for colors). As a conclusion, omitting chromaticity coordinates X, Y for colors from the logarithmic regression models does not affect the accuracy of the prediction.