Biofuels have been widely recognized as a potential renewable energy source that can lessen the United States’ dependence on imported petroleum and enhance the domestic economy. Particularly, biofuels derived from lignocellulosic biomass (LCB) have been the focus in the development of a sustainable biofuels industry. However, technical barriers in the LCB feedstock supply chain have been one of the major challenges impeding the economic viability of this industry. To expedite the commercialization process of LCB-based biofuels production, this paper employed a spatial mixed-integer mathematical model to explore the optimal biomass logistic system for a switchgrass-based biofuels biorefinery in East Tennessee. The evaluated logistic systems in this study included five conventional systems (one round bale system, one square bale system, and three mixed bale systems) in the baseline scenario and one stretch-wrap bale system in the preprocessing scenario. Results showed that the stretch-wrap bale system could potentially reduce total logistic cost of switchgrass by 12 to 21% compared that of the conventional systems. Also, the result of the optimal case in the conventional systems suggested that the mixed bale system without storage protection is most economical after taking into account the dry matter loss during storage. This study also provided information regarding the optimal location of a biorefinery, a switchgrass production plan, monthly harvested and delivered tonnage, and the draw area of switchgrass under each logistic system. The optimal location of a commercial-scale biorefinery was identified to be located in the northwest of Monroe County, a location close to the demonstration plant in Venore, Tennessee. Additionally, this study showed that the percentage of available hay land used for switchgrass production, the switchgrass-ethanol conversion rate, the energy prices, and the storage dry matter loss of compact switchgrass bale produce significant impacts on the total logistic cost of switchgrass for the biorefinery.

Meditation training has been shown to improve attention and emotion regulation. However, the mechanisms responsible for these effects are largely unknown. In order to make further progress, a rigorous interdisciplinary approach that combines both empirical and theoretical experiments is required. This dissertation uses such an approach to analyze electroencephalogram (EEG) data collected during two three-month long intensive meditation retreats in four steps. First, novel tools were developed for preprocessing the EEG data. These tools helped remove ocular artifacts, muscular artifacts, and interference from power lines in a semi-automatic fashion. Second, in order to identify the cortical correlates of meditation, longitudinal changes in the cortical activity were measured using spectral analysis. Three main longitudinal changes were observed in the retreat participants: (1) reduced individual alpha frequency after training, similar reduction has been consistently found in experienced meditators; (2) reduced alpha-band power in the midline frontal region, which correlated with improved vigilance performance; and (3) reduced beta-band power in the parietal-occipital regions, which correlated with daily time spent in meditation and enhanced self-reported psychological well-being. Third, a formal computational model was developed to provide a concrete and testable theory about the underlying mechanisms. Four theoretical experiments were run, which showed, (1) reduced intrathalamic gain after training, suggesting enhanced alertness; (2) increased cortico-thalamic delay, which strongly correlated with the reduction in individual alpha frequency (found during spectral analysis); (3) reduction in intrathalamic gain provided increased stability to the brain; and (4) anterior-posterior division in the modeled reticular nucleus of the thalamus (TRN) layer and increased connectivity in the posterior region of TRN after training. Fourth, correlation analysis was performed to ground the changes in cortical activity and model parameters into changes in behavior and self-reported psychological functions. Through these four steps, a concrete theory of the mechanisms underlying focused-attention meditation was constructed. This theory provides both mechanistic and teleological reasoning behind the changes observed during meditation training. The theory further leads to several predictions, including the possibility that customized meditation techniques can be used to treat patients suffering from neurodevelopmental disorders and epilepsy. Lastly, the dissertation attempts to link the theory to the long-held views that meditation improves awareness, attention, stability, and psychological well-being. / text

Les travaux présentés dans cette thèse visent à proposer des méthodes pour résoudre les problèmes de la sectorisation multicritère en cartographie. En premier temps, nous avons défini les problèmes différents de la sectorisation et nous avons établi les liens entre ces problèmes avec les problèmes classiques qui sont bien étudiés dans la littérature : le problème de découpage de district politique, les problèmes de localisation et le problème du partitionnement de graphe. Deux types de méthodes ont été abordés pour résoudre les problèmes de sectorisation. Des heuristiques ont été développées et elles consistent à calculer un optimum de Pareto pour les différents problèmes. Et pour le problème de sectorisation à partir de pôles, nous avons aussi utilisé et expérimenté un algorithme de boîte pour trouver une représentation du front de pareto. La méthode exacte branch and bound a été utilisée pour résoudre le problème de sectorisation sans pôle prédéfini optimalement. Avant que nous appliquons cette procédure, nous ajoutons quelques inégalités valides dans la formulation mathématique pour restreindre l'espace des solutions et nous développons une procédure de prétraitement pour réduire la taille du problème. / The work presented in this thesis aims to propose methods to solve the multicriteria map sectorization problem in cartography. Firstlly, we have defined the different sectorization problems and we have established the links between these problems with some classical problems which are well studied in the literature : political districting problem, locationallocation problems and constrained graph partitioning problems. Two types of methods have been proposed to solve the sectorization problem. Heuristics have been developed and they compute an optimum Pareto for the different sectorization problems. And for the sectorization problem with predefined centers, we have used a box algorithm and experimented it to find a representation of the Pareto front. The branch and bound method was used to solve optimally the sectorization problem without predefined centers. Before we apply this procedure, we add some valid inequalities in the mathematical formulation for restrict the space of solutions and we develop a preprocessing procedure to reduce the size of the problem.

abstract: Splicing of digital images is a powerful form of tampering which transports regions of an image to create a composite image. When used as an artistic tool, this practice is harmless but when these composite images can be used to create political associations or are submitted as evidence in the judicial system they become more impactful. In these cases, distinction between an authentic image and a tampered image can become important. Many proposed approaches to image splicing detection follow the model of extracting features from an authentic and tampered dataset and then classifying them using machine learning with the goal of optimizing classification accuracy. This thesis approaches splicing detection from a slightly different perspective by choosing a modern splicing detection framework and examining a variety of preprocessing techniques along with their effect on classification accuracy. Preprocessing techniques explored include Joint Picture Experts Group (JPEG) file type block line blurring, image level blurring, and image level sharpening. Attention is also paid to preprocessing images adaptively based on the amount of higher frequency content they contain. This thesis also recognizes an identified problem with using a popular tampering evaluation dataset where a mismatch in the number of JPEG processing iterations between the authentic and tampered set creates an unfair statistical bias, leading to higher detection rates. Many modern approaches do not acknowledge this issue but this thesis applies a quality factor equalization technique to reduce this bias. Additionally, this thesis artificially inserts a mismatch in JPEG processing iterations by varying amounts to determine its effect on detection rates. / Dissertation/Thesis / Masters Thesis Computer Science 2015

Data Ductus is a Swedish IT-consultant company, their customer base ranging from small startups to large scale cooperations. The company has steadily grown since the 80s and has established offices in both Sweden and the US. With the help of machine learning, this project will present a possible solution to the errors caused by the human factor in the logistic business.A way of preprocessing data before applying it to a machine learning algorithm, as well as a couple of algorithms to use will be presented. / Data Ductus är ett svenskt IT-konsultbolag, deras kundbas sträcker sig från små startups till stora redan etablerade företag. Företaget har stadigt växt sedan 80-talet och har etablerat kontor både i Sverige och i USA. Med hjälp av maskininlärning kommer detta projket att presentera en möjlig lösning på de fel som kan uppstå inom logistikverksamheten, orsakade av den mänskliga faktorn.Ett sätt att förbehandla data innan den tillämpas på en maskininlärning algoritm, liksom ett par algoritmer för användning kommer att presenteras.

Processes are running everywhere. Understanding and analyzing business and software processes and their interactions is critical if we wish to improve them. There are many event logs generated from Information Systems and applications related to fraud detection, healthcare processes, e-commerce processes, and others. These event logs are the starting point for process mining. Process mining aims to discover, monitor, and improve real processes by extracting knowledge from event logs available in information systems. Process mining provides fact-based insight from real event logs that helps analyze and improve existing business processes by answering, for example performance or conformance questions. As the number of applications developed in a cloud infrastructure (often called Software as a Service – SaaS at the application level) is increasing, it becomes essential and useful to study and discover these processes. However, SaaS applications bring new challenges to the problem of process mining. Using the Design Science Research Methodology, this thesis introduces a new method to study, discover, and analyze cloud-based application processes using process mining techniques. It explores the applications and known challenges related to process mining in cloud applications through a systematic literature review (SLR). It then contributes a new Application Programming Interface (API), with an implementation in R, and a companion method called Cloud Pattern API – Process Mining (CPA-PM), for the preprocessing of event logs in a way that addresses many of the challenges identified in the SLR. A case study involving a SaaS company and real event logs related to the trial process of their online service is used to validate the proposed solution.

Analytics such as machine learning are of big interest in many types of industries. Optical character recognition is essentially a solved problem, whereas number recognition on real-world images which can be one form of machine learning are a more challenging obstacle. The purpose of this study was to implement a system that can detect and read numbers on given dataset originating from the forest industry being images of color-stamped logs. This study evaluated accuracy of segmentation and number recognition on images of color-stamped logs when using a pre-trained model of the street view house numbers dataset. The general approach of preprocessing was based on car number plate segmentation because of the similar problem of identifying an object to then locate individual digits. Color segmentation were the biggest asset for the preprocessing because of the distinct red color of digits compared to the rest of the image. The accuracy of number recognition was significantly lower when using the pre-trained model on color-stamped logs being 26% in comparison to street view house numbers with 95% but could still reach over 80% per digit accuracy rate for some image classes when excluding accuracy of segmentation. The highest segmentation accuracy among classes was 93% and the lowest was 32%. From the results it was concluded that unclear digits on images lessened the number recognition accuracy the most. There are much to consider for future work, but the most obvious and impactful change would be to train a more accurate model by basing it on the dataset of color-stamped logs.

Text analysis is a significant branch of natural language processing, and includes manydifferent sub-fields such as topic modeling, document classification, and sentiment analysis.Unsurprisingly, those who do text analysis are concerned with the runtime of their algorithmsSome of these algorithms have runtimes that depend jointly on the size of the corpus beinganalyzed, as well as the size of that corpus's vocabulary. Trivially, a user may reduce theamount of data they feed into their model to speed it up, but we assume that users will behesitant to do this as more data tends to lead to better model quality. On the other hand,when the runtime also depends on the vocabulary of the corpus, a user may instead modifythe vocabulary to attain a faster runtime. Because elements of the vocabulary also add tomodel quality, this puts users into the position of needing to modify the corpus vocabulary inorder to reduce the runtime of their algorithm while maintaining model quality. To this end,we look at the relationship between model quality and runtime for text analysis by looking atthe effect that current techniques in vocabulary reduction have on algorithmic runtime andcomparing that with their effect on model quality. Despite the fact that this is an importantrelationship to investigate, it appears little work has been done in this area. We find thatmost preprocessing methods do not have much of an effect on more modern algorithms, butproper rare word filtering gives the best results in the form of significant runtime reductionstogether with slight improvements in accuracy and a vocabulary size that scales efficiently aswe increase the size of the data.

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Yes / A confocal microscope provides a sequence of images of the various corneal layers and structures at different depths from which medical clinicians can extract clinical information on the state of health of the patient’s cornea. Preprocessing the confocal corneal images to make them suitable for analysis is very challenging due the nature of these images and the amount of the noise present in them. This paper presents an efficient preprocessing approach for confocal corneal images consisting of three main steps including enhancement, binarisation and refinement. Improved visualisation, cell counts and measurements of cell properties have been achieved through this system and an interactive graphical user interface has been developed.