Whenever a student submits their final version of their thesis, a series of processes is triggered to finalize and archive the report. These processes are often handled in a less than efficient way which results in excessive manual labor and costs that can be prevent if automated. This report describes a solution that automates the series of processes that occur following a final thesis report submission. By utilizing the available information in a Canvas course and the content in the submitted thesis much of the manual cut-and-paste effort is avoided. Entering this data into DiVA is done by automated interaction via a browser, as DiVA does not have an application programming interface that could be used. The conclusion is that it is possible to automate this process through a headless browser. However, the automated parsing of the PDF version of the thesis proven to be inconsistent which results in the extracted data being inconsistent. With some improvements to the parsing module, the entire process could be fully automated. / Varje gång en student skickar in sin slutgiltiga version av sitt examensarbete, utlöses en serie av processer för att slutföra och arkivera examensarbetet. Dessa processer hanteras ofta på ett mindre än effektivt sätt vilket resulterar i extra mycket manuellt arbete och kostnader som kan förhindras ifall de automatiseras. Denna uppsats beskriver en lösning som automatiserar serien av processer som inträffar efter att ett slutgiltigt examensarbete har godkänts. Genom att använda tillgänglig information i en Canvas-kurs och innehållet i det inlämnade examensarbetet undviks mycket av den manuella ”klipp-och-klistra”-insatsen. Inmatning av den relevanta data från examensarbetet måste göras via automatiserad interaktion via en webbläsare i DiVA, eftersom DiVA inte hade ett API som kunde användas. Slutsatsen är att det är möjligt att automatisera detta genom en huvudlös webbläsare, även om modulen som behandlar PDF har visat sig vara inkonsekvent vilket i sin tur har resulterat i att den automatiska interaktionen är inkonsekvent. Med några optimeringar i analysmodulen kan hela processen automatiseras.

This dissertation addresses the problem of elevation of the T wave of an electrocardiogram (ECG) signal in the magnetic resonance imaging (MRI). In the MRI, due to the strong static magnetic field the interaction of the blood flow with this strong magnetic field induces a voltage in the body. This voltage appears as a superimposition at the locus of the T wave of the ECG signal. This looses important information required by the doctors to interpret the ST segment of the ECG and detect diseases such as myocardial infarction. This dissertation aims at finding a solution to the problem of elevation of the T wave of an ECG signal in the MRI. The first step is to simulate the entire situation and obtain the magnetic field dependent T wave elevation. This is achieved by building a model of the aorta and simulating the blood flow in it. This model is then subjected to a static magnetic field and the surface potential on the thorax is measured to observe the T wave elevation. The various parameters on which the T wave elevation is dependent are then analyzed. Different approaches are used to reduce this T wave elevation problem. The direct approach aims at computing the magnitude of T wave elevation using magneto-hydro-dynamic equations. The indirect approach uses digital signal processing tools like the least mean square adaptive filter to remove the T wave elevation and obtain artifact free ECG signal in the MRI. Excellent results are obtained from the simulation model. The model perfectly simulates the ECG signal in the MRI at all the 12 leads of the ECG. These results are compared with ECG signals measured in the MRI. A simulation package is developed in MATLAB based on the simulation model. This package is a graphical user interface allowing the user to change the strength of magnetic field, the radius of the aorta and the orientation of the aorta with respect to the heart and observe the ECG signals with the elevation at the 12 leads of the ECG. Also the artifacts introduced due to the magnetic field can be removed by the least mean square adaptive filter. The filter adapts the ECG signal in the MRI to the ECG signal of the patient outside the MRI. Before the adaptation, the heart rate of the ECG outside the MRI is matched to the ECG in the MRI by interpolation or decimation. The adaptive filter works excellently to remove the T wave artifacts. When the cardiac output of the patient changes, the simulation model is used along with the adaptive filter to obtain the artifact free ECG signal.

De Förenta Nationernas Agenda 2030 fastställer som ett delmål att säkerställa utbildning av hög kvalitet och främja livslångt lärande för alla som en del av arbetet för ett mer hållbart samhälle. Vikten av detta delmål blir särskilt tydlig i och med det observerbara sambandet mellan en fullständig gymnasieexamen och allmän hälsa i Sverige; gymnasiestudenter som går ut med en gymnasieexamen tenderar att erhålla bättre allmän hälsa. Learning Analytics är ett relativt nytt område inom utbildningsvetenskaplig forskning som syftar till att förbättra utbildning med hjälp av elevdata. Detta arbete undersökte vilken möjlig påverkan och begränsningar som förekommer vid implementering av en multipel linjär regressionsmodell utvecklad för en matematikkurs i en gymnasieskola. Vid utvecklingen av denna modell fastställdes tre signifikanta indikatorer för att förutsäga elevernas slutbetyg; Diagnos resultat,resultat på nationella proven och frånvaro. Prognosmodellen har utvärderats statistiskt varpå den visade sig vara tillförlitlig i 90% av bedömningarna, vilket inte är tillräckligt säkert för att användas i verkliga bedömningstillfällen eftersom lärare kräver att resultaten är obestridliga. Genom en fokusgruppsintervju med lärare granskas dessa resultat och deltagarna uttrycker sitt intresse för prognosmodeller tillsammans med en reflektion över elevers potentiella negativa reaktioner på en ogynnsam prognos. Utvärdering av modellen visar att den i dagsläget har en rimlig förmåga att förutsäga elevers slutbetyg men att det finns ett starkt behov av insamling av mer nyanserade data för att öka möjligheten till innovation i framtida arbeten / The 2030 Agenda establishes the goal to ensure quality education and promote lifelong learning opportunities for all. The importance of this goal becomes particularly clear when taking into account the link between upper secondary school graduation and general health in Sweden; Upper secondary school graduates tend to have better general health. Learning Analytics is a relatively new area of education research which aims to improve education using student data. This report examines the possible impact and limitations when implementing a multiple linear regression model developed for a mathematics course in an upper secondary school. In developing this model, three major indicators are established to be significant in predicting students' final grade; Diagnosis results, national test results and the amount of student absence. The model was statistically evaluated and found to be reliable in 90% of cases, which is not secure enough to be used in real assessment situations as teachers require the results to be indisputable. Through a focus group interview with teachers these results are evaluated and the participants establish their interest in predictive tools along with concerns for students' negative reactions to poor results. Evaluation of the model shows it has a reasonable ability to anticipate students' final grades but with a strong need for improvement in data collection methods and acquisition of more nuanced data to support greater possibility for innovation in future works.

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No description available.

One of the problems with the modern radio communication is the lack of availableradio frequencies. Recent studies have shown that, while the available licensed radiospectrum becomes more occupied, the assigned spectrum is significantly underutilized.To alleviate the situation, cognitive radio (CR) technology has been proposedto provide an opportunistic access to the licensed spectrum areas. Secondary CRsystems need to cyclically detect the presence of a primary user by continuouslysensing the spectrum area of interest. Radiowave propagation effects like fading andshadowing often complicate sensing of spectrum holes. When spectrum sensing isperformed in a cooperative manner, then the resulting sensing performance can beimproved and stabilized. In this thesis, two fully distributed and adaptive cooperative Primary User (PU)detection solutions for CR networks are studied. In the first part of this thesis we study a distributed energy detection schemewithout using any fusion center. Due to reduced communication such a topologyis more energy efficient. We propose the usage of distributed, diffusion least meansquare (LMS) type of power estimation algorithms with different network topologies.We analyze the resulting energy detection performance by using a commonframework and verify the theoretical findings through simulations. In the second part of this thesis we propose a fully distributed detection scheme,based on the largest eigenvalue of adaptively estimated correlation matrices, assumingthat the primary user signal is temporally correlated. Different forms of diffusionLMS algorithms are used for estimating and averaging the correlation matrices overthe CR network. The resulting detection performance is analyzed using a commonframework. In order to obtain analytic results on the detection performance, theadaptive correlation matrix estimates are approximated by a Wishart distribution.The theoretical findings are verified through simulations. / <p>QC 20170908</p>

Esta tese propõe conceitos, processos e uma arquitetura de sistemas para Monitoração e Acompanhamento da Aprendizagem Eletrônica (MAAE). A arquitetura é definida pelo seu modelo conceitual, pela interação com os serviços externos e pela representação XML dos conceitos e dos serviços. Ela independe de abordagem pedagógica específica. O Ponto de Observação é inserido em vários locais do Conteúdo da Aprendizagem Eletrônica. Um Elemento de Observação é associado ao Ponto de Observação, e é o responsável pela captura das interações do Processo de Aprendizagem Eletrônica. O Agenciador de Observação (Agenciador de Monitoração e Acompanhamento da Aprendizagem Eletrônica) recebe os eventos e solicitações dos Elementos de Observação e interage com os serviços externos. Os eventos são gravados no Repositório de Observação. A definição dos Processos de Aprendizagem Eletrônica é útil para a definição da estratégia de monitoração (Modelagem do Processo da Aprendizagem Eletrônica). Através da inserção dos mecanismos de observação nas Atividades de Aprendizagem (Processo da Produção do Conteúdo de Aprendizagem Eletrônica) é feita a monitoração do aprendiz (Processo da Aprendizagem Eletrônica) e se obtém as informações para análise (Avaliação e Análise da Aprendizagem Eletrônica). / This thesis proposes concepts, processes and a system architecture for Monitoring and Tracking E-Learning. The architecture is defined by a conceptual model, the interaction with external services and representation XML of the concepts and the services. It does not depend on any specific pedagogical boarding. The Monitoring Point is inserted in some places of the E-Learning Content. A Monitoring Element is associated to the Monitoring Point and it is for responsible of one of the interactions of the E-Learning Process. The Monitoring Service (E-Learning Monitoring and Following Service) receives the events and requests from the Monitoring Elements and it interacts with the external services. The events are recorded in the Monitoring Repository. The E-Learning Processes definition is useful to modeling the monitoring strategy (Learning Process Modeling), and insert to the monitoring mechanisms in the E-Learning Activities (Learning Content Production Process). The learner interaction monitoring occurs by getting the information according to the previous planning (Learning Process), and the generated information (Learning Analysis and Evaluation Process) is used in the analysis of learning tracking.

Esta tese propõe conceitos, processos e uma arquitetura de sistemas para Monitoração e Acompanhamento da Aprendizagem Eletrônica (MAAE). A arquitetura é definida pelo seu modelo conceitual, pela interação com os serviços externos e pela representação XML dos conceitos e dos serviços. Ela independe de abordagem pedagógica específica. O Ponto de Observação é inserido em vários locais do Conteúdo da Aprendizagem Eletrônica. Um Elemento de Observação é associado ao Ponto de Observação, e é o responsável pela captura das interações do Processo de Aprendizagem Eletrônica. O Agenciador de Observação (Agenciador de Monitoração e Acompanhamento da Aprendizagem Eletrônica) recebe os eventos e solicitações dos Elementos de Observação e interage com os serviços externos. Os eventos são gravados no Repositório de Observação. A definição dos Processos de Aprendizagem Eletrônica é útil para a definição da estratégia de monitoração (Modelagem do Processo da Aprendizagem Eletrônica). Através da inserção dos mecanismos de observação nas Atividades de Aprendizagem (Processo da Produção do Conteúdo de Aprendizagem Eletrônica) é feita a monitoração do aprendiz (Processo da Aprendizagem Eletrônica) e se obtém as informações para análise (Avaliação e Análise da Aprendizagem Eletrônica). / This thesis proposes concepts, processes and a system architecture for Monitoring and Tracking E-Learning. The architecture is defined by a conceptual model, the interaction with external services and representation XML of the concepts and the services. It does not depend on any specific pedagogical boarding. The Monitoring Point is inserted in some places of the E-Learning Content. A Monitoring Element is associated to the Monitoring Point and it is for responsible of one of the interactions of the E-Learning Process. The Monitoring Service (E-Learning Monitoring and Following Service) receives the events and requests from the Monitoring Elements and it interacts with the external services. The events are recorded in the Monitoring Repository. The E-Learning Processes definition is useful to modeling the monitoring strategy (Learning Process Modeling), and insert to the monitoring mechanisms in the E-Learning Activities (Learning Content Production Process). The learner interaction monitoring occurs by getting the information according to the previous planning (Learning Process), and the generated information (Learning Analysis and Evaluation Process) is used in the analysis of learning tracking.

The aims of this paper are to present some of the findings about the creation and utilization of online assignments and choice of support software for several calculus classes at Simon Fraser University (SFU) by considering the needs and perspectives of the instructors, students, and administrators. The term online assignment is used for a set of problems that are posted, submitted, graded, and recorded electronically through a course learning management system (LMS) of choice. The purpose of this note is to contribute to the discussion about a common question detected among research papers on the theme of online assignments; how can technology be used in teaching so that students benefit the most? Questions are provided to guide an instructor in choosing online assignment problems, and a list of necessary skills is supplied for an instructor to be able to deal effectively with this pedagogical tool.

Cette thèse porte sur la caractérisation de structures mécaniques, au travers de leurs paramètres structuraux, à partir d'observations perturbées par des bruits de mesure, supposés additifs blancs gaussiens et centrés. Pour cela, nous proposons d'utiliser des modèles à temps discret à parties linéaire et non-linéaire séparables. La première permet de retrouver les paramètres recherchés tandis que la seconde renseigne sur la non-linéarité présente. Dans le cadre d'une modélisation non-récursive par des séries de Volterra, nous présentons une approche à erreurs-dans-les-variables lorsque les variances des bruits ne sont pas connues ainsi qu'un algorithme adaptatif du type LMS nécessitant la connaissance de la variance du bruit d'entrée. Dans le cadre d'une modélisation par un modèle récursif polynomial, nous proposons deux méthodes à partir d'algorithmes évolutionnaires. La première inclut un protocole d'arrêt tenant compte de la variance du bruit de sortie. Dans la seconde, les fonctions fitness sont fondées sur des fonctions de corrélation dans lesquelles l'influence du bruit est supprimée ou compensée. / This PhD deals with the caracterisation of mechanical structures, by its structural parameters, when only noisy observations disturbed by additive measurement noises, assumed to be zero-mean white and Gaussian, are available. For this purpose, we suggest using discrete-time models with distinct linear and nonlinear parts. The first one allows the structural parameters to be retrieved whereas the second one gives information on the nonlinearity. When dealing with non-recursive Volterra series, we propose an errors-in-variables (EIV) method to jointly estimate the noise variances and the Volterra kernels. We also suggest a modified unbiased LMS algorithm to estimate the model parameters provided that the input-noise variance is known. When dealing with recursive polynomial model, we propose two methods using evolutionary algorithms. The first includes a stop protocol that takes into account the output-noise variance. In the second one, the fitness functions are based on correlation criteria in which the noise influence is removed or compensated.