As plataformas de Mídias Sociais se tornaram um meio essencial para a disponibilização de informações. Dentre elas, o Twitter tem se destacado, devido ao grande volume de mensagens que são compartilhadas todos os dias, principalmente mencionando eventos ao redor do mundo. Tais mensagens são uma importante fonte de informação e podem ser utilizadas em diversas aplicações. Contudo, a classificação de texto em tweets é uma tarefa não trivial. Além disso, não há um consenso quanto à quais tarefas devem ser executadas para Identificação e Classificação de Eventos em tweets, uma vez que as abordagens existentes trabalham com tipos específicos de eventos e determinadas suposições, que dificultam a reprodução e a comparação dessas abordagens em eventos de natureza distinta. Neste trabalho, nós elaboramos um framework para a classificação de eventos de natureza distinta. O framework possui os seguintes elementos chave: a) enriquecimento externo a partir da exploração de páginas web relacionadas, como uma forma de complementar a extração de features conceituais do conteúdo dos tweets; b) enriquecimento semântico utilizando recursos da Linked Open Data cloud para acrescentar features semânticas relacionadas; e c) técnica de poda para selecionar as features semânticas mais discriminativas Nós avaliamos o framework proposto através de um vasto conjunto de experimentos, que incluem: a) sete eventos alvos de natureza distinta; b) diferentes combinações das features conceituais propostas (i.e. entidades, vocabulário, e a combinação de ambos); c) estratégias distintas para a extração de features (i.e. a partir do conteúdo dos tweets e das páginas web); d) diferentes métodos para a seleção das features semânticas mais relevantes de acordo com o domínio (i.e. poda, seleção de features, e a combinação de ambos); e) dois algoritmos de classificação. Nós também comparamos o desempenho do framework em relação a outro método utilização para o enriquecimento contextual, o qual tem como base word embeddings. Os resultados mostraram as vantagens da utilização do framework proposto e que a nossa solução é factível e generalizável, dando suporte a classificação de diferentes tipos de eventos. / Social Media platforms have become key as a means of spreading information, opinions or awareness about real-world events. Twitter stands out due to the huge volume of messages about all sorts of topics posted every day. Such messages are an important source of useful information about events, presenting many useful applications (e.g. the detection of breaking news, real-time awareness, updates about events). However, text classification on Twitter is by no means a trivial task that can be handled by conventional Natural Language Processing techniques. In addition, there is no consensus about the definition of which kind of tasks are executed in the Event Identification and Classification in tweets, since existing approaches often focus on specific types of events, based on specific assumptions, which makes it difficult to reproduce and compare these approaches in events of distinct natures. In this work, we aim at building a unifying framework that is suitable for the classification of events of distinct natures. The framework has as key elements: a) external enrichment using related web pages for extending the conceptual features contained within the tweets; b) semantic enrichment using the Linked Open Data cloud to add related semantic features; and c) a pruning technique that selects the semantic features with discriminative potential We evaluated our proposed framework using a broad experimental setting, that includes: a) seven target events of different natures; b) different combinations of the conceptual features proposed (i.e. entities, vocabulary and their combination); c) distinct feature extraction strategies (i.e. from tweet text and web related documents); d) different methods for selecting the discriminative semantic features (i.e. pruning, feature selection, and their combination); and e) two classification algorithms. We also compared the proposed framework against another kind of contextual enrichment based on word embeddings. The results showed the advantages of using the proposed framework, and that our solution is a feasible and generalizable method to support the classification of distinct event types.

3D GIS is the key point of the development in the area in geospatial information domain. 3D visualization techniques, including 3D terrain data processing, the 3D objects modelling , satellite or air photo image texture, model material, level-of-detail terrain, is already gradually mature and had the outstanding performance in the existing 3D GIS software. But 3D spatial analysis function was still lacking in the field of the 3D spatial data processing. The goal of this research is to develop a 3D spatial analysis function for sun shadow in horizontal and vertical direction and to construct a system for 3D geographic objects visualization by using MFC and OpenGL as the development kit. It has some fundamental functions including: camera control, selecting a single face of a object, linking to a database, level-of-detail terrain. The shadow volume algorithm was used for 3D shadow visualization, and light tracing algorithm was used to compute a single face¡¦s culling area, finally sunshine formula of four seasons was utilitized to realize the shadow analysis function.

Dagvattenmagasin byggs för att fördröja och avlasta ledningssystemet dådagvattenmängderna i dem är stora för att sedan leda tillbaka vattnet i ledningssystemetnär vattenmängderna har sjunkit. Examensarbetet syftar till att undersöka och analysera vad som behövs göras för attanvända ett dagvattenmagsinet som är dimensionerat för tioårsregn redan vid femårs- ochtvåårsregn. Arbetet grundas på fältstudier och analys av hur systemet är uppbyggt. . Beräkningar hargjorts för studera flöden i ledningar och fyllnadsgrad i magasin. Resultatet visar att det ärmöjligt att med relativt enkla åtgärder kunna utnyttja det aktuella magasinet redan vid 2-och 5-årsregn.

Examensarbetet har genomförts som den avslutande delen på högskoleingenjörsutbildningen i byggteknik vid Karlstads Universitet. Arbetet omfattar 22,5 högskolepoäng och har utförts på uppdrag av Säffle-Åmål kommuns VA-enhet. Vatten från avlopp innehåller förhöjda nivåer av kväve och fosfor som reningsverken tvingas hantera. Vid dålig rening släpps dessa ämnen ut i sjöar och andra vattendrag, vilket kan leda till övergödning. I det dalsländska samhället Fengersfors används två mindre biodammar för rening av avloppsvatten. Biodammarna uppfyller inte myndigheternas reningskrav, vilket har föranlett kommunen att uppgradera anläggningen. Vid utbyggnad av reningsanläggningen krävs det att dagens volymer av tillskottsvatten reduceras. Detta för att en dimensionering skall vara möjlig i proportion till samhällets storlek. Tillskottsvatten är övrigt vatten som når reningsverken utöver bad-, disk-, klosett- och tvättvatten. Exempel på tillskottsvatten kan vara vatten från nederbörd och grundvatten via inläckage, felkopplingar och överläckage. Man eftersträvar att minimera andelen tillskottsvatten i avloppsvattnet. Detta för att undvika kostnadskrävande reningsprocesser. Syftet med studien är att undersöka hur flödet av tillskottsvatten kan minskas till de biodammar som idag renar Fengersfors spillvatten. Studien skall besvara följande fråga: Vilka källor till tillskottsvatten finns i Fengersfors idag och vilka åtgärder bör kommunen prioritera för att uppnå sitt mål om att minska andelen tillskottsvatten i spillvattennätet? Att kartlägga källor och volymer av tillskottsvatten i avloppsledningsnätet är ett tidskrävande arbete. För att komma till rätta med problemet utan kostnadskrävande dupliceringsåtgärder är det viktigt att först skapa en bild över området. I denna studie görs detta genom fyra undersökningsmoment: fältinventering av bostadsbebyggelse, fältinventering av ytavledning, undersökning av grundvattennivåer och spårfärgning av osäkra kopplingar. Momenten är lätta att utföra och behöver inte utföras av personer med särskild kompetens. Undersökningsmomenten sammanställs i två översiktskartor, Fengersfors Norra och Fengersfors Södra. Kartorna används därefter för enklare beräkningar över vilka områden som bidrar med stora volymer tillskottsvatten. Flertalet fastigheter i Fengersfors har sina takytor anslutna till spillvattennätet. Enklare åtgärder, som omkoppling till utkastare med tät vattenavledare, kan reducera volymen nederbördsvatten som når biodammarna. Dikningsunderhållet är eftersatt i hela Fengersfors, vilket medfört att trummor och brunnar satts igen. Ett fungerande dikessystem är nödvändigt för transport av dag- och dräneringsvatten till recipient. Under spårfärgningsarbetet hittades delar av dagvattenledningar som inte fanns med i ledningskartan. Kartor kan nu uppdateras och bli en viktig pusselbit för framtida åtgärder. Resultaten visar också att cirka 60 procent av det vatten som når reningsanläggningen i Fengersfors består av dränerat grundvatten, vilket gör reningen ineffektiv och en utbyggnad svår att dimensionera. Studien ger en fingervisning över vilka områden som behöver åtgärdas eller utredas vidare av kommunen. / This work has been carried out as the final part of the Bachelor program in structural engineering at the University of Karlstad. The work comprises 22,5 credits and is performed on behalf of the Water and Sewage Department in Säffle-Åmål Municipality. Wastewater contains elevated levels of nitrogen and phosphorus which the treatment plants are forced to deal with. If the purification in the plant are substandard these substances is emitted in lakes and streams, which can lead to eutrophication. The small village of Fengersfors, in the province of Dalsland, uses two small stabilization pounds to cleanse their wastewater. These stabilization pounds do not meet the authorities’ treatment requirements, which have led the municipality to upgrade the facility. Before expanding the treatment plant, today’s volumes of extraneous water must be reduced to be able to dimension the new pounds, in proportion to the size of Fengersfors. Extraneous water is clean water that reaches the treatment plants in addition to water from baths, showers, washing machines and toilets. Examples of additional water can be storm water and groundwater. It is desired to minimize the percentage of extraneous water in wastewater systems to avoid costly purification processes. The purpose of this study is to reduce the flow of extraneous water which transports to the stabilization pounds. What sources to extraneous water is there in Fengersfors and which actions should the municipality take to achieve their goal of reduced flow of extraneous water to the stabilization pounds? To identify seepage of extraneous water in wastewater systems is a time consuming job. To address the problem, without being forced to duplicate the system, it is important to first build an image of the area. In this study this is done by four examinations: field inventory of residential areas, field inventory of transfer schemes, investigation of groundwater levels and dye tracing of bad connections. These steps are easy to preform, requires no large financial means and need not to be performed by individuals with special skills. The survey is completed in two overview maps, Fengersfors Norra and Fengersfors Södra. These are then used for basic calculations to point out areas which are contributing large volumes of groundwater, trough foundation drainage, to the treatment plant. Several properties in Fengersfors have their roof surfaces connected to the wastewater system. Simple measures, such as switching to drain spouts with dense water deflector, can reduce the volume of rainwater that reaches the stabilization pounds. General for the area is that ditches have been neglected for a long time, resulting in drums and wells clogged. A functioning ditch system is necessary for transport of storm and drainage water to the recipient. During the dye tracing process parts of previously unknown storm water systems were found. Sewer system maps can now be updated and become an important part of future actions. The study provides an indication of which areas need to be addressed or further investigated by the municipality.

As plataformas de Mídias Sociais se tornaram um meio essencial para a disponibilização de informações. Dentre elas, o Twitter tem se destacado, devido ao grande volume de mensagens que são compartilhadas todos os dias, principalmente mencionando eventos ao redor do mundo. Tais mensagens são uma importante fonte de informação e podem ser utilizadas em diversas aplicações. Contudo, a classificação de texto em tweets é uma tarefa não trivial. Além disso, não há um consenso quanto à quais tarefas devem ser executadas para Identificação e Classificação de Eventos em tweets, uma vez que as abordagens existentes trabalham com tipos específicos de eventos e determinadas suposições, que dificultam a reprodução e a comparação dessas abordagens em eventos de natureza distinta. Neste trabalho, nós elaboramos um framework para a classificação de eventos de natureza distinta. O framework possui os seguintes elementos chave: a) enriquecimento externo a partir da exploração de páginas web relacionadas, como uma forma de complementar a extração de features conceituais do conteúdo dos tweets; b) enriquecimento semântico utilizando recursos da Linked Open Data cloud para acrescentar features semânticas relacionadas; e c) técnica de poda para selecionar as features semânticas mais discriminativas Nós avaliamos o framework proposto através de um vasto conjunto de experimentos, que incluem: a) sete eventos alvos de natureza distinta; b) diferentes combinações das features conceituais propostas (i.e. entidades, vocabulário, e a combinação de ambos); c) estratégias distintas para a extração de features (i.e. a partir do conteúdo dos tweets e das páginas web); d) diferentes métodos para a seleção das features semânticas mais relevantes de acordo com o domínio (i.e. poda, seleção de features, e a combinação de ambos); e) dois algoritmos de classificação. Nós também comparamos o desempenho do framework em relação a outro método utilização para o enriquecimento contextual, o qual tem como base word embeddings. Os resultados mostraram as vantagens da utilização do framework proposto e que a nossa solução é factível e generalizável, dando suporte a classificação de diferentes tipos de eventos. / Social Media platforms have become key as a means of spreading information, opinions or awareness about real-world events. Twitter stands out due to the huge volume of messages about all sorts of topics posted every day. Such messages are an important source of useful information about events, presenting many useful applications (e.g. the detection of breaking news, real-time awareness, updates about events). However, text classification on Twitter is by no means a trivial task that can be handled by conventional Natural Language Processing techniques. In addition, there is no consensus about the definition of which kind of tasks are executed in the Event Identification and Classification in tweets, since existing approaches often focus on specific types of events, based on specific assumptions, which makes it difficult to reproduce and compare these approaches in events of distinct natures. In this work, we aim at building a unifying framework that is suitable for the classification of events of distinct natures. The framework has as key elements: a) external enrichment using related web pages for extending the conceptual features contained within the tweets; b) semantic enrichment using the Linked Open Data cloud to add related semantic features; and c) a pruning technique that selects the semantic features with discriminative potential We evaluated our proposed framework using a broad experimental setting, that includes: a) seven target events of different natures; b) different combinations of the conceptual features proposed (i.e. entities, vocabulary and their combination); c) distinct feature extraction strategies (i.e. from tweet text and web related documents); d) different methods for selecting the discriminative semantic features (i.e. pruning, feature selection, and their combination); and e) two classification algorithms. We also compared the proposed framework against another kind of contextual enrichment based on word embeddings. The results showed the advantages of using the proposed framework, and that our solution is a feasible and generalizable method to support the classification of distinct event types.

As plataformas de Mídias Sociais se tornaram um meio essencial para a disponibilização de informações. Dentre elas, o Twitter tem se destacado, devido ao grande volume de mensagens que são compartilhadas todos os dias, principalmente mencionando eventos ao redor do mundo. Tais mensagens são uma importante fonte de informação e podem ser utilizadas em diversas aplicações. Contudo, a classificação de texto em tweets é uma tarefa não trivial. Além disso, não há um consenso quanto à quais tarefas devem ser executadas para Identificação e Classificação de Eventos em tweets, uma vez que as abordagens existentes trabalham com tipos específicos de eventos e determinadas suposições, que dificultam a reprodução e a comparação dessas abordagens em eventos de natureza distinta. Neste trabalho, nós elaboramos um framework para a classificação de eventos de natureza distinta. O framework possui os seguintes elementos chave: a) enriquecimento externo a partir da exploração de páginas web relacionadas, como uma forma de complementar a extração de features conceituais do conteúdo dos tweets; b) enriquecimento semântico utilizando recursos da Linked Open Data cloud para acrescentar features semânticas relacionadas; e c) técnica de poda para selecionar as features semânticas mais discriminativas Nós avaliamos o framework proposto através de um vasto conjunto de experimentos, que incluem: a) sete eventos alvos de natureza distinta; b) diferentes combinações das features conceituais propostas (i.e. entidades, vocabulário, e a combinação de ambos); c) estratégias distintas para a extração de features (i.e. a partir do conteúdo dos tweets e das páginas web); d) diferentes métodos para a seleção das features semânticas mais relevantes de acordo com o domínio (i.e. poda, seleção de features, e a combinação de ambos); e) dois algoritmos de classificação. Nós também comparamos o desempenho do framework em relação a outro método utilização para o enriquecimento contextual, o qual tem como base word embeddings. Os resultados mostraram as vantagens da utilização do framework proposto e que a nossa solução é factível e generalizável, dando suporte a classificação de diferentes tipos de eventos. / Social Media platforms have become key as a means of spreading information, opinions or awareness about real-world events. Twitter stands out due to the huge volume of messages about all sorts of topics posted every day. Such messages are an important source of useful information about events, presenting many useful applications (e.g. the detection of breaking news, real-time awareness, updates about events). However, text classification on Twitter is by no means a trivial task that can be handled by conventional Natural Language Processing techniques. In addition, there is no consensus about the definition of which kind of tasks are executed in the Event Identification and Classification in tweets, since existing approaches often focus on specific types of events, based on specific assumptions, which makes it difficult to reproduce and compare these approaches in events of distinct natures. In this work, we aim at building a unifying framework that is suitable for the classification of events of distinct natures. The framework has as key elements: a) external enrichment using related web pages for extending the conceptual features contained within the tweets; b) semantic enrichment using the Linked Open Data cloud to add related semantic features; and c) a pruning technique that selects the semantic features with discriminative potential We evaluated our proposed framework using a broad experimental setting, that includes: a) seven target events of different natures; b) different combinations of the conceptual features proposed (i.e. entities, vocabulary and their combination); c) distinct feature extraction strategies (i.e. from tweet text and web related documents); d) different methods for selecting the discriminative semantic features (i.e. pruning, feature selection, and their combination); and e) two classification algorithms. We also compared the proposed framework against another kind of contextual enrichment based on word embeddings. The results showed the advantages of using the proposed framework, and that our solution is a feasible and generalizable method to support the classification of distinct event types.

Context. Collision is a basic necessity in most simulated environments, especially video games, which demand user interaction. Octrees are a way to divide the simulated environments into smaller, more manageable parts,and is a hierarchical tree-structure, where each node has eight children. Octrees and similar tree-structural methods have been used frequently to optimize collision calculations and partition the objects in the 3D space. Objectives. The aim of this thesis is to find a way to further improve upon the octree structure, by using a two-level octree structure, and simplify the collision of objects that do not demand much complexity, due to their size or the geometric simplicity of their 3D models, this is done by calculating how many pixels the objects occupy on the screen, and use that as a factor when deciding the depth of their individual octrees. Methods. Each object in the 3D environment is divided using an octree. These octrees generated for the objects are then placed in a larger octree. This large octree use the smaller ones to check collision between the objects. The pixel area occupied on the screen by the objects’ octrees is used to determine what depth of the octrees will be check for intersection. Two test scenes were set up to test our model. Results. Our implementation could effectively reduce the depth of octrees belonging to objects occupying little space on the screen. The experiments also showed that the reduced depth could be used with only a slight loss in accuracy. The accuracy loss increased when more objects were used. Conclusions. The results gained in the thesis show that the pixel area can be used effectively, and the simplified octrees can still represent the objects adequately, resulting in a cheaper but slightly less accurate collision.

This thesis is focused on the improvement of an existing implementation of a waveform visualizer. The problem area handled in this work has its focus on how to reduce the number of points to be plotted. The given waveform visualizer was extended by the use of two additional algorithms. First, a Level Of Detail (LOD) algorithm that gives the subset of points that are necessary to plot the waveform in the current zoom level. Second, a straight line identification algorithm to find a series of points aligned in a straight line, only leaving the end points and then drawing a line between them. These two optimizations are the main focus of this work.Additionally, an exporting functionality was implemented to export the plot data into several different data formats. Also some improvements of zooming, panning, some GUI design, and a new drag and drop functionality was constructed.

This PhD thesis focuses on improving the limit of detection (LOD) of Raman biosensors by using surface enhanced Raman scattering (SERS) and/or hollow core photonic crystal fibers (HC-PCF), in conjunction with statistical methods. Raman spectroscopy is a multivariate phenomenon that requires statistical analysis to identify the relationship between recorded spectra and the property of interest. The objective of this research is to improve the performance of Raman biosensors using SERS techniques and/or HC-PCF, by applying partial least squares (PLS) regression and principal component analysis (PCA). I began my research using Raman spectroscopy, PLS analysis and two different validation methods to monitor heparin, an important blood anti-coagulant, in serum at clinical levels. I achieved lower LOD of heparin in serum using the Test Set Validation (TSV) method. The PLS analysis allowed me to distinguish between weak Raman signals of heparin in serum and background noise. I then focused on using SERS to further improve the LOD of analytes, and accomplished simultaneous detection of GLU-GABA in serum at clinical levels using the SERS and PLS models. This work demonstrated the applicability of using SERS in conjunction with PLS to measure properties of samples in blood serum. I also used SERS with HC-PCF configuration to detect leukemia cells, one of the most recurrent types of pediatric cancers. This was achieved by applying PLS regression and PCA techniques. Improving LOD was the next objective, and I was able to achieve this by improving the PLS model to decrease errors and remove outliers or unnecessary variables. The results of the final optimized models were evaluated by comparing them with the results of previous models of Heparin and Leukemia cell detection in previous sections. Finally, as a clinical application of Raman biosensors, I applied the enhanced Raman technique to detect polycystic ovary syndrome (PCOS) disease, and to determine the role of chemerin in this disease. I used SERS in conjunction with PCA to differentiate between PCOS and non-PCOS patients. I also confirmed the role of chemerin in PCOS disease, measured the level of chemerin, a chemoattractant protein, in PCOS and non-PCOS patients using PLS, and further improved LOD with the PLS regression model, as proposed in previous section.

The finite difference time domain (FDTD) method is commonly used for numerical simulations of the electromagnetic wave propagation in time domain. The FDTD method is easy to implement and the computational results are highly relevant to the analytical solution, so that the FDTD method is applied to variety application problems. However, the computational efficiency of the FDTD method is constrained by the upper limit of the temporal discretisation. The Courant Friedrich Lewy (CFL) stability condition limits the time step for the computation of the FDTD method, so that if the spatial discretisation of the computation is set to be small in order to obtain high accurate results, the size of the temporal discretisation need to be satisfy the CFL stability condition. The locally one dimensional FDTD (LOD-FDTD) method is unconditionally stable. The time step and the spatial step can be independently chosen for the computation of the LOD-FDTD method. The arithmetic operations of the LOD-FDTD method is fewer than that of the other implicit FDTD method, such as the Crank Nicolson FDTD (CN-FDTD) method and the alternating direction implicit FDTD (ADI-FDTD) method. Although the implementation of the LOD-FDTD method is simpler than that of the ADI-FDTD method,the numerical error in the computational results of the LOD-FDTD method is equivalent to that in the computational results of the ADI-FDTD method. In this thesis, a new three dimensional (3D) frequency dependent (FD) LOD-FDTD method is proposed. The one pole Debye model is incorporated into the 3D-FD-LOD-FDTD method in order to deal with practical applications. The proposed method is implemented in Fortran 90 and parallelised with OpenMP. A simulation model of the human phantom is developed in the 3D-FD-LOD-FDTD method with fine structures and frequency dependent dielectric properties of the human tissues, and numerical simulation of electromagnetic wave propagation inside the human head is shown.