During the past several decades, journalists have been struggling with the ever growing amount of data on the internet. Investigating the validity of the sources or finding similar articles for a story can consume a lot of time and effort. These issues are even amplified by the declining size of the staff of news agencies. The solution is to empower the remaining professional journalists with digital tools created by computer scientists. This thesis project is inspired by an idea to provide software support for journalistic work with interactive visual interfaces and artificial intelligence. More specifically, within the scope of this thesis project, we created a backend module that supports several text mining methods such as keyword extraction, named entity recognition, sentiment analysis, fake news classification and also data collection from various data sources to help professionals in the field of journalism. To implement our system, first we gathered the requirements from several researchers and practitioners in journalism, media studies, and computer science, then acquired knowledge by reviewing literature on current approaches. Results are evaluated both with quantitative methods such as individual component benchmarks and also with qualitative methods by analyzing the outcomes of the semi-structured interviews with collaborating and external domain experts. Our results show that there is similarity between the domain experts' perceived value and the performance of the components on the individual evaluations. This shows us that there is potential in this research area and future work would be welcomed by the journalistic community.

Social networking sites such as Twitter, Facebook and Flickr play an important role in disseminating breaking news about natural disasters, terrorist attacks and other events. They serve as sources of first-hand information to deliver instantaneous news to the masses, since millions of users visit these sites to post and read news items regularly. Hence, by exploring e fficient mathematical techniques like Dempster-Shafer theory and Modi ed Dempster's rule of combination, we can process large amounts of data from these sites to extract useful information in a timely manner. In surveillance related applications, the objective of processing voluminous social network data is to predict events like revolutions and terrorist attacks before they unfold. By fusing the soft and often unreliable data from these sites with hard and more reliable data from sensors like radar and the Automatic Identi cation System (AIS), we can improve our event prediction capability. In this paper, we present a class of algorithms to fuse hard sensor data with soft social network data (tweets) in an e ffective manner. Preliminary results using are also presented. / Thesis / Master of Applied Science (MASc)

Remote Sensing (RS) technology provides valuable information about Earth by enabling an overview of the planet from above, making it a much-needed resource for many applications. Given the abundance of RS data and continued urbanisation, there is a need for efficient approaches to leverage RS data and its unique characteristics for the assessment and management of urban areas. Consequently, employing Deep Learning (DL) for RS applications has attracted much attention over the past few years. In this thesis, novel datasets consisting of satellite RS images over urban areas in Sweden were compiled from Sentinel-2 multispectral, Sentinel-1 Synthetic Aperture Radar (SAR) and Urban Atlas 2018 Land Use/Land Cover (LULC) data. Then, DL was applied for multiband and multiclass semantic image segmentation of LULC. The contributions of complementary spectral, temporal and SAR data and spectral indices to LULC classification performance compared to using only Sentinel-2 data with red, green and blue spectral bands were investigated by implementing DL models based on the fully convolutional network-based architecture, U-Net, and performing data fusion. Promising results were achieved with 25 possible LULC classes. Furthermore, almost all DL models at an overall model level and all DL models at an individual class level for most LULC classes benefited from complementary satellite RS data with varying degrees of classification improvement. Additionally, practical knowledge and insights were gained from evaluating the results and are presented regarding satellite RS data characteristics and semantic segmentation of LULC in urban areas. The obtained results are helpful for practitioners and researchers applying or intending to apply DL for semantic segmentation of LULC in general and specifically in Swedish urban environments.

No description available.

Due to the escalating demand for traffic information and management, the significance of traffic state estimation, which involves the assessment of traffic conditions on road segments with limited measurement data, is increasing. Two primary estimation methods are model-driven and data-driven. The former uses traffic flow models, while the latter relies on extensive historical data to explore relationships between traffic states. Due to the uninterrupted nature of highway traffic flow, conventional model-driven approach is adopted in the study to estimate traffic information from sensing data. Data-driven approach is applied to enhance the estimation results. The project mainly focuses on comparing the estimation performance between the Particle Filter and the commonly used Extended Kalman Filter. These two methods are implemented in combination with two typical traffic flow models: Cell Transmission Model and METANET. Moreover, the project investigates the potential of using vehicle-to-everything (V2X) data in traffic state estimation, either alone or combined with traditional inductive loop detector (ILD) data. Being an emerging traffic data source, V2X communication has been recently installed and tested on the motorways near Stockholm. This study provides essential insights into how V2X data can benefit existing traffic information estimation and its performance. To evaluate the models mentioned above, the estimation algorithms and traffic flow models are implemented in a self-developed platform, which may be useful for further work. Results from simulation experiments show that Particle Filter can carry out traffic state estimation with comparable accuracy to Extended Kalman Filter. While standalone V2X speed data falls short, effective fusion methods are implemented to combine both data types, ultimately achieving the desired accuracy. These fusion methods encompass direct filtering, weighted averaging, and linear regression. Future investigations could broaden their scope to include new data sources, such as unmanned aerial vehicles (UAVs), and delve into advanced data fusion techniques, such as deep learning. / På grund av den ökande efterfrågan på trafikinformation och trafikhantering ökar betydelsen av trafiklägesuppskattning, vilket innebär bedömning av trafikförhållandena på vägsegment med begränsade mätningsdata. Två primära uppskattningsmetoder är modellbaserade och datadrivna metoder. Den förra använder trafikflödesmodeller, medan den senare förlitar sig på omfattande historiska data för att utforska samband mellan trafiklägen. På grund av det oavbrutna vägtrafikflödet antas en konventionell modellbaserad metod i studien för att uppskatta trafikinformation från sensordata. Den datadrivna metoden används för att förbättra estimatresultaten. Projektet fokuserar främst på att jämföra prestandan i uppskattningen mellan Partikelfiltret och den vanligtvis använda Extended Kalman Filter. Dessa två metoder implementeras i kombination med två typiska trafikflödesmodeller: Cell Transmission Model och METANET. Dessutom undersöker projektet möjligheterna att använda fordons-till-allt (V2X) data i trafiklägesuppskattning, antingen ensamt eller i kombination med data från traditionella induktiva slingdetektorer (ILD). Som en framväxande källa till trafikdata har V2X-kommunikation nyligen installerats och testats på motorvägarna nära Stockholm. Denna studie ger väsentlig inblick i hur V2X-data kan gynna befintlig uppskattning av trafikinformation och dess prestanda. För att utvärdera ovan nämnda modeller implementeras uppskattningsalgoritmerna och trafikflödesmodellerna i en självutvecklad plattform, vilket kan vara användbart för framtida arbete. Resultaten från simuleringsexperiment visar att Partikelfiltret kan utföra trafiklägesuppskattning med jämförbar noggrannhet jämfört med Extended Kalman Filter. Medan fristående V2X-hastighetsdata inte når hela vägen fram implementeras effektiva sammanslagningsmetoder för att kombinera båda datatyperna och slutligen uppnå önskad noggrannhet. Dessa sammanslagningsmetoder omfattar direkt filtrering, viktad medelvärdesbildning och linjär regression. Framtida undersökningar kan utvidga deras omfattning för att inkludera nya datakällor, såsom obemannade flygfordon (UAV:er), och utforska avancerade tekniker för datafusion, såsom djupinlärning.

Approved for public release, distribution is unlimited / The objective of this thesis is to provide an aircraft with an optimal route to its destination that avoids encroaching into surface-to-air weapons killing envelopes in real time. The optimal route computed will be updated dynamically, depending on the location of the vehicle and the location of the Surface to Air Missile (SAM) sites. The problem was solved using heuristic algorithms instead of the conventional Dijkstra's & Bellman Ford algorithms, which are computationally expensive. Data fusion techniques such as spatial correlation and triangulation algorithms are presented in detail. Such techniques are important for situational awareness in a real time combat environment. Important information provided by onboard sensors are merged with the preplanned data to provide the operator with a better integrated picture of the combat environment. / Civilian, Singapore Ministry of Defense

L'analyse de la marche et la mesure du déplacements des articulations humaines ont été largement étudiées. Les artefacts de tissus «mous» sont une source fréquente d'erreur pour la plupart des méthodes de mesure utilisées. La procédure standard en analyse de la marche consiste à utiliser une combinaison de mesures pour l'estimation efficace des angles articulaires et de la position des segments du corps humain. Ce travail propose le développement d'un système d'analyse de la marche reposant sur un robot parallèle à câbles équipé de plusieurs capteurs mesurant spécifiquement les déplacements du genou. Nous considérons le cas général pour lequel les articulations humaines se comportent comme des joints à 6 degrés de liberté reliant deux segments du corps. Afin de déterminer la position et l'orientation de ces segments, 14 câbles y sont attachés, ce qui permet de considérer ces segments comme les organes effecteurs de robots parallèles. Leur position peut alors être calculée à partir de la mesure de la longueur des câbles. Cependant, ces mesures sont entachées de bruit à cause des artefacts de tissus «mous». Afin d'améliorer la précision des résultats, le système propose aussi l'utilisation d'autres capteurs de nature différente : plusieurs capteurs inertiels (avec accéléromètres et gyroscopes), un système de motion capture, des capteurs de pression plantaire, des capteurs de distance (IR et résistance variable) et des capteurs de force pour mesurer la contraction musculaire. Plusieurs approches globales sont disponibles pour l'analyse du genou lors de la marche. Les choix technologiques effectués impactent directement sur la conception de notre système et imposent le développement de matériel spécifique pour mener à bien les mesures, tel que le collier flexible utilisé d'une part pour permettre l'attache des câbles sur les segments du patient et d'autres part pour supporter les capteurs supplémentaires. Nous traitons le collier comme une chaîne cinématique sérielle et nous proposons une méthode d'étalonnage qui ne nécessite pas d'utiliser les mesures angulaires des articulations contrairement aux méthodes existantes. Nous décrivons le protocole expérimental ainsi que les méthodes utilisées pour synchroniser les données issues de plusieurs ordinateurs. Les données sont ensuite fusionnées pour obtenir la pose du collier et donc celle des segments du patient. Enfin, ce travail permet d'identifier les modifications à apporter au système pour une meilleure analyse de la marche, ce qui pourra servir de base à un système de rééducation complet. / Gait analysis and human joint motion measurement has been studied extensively in the recent past. In order to address the effects of soft tissue artifacts (STA), a common source of error in most type of measurements, the standard procedure in gait analysis has been to use a combination of measurement methods for efficient estimation of joint angles and the body segment poses. This work proposes a gait analysis system based on a multi-sensor cable-driven parallel manipulator, focusing specifically on tracking the human knee. Our system assumes a human joint to be a general 6 DOF joint between 2 body segments. In order to measure pose of these body segments, up to 14 wires are attached to these human body segments and this permits the system to treat each of these body segments as the end-effector of a parallel mechanism. The pose of the body segments can thus be determined by measuring the wire lengths and solving the forward kinematics of this parallel architecture. The system is also equipped to use additional sensors including inertial sensors (accelerometers and gyroscopes), a 12 camera optical tracking system, in-shoe pressure sensors, variable length resistive wires, IR distance sensors, force sensors to measure muscle contraction. A number of choices are available in the approach for analyzing the knee during gait activity and the design of the setup depends on these choices. This work discuses the options available and details how they have impacted the choices we make in developing the experimental setup. We discuss the hardware developed and used, and specifically discuss the flexible collar used to attach wires to the patient body and to hold the additional sensors. We treat the collar as a serial kinematic chain and propose a calibration method for it that, unlike commonly used calibration techniques, avoids using joint angle measurements. We then outline the experiment and the methods used to synchronize and fuse the data from all sensors to obtain a pose estimate for the collar and thus, the body segments. Finally, this work helps identify steps necessary to improve the current setup and lays the groundwork for a complete rehabilitation system.

Floods are one of the most devastating types of worldwide disasters in terms of human, economic, and social losses. If authoritative data is scarce, or unavailable for some periods, other sources of information are required to improve streamflow estimation and early flood warnings. Georeferenced social media messages are increasingly being regarded as an alternative source of information for coping with flood risks. However, existing studies have mostly concentrated on the links between geo-social media activity and flooded areas. This thesis aims to show a novel methodology that shows a way to close the research gap regarding the use of social networks as a proxy for precipitation-runoff and flood forecast estimates. To address this, it is proposed to use a transformation function that creates a proxy variable for rainfall by analysing messages from geo-social media and precipitation measurements from authoritative sources, which are then incorporated into a hydrological model for the flow estimation. Then the proxy and authoritative rainfall data are merged to be used in a data assimilation scheme using the Ensemble Kalman Filter (EnKF). It is found that the combined use of authoritative rainfall values with the social media proxy variable as input to the Probability Distributed Model (PDM), improves flow simulations for flood monitoring. In addition, it is found that when these models are made under a scheme of fusion-assimilation of data, the results improve even more, becoming a tool that can help in the monitoring of \"ungauged\" or \"poorly gauged\" catchments. The main contribution of this thesis is the creation of a completely original source of rain monitoring, which had not been explored in the literature in a quantitative way. It also shows how the joint use of this source and data assimilation methodologies aid to detect flood events. / As inundações são um dos tipos mais devastadores de desastres em todo o mundo em termos de perdas humanas, econômicas e sociais. Se os dados oficiais forem escassos ou indisponíveis por alguns períodos, outras fontes de informação são necessárias para melhorar a estimativa de vazões e antecipar avisos de inundação. Esta tese tem como objetivo mostrar uma metodologia que mostra uma maneira de fechar a lacuna de pesquisa em relação ao uso de redes sociais como uma proxy para as estimativas de precipitação e escoamento. Para resolver isso, propõe-se usar uma função de transformação que cria uma variável proxy para a precipitação, analisando mensagens de medições geo-sociais e precipitação de fontes oficiais, que são incorporadas em um modelo hidrológico para a estimativa de fluxo. Em seguida, os dados de proxy e precipitação oficial são fusionados para serem usados em um esquema de assimilação de dados usando o Ensemble Kalman Filter (EnKF). Descobriu-se que o uso combinado de valores oficiais de precipitação com a variável proxy das mídias sociais como entrada para o modelo distribuído de probabilidade (Probability Distributed Model - PDM) melhora as simulações de fluxo para o monitoramento de inundações. A principal contribuição desta tese é a criação de uma fonte completamente original de monitoramento de chuva, que não havia sido explorada na literatura de forma quantitativa.

Floods are one of the most devastating types of worldwide disasters in terms of human, economic, and social losses. If authoritative data is scarce, or unavailable for some periods, other sources of information are required to improve streamflow estimation and early flood warnings. Georeferenced social media messages are increasingly being regarded as an alternative source of information for coping with flood risks. However, existing studies have mostly concentrated on the links between geo-social media activity and flooded areas. This thesis aims to show a novel methodology that shows a way to close the research gap regarding the use of social networks as a proxy for precipitation-runoff and flood forecast estimates. To address this, it is proposed to use a transformation function that creates a proxy variable for rainfall by analysing messages from geo-social media and precipitation measurements from authoritative sources, which are then incorporated into a hydrological model for the flow estimation. Then the proxy and authoritative rainfall data are merged to be used in a data assimilation scheme using the Ensemble Kalman Filter (EnKF). It is found that the combined use of authoritative rainfall values with the social media proxy variable as input to the Probability Distributed Model (PDM), improves flow simulations for flood monitoring. In addition, it is found that when these models are made under a scheme of fusion-assimilation of data, the results improve even more, becoming a tool that can help in the monitoring of \"ungauged\" or \"poorly gauged\" catchments. The main contribution of this thesis is the creation of a completely original source of rain monitoring, which had not been explored in the literature in a quantitative way. It also shows how the joint use of this source and data assimilation methodologies aid to detect flood events. / As inundações são um dos tipos mais devastadores de desastres em todo o mundo em termos de perdas humanas, econômicas e sociais. Se os dados oficiais forem escassos ou indisponíveis por alguns períodos, outras fontes de informação são necessárias para melhorar a estimativa de vazões e antecipar avisos de inundação. Esta tese tem como objetivo mostrar uma metodologia que mostra uma maneira de fechar a lacuna de pesquisa em relação ao uso de redes sociais como uma proxy para as estimativas de precipitação e escoamento. Para resolver isso, propõe-se usar uma função de transformação que cria uma variável proxy para a precipitação, analisando mensagens de medições geo-sociais e precipitação de fontes oficiais, que são incorporadas em um modelo hidrológico para a estimativa de fluxo. Em seguida, os dados de proxy e precipitação oficial são fusionados para serem usados em um esquema de assimilação de dados usando o Ensemble Kalman Filter (EnKF). Descobriu-se que o uso combinado de valores oficiais de precipitação com a variável proxy das mídias sociais como entrada para o modelo distribuído de probabilidade (Probability Distributed Model - PDM) melhora as simulações de fluxo para o monitoramento de inundações. A principal contribuição desta tese é a criação de uma fonte completamente original de monitoramento de chuva, que não havia sido explorada na literatura de forma quantitativa.

Les systèmes de nez et de langues électroniques à base de capteurs chimiques et électrochimiques constituent une solution avantageuse pour la caractérisation des odeurs et des saveurs émanant des produits agroalimentaires. La sélectivité croisée de la matrice des capteurs couplée aux méthodes de reconnaissance de formes est l'élément clé dans la conception et le développement de ces systèmes. Dans cette optique, nous avons démontré la capacité d'un dispositif expérimental de nez électronique à discriminer entre les différents types de drogues, à analyser la fraîcheur des fromages, à identifier entre les fromages adultérés et à différentier entre les eaux potables et usées. Nous avons également réussi à classifier correctement les eaux potables (minérales, de source, gazeuse et de robinet) et usées par utilisation d'une langue électronique potentiométrique. Cette étude a été validée par la Chromatographie en Phase Gazeuse couplée à la Spectrométrie de Masse (CPG-MS). En outre, nous avons développé une langue électronique voltammétrique à base d'une électrode de Diamant Dopé au Bore pour différencier les phases de traitement des eaux usées domestiques et hospitaliers et pour identifier les différents métaux lourds (Pb, Hg, Cu, Cd, Ni et le Zn) contenus dans l'eau du fleuve Rhône. La Voltammétrie à Redissolution Anodique à Impulsion Différentielle (DPASV) a été utilisée comme une méthode électrochimique pour caractériser les eaux étudiées. Enfin, les systèmes multicapteurs hybrides se sont avérés un bon outil analytique pour caractériser les produits de l'industrie agroalimentaire tels que les jus tunisiens et les huiles d'olives marocaines / Electronic noses and tongues systems based on chemical and electrochemical sensors are an advantageous solution for the characterisation of odours and tastes that are emanating from food products. The cross-selectivity of the sensor array coupled with patter recognition methods is the key element in the design and development of these systems. In this context, we have demonstrated the ability of an electronic nose device to discriminate between different types of drugs, to analyse cheeses freshness, to identify adulterated cheeses and to differentiate between potable and wastewaters. We have also succeeded to correctly classify drinking waters (mineral, natural, sparkling and tap) and wastewaters by using a potentiometric electronic tongue. This study was validated by Gas Chromatography coupled with Mass Spectrometry (GC-MS). Furthermore, we have developed a voltammetric electronic tongue based on a Diamond Doped Boron electrode to differentiate treatment stages of domestic and hospital wastewaters and to identify different heavy metals (Pb, Hg, Cu, Cd, Ni and Zn) contained in Rhône river. The Differential Pulse Anodic Stripping Voltammetry (DPASV) was used as an electrochemical method to characterise the studied waters. Finally, the hybrid multisensor systems have proven to be good analytical tools to characterise the products of food industry such as Tunisian juices and Moroccan olive oils