Opencast lignite mines leave behind large cratered landscapes, which also represent a large intervention in nature. In addition to being unusable, these areas also pose a hazard to the surrounding areas. Therefore, the goal is to renaturalize and recultivate these areas in order to recreate natural and usable areas again. These areas can then be used as nature reserves, for agriculture or tourism. The process of recultivation is based on a large amount of information, such as how should an area be implemented, what restrictions should be observed, and under what conditions may the whole recultivation process take place. While all of this information is available and there are many tools for displaying weather data, for example, this information is not linked to the specific area data. Linking a weather-related restriction (that only exists in a purely textual report for an area) to the weather data for a specific area at a specific time is very time-consuming and cumbersome. We support the experts in performing their tasks by linking the different types of data and providing visualizations of these data together with an appropriate filtering mechanism and selection mechanisms, implemented in an interactive decision support system. Textual information and explanations of the various restrictions are additionally linked to provide a clear explanation of the validity of specific restrictions.

A realistic field monitoring application to evaluate close proximity tunneling effects of a new tunnel on an existing tunnel is presented. A blind source separation (BSS)-based monitoring framework was developed using sensor data collected from the existing tunnel while the new tunnel was excavated. The developed monitoring framework is particularly useful to analyze underdetermined systems due to insufficient sensor data for explicit input force-output deformation relations. The analysis results show that the eigen-parameters obtained from the correlation matrix of raw sensor data can be used as excellent indicators to assess the tunnel structural behaviors during the excavation with powerful visualization capability of tunnel lining deformation. Since the presented methodology is data-driven and not limited to a specific sensor type, it can be employed in various proximity excavation monitoring applications.

No description available.

No description available.

Among the various natural calamities, flood is considered one of the most catastrophic natural hazards, which has a significant impact on the socio-economic lifeline of a country. The Assessment of flood risks facilitates taking appropriate measures to reduce the consequences of flooding. The flood risk assessment requires Big data which are coming from different sources, such as sensors, social media, and organizations. However, these data sources contain various types of uncertainties because of the presence of incomplete and inaccurate information. This paper presents a Belief rule-based expert system (BRBES) which is developed in Big data platform to assess flood risk in real time. The system processes extremely large dataset by integrating BRBES with Apache Spark while a web-based interface has developed allowing the visualization of flood risk in real time. Since the integrated BRBES employs knowledge driven learning mechanism, it has been compared with other data-driven learning mechanisms to determine the reliability in assessing flood risk. Integrated BRBES produces reliable results comparing from the other data-driven approaches. Data for the expert system has been collected targeting different case study areas from Bangladesh to validate the integrated system.

O uso de veículos aéreos não tripulados (VANTs) tem se tornado cada vez mais comum, principalmente em aplicações de uso civil. No cenário militar, o uso de VANTs tem focado o cumprimento de missões específicas que podem ser divididas em duas grandes categorias: sensoriamento remoto e transporte de material de emprego militar. Este trabalho se concentra na categoria do sensoriamento remoto. O trabalho foca a definição de um modelo e uma arquitetura de referência para o desenvolvimento de sensores inteligentes orientados a missões específicas. O principal objetivo destas missões é a geração de mapas temáticos. Neste trabalho são investigados processos e mecanismos que possibilitem a geração desta categoria de mapas. Neste sentido, o conceito de MOSA (Mission Oriented Sensor Array) é proposto e modelado. Como estudos de caso dos conceitos apresentados são propostos dois sistemas de mapeamento automático de fontes sonoras, um para o caso civil e outro para o caso militar. Essas fontes podem ter origem no ruído gerado por grandes animais (inclusive humanos), por motores de combustão interna de veículos ou por atividade de artilharia (incluindo caçadores). Os MOSAs modelados para esta aplicação são baseados na integração de dados provenientes de um sensor de imageamento termal e uma rede de sensores acústicos em solo. A integração das informações de posicionamento providas pelos sensores utilizados, em uma base cartográfica única, é um dos aspectos importantes tratados neste trabalho. As principais contribuições do trabalho são a proposta de sistemas MOSA, incluindo conceitos, modelos, arquitetura e a implementação de referência representada pelo sistema de mapeamento automático de fontes sonoras. / The use of unmanned aerial vehicles (UAV) has become increasingly common, particularly in civilian applications. In the military scenario, the use of UAVs has focused the accomplishment of specific tasks in two broad categories: remote sensing and transport of military material. This work focuses the remote sensing category. It address the definition of a model and reference architecture for the development of smart sensors oriented to specific tasks. The main objective of these missions is to generate thematic maps. This work investigates processes and mechanisms that enable the automatic generation of thematic maps. In this sense, the concept of MOSA (Mission Oriented Sensor Array) is proposed and modeled. As case studies, we propose two automatic mapping systems for on-the-ground generated sound sources, one for the civilian case and one for the military case. These sounds may come from the noise generated by large animals (including humans), from internal combustion engine vehicles or from artillery activity (including hunters). The MOSAs modeled for this application integrate data from a thermal imaging sensor and an on-the-ground network of acoustic sensors. The fusion of position information, provided by the two sensors, into a single cartographic basis is one of the key aspects addressed in this work. The main contributions are the proposed MOSA systems, including concepts, models and architecture and the reference implementation comprised by the system for automatic mapping of sound sources.

<p>Den här rapporten beskriver och utvärderar ett antal algoritmer för multisensordatafusion av radar och IR/TV-data på rådatanivå. Med rådatafusion menas att fusionen ska ske innan attribut- eller objektextrahering. Attributextrahering kan medföra att information går förlorad som skulle kunna förbättra fusionen. Om fusionen sker på rådatanivå finns mer information tillgänglig och skulle kunna leda till en förbättrad attributextrahering i ett senare steg. Två tillvägagångssätt presenteras. Den ena metoden projicerar radarbilden till IR-vyn och vice versa. Fusionen utförs sedan på de par av bilder med samma dimensioner. Den andra metoden fusionerar de två ursprungliga bilderna till en volym. Volymen spänns upp av de tre dimensionerna representerade i ursprungsbilderna. Metoden utökas också genom att utnyttja stereoseende. Resultaten visar att det kan vara givande att utnyttja stereoseende då den extra informationen underlättar fusionen samt ger en mer generell lösning på problemet.</p> / <p>This thesis describes and evaluates a number of algorithms for multi sensor fusion of radar and IR/TV data. The fusion is performed on raw data level, that is prior to attribute extraction. The idea is that less information will be lost compared to attribute level fusion. Two methods are presented. The first method transforms the radar image to the IR-view and vice versa. The images sharing the same dimension are then fused together. The second method fuses the original images to a three dimensional volume. Another version is also presented, where stereo vision is used. The results show that stereo vision can be used with good performance and gives a more general solution to the problem.</p>

Den här rapporten beskriver och utvärderar ett antal algoritmer för multisensordatafusion av radar och IR/TV-data på rådatanivå. Med rådatafusion menas att fusionen ska ske innan attribut- eller objektextrahering. Attributextrahering kan medföra att information går förlorad som skulle kunna förbättra fusionen. Om fusionen sker på rådatanivå finns mer information tillgänglig och skulle kunna leda till en förbättrad attributextrahering i ett senare steg. Två tillvägagångssätt presenteras. Den ena metoden projicerar radarbilden till IR-vyn och vice versa. Fusionen utförs sedan på de par av bilder med samma dimensioner. Den andra metoden fusionerar de två ursprungliga bilderna till en volym. Volymen spänns upp av de tre dimensionerna representerade i ursprungsbilderna. Metoden utökas också genom att utnyttja stereoseende. Resultaten visar att det kan vara givande att utnyttja stereoseende då den extra informationen underlättar fusionen samt ger en mer generell lösning på problemet. / This thesis describes and evaluates a number of algorithms for multi sensor fusion of radar and IR/TV data. The fusion is performed on raw data level, that is prior to attribute extraction. The idea is that less information will be lost compared to attribute level fusion. Two methods are presented. The first method transforms the radar image to the IR-view and vice versa. The images sharing the same dimension are then fused together. The second method fuses the original images to a three dimensional volume. Another version is also presented, where stereo vision is used. The results show that stereo vision can be used with good performance and gives a more general solution to the problem.

<p><p>Context represents any information regarding the situation of entities, being these a person, place or object that is considered relevant to the interaction between a user and an application.</p><p><p>The results obtained permits an user to save context information attached to a picture in a database, as well as retrieve pictures from that database and show it in a web interface with its context information associated. The web interface also allows the user to perform searches by using different criteria, so only the pictures that matches with that criteria will be shown.</p></p></p> / Final Degree Project - Thesis

O uso de veículos aéreos não tripulados (VANTs) tem se tornado cada vez mais comum, principalmente em aplicações de uso civil. No cenário militar, o uso de VANTs tem focado o cumprimento de missões específicas que podem ser divididas em duas grandes categorias: sensoriamento remoto e transporte de material de emprego militar. Este trabalho se concentra na categoria do sensoriamento remoto. O trabalho foca a definição de um modelo e uma arquitetura de referência para o desenvolvimento de sensores inteligentes orientados a missões específicas. O principal objetivo destas missões é a geração de mapas temáticos. Neste trabalho são investigados processos e mecanismos que possibilitem a geração desta categoria de mapas. Neste sentido, o conceito de MOSA (Mission Oriented Sensor Array) é proposto e modelado. Como estudos de caso dos conceitos apresentados são propostos dois sistemas de mapeamento automático de fontes sonoras, um para o caso civil e outro para o caso militar. Essas fontes podem ter origem no ruído gerado por grandes animais (inclusive humanos), por motores de combustão interna de veículos ou por atividade de artilharia (incluindo caçadores). Os MOSAs modelados para esta aplicação são baseados na integração de dados provenientes de um sensor de imageamento termal e uma rede de sensores acústicos em solo. A integração das informações de posicionamento providas pelos sensores utilizados, em uma base cartográfica única, é um dos aspectos importantes tratados neste trabalho. As principais contribuições do trabalho são a proposta de sistemas MOSA, incluindo conceitos, modelos, arquitetura e a implementação de referência representada pelo sistema de mapeamento automático de fontes sonoras. / The use of unmanned aerial vehicles (UAV) has become increasingly common, particularly in civilian applications. In the military scenario, the use of UAVs has focused the accomplishment of specific tasks in two broad categories: remote sensing and transport of military material. This work focuses the remote sensing category. It address the definition of a model and reference architecture for the development of smart sensors oriented to specific tasks. The main objective of these missions is to generate thematic maps. This work investigates processes and mechanisms that enable the automatic generation of thematic maps. In this sense, the concept of MOSA (Mission Oriented Sensor Array) is proposed and modeled. As case studies, we propose two automatic mapping systems for on-the-ground generated sound sources, one for the civilian case and one for the military case. These sounds may come from the noise generated by large animals (including humans), from internal combustion engine vehicles or from artillery activity (including hunters). The MOSAs modeled for this application integrate data from a thermal imaging sensor and an on-the-ground network of acoustic sensors. The fusion of position information, provided by the two sensors, into a single cartographic basis is one of the key aspects addressed in this work. The main contributions are the proposed MOSA systems, including concepts, models and architecture and the reference implementation comprised by the system for automatic mapping of sound sources.