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  • CA3 - Computational Intelligence Research Group
    Visual Analysis framework for large 4 Dimensional fields processes and dynamics The framework is aimed as a decision support system targeting different users and contexts as well as a collaborative platform for data exploration and research Five demonstration scenarios will be acting as a source of requirements conceptual abstractions and also as demonstrators The tool design will support in a user friendly visual way the exploration of complex including uncertainty

    Original URL path: http://www.ca3-uninova.org/project_va4d (2016-02-17)
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  • CA3 - Computational Intelligence Research Group
    in the preceding precursor and mission feasibility studies and are optimized in order to reach the following sometimes conflicting objectives minimum fuel consumption required landing precision maximum divert range specified touchdown conditions specified navigation performance provide best possible viewing conditions for hazard avoidance Hazard Detection and Avoidance starts at 100 seconds prior to the planned touchdown with an evaluation of the initially selected landing area The risk mapping combines Lidar and CCD camera images for shadow maps slope maps and roughness map Selection of a safe site is made by a combined evaluation of the risk maps and the current divert range of the lander at that time A second and final site evaluation is planned at low altitude allowing the detection of hazards down to the threshold size of 50 cm For hazard avoidance horizontal divert maneuvers are planned in real time at arrival of the site selection decision In the base project IPSIS UNINOVA has developed innovative autonomous decision making software for safe landing site selection during an interplanetary landing IPSIS was a complex decision model enhanced with artificial intelligence techniques which adhered to the tight computational budget allowed by the on board processor The decision model was based on fuzzy multi criteria concepts and techniques In LuLaB UNINOVA is responsible for analyzing the HDA system requirements and impact on the decision process reusing IPSIS background as starting point Research Areas Fuzzy multi criteria decision model Aggregation operators Evolutionary computing Safe landing site selection Partners Relevant Publications Bourdarias C Da Cunha P Drai R Simões L F and Ribeiro R A 2010 Optimized and flexible multi criteria decision making for hazard avoidance In Proceedings of the 33rd Annual AAS Rocky Mountain Guidance and Control Conference Breckenridge Colorado American Astronautical Society bib pdf Reynaud S Drieux M Bourdarias C

    Original URL path: http://www.ca3-uninova.org/project_lulab (2016-02-17)
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  • CA3 - Computational Intelligence Research Group
    events with a clear correlation between image resolution and radiation exposure higher doses or acquisition times correspond to clearer pictures but also represent increased risks for the patient The aim of this project is to apply Soft Computing ideas and methods to push the current limit and develop new reconstruction techniques that will provide higher resolution images without requiring risky exams Research Areas Soft Computing and Aggregation Functions Medical Imaging

    Original URL path: http://www.ca3-uninova.org/project_hrpet (2016-02-17)
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  • CA3 - Computational Intelligence Research Group
    power and Technologies we are now able to collect and store considerable large amounts of data However existing tools to explore analyze and visualize these large data sets are rather insufficient and quite limited We need to dramatically improve scalability on ordinary and or parallel computers to handle Large and Complex Data Sets and discover new information knowledge Examples of astronomical missions that collect considerable amounts of data yet barely studied in depth are Sloan Digital Sky Survey which stores around 53 million unique objects Hipparcos which stores around 10 5 unique objects and GAIA future mission which will collect 10 9 unique objects with 10 being variable objects 10 8 This tool is targeted for astronomers physicists mission control teams and other interested parties because it will allow exploration of data during and after the missions to obtain more knowledge about the Universe The aim of KD LADS is to allow an in depth knowledge discovery process complemented with specialized visualizations techniques e g SOM Self Organizing Maps to better visualize the data clusters and provide more insights for exploring and analyzing the data thus improving any knowledge discovery process Research Areas Knowledge Discovery Data Mining Parallel computing Partners

    Original URL path: http://www.ca3-uninova.org/project_kdlads (2016-02-17)
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  • CA3 - Computational Intelligence Research Group
    driven system design As a decission support system for Spacecraft Operators with data acquisition and consolidation of spacecraft and space weather data providing near realtime information display on monitoring tools as well as reporting and analysis tools Forecasting and knowledge extraction are also contemplated with this system SEISOP intends to support multiple missions and types including Space exploration and observations missions Earth observation and Navigation and Telecom missions User groups include Flight Control Teams Lancher Teams Scientists and Spacecraft designers Uninova s responsibility on this version of the project are the Data Processing Module which includes a set of services and tools for performing Extraction Transformation and Loading of heterogeneous text based data from international data repositories and internal mission data repositories Supporting graphical user interfaces include a tool for the definition and status monitoring of the various data sources and a tool for visually defining the file format and extraction and transformation workflow Also we are further developing the Monitoring Tool MT and Reporting and Analysis Tool RAT desktop applications The MT is a multi user tool for mission controllers providing a customizable and flexible interface for near realtime monitoring of spacecraft and spaceweather data The RAT is an

    Original URL path: http://www.ca3-uninova.org/project_seisop (2016-02-17)
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  • CA3 - Computational Intelligence Research Group
    is no anatomic evidence for a central sensing and decision unit and considering the rather low computational capacity of a plant cell it appears meaningful to consider the apex as a simple autonomous unit taking decisions on own account Yet when looking at the root as a collective growth patterns are not chaotic but seem to follow a higher order and emerge as a result of the individual decision making of the apexes Analyzing and subsequently simulating root growth has been in the focus of previous research The major justification for these analyses derives from agricultural physiological questions on root efficiency soil exploitation nutrient uptake per volume root etc The main means used are fractal methods i e describing root architecture as a fractal That work nicely models root architectures however the technique involves recursive formulation and hierarchical levels and although the simulations of roots match quite well the observed growth patterns in real plants it does not reflect the decision processes actually going on during root growth We introduce a cellular automata model of soil and root dynamics This model captures a set of essential features that condition root development and enables the evaluation of candidate root systems in

    Original URL path: http://www.ca3-uninova.org/project_simorg (2016-02-17)
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  • CA3 - Computational Intelligence Research Group
    to be installed in ferries based on Automatic Identification System AIS to help the navigation on regular routes across river Tejo The system allows the specification of allowed routes to guide the driver through the river channels what is especially important in low visibility conditions It also registers navigational information route speed and others in order to optimize costs and increase ferries efficiencies and security Research Areas AIS Monitoring Systems

    Original URL path: http://www.ca3-uninova.org/project_sinais (2016-02-17)
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  • CA3 - Computational Intelligence Research Group
    imaging sensor throughout the descent so that its estimated characteristics can be continuously updated The IPSIS Intelligent Planetary SIte Selection project developed innovative software for autonomous Decision Making in the selection of a safe landing site during an interplanetary landing The main objective of IPSIS was to formalize and implement an autonomous dynamic and adaptable multi criteria mathematical model to choose the best target landing sites and to determine when re targetings should occur A complex decision model was developed in this project for dealing with its challenging requirements past historic information influences the assessment of the present alternatives dynamics sophisticated data preparation processes normalize and fuse the data while handling its inherent uncertainty adaptability and a retargeting process tracks the evolution in the quality of the best identified sites and recommends at a time of its choosing new landing coordinates towards which the lander should move The adherence to the tight computational budgets allowed by the on board processors also prompted the introduction of non exhaustive approaches to site selection where the evaluation of the high number of sites scanned by the lander s sensors is guided by meta heuristics which allow an efficient identification of the best sites from a minimal sampling of the available alternatives Developed Architecture Research Areas Planetary landing site selection Autonomous hazard avoidance systems Fuzzy multiple criteria dynamic decision making Dynamically changing input data Aggregation operators Particle Swarm Optimization Tabu Search Partners Publications 2010 Bourdarias C Da Cunha P Drai R Simões L F and Ribeiro R A 2010 Optimized and flexible multi criteria decision making for hazard avoidance In Proceedings of the 33rd Annual AAS Rocky Mountain Guidance and Control Conference Breckenridge Colorado American Astronautical Society bib pdf 2009 Pais T C Ribeiro R A and Simões L F 2009 Computational Intelligence in

    Original URL path: http://www.ca3-uninova.org/project_ipsis (2016-02-17)
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