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  • CA3 - Computational Intelligence Research Group
    05 Jornadas de Engenharia de Electrónica Telecomunicacoes e Computadores Lisboa Portugal 2005 J M Fonseca A D Mora A C Marques A multi agent medical information system for Bioprofile collection CIMED 2005 Second International Conference on Computacional Intelligence in Medicine and Healthcare Costa da Caparica Lisbon Portugal 29th June to 1st July 2005 A Mora P Vieira J Fonseca MODELLING OF DRUSEN DEPOSITS BASED ON RETINA IMAGE TRIDIMENSIONAL INFORMATION CIMED 2005 Second International Conference on Computacional Intelligence in Medicine and Healthcare Costa da Caparica Lisbon Portugal 29th June to 1st July 2005 Mora A Fonseca J Vieira P Drusen Deposits Modeling with Illumination Correction in Proc Biomed 2005 Innsbruck Austria 16 18 February 2005 Fonseca J M Mora A D Marques A C MAMIS A Multi Agent Medical Information System in Proc Biomed 2005 Innsbruck Austria 16 18 February 2005 2003 2004 A Mora P Vieira J Fonseca DRUSEN DEPOSITS ON RETINA IMAGES DETECTION AND MODELING 2nd International Conference on Advances in Medical Signal and Information Processing MEDSIP 2004 Malta 5 8 September 2004 J M Fonseca and A D Mora An AI based approach to the Learners Profile Estimation CE2004 The 11th ISPE International Conference on Concurrent Engineering Research and Applications Pequim R P China 26 30 July 2004 A Mora P Vieira J Fonseca A Modeling Approach for Automatic Detection of Drusen Deposits on Retina Images 4th European Symposium on Biomedical Engineering Patras Greece 25 27 June 2004 José Manuel Fonseca André Mora Personal Assistant Autonomous Agents for Intelligent e Learning Systems IASTED International Conference WEB BASED EDUCATION WBE 2004 Innsbruck Austria February 16 18 2004 Pedro Barroso Joaquim Amaral André Mora José Manuel Fonseca Adolfo Steiger Garção A Quadtree Based Vehicles Recognition System 4th WSEAS Int Conf on OPTICS PHOTONICS LASERS and IMAGING ICOPLI 2004 Taiwan January

    Original URL path: http://www.ca3-uninova.org/member_andre_mora (2016-02-17)
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  • CA3 - Computational Intelligence Research Group
    family have enough time to think and decide for life and financial matters caused by the disease and the care needed in the advanced stages ii early diagnosis of AD gives the patient the opportunity of benefiting from symptom delaying medications which are most useful in the early stages In recent years electroencephalography EEG as a cheap potentially mobile and easy to record measure of brain activity has gained increasing attention of researchers for early diagnosis of AD Although the progress in exploring the capability of EEG for early diagnosis of AD is considerable a lot of problems still remain unsolved in this area According to literature from signal processing point of view the reported effects of AD on EEG are promising but not consistent and practical Moreover it seems there exists a lack of discriminative feature selection and efficient classification methods in order to report reliable classification error rate The aim of this project is to fill this gap using signal processing methods in particular time frequency analysis and artificial intelligence techniques The target group of the project involves groups of patients in early stages of the disease i e mild cognitive impairment MCI or mild AD Overall Objectives The project will contribute to the investigating the potential of EEG for early diagnosis or even prognosis of AD If the capability of EEG is confirmed in this respect it can be included as a screening tool for AD in yearly check ups of people after the age of 50 The specific objectives of the project include Analysis the capability of proposed features so far from the consistency and effectiveness point of view Explore the potential of time frequency methods in describing the effect of AD on EEG more in depth than existing literature Find a proper feature selection method

    Original URL path: http://www.ca3-uninova.org/project_edad (2016-02-17)
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  • CA3 - Computational Intelligence Research Group
    to tackle two main challenges how to interactively visualize a large amount of data and how to perform complex selections in 3D environments The emergence of widespread 3D interaction devices such as Microsoft Kinect and Leap Motion brought a hope that perhaps this problem can be soon addressed Those devices use machine learning algorithms with image processing and fusion for enabling the computer to recognize humans and to perform motion capture However for effective usage in data selection the system should also be able to infer the user s motion intention It should be able to answer questions as does the user want to select a sphere or an arbitrarily complex region Hence IVELA will recognize gestures that humans intuitively use for data selection and volume definition This will involve the adoption of machine learning algorithms as Support Vector Machines to infer the regions from the user s gesture Currently there is no scientific engineering or mission analysis software tool that allows the user to perform data selection of complex regions in the 3D space using such human computer interaction devices Finally even if IVELA is a demonstrator for a 3D interactive visualization environment for large datasets such as Gaia

    Original URL path: http://www.ca3-uninova.org/project_ivela (2016-02-17)
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  • CA3 - Computational Intelligence Research Group
    of the instruments The objectives of the project are 1 Analyse of end to end processing models for ESA missions SMOS SWARM and forthcoming EarthCARE in relation to processing facilities such as MMFI and or Grid Processing On Demand G POD 2 Select most suitable technology and develop a prototype implementing the required automation 3 Demonstrate the capability of the prototype to deal with end to end processing orchestration for

    Original URL path: http://www.ca3-uninova.org/project_geaf (2016-02-17)
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  • CA3 - Computational Intelligence Research Group
    degradation of internal structures It is poorly understood how these problems were solved by the evolutionary process of these organisms The objective of this project is to study the kinetics of segregation to the poles and the partitioning in division of aggregates in live bacteria one event at a time with single molecule resolution various environmental and stress conditions Also it will be established the correlation between this and aging i e loss of reproductive vitality across lineages A major component of this work is the development of image processing and segmentation techniques to extract the information from time series of microscope images With these tools we will characterize the kinetics of segregation at the single event level over time and its adaptability to less favorable conditions In particular we will Establish methods for image segmentation of cells and aggregates from temporal images by confocal microscopy Establish automated methods to track cells and aggregates in time from temporal images by confocal microscopy and characterize the kinetics of the aggregates Develop methods to detect cell divisions and characterize the partitioning of aggregates by the daughter cells Study the kinetics of segregation of unwanted fluorescently tagged aggregates in various conditions Study how

    Original URL path: http://www.ca3-uninova.org/project_sadac (2016-02-17)
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  • CA3 - Computational Intelligence Research Group
    Currently the focus is on general image processing modules for solar images as well as modules for sunspots and coronal bright points CBPs This research puts a special emphasis on tracking solar features and proposes solutions using evolutionary algorithms e g hybrid of swarm intelligence and snake model algorithms and fuzzy sets theory After solar features are detected the corresponding modules calculate their characterizations such as shape area chirality speed and coordination The tracking module is responsible for tracing the solar features throughout their life span PhD Research Topics Image processing Active contour models Evolutionary computing Swarm intelligence Fuzzy theory Solar physics Space weather Relevant Publications Shahamatnia E Ebadzadeh M M 2011 Application of Particle Swarm Optimization and Snake Model Hybrid on Medical Imaging In the proceedings of Third International Workshop on Computational Intelligence in Medical Imaging CIMI IEEE Symposium Series on Computational Intelligence Paris France 11 15 April Shahamatnia E Dorotovi I Ribeiro R A and Fonseca J M 2012 Towards an automatic sunspot tracking Swarm intelligence and snake model hybrid Acta Futura 5 pp 151 159 DOI 10 2420 AF05 2012 153 Dorotovic I Shahamatnia E Lorenc M Rybanský M Ribeiro R A Fonseca J M Sunspots and

    Original URL path: http://www.ca3-uninova.org/project_solatic (2016-02-17)
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  • CA3 - Computational Intelligence Research Group
    future data from Gaia There are 8 coordination units in the DPAC CU1 System Architecture CU2 Data Simulations CU3 Core Processing CU4 Object Processing CU5 Photometric Processing CU6 Spectroscopic Processing CU7 Variability Processing CU8 Astrophysical Parameters The Portuguese participation in the DPAC will be on CU1 CU2 CU5 CU7 CU8 Specifically UNINOVA will participate on CU7 providing non supervised algorithms for the variability studies as well as required implementations in

    Original URL path: http://www.ca3-uninova.org/project_gaia (2016-02-17)
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  • CA3 - Computational Intelligence Research Group
    de Embalagens PoVeRE pretende desenvolver uma ferramenta de cálculo para determinar um Valor Ponto Verde VPV inteligente e sustentável i e que inclua não só aspectos económicos mas também ambientais e sociais de modo a dar indicações aos produtores de embalagens sobre como deverão produzi las com vista aos impactes que terão no seu fim de vida O VPV a calcular terá em consideração diversos patamares classes diferenciadas de taxa

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