Data Assimilation Technique For Flood Monitoring and Prediction

This paper focuses on the development of methods and cascade of models for flood monitoring and forecasting and its implementation in Grid environment. The processing of satellite data for flood extent mapping is done using neural networks. For flood forecasting we use cascade of models: regional numerical weather prediction (NWP) model, hydrological model and hydraulic model. Implementation of developed methods and models in the Grid infrastructure and related projects are discussed

Intelligent Model of User Behavior in Distributed Systems

We present a complex neural network model of user behavior in distributed systems. The model reflects both dynamical and statistical features of user behavior and consists of three components: on-line and off-line models and change detection module. On-line model reflects dynamical features by predicting user actions on the basis of previous ones. Off-line model is based on the analysis of statistical parameters of user behavior. In both cases neural networks are used to reveal uncharacteristic activity of users. Change detection module is intended for trends analysis in user behavior. The efficiency of complex model is verified on real data of users of Space Research Institute of NASU-NSAU

WORKFLOW MODELLING IN GRID SYSTEM FOR SATELLITE DATA PROCESSING

Abstract: This paper focuses on a problem of Grid system decomposition by developing its object model. Unified Modelling Language (UML) is used as a formalization tool. This approach is motivated by the complexity of the system being analysed and the need for simulation model design

Обеспечение безопасности Grid-систем на основе модели поведения пользователей

Приведен обзор моделей и методов обеспечения безопасности в Grid-системах, базирующихся на мониторинге поведения пользователей. Предложен подход к обеспечению безопасности Grid-систем на основе построения профиля пользователя с параметрами запускаемых им задач. Приведены результаты экспериментов на основе реальных данных, полученных в Grid-системе GILDA-EGEE

A web portal for Portuguese brain imaging network

Mestrado em Engenharia de Computadores e TelemáticaA Imagiologia Cerebral (IC) está na fronteira entre a neurologia, engenharia e física. écnicas de imagens médicas multimodais, tais como a Ressonância Magnética (MRI e fMRI) e Espectroscopia (MRS), Tomografia Computadorizada por Emissão de Fotões/Positrões (SPECT/PET), entre outros, são emergentes ferramentas de pesquisa médica que pode fornecer informações valiosas para o diagnóstico de doenças do cérebro. Eletroencefalograma de alta resolução (HR-EEG), técnicas para sincronizar e fundir seus resultados de análise e várias técnicas de imagem são também parte de IC. Em Portugal, dado o facto que a maioria das áreas relacionadas com IC (por exemplo, medicina, engenharia ou física) são assuntos de investigação em muitos grupos de P&D, um consórcio de universidades de Aveiro, Coimbra, Minho e Porto criou a Rede Nacional de Imagiologia Funcional Cerebral (RNIFC). A RNIFC é uma associação sem fins lucrativos que foi formalizada e assinada em fevereiro de 2009. Actualmente, com o suporte de sistemas digitais para armazenar imagens médicas, é possível partilhar dados entre essas instituições para melhorar o diagnóstico, e permitir investigações entre a comunidade médica de diferentes instituições. O principal objectivo desta dissertação é descrever a implementação dos serviços de sistemas de informação essenciais para a Brain Imaging Network (BIN) que suportam actualmente o RNIFC acessível através do Portal BIN, o principal ponto de entrada para a BING. O Portal BIN permite aos pesquisadores na comunidade BING espalhadas pelo país e no estrangeiro, quer para solicitar o acesso a instrumentos científicos ou para recuperar os seus casos e executar as suas análises. ABSTRACT: Brain Imaging is in the frontier between neurology, engineering and physics. Multimodal medical imaging techniques, such as Magnetic Resonance Imaging (MRI and fMRI) and Spectroscopy (MRS), Single Photon/Positron Emitting Tomography (SPECT/PET) among others, are emergent medical research tools that can provide valuable information for diagnosis of brain diseases. High-resolution electroencephalogram (HR-EEG), techniques for synchronizing and fuse its analysis results and several imaging techniques are also part of BI. In Portugal, given fact that most of the BI related areas (e.g. medical, engineering or physics) are subjects of research in many R&D groups, a consortium of the universities of Aveiro, Coimbra, Minho and Porto created the National Functional Brain Imaging Network (RNIFC). The RNIFC is a non-profitable association that was formalized and signed in February 2009. Currently, with the support of digital systems to store medical images, it is possible to share data among these institutions to improve diagnosis, and allow investigations by the medical community among different institutions. The main objective of this thesis is to describe the implementation of the essential Brain Imaging Network (BIN) information systems services that currently support the RNIFC accessible through the BIN Portal, the main entry point for the BING. BIN Portal enables researchers in the BING community scattered along the country and abroad either to apply for access to the scientific instruments or to retrieve their cases and run their analysis

Aerospace medicine and biology: A cumulative index to a continuing bibliography (supplement 345)

This publication is a cumulative index to the abstracts contained in Supplements 333 through 344 of Aerospace Medicine and Biology: A Continuing Bibliography. Seven indexes are included -- subject, personal author, corporate source, foreign technology, contract number, report number, and accession number

Scientific Analysis by the Crowd: A System for Implicit Collaboration between Experts, Algorithms, and Novices in Distributed Work.

Crowd sourced strategies have the potential to increase the throughput of tasks historically constrained by the performance of individual experts. A critical open question is how to configure crowd-based mechanisms, such as online micro-task markets, to accomplish work normally done by experts. In the context of one kind of expert work, feature extraction from electron microscope images, this thesis describes three experiments conducted with Amazon’s Mechanical Turk to explore the feasibility of crowdsourcing for tasks that traditionally rely on experts. The first experiment combined the output from learning algorithms with judgments made by non-experts to see whether the crowd could efficiently and accurately detect the best algorithmic performance for image segmentation. Image segmentation is an important but rate limiting step in analyzing biological imagery. Current best practice relies on extracting features by hand. Results showed that crowd workers were able to match the results of expert workers in 87.5% of the cases given the same task and that they did so with very little training. The second experiment used crowd responses to progressively refine task instructions. Results showed that crowd workers were able to consistently add information to the instructions and produced results the crowd perceived as more clear by an average of 8.7%. Finally, the third experiment mapped images to abstract representations to see whether the crowd could efficiently and accurately identify target structures. Results showed that crowd workers were able to find 100% of known structures with an 82% decrease in false positives compared to conventional automated image processing. This thesis makes a number of contributions. First, the work demonstrates that tasks previously performed by highly-trained experts, such as image extraction, can be accomplished by non-experts in less time and with comparable accuracy when organized through a micro-task market. Second, the work shows that engaging crowd workers to reflect on the description of tasks can be used to have them refine tasks to produce increased engagement by subsequent crowd workers. Finally, the work shows that abstract representations perform nearly as well as actual images in terms of using a crowd of non-experts to locate targeted features.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102368/1/dlzz_1.pd

Optimisation of the enactment of fine-grained distributed data-intensive work flows

The emergence of data-intensive science as the fourth science paradigm has posed a data deluge challenge for enacting scientific work-flows. The scientific community is facing an imminent flood of data from the next generation of experiments and simulations, besides dealing with the heterogeneity and complexity of data, applications and execution environments. New scientific work-flows involve execution on distributed and heterogeneous computing resources across organisational and geographical boundaries, processing gigabytes of live data streams and petabytes of archived and simulation data, in various formats and from multiple sources. Managing the enactment of such work-flows not only requires larger storage space and faster machines, but the capability to support scalability and diversity of the users, applications, data, computing resources and the enactment technologies. We argue that the enactment process can be made efficient using optimisation techniques in an appropriate architecture. This architecture should support the creation of diversified applications and their enactment on diversified execution environments, with a standard interface, i.e. a work-flow language. The work-flow language should be both human readable and suitable for communication between the enactment environments. The data-streaming model central to this architecture provides a scalable approach to large-scale data exploitation. Data-flow between computational elements in the scientific work-flow is implemented as streams. To cope with the exploratory nature of scientific work-flows, the architecture should support fast work-flow prototyping, and the re-use of work-flows and work-flow components. Above all, the enactment process should be easily repeated and automated. In this thesis, we present a candidate data-intensive architecture that includes an intermediate work-flow language, named DISPEL. We create a new fine-grained measurement framework to capture performance-related data during enactments, and design a performance database to organise them systematically. We propose a new enactment strategy to demonstrate that optimisation of data-streaming work-flows can be automated by exploiting performance data gathered during previous enactments