Intelligent Computations for Flood Monitoring

Floods represent the most devastating natural hazards in the world, affecting more people and causing more property damage than any other natural phenomena. One of the important problems associated with flood monitoring is flood extent extraction from satellite imagery, since it is impractical to acquire the flood area through field observations. This paper presents a method to flood extent extraction from synthetic-aperture radar (SAR) images that is based on intelligent computations. In particular, we apply artificial neural networks, self-organizing Kohonen’s maps (SOMs), for SAR image segmentation and classification. We tested our approach to process data from three different satellite sensors: ERS-2/SAR (during flooding on Tisza river, Ukraine and Hungary, 2001), ENVISAT/ASAR WSM (Wide Swath Mode) and RADARSAT-1 (during flooding on Huaihe river, China, 2007). Obtained results showed the efficiency of our approach.

Self-organising zooms for decentralised redundancy management in visual sensor networks

When visual sensor networks are composed of cameras which can adjust the zoom factor of their own lens, one must determine the optimal zoom levels for the cameras, for a given task. This gives rise to an important trade-off between the overlap of the different cameras’ fields of view, providing redundancy, and image quality. In an object tracking task, having multiple cameras observe the same area allows for quicker recovery, when a camera fails. In contrast having narrow zooms allow for a higher pixel count on regions of interest, leading to increased tracking confidence. In this paper we propose an approach for the self-organisation of redundancy in a distributed visual sensor network, based on decentralised multi-objective online learning using only local information to approximate the global state. We explore the impact of different zoom levels on these trade-offs, when tasking omnidirectional cameras, having perfect 360-degree view, with keeping track of a varying number of moving objects. We further show how employing decentralised reinforcement learning enables zoom configurations to be achieved dynamically at runtime according to an operator’s preference for maximising either the proportion of objects tracked, confidence associated with tracking, or redundancy in expectation of camera failure. We show that explicitly taking account of the level of overlap, even based only on local knowledge, improves resilience when cameras fail. Our results illustrate the trade-off between maintaining high confidence and object coverage, and maintaining redundancy, in anticipation of future failure. Our approach provides a fully tunable decentralised method for the self-organisation of redundancy in a changing environment, according to an operator’s preferences.

Self-aware and self-expressive camera networks

Smart cameras perform on-board image analysis, adapt their algorithms to changes in their environment, and collaborate with other networked cameras to analyze the dynamic behavior of objects. A proposed computational framework adopts the concepts of self-awareness and self-expression to more efficiently manage the complex tradeoffs among performance, flexibility, resources, and reliability. The Web extra at http://youtu.be/NKe31-OKLz4 is a video demonstrating CamSim, a smart camera simulation tool, enables users to test self-adaptive and self-organizing smart-camera techniques without deploying a smart-camera network.

Análise do jogo de goalball : modelação e interpretação dos padrões de jogo da Paralimpíada de Pequim 2008

Universidade Estadual de Campinas . Faculdade de Educação Física

Improvements on ICA mixture models for image pre-processing and segmentation

Today several different unsupervised classification algorithms are commonly used to cluster similar patterns in a data set based only on its statistical properties. Specially in image data applications, self-organizing methods for unsupervised classification have been successfully applied for clustering pixels or group of pixels in order to perform segmentation tasks. The first important contribution of this paper refers to the development of a self-organizing method for data classification, named Enhanced Independent Component Analysis Mixture Model (EICAMM), which was built by proposing some modifications in the Independent Component Analysis Mixture Model (ICAMM). Such improvements were proposed by considering some of the model limitations as well as by analyzing how it should be improved in order to become more efficient. Moreover, a pre-processing methodology was also proposed, which is based on combining the Sparse Code Shrinkage (SCS) for image denoising and the Sobel edge detector. In the experiments of this work, the EICAMM and other self-organizing models were applied for segmenting images in their original and pre-processed versions. A comparative analysis showed satisfactory and competitive image segmentation results obtained by the proposals presented herein. (C) 2008 Published by Elsevier B.V.

A mathematical view of biological complexity

This essay is a trial on giving some mathematical ideas about the concept of biological complexity, trying to explore four different attributes considered to be essential to characterize a complex system in a biological context: decomposition, heterogeneous assembly, self-organization, and adequacy. It is a theoretical and speculative approach, opening some possibilities to further numerical and experimental work, illustrated by references to several researches that applied the concepts presented here. (C) 2008 Elsevier B.V. All rights reserved.

Planeamento e optimização em redes celulares móveis auto-geridas assentes em LTE

O objectivo deste trabalho consiste em avaliar os benefícios das Self Organizing Networks (SON), no que concerne ao planeamento e optimização de redes Long Term Evolution (LTE), não só através do seu estudo, como também através do desenvolvimento e teste de algoritmos, que permitem avaliar o funcionamento de algumas das suas principais funções. O estudo efectuado sobre as SON permitiu identificar um conjunto de funções, tais como a atribuição automática de Physical Cell Id (PCI), o Automatic Neighbour Relation (ANR) e a optimização automática de parâmetros de handover, que permitem facilitar ou mesmo substituir algumas das tarefas mais comuns em planeamento e optimização de redes móveis celulares, em particular, redes LTE. Recorrendo a um simulador LTE destinado à investigação académica, em código aberto e desenvolvido em Matlab®, foi desenvolvido um conjunto de algoritmos que permitiram a implementação das funções em questão. Para além das funções implementadas, foram também introduzidas alterações que conferem a este simulador a capacidade de representar e simular redes reais, permitindo uma análise mais coerente dos algoritmos desenvolvidos. Os resultados obtidos, para além de evidenciarem claramente o benefício dos algoritmos desenvolvidos, foram ainda comparados com os obtidos pela ferramenta profissional de planeamento e optimização Atoll®, tendo-se verificado a franca proximidade de desempenho em algumas das funções. Finalmente, foi desenvolvida uma interface gráfica que permite o desenho, configuração e simulação de cenários, bem como a análise de resultados.

Planeamento e otimização de femto-células em ambiente empresarial

O objetivo deste trabalho consiste em avaliar as capacidades das femto-células, no âmbito do planeamento e otimização de redes Universal Mobile Telecommunication System (UMTS) instaladas no interior de edifícios num ambiente empresarial. A avaliação será feita através do estudo da tecnologia das femto-células, e do planeamento efetuado num cenário real onde, através do desenvolvimento e teste de um conjunto parametrizações, será avaliado o funcionamento das femto-células assim como forma de otimizar o seu desempenho. O estudo realizado permitiu identificar um conjunto de características que as femto-células partilham com as Self-Organizing Networks (SON), como a auto-configuração, auto-otimização de parâmetros rádio, ajuste dinâmico da área de cobertura, atribuição automática de Scrambling Codes (SC) e da frequência da portadora, criação automática de relações de vizinhança, entre outras, que permitem facilitar o processo de planeamento e otimização de redes móveis UMTS. Recorrendo a um cenário empresarial real, foi efetuado um planeamento celular indoor de raiz, através do qual foi possível testar o funcionamento da algumas das principais funções das femto-células, nomeadamente a capacidade de ajuste dinâmico da área de cobertura. Foi também avaliado o funcionamento de um grupo co-localizado de femto-células, onde foi possível testar parametrizações com o objetivo de melhorar o processo de handover entre as femto-células do grupo, e entre estas e a rede macro Global System for Mobile Communications (GSM). A avaliação de cada um de cada uma das parametrizações testada, é efetuada a partir das medidas recolhidas no terreno, recorrendo à ferramenta TEMS® Investigation, assim como aos Key Performance Indicators (KPIs) que as femto-células disponibilizam. Os resultados obtidos mostram o benefício da utilização das femto-células num ambiente empresarial real, assim como os eventuais problemas e desafios que podem surguir do planeamento celular indoor recorrendo à tecnologia das femto-células, sendo apresentada a parametrização que permite obter o melhor desempenho da rede instalada.

Detecção de sectores cruzados e optimização de cobertura em redes LTE

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações

Optimização da lista de células vizinhas em redes LTE

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações

Improving accuracy for OTD based 3G geolocation in real urban/suburban environments

This paper presents the recent research results about the development of a Observed Time Difference (OTD) based geolocation algorithm based on network trace data, for a real Universal Mobile Telecommunication System (UMTS) Network. The initial results have been published in [1], the current paper focus on increasing the sample convergence rate, and introducing a new filtering approach based on a moving average spatial filter, to increase accuracy. Field tests have been carried out for two radio environments (urban and suburban) in the Lisbon area, Portugal. The new enhancements produced a geopositioning success rate of 47% and 31%, and a median accuracy of 151 m and 337 m, for the urban and suburban environments, respectively. The implemented filter produced a 16% and 20% increase on accuracy, when compared with the geopositioned raw data. The obtained results are rather promising in accuracy and geolocation success rate. OTD positioning smoothed by moving average spatial filtering reveals a strong approach for positioning trace extracted events, vital for boosting Self-Organizing Networks (SON) over a 3G network.

Neighbour list optimization for real LTE radio network

With the increasing complexity of current networks, it became evident the need for Self-Organizing Networks (SON), which aims to automate most of the associated radio planning and optimization tasks. Within SON, this paper aims to optimize the Neighbour Cell List (NCL) for Long Term Evolution (LTE) evolved NodeBs (eNBs). An algorithm composed by three decisions were were developed: distance-based, Radio Frequency (RF) measurement-based and Handover (HO) stats-based. The distance-based decision, proposes a new NCL taking account the eNB location and interference tiers, based in the quadrants method. The last two algorithms consider signal strength measurements and HO statistics, respectively; they also define a ranking to each eNB and neighbour relation addition/removal based on user defined constraints. The algorithms were developed and implemented over an already existent radio network optimization professional tool. Several case studies were produced using real data from a Portuguese LTE mobile operator. © 2014 IEEE.

Caracterização das NUTS da Europa de acordo com a dinâmica de ocupações/uso do solo entre 1990 e 2000

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

A aplicação de redes neuronais na deteção da influência do High Frequency Trading na negociação de ações (caso português)

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão da Informação

Exploraty Multivariate Statistical Methods Applied to Pharmaceutical Industry CRM Data

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação