A type system for access control in an object-oriented language

Trabalho apresentado no ��mbito do Mestrado em Engenharia Inform��tica, como requisito parcial para obten����o do grau de Mestre em Engenharia Inform��tica

2D PCA-Based localization for mobile robots in unstructured environments

In this paper a new PCA-based positioning sensor and localization system for mobile robots to operate in unstructured environments (e. g. industry, services, domestic ...) is proposed and experimentally validated. The inexpensive positioning system resorts to principal component analysis (PCA) of images acquired by a video camera installed onboard, looking upwards to the ceiling. This solution has the advantage of avoiding the need of selecting and extracting features. The principal components of the acquired images are compared with previously registered images, stored in a reduced onboard image database, and the position measured is fused with odometry data. The optimal estimates of position and slippage are provided by Kalman filters, with global stable error dynamics. The experimental validation reported in this work focuses on the results of a set of experiments carried out in a real environment, where the robot travels along a lawn-mower trajectory. A small position error estimate with bounded co-variance was always observed, for arbitrarily long experiments, and slippage was estimated accurately in real time.

Land use / land cover change detection: an object oriented approach, M��nster, Germany

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

Digital-domain self-calibration technique for video-rate pipeline A/D converters using Gaussian white noise

Electronics Letters Vol.38, n�� 19

Anota����o colaborativa de conte��dos utilizando jogos com prop��sito

Com o crescimento da informa����o dispon��vel na Web, arquivos pessoais e profissionais, protagonizado tanto pelo aumento da capacidade de armazenamento de dados, como pelo aumento exponencial da capacidade de processamento dos computadores, e do f��cil acesso a essa mesma informa����o, um enorme fluxo de produ����o e distribui����o de conte��dos audiovisuais foi gerado. No entanto, e apesar de existirem mecanismos para a indexa����o desses conte��dos com o objectivo de permitir a pesquisa e acesso aos mesmos, estes apresentam normalmente uma grande complexidade algor��tmica ou exigem a contrata����o de pessoal altamente qualificado, para a verifica����o e categoriza����o dos conte��dos. Nesta disserta����o pretende-se estudar solu����es de anota����o colaborativa de conte��dos e desenvolver uma ferramenta que facilite a anota����o de um arquivo de conte��dos audiovisuais. A abordagem implementada �� baseada no conceito dos ���Jogos com Prop��sito��� (GWAP ��� Game With a Purpose) e permite que os utilizadores criem tags (metadatos na forma de palavras-chave) de forma a atribuir um significado a um objecto a ser categorizado. Assim, e como primeiro objectivo, foi desenvolvido um jogo com o prop��sito n��o s�� de entretenimento, mas tamb��m que permita a cria����o de anota����es audiovisuais perante os v��deos que s��o apresentados ao jogador e, que desta forma, se melhore a indexa����o e categoriza����o dos mesmos. A aplica����o desenvolvida permite ainda a visualiza����o dos conte��dos e metadatos categorizados, e com o objectivo de cria����o de mais um elemento informativo, permite a inser����o de um like num determinado instante de tempo do v��deo. A grande vantagem da aplica����o desenvolvida reside no facto de adicionar anota����es a pontos espec��ficos do v��deo, mais concretamente aos seus instantes de tempo. Trata-se de uma funcionalidade nova, n��o dispon��vel em outras aplica����es de anota����o colaborativa de conte��dos audiovisuais. Com isto, o acesso aos conte��dos ser�� bastante mais eficaz pois ser�� poss��vel aceder, por pesquisa, a pontos espec��ficos no interior de um v��deo.

Design of an extensible metadata editor based on RDF

The content of a Learning Object is frequently characterized by metadata from several standards, such as LOM, SCORM and QTI. Specialized domains require new application profiles that further complicate the task of editing the metadata of learning object since their data models are not supported by existing authoring tools. To cope with this problem we designed a metadata editor supporting multiple metadata languages, each with its own data model. It is assumed that the supported languages have an XML binding and we use RDF to create a common metadata representation, independent from the syntax of each metadata languages. The combined data model supported by the editor is defined as an ontology. Thus, the process of extending the editor to support a new metadata language is twofold: firstly, the conversion from the XML binding of the metadata language to RDF and vice-versa; secondly, the extension of the ontology to cover the new metadata model. In this paper we describe the general architecture of the editor, we explain how a typical metadata language for learning objects is represented as an ontology, and how this formalization captures all the data required to generate the graphical user interface of the editor.

Efficient digital self-calibration of video-rate pipeline ADCs using white gaussian noise

Proceedings of IEEE, ISCAS 2003, Vol.I, pp. 877-880

In the midst of confusion : da dan��a em cinema e da adapta����o de dan��a para o ecr��: processos de cria����o intermediais: trabalho de projecto

Trabalho de Projeto submetido �� Escola Superior de Teatro e Cinema para cumprimento dos requisitos necess��rios �� obten����o do grau de Mestre em Desenvolvimento de Projeto Cinematogr��fico - especializa����o em Dramaturgia e Realiza����o

TV Recommendation and Personalization Systems: Integrating Broadcast and Video On-demand Services

he expansion of Digital Television and the convergence between conventional broadcasting and television over IP contributed to the gradual increase of the number of available channels and on demand video content. Moreover, the dissemination of the use of mobile devices like laptops, smartphones and tablets on everyday activities resulted in a shift of the traditional television viewing paradigm from the couch to everywhere, anytime from any device. Although this new scenario enables a great improvement in viewing experiences, it also brings new challenges given the overload of information that the viewer faces. Recommendation systems stand out as a possible solution to help a watcher on the selection of the content that best fits his/her preferences. This paper describes a web based system that helps the user navigating on broadcasted and online television content by implementing recommendations based on collaborative and content based filtering. The algorithms developed estimate the similarity between items and users and predict the rating that a user would assign to a particular item (television program, movie, etc.). To enable interoperability between different systems, programs characteristics (title, genre, actors, etc.) are stored according to the TV-Anytime standard. The set of recommendations produced are presented through a Web Application that allows the user to interact with the system based on the obtained recommendations.

Parallel GPU architecture for hyperspectral unmixing based on augmented Lagrangian method

Hyperspectral imaging has become one of the main topics in remote sensing applications, which comprise hundreds of spectral bands at different (almost contiguous) wavelength channels over the same area generating large data volumes comprising several GBs per flight. This high spectral resolution can be used for object detection and for discriminate between different objects based on their spectral characteristics. One of the main problems involved in hyperspectral analysis is the presence of mixed pixels, which arise when the spacial resolution of the sensor is not able to separate spectrally distinct materials. Spectral unmixing is one of the most important task for hyperspectral data exploitation. However, the unmixing algorithms can be computationally very expensive, and even high power consuming, which compromises the use in applications under on-board constraints. In recent years, graphics processing units (GPUs) have evolved into highly parallel and programmable systems. Specifically, several hyperspectral imaging algorithms have shown to be able to benefit from this hardware taking advantage of the extremely high floating-point processing performance, compact size, huge memory bandwidth, and relatively low cost of these units, which make them appealing for onboard data processing. In this paper, we propose a parallel implementation of an augmented Lagragian based method for unsupervised hyperspectral linear unmixing on GPUs using CUDA. The method called simplex identification via split augmented Lagrangian (SISAL) aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The efficient implementation of SISAL method presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory.

Behind video lectures in a MOOC

The year 2012 was the ���boom year��� in MOOC and all its outstanding growth until now, made us move forward in designing the first MOOC in our Institution (and the third in our country, Portugal). Most MOOC are video lectured based and the learning analytic process to these ones is just taking its first steps. Designing a video-lecture seems, at a first glance, very easy: one can just record a live lesson or lecture and turn it, directly, into a video-lecture (even here one may experience some ���sound��� and ���camera��� problems); but developing some engaging, appealing video-lecture, that motivates students to embrace knowledge and that really contributes to the teaching/learning process, it is not an easy task. Therefore questions like: ���What kind of information can induce knowledge construction, in a video-lecture?���, ���How can a professor interact in a video-lecture when he is not really there?���, ���What are the video-lectures attributes that contribute the most to viewer���s engagement?���, ���What seems to be the maximum ���time-resistance��� of a viewer?���, and many others, raised in our minds when designing video-lectures to a Mathematics MOOC from the scratch. We believe this technological resource can be a powerful tool to enhance students' learning process. Students that were born in digital/image era, respond and react slightly different to outside stimulus, than their teachers/professors ever did or do. In this article we will describe just how we have tried to overcome some of the difficulties and challenges we tackled when producing our own video-math-lectures and in what way, we feel, videos can contribute to the teaching and learning process at higher education level.

Vision based context categorization for all-terrain robot

Dissertation presented at the Faculdade de Ci��ncias e Tecnologia da Universidade Nova de Lisboa to obtain the Master degree in Electrical and Computer Engineering.

Decentralized Target Tracking based on Multi-Robot Cooperative Triangulation

Target tracking with bearing-only sensors is a challenging problem when the target moves dynamically in complex scenarios. Besides the partial observability of such sensors, they have limited field of views, occlusions can occur, etc. In those cases, cooperative approaches with multiple tracking robots are interesting, but the different sources of uncertain information need to be considered appropriately in order to achieve better estimates. Even though there exist probabilistic filters that can estimate the position of a target dealing with incertainties, bearing-only measurements bring usually additional problems with initialization and data association. In this paper, we propose a multi-robot triangulation method with a dynamic baseline that can triangulate bearing-only measurements in a probabilistic manner to produce 3D observations. This method is combined with a decentralized stochastic filter and used to tackle those initialization and data association issues. The approach is validated with simulations and field experiments where a team of aerial and ground robots with cameras track a dynamic target.

Gesture based interface for image annotation

Disserta����o apresentada para obten����o do Grau de Mestre em Engenharia Inform��tica pela Universidade Nova de Lisboa, Faculdade de Ci��ncias e Tecnologia

Automatic Small Bowel Tumor Diagnosis by Using Multi-Scale Wavelet-Based Analysis in Wireless Capsule Endoscopy Images

BACKGROUND: Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity. METHOD: The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis. RESULTS: The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice.