Interfaces persuasivas e tangíveis

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática.

GPU implementation of the simplex identification via split augmented Lagrangian

Hyperspectral imaging can be used for object detection and for discriminating between different objects based on their spectral characteristics. One of the main problems of hyperspectral data analysis is the presence of mixed pixels, due to the low spatial resolution of such images. This means that several spectrally pure signatures (endmembers) are combined into the same mixed pixel. Linear spectral unmixing follows an unsupervised approach which aims at inferring pure spectral signatures and their material fractions at each pixel of the scene. The huge data volumes acquired by such sensors put stringent requirements on processing and unmixing methods. This paper proposes an efficient implementation of a unsupervised linear unmixing method on GPUs using CUDA. The method finds the smallest simplex by solving a sequence of nonsmooth convex subproblems using variable splitting to obtain a constraint formulation, and then applying an augmented Lagrangian technique. The parallel implementation of SISAL presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory. The results herein presented indicate that the GPU implementation can significantly accelerate the method's execution over big datasets while maintaining the methods accuracy.

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.

Parallel hyperspectral unmixing method via split augmented lagrangian on GPU

One of the main problems of hyperspectral data analysis is the presence of mixed pixels due to the low spatial resolution of such images. Linear spectral unmixing aims at inferring pure spectral signatures and their fractions at each pixel of the scene. The huge data volumes acquired by hyperspectral sensors put stringent requirements on processing and unmixing methods. This letter proposes an efficient implementation of the method called simplex identification via split augmented Lagrangian (SISAL) which exploits the graphics processing unit (GPU) architecture at low level using Compute Unified Device Architecture. 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 proposed implementation is performed in a pixel-by-pixel fashion using coalesced accesses to memory and exploiting shared memory to store temporary data. Furthermore, the kernels have been optimized to minimize the threads divergence, therefore achieving high GPU occupancy. The experimental results obtained for the simulated and real hyperspectral data sets reveal speedups up to 49 times, which demonstrates that the GPU implementation can significantly accelerate the method's execution over big data sets while maintaining the methods accuracy.

Interface multitoque para exploração de colecção de arte

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

Paradigmas de interfaces dos novos media: desafios para o design

Trabalho de Projecto de Mestrado em Ciências da Comunicação Variante Novos Media e Práticas Web

Time – intangible tangible –

Dissertação para obtenção do Grau de Mestre em Arte e Ciência do Vidro

Interfaces Cérebro-Computador Utilização do Emotiv EPOC para controlar software lúdico

Uma interface cérebro-computador (BCI) não é mais do que um dispositivo que lê e analisa ondas cerebrais e as converte em ações sobre um computador. Com a evolução das BCI e a possibilidade de acesso às mesmas por parte do público começou a ser possível o uso de BCIs para fins lúdicos. Nesse sentido nesta tese foi feito um estudo sobre interfaces cérebro-computador, o que são, que tipos de BCI existem, o seu uso para entretenimento, as suas limitações e o futuro deste tipo de interfaces. Foi ainda criado um software lúdico controlado por BCI (Emotiv EPOC) que é composto por um jogo tipo Pong e um reprodutor de música. O reprodutor de música através de BCI classifica e recomenda músicas ao utilizador. Com esta tese foi possível chegar à conclusão que é possível utilizar BCI para entretenimento (jogos e recomendação de conteúdos) apesar de se ter verificado que para jogos os dispositivos tradicionais de controlo (rato e teclado) ainda têm uma precisão muito superior.

Interfaces com o utilizador em sistemas de informação web

A crescente quantidade de informação com que se depara a sociedade tem vindo a fomentar a necessidade de auto-formação quer em meios empresariais, quer em instituições vocacionadas para a educação. Atcualmente, essa auto-formação recorre tendencialmente cada vez mais, a processos Multimédia e de Internet World Wide Web, visto estes permitirem a ausência do utilizador dos locas tradicionais de divulgação de informação. Por outro lado, as empresas e demais instituições poderão encontrar nestes recursos, uma forma privilegiada de redução de custos e de abrangência de utilizadores de segmentos geográficos e culturais longínquos.

Bridging present and future of brain-computer interfaces: an assessment of impacts

Based on the report for the “Project III” unit of the PhD programme on Technology Assessment under the supervision of Prof. António B. Moniz. This report was discussed also at the 2nd Winter School on Technology Assessment held at Universidade Nova de Lisboa, Caparica Campus, Portugal on December 2011.

Mobile persuasive interfaces for public ambient displays

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Informática

Estudo comparativo entre interfaces de programação de aplicações de mapas

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Gesture based persuasive interfaces for public ambient displays

Dissertação de Mestrado em Engenharia Informática 2º Semestre, 2011/2012

Impairment of tangible fixed assets in the Portuguese listed companies: Disclosures

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

The value relevance of tangible fixed assets

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics