Atas do VI Encontro do CIED – I Encontro Internacional em estudos educacionais. avaliação: desafios e riscos

Esta publicação contém as comunicações apresentadas no VI Encontro do CIED – I Encontro Internacional em Estudos Educacionais. Avaliação: Desafios e Riscos, nos dias 15 e 16 de novembro de 2013, na Escola Superior de Educação de Lisboa, Instituto Politécnico de Lisboa, organizado pelo CIED, com o apoio da Escola Superior de Educação de Lisboa.

Coding mode decision algorithm for binary descriptor coding

In visual sensor networks, local feature descriptors can be computed at the sensing nodes, which work collaboratively on the data obtained to make an efficient visual analysis. In fact, with a minimal amount of computational effort, the detection and extraction of local features, such as binary descriptors, can provide a reliable and compact image representation. In this paper, it is proposed to extract and code binary descriptors to meet the energy and bandwidth constraints at each sensing node. The major contribution is a binary descriptor coding technique that exploits the correlation using two different coding modes: Intra, which exploits the correlation between the elements that compose a descriptor; and Inter, which exploits the correlation between descriptors of the same image. The experimental results show bitrate savings up to 35% without any impact in the performance efficiency of the image retrieval task. © 2014 EURASIP.

H.264/AVC backward compatible Bit-Depth scalable video coding

As high dynamic range video is gaining popularity, video coding solutions able to efficiently provide both low and high dynamic range video, notably with a single bitstream, are increasingly important. While simulcasting can provide both dynamic range videos at the cost of some compression efficiency penalty, bit-depth scalable video coding can provide a better trade-off between compression efficiency, adaptation flexibility and computational complexity. Considering the widespread use of H.264/AVC video, this paper proposes a H.264/AVC backward compatible bit-depth scalable video coding solution offering a low dynamic range base layer and two high dynamic range enhancement layers with different qualities, at low complexity. Experimental results show that the proposed solution has an acceptable rate-distortion performance penalty regarding the HDR H.264/AVC single-layer coding solution.

Perceptually driven video error protection using a distributed source coding approach

In video communication systems, the video signals are typically compressed and sent to the decoder through an error-prone transmission channel that may corrupt the compressed signal, causing the degradation of the final decoded video quality. In this context, it is possible to enhance the error resilience of typical predictive video coding schemes using as inspiration principles and tools from an alternative video coding approach, the so-called Distributed Video Coding (DVC), based on the Distributed Source Coding (DSC) theory. Further improvements in the decoded video quality after error-prone transmission may also be obtained by considering the perceptual relevance of the video content, as distortions occurring in different regions of a picture have a different impact on the user's final experience. In this context, this paper proposes a Perceptually Driven Error Protection (PDEP) video coding solution that enhances the error resilience of a state-of-the-art H.264/AVC predictive video codec using DSC principles and perceptual considerations. To increase the H.264/AVC error resilience performance, the main technical novelties brought by the proposed video coding solution are: (i) design of an improved compressed domain perceptual classification mechanism; (ii) design of an improved transcoding tool for the DSC-based protection mechanism; and (iii) integration of a perceptual classification mechanism in an H.264/AVC compliant codec with a DSC-based error protection mechanism. The performance results obtained show that the proposed PDEP video codec provides a better performing alternative to traditional error protection video coding schemes, notably Forward Error Correction (FEC)-based schemes. (C) 2013 Elsevier B.V. All rights reserved.

Tris(2-Ethylhexyl) trimellitate (TOTM) a potential reference fluid for high viscosity. Part I: viscosity measurements at temperatures from (303 to 373)K and pressures up to 65MPa, using a novel vibrating-wire instrument

No literature data above atmospheric pressure could be found for the viscosity of TOTIVI. As a consequence, the present viscosity results could only be compared upon extrapolation of the vibrating wire data to 0.1 MPa. Independent viscosity measurements were performed, at atmospheric pressure, using an Ubbelohde capillary in order to compare with the vibrating wire results, extrapolated by means of the above mentioned correlation. The two data sets agree within +/- 1%, which is commensurate with the mutual uncertainty of the experimental methods. Comparisons of the literature data obtained at atmospheric pressure with the present extrapolated vibrating-wire viscosity measurements have shown an agreement within +/- 2% for temperatures up to 339 K and within +/- 3.3% for temperatures up to 368 K. (C) 2014 Elsevier B.V. All rights reserved.

Paralisia do VI par craniano vs. Síndrome de Duane tipo 1

O VI par craniano, é responsável pela inervação do músculo recto externo. Uma lesão que afecta o VI par irá resultar numa limitação da abdução. A Síndrome de Duane, do tipo I, caracteriza-se por limitação da abdução unilateral, em que o olho afectado não passa da linha média, por retração do globo ocular e estreitamento da fenda palpebral, em adução. O diagnóstico diferencial é de extrema importância para se proceder a um prognóstico correcto e respectivo plano terapêutico. Objectivos: Esquematizar as diversas características motoras e sensoriais da paralisia do VI par craniano comparando-as com as da Síndrome de Duane Tipo I, designando os testes de ortóptica respectivos. Identificar os testes de diagnóstico diferencial entre as duas entidades e as suas características principais, nomeadamente os exames coordimétricos e o teste das ducções forçadas.

Machine learning Vasicek model calibration with Gaussian processes

In this article, we calibrate the Vasicek interest rate model under the risk neutral measure by learning the model parameters using Gaussian processes for machine learning regression. The calibration is done by maximizing the likelihood of zero coupon bond log prices, using mean and covariance functions computed analytically, as well as likelihood derivatives with respect to the parameters. The maximization method used is the conjugate gradients. The only prices needed for calibration are zero coupon bond prices and the parameters are directly obtained in the arbitrage free risk neutral measure.