Theoretical Analysis of Multimodal Four-Wave Mixing in Optical Microwires

Optical fiber microwires (OFMs) are nonlinear optical waveguides that support several spatial modes. The multimodal generalized nonlinear Schrodinger equation (MM-GNLSE) is deduced taking into account the linear and nonlinear modal coupling. A detailed theoretical description of four-wave mixing (FWM) considering the modal coupling is developed. Both, the intramode and the intermode phase-matching conditions is calculated for an optical microwire in a strong guiding regime. Finally, the FWM dynamics is studied and the amplitude evolution of the pump beams, the signal and the idler are analyzed.

Classification and staging of chronic liver disease from multimodal data

Chronic liver disease (CLD) is most of the time an asymptomatic, progressive, and ultimately potentially fatal disease. In this study, an automatic hierarchical procedure to stage CLD using ultrasound images, laboratory tests, and clinical records are described. The first stage of the proposed method, called clinical based classifier (CBC), discriminates healthy from pathologic conditions. When nonhealthy conditions are detected, the method refines the results in three exclusive pathologies in a hierarchical basis: 1) chronic hepatitis; 2) compensated cirrhosis; and 3) decompensated cirrhosis. The features used as well as the classifiers (Bayes, Parzen, support vector machine, and k-nearest neighbor) are optimally selected for each stage. A large multimodal feature database was specifically built for this study containing 30 chronic hepatitis cases, 34 compensated cirrhosis cases, and 36 decompensated cirrhosis cases, all validated after histopathologic analysis by liver biopsy. The CBC classification scheme outperformed the nonhierachical one against all scheme, achieving an overall accuracy of 98.67% for the normal detector, 87.45% for the chronic hepatitis detector, and 95.71% for the cirrhosis detector.

Enhancing Multimodal Silent Speech Interfaces With Feature Selection

In research on Silent Speech Interfaces (SSI), different sources of information (modalities) have been combined, aiming at obtaining better performance than the individual modalities. However, when combining these modalities, the dimensionality of the feature space rapidly increases, yielding the well-known "curse of dimensionality". As a consequence, in order to extract useful information from this data, one has to resort to feature selection (FS) techniques to lower the dimensionality of the learning space. In this paper, we assess the impact of FS techniques for silent speech data, in a dataset with 4 non-invasive and promising modalities, namely: video, depth, ultrasonic Doppler sensing, and surface electromyography. We consider two supervised (mutual information and Fisher's ratio) and two unsupervised (meanmedian and arithmetic mean geometric mean) FS filters. The evaluation was made by assessing the classification accuracy (word recognition error) of three well-known classifiers (knearest neighbors, support vector machines, and dynamic time warping). The key results of this study show that both unsupervised and supervised FS techniques improve on the classification accuracy on both individual and combined modalities. For instance, on the video component, we attain relative performance gains of 36.2% in error rates. FS is also useful as pre-processing for feature fusion. Copyright © 2014 ISCA.

C3G - A portuguese collaboration for geology, geodesy and geophysics

Portugal joined the effort to create the EPOS infrastructure in 2008, and it became immediately apparent that a national network of Earth Sciences infrastructures was required to participate in the initiative. At that time, FCT was promoting the creation of a national infrastructure called RNG - Rede Nacional de Geofísica (National Geophysics Network). A memorandum of understanding had been agreed upon, and it seemed therefore straightforward to use RNG (enlarged to include relevant participants that were not RNG members) as the Portuguese partner to EPOS-PP. However, at the time of signature of the EPOS-PP contract with the European Commission (November 2010), RNG had not gained formal identity yet, and IST (one of the participants) signed the grant agreement on behalf of the Portuguese consortium. During 2011 no progress was made towards the formal creation of RNG, and the composition of the network – based on proposals submitted to a call issued in 2002 – had by then become obsolete. On February 2012, the EPOS national contact point was mandated by the representatives of the participating national infrastructures to request from FCT the recognition of a new consortium - C3G, Collaboratory for Geology, Geodesy and Geophysics - as the Portuguese partner to EPOS-PP. This request was supported by formal letters from the following institutions: ‐ LNEG. Laboratório Nacional de Energia e Geologia (National Geological Survey); ‐ IGP ‐ Instituto Geográfico Português (National Geographic Institute); ‐ IDL, Instituto Dom Luiz – Laboratório Associado ‐ CGE, Centro de Geofísica de Évora; ‐ FCTUC, Faculdade de Ciências e Tecnologia da Universidade de Coimbra; ‐ Instituto Superior de Engenharia de Lisboa; ‐ Instituto Superior Técnico; ‐ Universidade da Beira Interior. While Instituto de Meteorologia (Meteorological Institute, in charge of the national seismographic network) actively supports the national participation in EPOS, a letter of support was not feasible in view of the organic changes underway at the time. C3G aims at the integration and coordination, at national level, of existing Earth Sciences infrastructures, namely: ‐ seismic and geodetic networks (IM, IST, IDL, CGE); ‐ rock physics laboratories (ISEL); ‐ geophysical laboratories dedicated to natural resources and environmental studies; ‐ geological and geophysical data repositories; ‐ facilities for data storage and computing resources. The C3G - Collaboratory for Geology, Geodesy and Geophysics will be coordinated by Universidade da Beira Interior, whose Department of Informatics will host the C3G infrastructure.