Estimation of muscular forces from SSA smoothed sEMG signals calibrated by inverse dynamics-based physiological static optimization

The estimation of muscular forces is useful in several areas such as biomedical or rehabilitation engineering. As muscular forces cannot be measured in vivo non-invasively they must be estimated by using indirect measurements such as surface electromyography (sEMG) signals or by means of inverse dynamic (ID) analyses. This paper proposes an approach to estimate muscular forces based on both of them. The main idea is to tune a gain matrix so as to compute muscular forces from sEMG signals. To do so, a curve fitting process based on least-squares is carried out. The input is the sEMG signal filtered using singular spectrum analysis technique. The output corresponds to the muscular force estimated by the ID analysis of the recorded task, a dumbbell weightlifting. Once the model parameters are tuned, it is possible to obtain an estimation of muscular forces based on sEMG signal. This procedure might be used to predict muscular forces in vivo outside the space limitations of the gait analysis laboratory.Postprint (published version

Influence of the strong metal support interaction effect (SMSI) of Pt/TiO2 and Pd/TiO2 systems in the photocatalytic biohydrogen production from glucose solution

Two different catalysts consisting of Pt/TiO2 and Pd/TiO 2 were submitted to diverse oxidative and reductive calcination treatments and tested for photocatalytic reforming of glucose water solution (as a model of biomass component) in H2 production. Oxidation and reduction at 850°C resulted in better photocatalysts for hydrogen production than Degussa P-25 and the ones prepared at 500°C, despite the fact that the former consisted in very low surface area (6-8 m2/g) rutile titania specimens. The platinum-containing systems prepared at 850°C give the most effective catalysts. XPS characterization of the systems showed that thermal treatment at 850°C resulted in electron transfer from titania to metal particles through the so-called strong metal-support interaction (SMSI) effect. Furthermore, the greater the SMSI effect, the better the catalytic performance. Improvement in photocatalytic behavior is explained in terms of avoidance of electron-hole recombination through the electron transfer from titania to metal particles

Gradual transition from insulator to semimetal of Ca1−x_{1-x}Eux_{x}B6_{6} with increasing Eu concentration

The local environment of Eu$^{2+}$ ($4f^{7}$, $S=7/2$) in Ca$_{1-x}$Eu$_{x}$B$_{6}$ ($0.003\leqslant x\leqslant 1.00$) is investigated by means of electron spin resonance (ESR). For $x\lesssim 0.003$ the spectra show resolved \textit{fine} and \textit{hyperfine} structures due to the cubic crystal \textit{electric} field and nuclear \textit{hyperfine} field, respectively. The resonances have Lorentzian line shape, indicating an \textit{insulating} environment for the Eu$^{2+}$ ions. For $0.003\lesssim x\lesssim 0.07$, as $x$ increases, the ESR lines broaden due to local distortions caused by the Eu/Ca ions substitution. For $0.07\lesssim x\lesssim 0.30$, the lines broaden further and the spectra gradually change from Lorentzian to Dysonian resonances, suggesting a coexistence of both \textit{insulating} and \textit{metallic} environments for the Eu$^{2+}$ ions. In contrast to Ca$_{1-x}$Gd$_{x}$B$_{6}$, the \textit{fine} structure is still observable up to $x\approx 0.15$. For $x\gtrsim 0.30$ the \textit{fine} and \textit{hyperfine} structures are no longer observed, the line width increases, and the line shape is purely Dysonian anticipating the \textit{semimetallic} character of EuB$_{6}$. This broadening is attributed to a spin-flip scattering relaxation process due to the exchange interaction between conduction and Eu$^{2+}$ $4f$ electrons. High field ESR measurements for $x\gtrsim 0.15$ reveal smaller and anisotropic line widths, which are attributed to magnetic polarons and Fermi surface effects, respectively.Comment: Submitted to PR

Quantum Oscillations in EuFe2As2 single crystals

Quantum oscillation measurements can provide important information about the Fermi surface (FS) properties of strongly correlated metals. Here, we report a Shubnikov-de Haas (SdH) effect study on the pnictide parent compounds EuFe$_{2}$As$_{2}$ (Eu122) and BaFe$_{2}$As$_{2}$ (Ba122) grown by In-flux. Although both members are isovalent compounds with approximately the same density of states at the Fermi level, our results reveal subtle changes in their fermiology. Eu122 displays a complex pattern in the Fourier spectrum, with band splitting, magnetic breakdown orbits, and effective masses sistematically larger when compared to Ba122, indicating that the former is a more correlated metal. Moreover, the observed pockets in Eu122 are more isotropic and 3D-like, suggesting an equal contribution from the Fe $3d$ orbitals to the FS. We speculate that these FS changes may be responsible for the higher spin-density wave ordering temperature in Eu122.Comment: 5 pages, 4 figure

One Deep Music Representation to Rule Them All? : A comparative analysis of different representation learning strategies

Inspired by the success of deploying deep learning in the fields of Computer Vision and Natural Language Processing, this learning paradigm has also found its way into the field of Music Information Retrieval. In order to benefit from deep learning in an effective, but also efficient manner, deep transfer learning has become a common approach. In this approach, it is possible to reuse the output of a pre-trained neural network as the basis for a new learning task. The underlying hypothesis is that if the initial and new learning tasks show commonalities and are applied to the same type of input data (e.g. music audio), the generated deep representation of the data is also informative for the new task. Since, however, most of the networks used to generate deep representations are trained using a single initial learning source, their representation is unlikely to be informative for all possible future tasks. In this paper, we present the results of our investigation of what are the most important factors to generate deep representations for the data and learning tasks in the music domain. We conducted this investigation via an extensive empirical study that involves multiple learning sources, as well as multiple deep learning architectures with varying levels of information sharing between sources, in order to learn music representations. We then validate these representations considering multiple target datasets for evaluation. The results of our experiments yield several insights on how to approach the design of methods for learning widely deployable deep data representations in the music domain.Comment: This work has been accepted to "Neural Computing and Applications: Special Issue on Deep Learning for Music and Audio