Electrochemical response of 70Co-30Ni highly branched 3D-dendritic structures for charge storage electrodes

A 70Co-30Ni dendritic alloy was produced on stainless steel by pulse electrodeposition in the cathodic domain, and oxidized by potential cycling. X-ray diffraction (XRD) identified the presence of two phases and scanning electron microscopy (SEM) evidenced an open 3D highly branched dendritic morphology. After potential cycling in 1 M KOH, SEM and X-ray photoelectron spectroscopy (XPS) revealed, respectively, the presence of thin nanoplates, composed of Co and Ni oxi-hydroxides and hydroxides over the original dendritic film. Cyclic voltammetry tests showd the presence of redox peaks assigned to the oxidation and reduction of Ni and Co centres in the surface film. Charge/discharge measurements revealed capacity values of 121 mAh g(1) at 1 mA cm(2). The capacity retention under 8000 cycles was above 70%, stating the good reversibility of these redox materials and its suitability to be used as charge storage electrodes. Electrochemical impedance spectroscopy (EIS) spectra, taken under different applied bias, showed that the capacitance increased when the electrode was fully oxidized and decreased when the electrode was reduced, reflecting different states-of-charge of the electrode. (C) 2015 Elsevier Ltd. All rights reserved.

Enhanced PCA-based localization using depth maps with missing data

In this paper a new method for self-localization of mobile robots, based on a PCA positioning sensor to operate in unstructured environments, is proposed and experimentally validated. The proposed PCA extension is able to perform the eigenvectors computation from a set of signals corrupted by missing data. The sensor package considered in this work contains a 2D depth sensor pointed upwards to the ceiling, providing depth images with missing data. The positioning sensor obtained is then integrated in a Linear Parameter Varying mobile robot model to obtain a self-localization system, based on linear Kalman filters, with globally stable position error estimates. A study consisting in adding synthetic random corrupted data to the captured depth images revealed that this extended PCA technique is able to reconstruct the signals, with improved accuracy. The self-localization system obtained is assessed in unstructured environments and the methodologies are validated even in the case of varying illumination conditions.

Large pseudoscalar Yukawa couplings in the complex 2HDM

We start by presenting the current status of a complex flavour conserving two-Higgs doublet model. We will focus on some very interesting scenarios where unexpectedly the light Higgs couplings to leptons and to b-quarks can have a large pseudoscalar component with a vanishing scalar component. Predictions for the allowed parameter space at end of the next run with a total collected luminosity of 300 fb(-1) and 3000 fb(-1) are also discussed. These scenarios are not excluded by present data and most probably will survive the next LHC run. However, a measurement of the mixing angle phi(tau), between the scalar and pseudoscalar component of the 125 GeV Higgs, in the decay h -> tau(+)tau(-) will be able to probe many of these scenarios, even with low luminosity. Similarly, a measurement of phi(t) in the vertex (t) over bar th could help to constrain the low tan beta region in the Type I model.

On the applicability of joint inversion of gravity and resistivity data to the study of a tectonic sedimentary basin in Northern Portugal

The Chaves basin is a pull-apart tectonic depression implanted on granites, schists, and graywackes, and filled with a sedimentary sequence of variable thickness. It is a rather complex structure, as it includes an intricate network of faults and hydrogeological systems. The topography of the basement of the Chaves basin still remains unclear, as no drill hole has ever intersected the bottom of the sediments, and resistivity surveys suffer from severe equivalence issues resulting from the geological setting. In this work, a joint inversion approach of 1D resistivity and gravity data designed for layered environments is used to combine the consistent spatial distribution of the gravity data with the depth sensitivity of the resistivity data. A comparison between the results from the inversion of each data set individually and the results from the joint inversion show that although the joint inversion has more difficulty adjusting to the observed data, it provides more realistic and geologically meaningful models than the ones calculated by the inversion of each data set individually. This work provides a contribution for a better understanding of the Chaves basin, while using the opportunity to study further both the advantages and difficulties comprising the application of the method of joint inversion of gravity and resistivity data.

Building a person home behavior model based on data from smart house sensors 2015

Dissertação para a obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia

Confronting predictive texture zeros in lepton mass matrices with current data

Several popular Ansatze of lepton mass matrices that contain texture zeros are confronted with current neutrino observational data. We perform a systematic chi(2) analysis in a wide class of schemes, considering arbitrary Hermitian charged-lepton mass matrices and symmetric mass matrices for Majorana neutrinos or Hermitian mass matrices for Dirac neutrinos. Our study reveals that several patterns are still consistent with all the observations at the 68.27% confidence level, while some others are disfavored or excluded by the experimental data. The well-known Frampton-Glashow-Marfatia two-zero textures, hybrid textures, and parallel structures (among others) are considered.

Fly by data link: feasibility of a relative navigation solution for aviation relying on a future L-band data link

Trabalho final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações

Mobile device-to-device distributed computing using data sets

The rapidly increasing computing power, available storage and communication capabilities of mobile devices makes it possible to start processing and storing data locally, rather than offloading it to remote servers; allowing scenarios of mobile clouds without infrastructure dependency. We can now aim at connecting neighboring mobile devices, creating a local mobile cloud that provides storage and computing services on local generated data. In this paper, we describe an early overview of a distributed mobile system that allows accessing and processing of data distributed across mobile devices without an external communication infrastructure. Copyright © 2015 ICST.

Velocity estimation of moving ships using C-band SLC SAR data

A new algorithm for the velocity vector estimation of moving ships using Single Look Complex (SLC) SAR data in strip map acquisition mode is proposed. The algorithm exploits both amplitude and phase information of the Doppler decompressed data spectrum, with the aim to estimate both the azimuth antenna pattern and the backscattering coefficient as function of the look angle. The antenna pattern estimation provides information about the target velocity; the backscattering coefficient can be used for vessel classification. The range velocity is retrieved in the slow time frequency domain by estimating the antenna pattern effects induced by the target motion, while the azimuth velocity is calculated by the estimated range velocity and the ship orientation. Finally, the algorithm is tested on simulated SAR SLC data.

Modelação do comportamento dinâmico de sistemas barragem-fundação-albufeira: análise sísmica de uma barragem de abóbadas múltiplas

Trabalho Final de Mestrado elaborado no Laboratório Nacional de Engenharia Civil (LNEC) para a obtenção do grau de Mestre em Engenharia Civil pelo Instituto Superior de Engenharia de Lisboa no âmbito do protocolo de cooperação entre o ISEL e o LNEC

Two-loop stability of a complex singlet extended Standard Model

Motivated by the dark matter and the baryon asymmetry problems, we analyze a complex singlet extension of the Standard Model with a Z(2) symmetry (which provides a dark matter candidate). After a detailed two-loop calculation of the renormalization group equations for the new scalar sector, we study the radiative stability of the model up to a high energy scale (with the constraint that the 126 GeV Higgs boson found at the LHC is in the spectrum) and find it requires the existence of a new scalar state mixing with the Higgs with a mass larger than 140 GeV. This bound is not very sensitive to the cutoff scale as long as the latter is larger than 10(10) GeV. We then include all experimental and observational constraints/measurements from collider data, from dark matter direct detection experiments, and from the Planck satellite and in addition force stability at least up to the grand unified theory scale, to find that the lower bound is raised to about 170 GeV, while the dark matter particle must be heavier than about 50 GeV.

Exploiting the bin-class histograms for feature selection on discrete data

In machine learning and pattern recognition tasks, the use of feature discretization techniques may have several advantages. The discretized features may hold enough information for the learning task at hand, while ignoring minor fluctuations that are irrelevant or harmful for that task. The discretized features have more compact representations that may yield both better accuracy and lower training time, as compared to the use of the original features. However, in many cases, mainly with medium and high-dimensional data, the large number of features usually implies that there is some redundancy among them. Thus, we may further apply feature selection (FS) techniques on the discrete data, keeping the most relevant features, while discarding the irrelevant and redundant ones. In this paper, we propose relevance and redundancy criteria for supervised feature selection techniques on discrete data. These criteria are applied to the bin-class histograms of the discrete features. The experimental results, on public benchmark data, show that the proposed criteria can achieve better accuracy than widely used relevance and redundancy criteria, such as mutual information and the Fisher ratio.

Processing and characterization of 3D dense chitosan pieces, for orthopedic applications, by adding plasticizers

In this work, plasticizer agents were incorporated in a chitosan based formulation, as a strategy to improve the fragile structure of chitosan based-materials. Three different plasticizers: ethylene glycol, glycerol and sorbitol, were blended with chitosan to prepare 3D dense chitosan specimens. The properties of the obtained structures were assessed for mechanical, microstructural, physical and biocompatibility behavior. The results obtained revealed that from the different specimens prepared, the blend of chitosan with glycerol has superior mechanical properties and good biological behavior, making this chitosan based formulation a good candidate to improve robust chitosan structures for the construction of bioabsorbable orthopedic implants.

Fast unsupervised technique for extraction of endmembers spectra from hyperspectral data

One of the most challenging task underlying many hyperspectral imagery applications is the linear unmixing. The key to linear unmixing is to find the set of reference substances, also called endmembers, that are representative of a given scene. This paper presents the vertex component analysis (VCA) a new method to unmix linear mixtures of hyperspectral sources. The algorithm is unsupervised and exploits a simple geometric fact: endmembers are vertices of a simplex. The algorithm complexity, measured in floating points operations, is O (n), where n is the sample size. The effectiveness of the proposed scheme is illustrated using simulated data.

Estimation of signal subspace on hyperspectral data

Dimensionality reduction plays a crucial role in many hyperspectral data processing and analysis algorithms. This paper proposes a new mean squared error based approach to determine the signal subspace in hyperspectral imagery. The method first estimates the signal and noise correlations matrices, then it selects the subset of eigenvalues that best represents the signal subspace in the least square sense. The effectiveness of the proposed method is illustrated using simulated and real hyperspectral images.