Incorporating of layered double hydroxide/sepiolite to improve the performance of sulfonated poly(ether ether ketone) composite membranes for proton exchange membrane fuel cells

Sulfonated poly(ether ether ketone) (SPEEK) with a low sulfonation degree was blended using nanofiller layered double hydroxide (LDH, Mg₂AlCl)/sepiolite nanostructured material as additive to use as an electrolyte membrane for fuel cell application. Characterization assessments, that is, mechanical stability, thermal gravimetric analysis, ion exchange capability, swelling properties, water uptake capacities, electrochemical impedance spectroscopy analysis, and Fourier transform infrared spectroscopy of the composite membranes were conducted. The presence of LDH/sepiolite nanoarchitectured material within SPEEK was found to have the highest water retention and proton conductivity value at high temperature rather than LDH/SPEEK and pristine SPEEK membranes.     

Gait-based person re-identification under covariate factors

Pattern Analysis and Applications - Gait is recognized as an effective behavioral biometric trait. Gait pattern information can be captured and perceived from a distance thanks to its noninvasive...     

Frequency-Domain Block Turbo-Equalization for Single-Carrier Transmission Over MIMO Broadband Wireless Channel

This paper presents a new class of block turbo-equalizers for single-carrier transmission over multiple-input multiple-output (MIMO) broadband wireless channels. The key underlying idea consists in equalizing (nonoverlapping) groups of symbols and detecting their individual space-time components in a disjoint and iterative fashion. This functional split naturally induces new design options that have been accurately listed and described, i.e., choice of distinct criteria for intergroup interference (IGI) equalization and intragroup components detection, yielding hybrid structures, multiple iterative loops, and related scheduling variants. Selected algorithms in the proposed class are compared in terms of performance under various transmission scenarios. For all of them, minimum mean square error IGI equalization certainly occupies a central role (at least for the first iteration) and may be identified as the computational bottleneck. Fortunately, block spread transmission together with cyclic prefix operations make the channel matrix block circulant, thus allowing low-complexity inversion in the Fourier domain     

Utility-Based Maintenance Optimization for Complex Water-Distribution Systems Using Bayesian Networks

Water supply systems (WSS), as well as other real-world systems, are characterized by complex configurations. For these systems, it is essential to ensure appropriate utility through optimal maintenance planning. The difficulties in decision-making are much increased by lack of information regarding the operation and failure conditions. When maintenance optimization is considered for systems configured as networks, comprising a large number of components, the main challenge is to model the reliability characteristics, such as availability, taking account of the interactions and dependencies between different components. The aim of this paper is to provide an optimal Preventive Maintenance (PM) plan with a view to maximizing the utility of a complex repairable system using Bayesian Networks (BNs). For each node of the BN, the optimal PM periodicity is obtained, in accordance with the policy of periodic imperfect PM with minimal repair at failure. The system availability is then computed, by Bayesian inference, for various combinations of nodes, or subsystems, periodicities and partial renewals before the complete renewal of the whole system. A utility function is then introduced to provide the maintenance plan for the system, leading to the implementation of the best policy. The methodology is illustrated by numerical application on WSS.     

Microstructural and Optical Properties of Porous Alumina Elaborated on Glass Substrate

A transparent porous anodized aluminum oxide (AAO) nanostructure was formed on a glass substrate using the anodization of a highly pure evaporated aluminum layer. A parametric study was carried out in order to achieve a fine control of the microstructural and optical properties of the elaborated films. The microstructural and surface morphologies of the porous alumina films were characterized by x-ray diffraction and atomic force microscopy. Pore diameter, inter-pore separation, and the porous structure as a function of anodization conditions were investigated. It was then found that the pores density decreases with increasing the anodization time. Regular cylindrical porous AAO films with a flat bottom structure were formed by chemical etching and anodization. A high transmittance in the 300-900 nm range is reported, indicating a fulfilled growth of the transparent sample (alumina) from the aluminum metal. The data showed typical interference oscillations as a result of the transparent characteristics of the film throughout the visible spectral range. The thickness and the optical constants (n and k) of the porous anodic alumina films, as a function of anodizing time, were obtained using spectroscopic ellipsometry in the ultraviolet-visible-near infrared (UV-vis-NIR) regions.     

Elaboration and characterization of nanocrystalline TiO2 thin films prepared by sol–gel dip-coating

Nanocrystalline TiO₂ thin films were deposited on a ITO coated glass substrate by sol–gel dip coating technique, the layers undergo a heat treatment at temperatures varying from 300 to 450 °C. The structural, morphological and optical characterizations of the as deposited and annealed films were carried out using X-ray diffraction (XRD), Raman spectroscopy, Atomic Force Microscopy (AFM), visible, (Fourier-Transform) infrared and ultraviolet spectroscopy, Fluorescence and spectroscopic ellipsometry. The results indicate that an anatase phase structure TiO₂ thin film with nanocrystallite size of about 15 nm can be obtained at the heat treatment temperature of 350 °C or above, that is to say, at the heat treatment temperature below 300 °C, the thin films grow in amorphous phase; while the heat treatment temperature is increased up to 400 °C or above, the thin film develops a crystalline phase corresponding to the titanium oxide anatase phase. We have accurately determined the layer thickness, refractive index and extinction coefficient of the TiO₂ thin films by the ellipsometric analysis. The optical gap decreases from 3.9 to 3.5 eV when the annealing temperature increases. Photocatalytic activity of the TiO₂ films was studied by monitoring the degradation of aqueous methylene blue under UV light irradiation and was observed that films annealed above 350 °C had good photocatalytic activity which is explained as due to the structural and morphological properties of the films.     

Enhancement of photoelectrochemical performance of CdSe sensitized seeded TiO<Subscript>2</Subscript> films

In this work, we highlight the effect of TiO₂ seed layer (SL) on the photoelectrochemical performances of CdSe/TiO₂ photoanodes (PAs). TiO₂ thin films were prepared by spin coating starting from a sol gel solution containing TiO₂ nanopowder, then sensitized with electrodeposited CdSe nanoparticles. Structural, optical and photoelectrochemical properties of the CdSe/TiO₂ PAs with and without the SL were investigated. Charge accumulation processes and charge transfer characteristics were identified by electrochemical impedance spectroscopy. The introduction of the compact TiO₂ SL was found to significantly increase the electron transport. The photocurrent density produced by the CdSe/TiO₂/SL PA reached 0.95 mA/cm², about two times higher than that performed by the CdSe/TiO₂ PAs. This enhancement might be attributed to a substantial decrease of the leakage current induced by a better crystallization of TiO₂ thin films as well as a higher sensitizing effect of the CdSe nanoparticles.     

A Hybrid Training Algorithm for Feedforward Neural Networks

Feedforward neural networks (FNN) have been proposed to solve complex problems in pattern recognition, classification and function approximation. Despite the general success of learning methods for FNN, such as the backpropagation (BP) algorithm, second-order algorithms, long learning time for convergence remains a problem to be overcome. In this paper, we propose a new hybrid algorithm for a FNN that combines unsupervised training for the hidden neurons (Kohonen algorithm) and supervised training for the output neurons (gradient descent method). Simulation results show the effectiveness of the proposed algorithm compared with other well-known learning methods.     

Visual servoing based on efficient histogram information

The robustness of a visual servoing task depends mainly on the efficiency of visual selections captured from a sensor at each robot’s position. A task function could be described as a regulation of the values sent via the control law to the camera velocities. In this paper we propose a new approach that does not depend on matching and tracking results. Thus, we replaced the classical minimization cost by a new function based on probability distributions and Bhattacharyya distance. To guarantee more robustness, the information related to the observed images was expressed using a combination of orientation selections. The new visual selections are computed by referring to the disposition of Histograms of Oriented Gradients (HOG) bins. For each bin we assign a random variable representing gradient vectors in a particular direction. The new entries will not be used to establish equations of visual motion but they will be directly inserted into the control loop. A new formulation of the interaction matrix has been presented according to the optical flow constraint and using an interpolation function which leads to a more efficient control behaviour and to more positioning accuracy. Experiments demonstrate the robustness of the proposed approach with respect to varying work space conditions.