Local spectral information in the near field with wavelet analysis and entropy

In near-field optical microscopy the resolution is strongly related to the experimental illumination conditions and to the separation between tip and sample. Therefore the spectral information in near-field data (related to the resolution in images) can be described only locally as a function of the tip-sample position. To make a local study of the spectral information in near-field data, we use wavelet decomposition that is associated with the calculation of entropy. We deduce the resolution from the characteristics of the wavelet, which leads to an automatic and numerical evaluation of the resolution in near-field data.     

Thickness effect on the properties of Mn-doped ZnO thin films synthesis by sol-gel and comparison to first-principles calculations

The current study investigates the effect of thickness on the structural, morphological, electronic, and optical properties of pure zinc oxide (ZnO) and 7% Mn-doped ZnO thin films, deposited by sol–gel spin coating method. All films exhibited a hexagonal wurtzite structure with a high preferential c-axis orientation. The surface morphology showed a good uniformity with cracks and wrinkles. The transmittance decreased with thickness. The bandgap energy was inversely varying with coating number. Photoluminescence spectra showed ultraviolet with strong and weak blue and weak green emission peaks. Density functional theory and Hubbard (DFT + U) method was then applied to study the structural, electronic, and optical properties of pure and 6.25% Mn-doped ZnO materials. A decrease in bandgap energy from pure to 6.25% Mn-doped ZnO material was shown using the DFT + U method. It also found that the Mn3d states were distributed far from Fermi level with a coexistence of both ionic and covalent nature bonds. A slight shift toward the lower energy was noticed for optical properties by Mn doping. The theoretical findings showed a similar behavior to those obtained by experiment.     

EEG efficient classification of imagined right and left hand movement using RBF kernel SVM and the joint CWT_PCA

Brain–machine interfaces are systems that allow the control of a device such as a robot arm through a person’s brain activity; such devices can be used by disabled persons to enhance their life and improve their independence. This paper is an extended version of a work that aims at discriminating between left and right imagined hand movements using a support vector machine (SVM) classifier to control a robot arm in order to help a person to find an object in the environment. The main focus here is to search for the best features that describe efficiently the electroencephalogram data during such imagined gestures by comparing two feature extraction methods, namely the continuous wavelet transform (CWT) and the empirical modal decomposition (EMD), combined with the principal component analysis (PCA) that were fed through a linear and radial basis function (RBF) kernel SVM classifier. The experimental results showed high performance achieving an average accuracy across all the subjects of 92.75% with an RBF kernel SVM classifier using CWT and PCA compared to 80.25% accuracy obtained with EMD and PCA. The proposed system has been implemented and tested using data collected from five male subjects and it enabled the control of the robot arm in the right and the left direction.