Recognition of Emotions using Energy Based Bimodal Information Fusion and Correlation

Multi-sensor information fusion is a rapidly developing research area which forms the backbone of numerous essential technologies such as intelligent robotic control, sensor networks, video and image processing and many more. In this paper, we have developed a novel technique to analyze and correlate human emotions expressed in voice tone & facial expression. Audio and video streams captured to populate audio and video bimodal data sets to sense the expressed emotions in voice tone and facial expression respectively. An energy based mapping is being done to overcome the inherent heterogeneity of the recorded bi-modal signal. The fusion process uses sampled and mapped energy signal of both modalities’s data stream and further recognize the overall emotional component using Support Vector Machine (SVM) classifier with the accuracy 93.06%

Magnetism of Ta Dichalcogenide Monolayers Tuned by Strain and Hydrogenation

The effects of strain and hydrogenation on the electronic and magnetic properties of monolayers of Ta based dichalcogenides (TaX2; X = S, Se, Te) are investigated using density-functional theo-ry. We predict a complex scenario of strain-dependent magnetic phase transitions involving par-amagnetic, ferromagnetic, and modulated antiferromagnetic states. Covering one of the two chalcogenide surfaces with hydrogen switches the antiferromagnetic/nonmagnetic TaX2 mono-layers to a semiconductor. Our research opens new pathways towards the manipulation of mag-netic properties for future optoelectronics and spintronics applications.Comment: 13 pages, 5 figure

Using LSTM for the Prediction of Disruption in ADITYA Tokamak

Major disruptions in tokamak pose a serious threat to the vessel and its surrounding pieces of equipment. The ability of the systems to detect any behavior that can lead to disruption can help in alerting the system beforehand and prevent its harmful effects. Many machine learning techniques have already been in use at large tokamaks like JET and ASDEX, but are not suitable for ADITYA, which is comparatively small. Through this work, we discuss a new real-time approach to predict the time of disruption in ADITYA tokamak and validate the results on an experimental dataset. The system uses selected diagnostics from the tokamak and after some pre-processing steps, sends them to a time-sequence Long Short-Term Memory (LSTM) network. The model can make the predictions 12 ms in advance at less computation cost that is quick enough to be deployed in real-time applications.Comment: 7 pages, 4 figure

Chemoenzymatic Synthesis of D-Glucitol-Based Non-Ionic Amphiphilic Architectures as Nanocarriers

Newer non-ionic amphiphiles have been synthesized using biocompatible materials and by following a greener approach i.e., D-glucitol has been used as a template, and hydrophobic and hydrophilic segments were incorporated on it by using click chemistry. The hydrophilic segments in turn were prepared from glycerol using an immobilized Candida antarctica lipase (Novozym-435)-mediated chemoenzymatic approach. Surface tension measurements and dynamic light scattering studies reflect the self-assembling behavior of the synthesized amphiphilic architectures in the aqueous medium. The results from UV-Vis and fluorescence spectroscopy establish the encapsulation of guests in the hydrophobic core of self-assembled amphiphilic architectures. The results of 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) assay indicate that the amphiphiles are well tolerated by the used A549 cell lines at all tested concentrations

Interface-Induced Spin Polarization in Graphene on Chromia

The induced spin polarization of graphene on Cr2O3 (001) is investigated using density-functional theory (DFT) and model calculations. The magnetic moment in graphene is a proximity effect and can be regarded as a second-order Stoner scenario, and similar mechanisms are likely realized for all graphene systems with an insulating magnetic substrate. In the absence of charge transfer, the magnetic moment would be quadratic in the exchange field, as contrasted to the usually encountered approximately linear dependence. The net magnetization of the graphene is small, of the order of 0.01 μB per atom, but the energy-dependent spin polarization exhibits pronounced peaks that have a disproportionally strong effect on the spin-polarized electron transport and are therefore important for spin-electronics applications

Magnetic and electron transport properties of Co\u3csub\u3e2\u3c/sub\u3eSi nanomagnets

Magnetotransport and ferromagnetism in thin films of Co2Si nanoclusters are investigated experimentally and theoretically. The nanoclusters are fabricated by an inert-gas condensation-type cluster-deposition method and have an average size of 11.3 nm. Unlike the bulk Co2Si that exhibits a very weak net magnetic moment only below 10 K, the nanoclusters exhibit room-temperature ferromagnetism with a substantial saturation magnetization. Key features of the system are its closeness to the Stoner transition, magnetic moments induced by spin polarization starting from surface atoms, and nonuniaxial anisotropy associated with the orthorhombic crystal structure of Co2Si. A method is introduced to determine the effective anisotropy using the experimental magnetization data of this complex system and its relationship with the two lowest-order nonuniaxial anisotropy constants. On decreasing temperature from 300 K, the nanoclusters show electron-transport properties unusual for a ferromagnetic metal, including an increase of Hall resistivity and a nonmonotonic change of negative magnetoresistance with a peak at around 100 K. The underlying physics is explained on the basis of the large polarization of surface spins and variation in the degree of their misalignments due to temperature-dependent effective anisotropy