Indian Institute of Technology Indore

About: Indian Institute of Technology Indore is a education organization based out in Indore, Madhya Pradesh, India. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 1606 authors who have published 4803 publications receiving 66500 citations.

A Space-based Observational Strategy for Characterizing the First Stars and Galaxies Using the Redshifted 21 cm Global Spectrum

Abstract: Author(s): Burns, JO; Bradley, R; Tauscher, K; Furlanetto, S; Mirocha, J; Monsalve, R; Rapetti, D; Purcell, W; Newell, D; Draper, D; Macdowall, R; Bowman, J; Nhan, B; Wollack, EJ; Fialkov, A; Jones, D; Kasper, JC; Loeb, A; Datta, A; Pritchard, J; Switzer, E; Bicay, M | Abstract: The redshifted 21 cm monopole is expected to be a powerful probe of the epoch of the first stars and galaxies (10 l z l 35). The global 21 cm signal is sensitive to the thermal and ionization state of hydrogen gas and thus provides a tracer of sources of energetic photons-primarily hot stars and accreting black holes-which ionize and heat the high redshift intergalactic medium (IGM). This paper presents a strategy for observations of the global spectrum with a realizable instrument placed in a low-altitude lunar orbit, performing night-time 40-120 MHz spectral observations, while on the farside to avoid terrestrial radio frequency interference, ionospheric corruption, and solar radio emissions. The frequency structure, uniformity over large scales, and unpolarized state of the redshifted 21 cm spectrum are distinct from the spectrally featureless, spatially varying, and polarized emission from the bright foregrounds. This allows a clean separation between the primordial signal and foregrounds. For signal extraction, we model the foreground, instrument, and 21 cm spectrum with eigenmodes calculated via Singular Value Decomposition analyses. Using a Markov Chain Monte Carlo algorithm to explore the parameter space defined by the coefficients associated with these modes, we illustrate how the spectrum can be measured and how astrophysical parameters (e.g., IGM properties, first star characteristics) can be constrained in the presence of foregrounds using the Dark Ages Radio Explorer (DARE).

Effect of growth temperature on structural, electrical and optical properties of dual ion beam sputtered ZnO thin films

Abstract: ZnO epitaxial thin films were grown on p-type Si(100) substrates by dual ion beam sputtering deposition system. The crystalline quality, surface morphology, optical and electrical properties of as-deposited ZnO thin films at different growth temperatures were studied. Substrate temperature was varied from 100 to 600 °C at constant oxygen percentage O2/(O2 + Ar) % of 66.67 % in a mixed gas of Ar and O2 with constant chamber pressure of 2.75 × 10−4 mBar. X-Ray diffraction analyses revealed that all the films had (002) preferred orientation. The minimum value of stress was reported to be −0.32 × 1010 dyne/cm2 from ZnO film grown at 200 °C. Photoluminescence measurements demonstrated sharp near-band-edge emission (NBE) was observed at ~375 nm along with deep level emission (DLE) in the visible spectral range at room temperature. The DLE Peak was found to have decrement as ZnO growth temperature was increased from 200 to 600 °C. The minimum FWHM of the NBE peak of 16.76 nm was achieved at 600 °C growth temperature. X-Ray photoelectron spectroscopy study revealed presence of oxygen interstitials and vacancies point defects in ZnO film grown at 400 °C. The ZnO thin film was found to be highly resistive when grown at 100 °C. The ZnO films were found to be n-type conducting with decreasing resistivity on increasing substrate temperature from 200 to 500 °C and again increased for film grown at 600 °C. Based on these studies a correlation between native point defects, optical and electrical properties has been established.

Automated Detection of Heart Valve Disorders From the PCG Signal Using Time-Frequency Magnitude and Phase Features

Abstract: In this letter, we propose a method for the automated detection of heart valve disorders namely, the aortic stenosis (AS), mitral stenosis (MS), and mitral regurgitation (MR) from the phonocardiogram (PCG) signal. The wavelet synchrosqueezing transform is used to obtain the time-frequency matrix from the segmented cycles of the PCG signal. From the time-frequency matrix, the magnitude and phase features are extracted. The random forest (RF) classifier is used for the classification. The results reveal that the proposed method has the average individual accuracy (IA) values of 98.83%, 97.66%, 91.16%, and 92.83% for normal, AS, MS, and MR classes.

Detection of Human Emotions Using Features Based on the Multiwavelet Transform of EEG Signals

Abstract: Emotion classification based on electroencephalogram (EEG) signals is a relatively new area of research in the development of brain computer interface (BCI) system with challenging issues like induction of the emotional states and the extraction of the features in order to obtain optimum classification of human emotions. The emotion classification system based on BCI can be useful in many areas like as entertainment, education, and health care. This chapter presents a new method for human emotion classification using multiwavelet transform of EEG signals. The EEG signal contains useful information related to the different emotional states, which helps us to understand the psychology and neurology of the human brain. The features namely, ratio of the norms based measure, Shannon entropy measure, and normalized Renyi entropy measure are computed from the sub-signals generated by multiwavelet decomposition of EEG signals. These features have been used as an input to multiclass least squares support vector machine (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for classification of human emotions from EEG signals. The classification performance of the proposed method for classification of emotions using EEG signals determined by computing the classification accuracy, ten-fold cross-validation, and confusion matrix. The proposed method has provided classification accuracy of 84.79 % for classification of human emotions namely happy, neutral, sadness, and fear from EEG signals with Morlet wavelet kernel function of MC-LS-SVM. The audio–video stimulus has been used for inducing the emotions in EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of human emotions from EEG signals.