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: Fading & Support vector machine. The organization has 1606 authors who have published 4803 publications receiving 66500 citations.

Selective and Recyclable Congo Red Dye Adsorption by Spherical Fe 3 O 4 Nanoparticles Functionalized with 1,2,4,5-Benzenetetracarboxylic Acid

Abstract: In this study, the new material Fe3O4@BTCA has been synthesized by immobilization of 1,2,4,5-Benzenetetracarboxylic acid (BTCA) on the surface of Fe3O4 NPs, obtained by co-precipitation of FeCl3.6H2O and FeCl2.4H2O in the basic conditions. Characterization by P-XRD, FE-SEM, and TEM confirm Fe3O4 has a spherical crystalline structure with an average diameter of 15 nm, which after functionalization with BTCA, increases to 20 nm. Functionalization also enhances the surface area and surface charge of the material, confirmed by BET and zeta potential analyses, respectively. The dye adsorption capacity of Fe3O4@BTCA has been investigated for three common dyes; Congo red (C.R), Methylene blue (M.B), and Crystal violet (C.V). The adsorption studies show that the material rapidly and selectively adsorbs C.R dye with very high adsorption capacity (630 mg/g), which is attributed to strong H-bonding ability of BTCA with C.R dye as indicated by adsorption mechanism study. The material also shows excellent recyclability without any considerable loss of adsorption capacity. Adsorption isotherm and kinetic studies suggest that the adsorption occurs by the Langmuir adsorption model following pseudo-second-order adsorption kinetics.

Multi-Stimuli Responsive Donor–Acceptor Tetraphenylethylene Substituted Benzothiadiazoles

Abstract: In order to understand how the donor (D)/acceptor (A) substituents and their substitution pattern affect the solution and solid-state optical properties, a series of symmetrical and unsymmetrical TPE substituted BTDs 3–8 were designed and synthesized by the Suzuki and Stille coupling reactions. Their solvatochromic, aggregation induced emission (AIE), mechanochromic, and vapochromic properties were studied and compared. The single-crystal X-ray structures of BTDs 5 and 6 are reported. The BTDs 3–8 are highly fluorescent with the tunable emissions. The solvent dependent emission was observed in BTDs 3–8 and their Lippert–Mataga plots show a linear correlation of the Stokes shift with solvent polarity. The emission study in different tetrahydrofuran (THF):water percentages show enhanced emission in aggregates. The BTDs exhibit a reversible multi-stimuli response toward mechanical force, solvent, and heat. The detailed study using single-crystal X-ray, photophysical properties, powder X-ray diffraction, scan...

Instantaneous voiced/non-voiced detection in speech signals based on variational mode decomposition

Abstract: In this paper, a variational mode decomposition (VMD) based method has been proposed for the instantaneous detection of voiced/non-voiced (V/NV) regions in the speech signals. In the proposed method, the VMD is applied in iterative way with specific input parameters. Firstly, the VMD decomposes the speech signal into two components, then, the VMD is applied successively on one of these two components based on suitably defined convergence criteria. It has been shown that the VMD applied in iterative way behaves as a low-pass filter and after convergence it provides separation of the fundamental frequency (F0) component from the speech signal. The envelope of the F0 component of the speech signal has been obtained using an analytical model based on single degree of freedom (SDOF). Automatic threshold has been computed from the obtained envelope in order to detect the V/NV regions in speech signals. The proposed method has been studied on speech signals and the corresponding electroglottograph (EGG) signals from the CMU-Arctic database in different noise conditions obtained from the NOISEX-92 database. Experimental results at various signal to noise ratios (SNRs) are included in order to show the effectiveness of the proposed method compared to the other existing methods for V/NV detection in speech signals.

An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism

Abstract: Graphical abstractDisplay Omitted HighlightsWe propose a new method for diagnosis of alcoholism using TQWT.New feature set based on correntropy derived from TQWT have been proposed.The effects of Q on classification performance have been evaluated.A novel Alcoholism Risk Index (ARI) is developed using 3 clinically significant features.Performance has been compared with existing methods. Alcoholism affects the structure and functioning of brain. Electroencephalogram (EEG) signals can depict the state of brain. The EEG signals are ensemble of various neuronal activity recorded from different scalp regions having different characteristics and very low magnitude in microvolts. These factors make human interpretation difficult and time consuming to analyze these signals. Moreover, these highly varying EEG signals are susceptible to inter/intra variability errors. So, a Computer-Aided Diagnosis (CAD) can be used to identify the alcoholic and normal subjects accurately. However, these EEG signals exhibit nonlinear and non-stationary properties. Therefore, it needs much effort in deciphering the diagnostic evidence from them using linear time and frequency-domain methods. The nonlinear parameters together with time-frequency/scale domain methods can help to detect tiny changes in these signals. The correntropy is nonlinear indicator which characterizes the dynamic behavior of EEG signals in time-scale domain. In this paper, we present a new way for diagnosis of alcoholism using Tunable-Q Wavelet Transform (TQWT) based features derived from EEG signals. The feature extraction is performed using TQWT based decomposition and extracted Centered Correntropy (CC) from the forth decomposed detail sub-band. The Principal Component Analysis (PCA) is used for feature reduction followed by Least Squares-Support Vector Machine (LS-SVM) for classifying normal and alcoholic EEG signals. In order to make sure reliable classification performance, 10-fold cross-validation scheme is adopted. Our proposed system is able to diagnose the alcoholic and normal EEG signals, with an average accuracy of 97.02%, sensitivity of 96.53%, specificity of 97.50% and Matthews correlation coefficient of 0.9494 for Q-factor (Q) varying between 3 and 8 using Radial Basis Function (RBF) kernel function. Also, we have established a novel Alcoholism Risk Index (ARI) using three clinically significant features to discriminate the given classes by means of a single number. This system can be used for automated diagnosis and monitoring of alcoholic subjects to evaluate the effect of treatment.