Anil Hazarika

Influence of CuO morphology on the enhanced catalytic degradation of methylene blue and methyl orange

In this work, CuO nanostructures of different morphologies were synthesized via a precursor mediated, two step hydrothermal method. The as obtained CuO nanostructures were systematically characterized by X-ray powder diffraction, Fourier transform infra-red spectroscopy, scanning electron microscopy, transmission electron microscopy, micro-Raman spectroscopy, Brunauer–Emmett–Teller N2 adsorption–desorption analysis and UV-visible spectroscopy. The results reveal that the CuO nanostructures are monoclinic, with definite morphologies, and they were employed for the catalytic degradation of cationic and anionic dyes in the presence of H2O2. The CuO nanostructures exhibit different efficiencies and rates towards the degradation reactions by the changing of the morphologies. Moreover, the effects of catalyst dosage and reaction temperature were also addressed in dye degradation reactions. The results imply that the increasing temperature led to higher reaction rates and efficacy. The kinetic data reveals that the process of degradation of the dyes was modeled by the pseudo-first-order kinetics involving adsorption and redox reaction. All the thermodynamic parameters, including activation energy (Ea), enthalpy (ΔH#), entropy (ΔS#) and free energy (ΔG#) of activation were calculated for the oxidation reaction. The generation of reactive ·OH from the homolytic cleavage of H2O2 was activated by the Cu2+ ions. The nanostructures also show good recyclability and high stability, up to the fifth cycle of the degradation reaction. Further, the degradation of dyes was confirmed by the FTIR analysis.

Rule based fuzzy approach for peripheral motor neuropathy (PMN) diagnosis based on NCS data

The development of artificial intelligence methodology (AIM) led to development of computer assist diagnosis systems which are based on expert medical knowledge. Medical diagnosis is a complex system as well as subjective in nature and needs expert person for interpretation of medical information. Moreover, abundance of data in database is often beyond human cognition and comprehension. It is widely pointed that the conventional diagnosis cannot sufficiently handle imprecise and vague knowledge for some real world applications, but expert system such as fuzzy model can effectively resolve/interpretate data and knowledge problems with uncertainty. This paper presents a novel fuzzy expert system (FES) for neuropathy decision support application. In this study group, 120 neuropathy patients, 4 nerves and 5 variables of each nerve were considered for analysis. 26 rules were evaluated based on medical knowledge. After the system is completely constructed, new data were encoded to linguistic variables and tested to predict the model performance. The simulation results have shown that the proposed FES can be used for medical data analysis effectively. The comparison results show that the linguistic rules extracted are competitive with or even superior to some well-known medical methods. Results are presented showing the effectiveness of the method for supporting differential diagnosis.

Two-fold feature extraction technique for biomedical signals classification

This presents a two-fold feature extraction technique for biomedical signals classification. In this work, signals are uniformly decomposed to form a set of uniform matrices. Then using Canonical Correlation Analysis (CCA) each pair matrices are mapped to orthogonal space. Next, Wavelet transformation is performed on original feature matrices and then mapped to orthogonal space. Both domains are statistically independent. From each domain, same dimensional feature vectors are extracted and concatenated them to form single embedding vectors. The embedding vectors are fed to classifier to recognize the healthy control and pathological signal patterns. For demonstration, we consider three groups of EMG signal vis Amyotrophic lateral sclerosis (ALS), Myopathy (Myo) and healthy control (Nor). Results indicate that adopted feature extraction technique and synchronization of features strongly enhances the quality of feature pattern. The optimum recognition rate under adopted feature technique are obtained 95.91%±3.6% and 95.58%±1.5 % in Myo-Nor and ALS-Nor respectively. The proposed feature extraction scheme is consistent not only in accuracies but also other quality assessment parameters. Hence it promises to provide a better strategic tool for signal classification.

Fractal to monolayer growth of AgCl and Ag/AgCl nanoparticles on vanadium oxides (VOx) for visible-light photocatalysis

A facile and simple methodology was adopted for the trapping of highly crystalline AgCl and Ag/AgCl nanoparticles (NPs) into the interlayer spacings of vanadium oxides (VOx). Self-organization of AgCl and Ag/AgCl-NPs on VOx was found to be governed by the nature of the dicarboxylic acids used during the synthesis of the nanocomposites. A “fractal-like” morphology of the AgCl@VOx nanocomposite was achieved in the presence of cis-1,2 cyclohexanedicarboxylic acid. Heating of the AgCl@VOx nanocomposite above 68 °C resulted in the growth of polydispersed and ultrafine (3–4 nm) Ag/AgCl-NPs and its self-organization into monolayer formation on a partly crystalline VOx matrix. Change in the conformation of the dicarboxylic acid to the trans-isomer resulted in the formation of a ‘rod-like’ structure of Ag/AgCl-NPs on a highly crystalline VOx matrix. The band gaps of the nanocomposites were within the range of 1.8 to 2.9 eV. Because of such a low band gap, the synthesized nanocomposites were found to be highly active toward the photooxidation of methylene (MB) and methyl orange (MO) under sunlight.

Comparison of direct interfacing and ADC based system for gas identification using E-Nose

This paper presents the design and characterization of a direct interfacing circuit (DIC) for sensor array and its comparison with the built-in analog to digital converter (ADC) of a PIC microcontroller. Before implementing the DIC for the sensor array, we examined its performance on simulated voltages to have a proper understanding of the output characteristics. To explore the DIC for multi-sensory system, an exemplary E-Nose setup was developed for experimentation. The sensor responses from the E-Nose system are concurrently measured by two microcontrollers, one using DIC and the other by the 10-bit internal ADC of the microcontroller. Principal component analysis (PCA) is then performed for visualizing and comparing the class separation for both the methods. To further explore the discriminating capability of the DIC based E-Nose, artificial neural network (ANN) is implemented. Finally, we delve into microcontroller based online gas discrimination by the E-Nose using DIC and its comparison with ADC results. Equivalent results were observed for both the cases with accuracy up to 97 %.

Fusion of projected feature for classification of EMG patterns

In medical domain, proper set of information fusion is the vital requirement for quantitative interpretation. In this paper, we present a novel feature fusion technique based on two-fold feature projection (FP) for EMG classification. Canonical Correlation Analysis (CCA) transformation is performed on original feature space and wavelet transformation of original feature. Based on subspace learning technique, relevant features are extracted from unified domain independent spaces and fused via proposed fusion algorithms. To demonstrate the outperform of the adopted algorithm three class of EMG subject groups are considered from online and Guwahati Neurological Research Centre (GNRC), Ghy, India. Outcomes indicate that the adopted dimension reduction strategy are consistent not only in accuracy but also in other quality assessment parameters. The overall accuracy is 98.80% ±2.0%. It provides an efficient and powerful way of feature extraction for fusion to improve recognition rate. Hence, it promises to prove a better strategic tool for medical data analysis in healthcare institutions.

Dielectric properties of a strongly polar nematic liquid crystal compound doped with gold nanoparticles

ABSTRACT This study focuses on the electrical characteristics of a strongly polar nematic liquid crystal, Hexyloxy-cyanobiphenyl (6OCB), doped with a low concentration (2% by weight) of citrate buffer stabilised gold nanoparticles (GNPs) at low frequencies between 20 Hz and 35 MHz. The doped samples have lower values of nematic–isotropic transition temperature, permittivity (both parallel and perpendicular to the field direction) and dielectric anisotropy; however, relaxation time and activation energy were increased. The observed results could be explained on the basis of weakly anisotropic nature of GNPs and a local rearrangement of liquid crystal molecules surrounding the nanoparticles. Moreover, a complimentary suggestion on a possible change in the dipole–dipole correlation is made to explain the difference in changes (qualitative and quantitative) observed for permittivity of the host nematic liquid crystal doped with GNP. Temperature dependent dielectric relaxation studies indicate an increase in viscosity and potential barrier; and hence a change in strength of inter-molecular and intra-molecular interactions is suggested. Graphical Abstract

F-SVD based algorithm for variability and stability measurement of bio-signals, feature extraction and fusion for pattern recognition

Abstract A support system with efficient learning framework helps eliciting complete knowledge of underlying phenomena of interest. It makes the analysis less-onerous, time-consuming and error-prone and thus promotes large scale applications. Such modeling requires profound understanding of available information and its appropriate utilization. Albeit success of electromyogram (EMG) support systems, challenges still exits specifically in early phase of design mainly due to inherent variations and complex data distribution patterns of signals. In this article, a frame singular value decomposition (F-SVD) based method-generalizing Canonical correlation analysis for automatic classification of EMG signals to diagnose amyotrophic lateral sclerosis (ALS), myopathy and normal subjects, is proposed. At first, signals are decomposed to formulate a set of vectors and performed subspace transformation to demonstrate the variability and stability of signals base on correlations between pairs of vectors. Besides, discrete Wavelet transformation is applied on generated vectors and correlation analysis is performed. Afterwards, taking highly correlated statistical measures a set of compact feature distributions are estimated and fused via two recently proposed parallel and serial feature fusion models. Finally two global descriptors for effective classifications of various EMG patterns are proposed. The efficacy of derived feature space is validated by intuitive, graphical and statistical analysis. The model performances are investigated over two datasets. It achieves accuracy of 98.10% and 97.60% over two and three-class groups of first dataset receptively. Accuracy over second dataset is 100% with a specificity of 100% and sensitivities of 100%. This is first time that F-SVD is employed for automatic classification of EMG. Experiments results on various datasets evince adequacy of our method. Further comparison of performance with state-of-the-art methods depicts that our method comparable or superior in terms of various performance metrics.

A Twofold Subspace Learning-Based Feature Fusion Strategy for Classification of EMG and EMG Spectrogram Images

We addressed an algorithm intuitively modeling multi-view information for pattern recognition application, specifically for electromyography (EMG) classification. The objective of the framework is to extract the low-dimensional embeddings (LDEs) inherent in multiple views that comprehensively represent the class information. We have shown that the algorithm is capable of providing robust solution to multitask learning relying on multi-view information. On two sets of EMG data, the learned LDEs comprehensively represent the multi-view information they were trained to represent, with consistency in performance across multiple sets of partitioned data sets. The significant aftermaths of the adopted learning strategy affirm the practical usability of the algorithm in healthcare applications for making correct diagnosis. Further, implementation of the algorithm for spectrogram image-based recognition is also of note.

Nonlinear Offset Measurement and Nullification for Effective Resistive Sensor Design

Over the decades, a number of methodologies have been introduced to meliorate the resistive sensor measurement protocol for complete knowledge of the phenomenon of interest. Nonetheless, such setting requires high degree of circuit components that result high level of errors (i.e., nonlinear) and thereby, its minimization for effective design is an open question. This article presents a technique that utilizes direct resistive circuit with microcontroller (\( \mu C \)), followed by subsequent estimation of curve-fitting models (CFMs) to curtail the errors involved and implementation in \( \mu C \) to update real-time data. Further, the study exploited the effectiveness of various employed CFMs in this context. The significant aftermaths with suitable choice of CFM and subsequent comparison with the state-of-the-art approaches manifest the efficacy of the adopted scheme.