Browsing by Author "Jha, Sunil K."

Now showing 1 - 6 of 6
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    Chemical vapor identification by plasma treated thick film tin oxide gas sensor array and pattern recognition
    (2011) Srivastava, J.K.; Pandey, Preeti; Jha, Sunil K.; Mishra, V.N.; Dwivedi, R.
    Present study deals the class recognition potential of a four element plasma treated thick film tin oxide gas sensor array exposed with volatile organic compounds (VOCs). Methanol, Ethanol and Acetone are selected as target VOCs and exposed on sensor array at different concentration in range from 100-1000 ppm. Sensor array consist of four tin oxide sensors doped with 1-4 % PbO concentrations were fabricated by thick film technology and then treated with oxygen plasma for 5-10 minute durations. Sensor signal is analyzed by principal component analysis (PCA) for visual classification of VOCs. Further output of PCA is used as input for classification of VOCs by four pattern classification techniques as: linear discriminant analysis (LDA), k-nearest neighbor (KNN), back propagation neural network (BPNN) and support vector machine (SVM). All the four classifier results 100 % correct classification rate of VOCs by response analysis of sensor array treated with plasma for 5 minute. © 2011 IFSA.
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    Data mining approach to polymer selection for making SAW sensor array based electronic nose
    (2012) Jha, Sunil K.; Yadava, R.D.S.
    In this paper, a simple application of principal component analysis and hierarchical clustering methods of classical data mining has been demonstrated for making selection of polymer coatings for surface acoustic wave (SAW) sensor array. The database consists of thermodynamic solvation parameters of the prospective polymers and the target vapors. The linear-solvation-energy relationship (LSER) has been used to calculate partition coefficients from the solvation parameters. The partition coefficient data for various vapor-polymer combinations is then arranged as a data matrix taking the polymers for instances (rows) and the vapors for variables (columns). Selection of polymer subset for optimal discrimination of target vapors from background interferents has been made by analyzing the principal component score and load plots. A simulation study for detection of trinitrotoluene (TNT) and dinitrotoluene (DNT) explosive vapors camouflaged in the background of 29 interferent organic vapors originating from varied sources such as soil, vegetation, body odor, and indoor and outdoor industrial/commercial environments has been carried out. The paper demonstrates that the application of data mining methods for the selection of polymeric sensor coatings could prove prudent in terms of reducing system complexity, cost and development time. © 2012 IFSA.
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    Denoising by singular value decomposition and its application to electronic nose data processing
    (2011) Jha, Sunil K.; Yadava, R.D.S.
    This paper analyzes the role of singular value decomposition (SVD) in denoising sensor array data of electronic nose systems. It is argued that the SVD decomposition of raw data matrix distributes additive noise over orthogonal singular directions representing both the sensor and the odor variables. The noise removal is done by truncating the SVD matrices up to a few largest singular value components, and then reconstructing a denoised data matrix by using the remaining singular vectors. In electronic nose systems this method seems to be very effective in reducing noise components arising from both the odor sampling and delivery system and the sensors electronics. The feature extraction by principal component analysis based on the SVD denoised data matrix is seen to reduce separation between samples of the same class and increase separation between samples of different classes. This is beneficial for improving classification efficiency of electronic noses by reducing overlap between classes in feature space. The efficacy of SVD denoising method in electronic nose data analysis is demonstrated by analyzing five data sets available in public domain which are based on surface acoustic wave (SAW) sensors, conducting composite polymer sensors and the tin-oxide sensors arrays. © 2006 IEEE.
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    Drugs of abuse and their detection methodologies: Contribution of chemical sensor
    (Bentham Science Publishers, 2015) Jha, Sunil K.; Hayashi, Kenshi; Yadava, R.D.S.
    Drug of abuse or illicit drugs have become a serious health issue and global evils during the last few decades. Their detection is a significant area of research, for preventing illegal traffic and toxic effects on human health and society. Numerous analytical methods, based on diverse principles, have been developed for the detection of drugs of abuse. The core intention of present review is to pioneer the reader with the varieties of drugs of abuses (availability, formation, and use and ill-use) as well detection techniques developed and employed in the past few years. The study includes a comparative review of analytical detection techniques. Effectiveness of chemical sensors over other analytical techniques is particularly emphasized. © 2015 Bentham Science Publishers
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    Neural, fuzzy and neuro-fuzzy approach for concentration estimation of volatile organic compounds by surface acoustic wave sensor array
    (Elsevier B.V., 2014) Jha, Sunil K.; Hayashi, Kenshi; Yadava, R.D.S.
    Present study evaluates application of adaptive neuro-fuzzy inference system (ANFIS) for concentration estimation of volatile organic compounds (VOCs) by analyzing response matrix of polymer-functionalized surface acoustic wave (SAW) sensor array. The performance of ANFIS is compared with that of subtractive clustering based fuzzy inference system (SC-FIS) and backpropagation artificial neural network (BP-ANN). For analysis, the raw SAW sensor array data is preprocessed by logarithmic scaling followed by dimensional autoscaling and the feature extraction by principal component analysis (PCA). For concentration prediction, the extracted feature vectors were fed as input to the three methods (ANFIS, SC-FIS and BP-ANN) independently. The performance of the three methods were evaluated on the basis of root mean square error (RMSE) and correlation value involving actual and estimated values of concentration. Five sets of SAW sensor array responses are analyzed. The analysis includes both experimental and synthetic (sensor model generated) data sets. It is found that the ANFIS has the least value of RMSE and highest value of correlation compared to SC-FIS and BP-ANN. This signifies the relative superiority of ANFIS method. © 2014 Elsevier Ltd. All rights reserved.
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    Preprocessing of SAW sensor array data and pattern recognition
    (2009) Jha, Sunil K.; Yadava, R.D.S.
    This paper discusses data preprocessing methods for feature extraction in polymer coated surface acoustic wave (SAW) vapor sensor array. The role of sensor response modeling is explored in developing an appropriate preprocessing strategy. The preprocessor should be designed to transform the experimentally measured data variables (sensor signals) into a format that relates the characteristic features of the object for recognition (vapor) linearly with the transformed data variables. This facilitates generation of greater dispersion in feature space defined by linear feature extraction method such as principal component analysis. Considering solvation parameters of the vapor molecules to be their characteristic descriptors (features) and prompted by the equilibrium response model of SAW sensors, it is demonstrated that by transforming the raw data space logarithmically generates greater dispersion in feature space defined by the principal component analysis, and enhances classification efficiency of the backpropagation neural network substantially.