Browsing by Author "Hebbar, R."

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(E)-1-(2,4-Dichlorophen-yl)-3-[3-(4-methoxyphenyl)-1-phenyl-1H-pyrazol-4- yl]prop-2-en-1-one
(2011) Fun, H.-K.; Ching Kheng, C.K.; Malladi, S.; Hebbar, R.; Isloor, A.M.
In the title molecule, C25H18Cl2N 2O2, the dihedral angles between the pyrazole ring and its N- and C-bonded benzene rings are 8.28 (11) and 40.89 (10)°, respectively. The dihedral angle between the benzene rings is 39.03 (11)°. The title molecule exists in a trans conformation with respect to the acyclic C=C bond. In the crystal, molecules are linked into inversion dimers by pairs of inter-molecular C - H?O hydrogen bonds, generating R2 2(14) loops. © Fun et al. 2011.
Investigation on grinding wear behaviour of austempered ductile iron as media material during comminution of iron ore in ball mills
(2011) Hebbar, R.
An attempt has been made to assess the grinding wear behaviour of austempered ductile iron (ADI) as media material in comminution of Kudremukh haematite iron ore in a ball mill. Spheroidal graphite (S.G) iron balls were austenitised at 900°C for one hour and austempered at 280°C and 380°C for different time durations. These materials were characterized by measuring hardness, carrying out X-ray diffraction analysis, studying microstructures using scanning electron microscope (SEM). Grinding wear behaviour of ADI was assessed during wet grinding at different pH of the mineral slurry. The wear resistance of ADI was compared with that of forged En 31 steel balls under similar grinding conditions. It was found that ADI balls austempered at 280°C for 30 minutes which contains lower bainite registered superior wear resistance. It was also noted that the wear resistance of ADI was more at higher pH range of the slurry. © 2011 TIIM, India.
Urban land cover classification using hyperspectral data
(2014) Hegde, G.; Ahamed, J.M.; Hebbar, R.; Raj, U.
Urban land cover classification using remote sensing data is quite challenging due to spectrally and spatially complex urban features. The present study describes the potential use of hyperspectral data for urban land cover classification and its comparison with multispectral data. EO-1 Hyperion data of October 05, 2012 covering parts of Bengaluru city was analyzed for land cover classification. The hyperspectral data was initially corrected for atmospheric effects using MODTRAN based FLAASH module and Minimum Noise Fraction (MNF) transformation was applied to reduce data dimensionality. The threshold Eigen value of 1.76 in VNIR region and 1.68 in the SWIR region was used for selection of 145 stable bands. Advanced per pixel classifiers viz., Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) were used for general urban land cover classification. Accuracy assessment of the classified data revealed that SVM was quite superior (82.4 per cent) for urban land cover classification as compared to SAM (67.1 per cent). Selecting training samples using end members significantly improved the classification accuracy by 20.1 per cent in SVM. The land cover classification using multispectral LISS-III data using SVM showed lower accuracy mainly due to limitation of spectral resolution. The study indicated the requirement of additional narrow bands for achieving reasonable classification accuracy of urban land cover. Future research is focused on generating hyperspectral library for different urban features.
Urban land cover classification using hyperspectral data
(International Society for Photogrammetry and Remote Sensing, 2014) Hegde, G.; Mohammed Ahamed, J.M.; Hebbar, R.; Raj, U.
Urban land cover classification using remote sensing data is quite challenging due to spectrally and spatially complex urban features. The present study describes the potential use of hyperspectral data for urban land cover classification and its comparison with multispectral data. EO-1 Hyperion data of October 05, 2012 covering parts of Bengaluru city was analyzed for land cover classification. The hyperspectral data was initially corrected for atmospheric effects using MODTRAN based FLAASH module and Minimum Noise Fraction (MNF) transformation was applied to reduce data dimensionality. The threshold Eigen value of 1.76 in VNIR region and 1.68 in the SWIR region was used for selection of 145 stable bands. Advanced per pixel classifiers viz., Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) were used for general urban land cover classification. Accuracy assessment of the classified data revealed that SVM was quite superior (82.4 per cent) for urban land cover classification as compared to SAM (67.1 per cent). Selecting training samples using end members significantly improved the classification accuracy by 20.1 per cent in SVM. The land cover classification using multispectral LISS-III data using SVM showed lower accuracy mainly due to limitation of spectral resolution. The study indicated the requirement of additional narrow bands for achieving reasonable classification accuracy of urban land cover. Future research is focused on generating hyperspectral library for different urban features.