Manoj K. Arora

PEC University of Technology, Chandigarh, Chandigarh, India

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Publications (77)88.61 Total impact

  • Source
    Reet Kamal Tiwari, Manoj K. Arora, Ravi P. Gupta
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    ABSTRACT: Mapping of debris-covered glacier boundaries using remote sensing technique is restricted by the presence of supraglacial debris (debris over the glacier) since it has similar spectral properties than that of periglacial debris (debris outside glacial boundary). However, earlier studies have suggested that the temperature differences between the supraglacial and periglacial debris and/or geo-morphometric parameters can be used to separate these two classes. Several automated and semi-automated approaches have been developed for the mapping of debris-covered glacial boundaries utilizing thermal information and/or geo-morphometric parameters. Most of the techniques utilizing multisource datasets use semi-automated time consuming method of classification. In this article, a novel hybrid classification scheme utilizing both the maximum likelihood classification and knowledge based classification has been used which integrates inputs from ASTER optical, thermal and DEM remote sensing data for mapping debris-covered glacier boundary in a test area in the Chenab basin, Himalayas, India. The results of this new proposed classification scheme were compared with the classification results of maximum likelihood classification which has been used earlier by several researchers for a similar type of mapping. Further, cloud is also considered as one of the major hindrance in mapping of the glaciers due to its similar reflectance as that of snow. Additionally, the low radiometric resolution of most of the optical remote sensing data may sometimes cause serious problem in mapping glacial terrain classes due to saturation towards higher DN values due to higher reflectance of snow. A contrast enhancement using band transformation has been proposed in this remote sensing based study to resolve such problems.
    Remote Sensing of Environment 01/2015; In Press. DOI:10.1016/j.rse.2014.10.026 · 6.39 Impact Factor
  • M S Ganesh Prasad, Manoj K. Arora
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    ABSTRACT: Representing the quality of thematic maps derived from remote-sensing image classification is important in assessing its fitness for use. Conventional approaches to represent the quality in terms of accuracy need information from the reference data at the same scale. Error-prone or dubious reference data may have an impact on the assessment of quality. Therefore, measures that complement the conventional accuracy measures are required to represent the quality. Uncertainty and confidence are such measures that do not require reference data. Few studies have been attempted to derive pixel-level confidence. However, these measures are not widely adopted by the remote-sensing community due to their limitations. In this article, a simple measure of confidence is derived to represent the quality of fuzzy classification. To derive the confidence value for a pixel, two values, viz. first highest class membership value as evidence and an associated degree of certainty, are required. When the difference between first and second highest membership values is used as degree of certainty in the proposed approach, the confidence measure derived is equal to the complement of existing measure of uncertainty, viz. confusion index in difference form.
    International Journal of Remote Sensing 12/2014; 35(24):8122-8137. DOI:10.1080/01431161.2014.979303 · 1.65 Impact Factor
  • M S Ganesh Prasad, Manoj K Arora
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    ABSTRACT: In recent years, uncertainty has become an important subject in assessing the quality of remote sensing image classification. Classification uncertainty is due to poor class definition, transition zones and the presence of mixed pixels in remote sensing data. Fuzzy classification approaches aim to estimate the proportions of specific classes that occur within each pixel. Partial class membership values derived from fuzzy classification serve as baseline information to assess classification uncertainties and allow the depiction of spatial variation of uncertainty. Providing uncertainty information at pixel level may assist in increasing the confidence in using thematic maps produced from remote sensing image classification. Many metrics have been developed to quantify pixel-wise classification uncertainty. In the present study, two formulations of confusion index are used. Literature state that, the two forms of confusion index provide similar information. The present study aims at examining whether these two formulations provide similar information or not. Multispectral image from Landsat- 7 ETM+ sensor was subjected to fuzzy c-means classification. The derived class membership values for each pixel were used in quantifying classification uncertainty. A comparative analysis of classification uncertainty provided by two forms of confusion index was carried out. The results from the study show that the two forms of confusion index provide dissimilar information on classification uncertainty.
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    ABSTRACT: The surface ice velocity has a major impact on the health and fate of the glacier. Measurement of ice velocity can help in modeling the glacier dynamics. This article presents sub-pixel image correlation technique (COSI-Corr) for calculating glacier ice velocity. It is difficult to obtain sufficient ice velocity data with conventional glaciological techniques (field measurements) due to the frequent loss of stakes and difficulty in the handling of measuring instruments at the site. A number of researchers have also used SAR interferometry/speckle tracking to map glacier ice velocities. However, it has been reported that SAR based works have limitations in highly rugged terrains like the Himalayas and especially for fast-moving glaciers. The literature suggests that optical image based correlation techniques appear to be more successful and robust matching method than SAR interferometry for the measurement of glacier ice velocity in Himalayan terrain, and therefore, the former is the focus in this study. The principle involved in this technique is that two images acquired at different times are correlated to find out the shift in position of any moving object, which is then treated as displacement in this time interval. Though it has been used to measure ground deformation but it has been suggested that the proposed technique would also allow for the measurement of surface displacements due to ice-flow or geomorphic processes, or for any other change detection application. The algorithm works in four fundamental steps: In the first step each pixel from the satellite focal plane is projected onto a ground reference system. This operation utilizes knowledge from both the imaging systems and the ground topography. The second step involves optimizing the satellite viewing parameters with respect to some reference frame. The third step involves resampling of the acquired images with the previously calculated parameters. This yields ground-projected images, called orthorectified images. Then in the the fourth step, image correlation is run to calculate surface ice velocities. This algorithm has now been implemented in a software package, Co-registration of Optically Sensed Images and Correlation (COSI-Corr), developed with Interactive Data Language (IDL) and integrated under ENVI. The described approach allows for the correction of offsets due to attitude effects and sensor distortions, as well as elevation errors. This methodology is thus well suited to generate accurate, low-cost glacier-ice velocity data of remote regions like Himalayan glaciers where ground instrumentation is difficult to implement and terrain conditions are inhospitable.
    National Conference on Himalayan Glaciology (NCHG-2014), Shimla; 10/2014
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    R K Tiwari, M K Arora
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    ABSTRACT: Debris cover over glaciers affects the rate of ablation and is considered as an indicator of glacier health. It also affects the ability to map glacier bodies and thereby has a bearing on the accuracy of modelling used for climate prediction, runoff estimation, etc. Mapping of debris-covered glacier boundaries using remote sensing technique is restricted by the presence of supraglacial debris since it has similar spectral properties than that of periglacial debris. However, earlier studies have suggested that the temperature differences between the supraglacial and periglacial debris and/or geo-morphometric parameters can be used to map the extent of these two classes. Several automated and semi-automated approaches have been developed for the mapping of debris-covered glacial boundaries utilizing thermal information and/or geo-morphometric parameters. Most of the techniques utilizing multisource datasets uses semi-automated method of classification which are time consuming. A novel hybrid classification scheme utilizing both the maximum likelihood classification and knowledge based classification has been used here which integrates inputs from ASTER optical, thermal and DEM remote sensing data for mapping debris-covered glacier boundary. Further, cloud is also considered as one of the major hindrance in mapping of the glaciers due to its similar reflectance as of snow. Additionally, the low radiometric resolution of most of the optical remote sensing data may sometimes cause serious problem in mapping glacial terrain classes due to saturation towards higher DN values due to higher reflectance of snow. A contrast enhancement using band transformation has been proposed in this remote sensing based study to resolve such problems.
    National Conference on Himalayan Glaciology (NCHG-2014), Shimla; 10/2014
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    ABSTRACT: Water Cloud Model (WCM) relates the backscatter coefficient (σo) with soil moisture. The backscatter coefficient includes the backscatter coefficient due to vegetation (σoveg), and the backscatter coefficient due to soil (σosoil). The σoveg of WCM depends upon vegetation characteristics. The present study is aimed to investigate the effect of different vegetation descriptors in estimating soil moisture from WCM. The study is carried out in Solani river catchment of India. ENVISAT ASAR images of three dates were acquired for the study. The field data, volumetric soil moisture from the upper 0-10 cm soil layer, soil texture, soil surface roughness, Leaf Area Index (LAI), Leaf Water Area Index (LWAI), Normalized Plant Water Content (NPWC) and average Plant Height (PH) corresponding to satellite pass dates were collected. Genetic Algorithm optimization technique is used to estimate the WCM vegetation parameters. The use of LAI as vegetation descriptor results in minimum root mean square error (RMSE) of 1.77 dB between WCM computed backscatter and ENVISAT ASAR observed backscatter. Also, use of LAI in WCM as vegetation descriptor results in the least RMSE of 4.19%, between estimated and observed soil moisture for the first field campaign while it was 5.64% for the last field campaign which was undertaken after 35 days of first campaign. It is concluded that LAI can be treated as the best vegetation descriptor in studies retrieving soil moisture and backscatter from microwave remote sensing data. This article is protected by copyright. All rights reserved.
    Hydrological Processes 10/2014; 29(9). DOI:10.1002/hyp.10344 · 2.70 Impact Factor
  • M. K. Arora, S. Chauhan, M. Sharma
    10th World Congress on Computational Mechanics; 05/2014
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    ABSTRACT: Although hyperspectral images contain a wealth of information due to its fine spectral resolution, the information is often redundant. It is therefore expedient to reduce the dimensionality of the data without losing significant information content. The aim of this paper is to show that proposed fractal based dimensionality reduction applied on high dimensional hyperspectral data can be proved to be a better alternative compared to some other popular conventional methods when similar classification accuracy is desired at a reduced computational complexity. Amongst a number of methods of computing fractal dimension, three have been applied here. The experiments have been performed on two hyperspectral data sets acquired from AVIRIS sensor.
    Optics and Lasers in Engineering 04/2014; 55:267–274. DOI:10.1016/j.optlaseng.2013.11.018 · 1.70 Impact Factor
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    ABSTRACT: Earthquake is one of the most destructive natural hazards which pose a real threat to India with nearly 59% of its geographical area vulnerable to seismic disturbance of varying intensities including the capital city of the country. India has experienced several major earthquakes mainly in Himalayan region and is also considered as one of the most earthquake prone regions in the world. Therefore, during past few decades, the Himalayan region has been studied extensively in terms of present ongoing displacements. It can be believed that a better estimate of the current Himalayan convergence rate and possible rupture can improve seismic hazard evaluations. Moreover, an improved convergence rate is also necessary to estimate if any slip deficit is available to drive future earthquakes in this region. In recent years, SAR interferometry has been successfully used for generating large scale surface displacement maps in radar look direction on a dense grid and with a centimeter to millimeter accuracy. In this context, the usefulness of SAR interferometry technique and its variations for estimation of displacements has been studied and presented in this paper. To study the displacement both conventional and multi-temporal Differential SAR interferometry has been used. In order to get the 3-D surface displacement, interferogram from both ascending and descending track can be used. However, due to the unavailability of ascending track data, a well-known mathematical model also has been used. Overall average displacement rates in the present study are found to be relatively lower as compared to the reported convergence rates. From geophysical point of view, the results presented in this paper for a small area are quite promising. Several explanations have also been presented in this paper to support the results. The reported low convergence rate may be due to the occurrence of silent/quite earthquakes, aseismic slip, differential movement of Delhi Hardwar ridge, etc. Therefore, in view of the contemporary seismicity and conspicuous displacements, a study of long-term observations of this surface movement has been recommended in future.
    Optics and Lasers in Engineering 02/2014; DOI:10.1016/j.optlaseng.2013.09.001 · 1.70 Impact Factor
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    H. S. Gusain, V. D. Mishra, M. K. Arora
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    ABSTRACT: The aim of this letter is to estimate incoming and net shortwave radiation fluxes of large snow covered area of western Himalaya and to evaluate the results with in situ observations. Radiation fluxes are estimated at spatial level using remote sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM) supplemented with sparse field data obtained from automatic weather stations (AWSs). Snow cover albedo has been estimated from MODIS data using narrowband to broadband conversion method for clear sky days. Geo-spatial maps of air temperature (AT) and relative humidity (Rh) have been generated for the study area using AWS recorded AT/Rh and DEM. Parameterization techniques have been used for estimating incoming and net shortwave radiation fluxes, which have been validated from in situ AWS observations. The root mean square error (RMSE) in estimation of incoming shortwave radiation flux and net shortwave radiation flux has been found to be 75 W m-2 and 84.9 W m-2 respectively. Further, the higher radiation fluxes have been observed on south aspect slopes than those observed on north aspect slopes.
    Remote Sensing Letters 01/2014; 5(1):83 - 92. DOI:10.1080/2150704X.2013.866287 · 1.43 Impact Factor
  • Journal of the Geological Society of India 01/2014; · 0.51 Impact Factor
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    ABSTRACT: The aim of this paper is to estimate soil moisture at spatial level by applying geostatistical techniques on the point observations of soil moisture in parts of Solani River catchment in Haridwar district of India. Undisturbed soil samples were collected at 69 locations with soil core sampler at a depth of 0–10 cm from the soil surface. Out of these, discrete soil moisture observations at 49 locations were used to generate a spatial soil moisture distribution map of the region. Two geostatistical techniques, namely, moving average and kriging, were adopted. Root mean square error (RMSE) between observed and estimated soil moisture at remaining 20 locations was determined to assess the accuracy of the estimated soil moisture. Both techniques resulted in low RMSE at small limiting distance, which increased with the increase in the limiting distance. The root mean square error varied from 7.42 to 9.77 in moving average method, while in case of kriging it varied from 7.33 to 9.99 indicating similar performance of the two techniques.
    01/2014; DOI:10.1007/s13201-014-0202-x
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    ABSTRACT: Landslides are the most damaging and threatening aftereffect of seismic events in Garhwal Himalayas. It is evident from past seismic events in Uttarakhand, India that no other phenomena can produce landslides of so great in size and number as a single seismic event can produce. Landslide inventories are produced for the study area before and after the occurrence of Chamoli Earthquake using Panchromatic (PAN) sharpened Linear Imaging Self Scanning-III (LISS-III) images. A sudden increase in number of landslides after the earthquake is observed. Further, two Landslide Susceptibility Zonation (LSZ) maps have been derived using pre- and post-Chamoli Earthquake landslide inventories. The difference of two LSZ indicates that landslides are very complex phenomenon and are affected by static factors in seismic conditions also. An attempt has been made to estimate the seismic displacements using Differential Synthetic Aperture Radar Interferometry (DIn SAR). European Remote Sensing Satellite-1/2 (ERS-1/ 2) SAR images have been used for preparing differential interferogram. Geometric and temporal decorrelation in SAR images is very high in the study area, which limits the use of DInSAR for displacement estimation. Theoretical displacement has been estimated using fault displacement modeling parameters for Chamoli earthquake. Post-Chamoli earthquake landslide inventory is overlaid over displacement map for understanding the impact of seismic displacement pattern with other static factors on the occurrence of landslides. It is observed that distribution and size of landslides is affected by displacement pattern controlled by other static factors also.
    Computers & Geosciences 12/2013; 61:50–63. DOI:10.1016/j.cageo.2013.07.018 · 2.05 Impact Factor
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    ABSTRACT: The Digital Elevation Models (DEMs), which represent the variation of elevation in a terrain at spatial level, are an important source of input to a variety of applications for deriving a number of terrain parameters such as relative relief, slope, aspect direction etc. In recent years, Synthetic Aperture Radar Interferometry has been viewed as a powerful approach to derive quality DEMs from a pair of SAR images. Despite the interferometric technique is often limited by several de-correlations several researchers demonstrate its effectiveness in topographic mapping. The DEM accuracy is strongly influenced by the effectiveness of the phase unwrapping technique. In this study an effective adaptive filtering approach has been used to reduce the phase noise due to de-correlation and in improving the accuracy of phase unwrapping. Two well known phase unwrapping approaches such as branch cut and minimum cost flow network have been used. Interferometric data from ASAR sensor onboard ENVISAT satellite have been used. A highly undulated terrain condition near Dehradun city situated in Uttarakhand state of India was selected to investigate the performance of this adaptive filtering approach. The RMS error between the InSAR derived elevations and the map derived elevations was obtained as 7.2 m using adaptive filter. However, elevation map of the study area could not be generated due to high de-correlation effect without the use of adaptive filter. This result clearly demonstrates the effectiveness of adaptive filtering approach for generation of DEM at meter level accuracy, which is sufficient for many engineering applications.
    Journal of the Geological Society of India 08/2013; 82(2):153-161. DOI:10.1007/s12594-013-0133-4 · 0.51 Impact Factor
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    ABSTRACT: Most military targets of strategic importance are very small in size. Though some of them may get spatially resolved, most cannot be detected due to lack of adequate spectral resolution. Hyperspectral data, acquired over hundreds of narrow contiguous wavelength bands, are extremely suitable for most military target detection applications. Target detection, however, still remains complicated due to a host of other issues. These include, first, the heavy volume of hyperspectral data, which leads to computational complexities; second, most materials in nature exhibit spectral variability and remain unpredictable; and third, most target detection algorithms are based on spectral modeling and availability of a priori target spectra is an essential requirement, a condition difficult to meet in practice. Independent component analysis (ICA) is a new evolving technique that aims at finding components that are statistically independent or as independent as possible. It does not have any requirement of a priori availability of target spectra and is an attractive alternative. This paper, presents a study of military target detection using four spectral matching algorithms, namely, orthogonal subspace projection (OSP), constrained energy minimisation, spectral angle mapper and spectral correlation mapper, four anomaly detection algorithms, namely, OSP anomaly detector (OSPAD), Reed-Xiaoli anomaly detector (RXD), uniform target detector (UTD), a combination of RXD-UTD. The performances of these spectrally modeled algorithms are then also compared with ICA using receiver operating characteristic analysis. The superior performance of ICA indicates that it may be considered a viable alternative for military target detection.
    Optical Engineering 02/2013; 52(2):6402-. DOI:10.1117/1.OE.52.2.026402 · 0.96 Impact Factor
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    ABSTRACT: Per pixel classification algorithms are incapable of mapping the land cover classes at its sub pixel level. The solution to this problem is to make the spatial resolution finer than the original data and arrange the sub pixels according to the fractional cover of each of the classes in the pixel and their class distribution in neighboring pixels. In this paper, an algorithm, named as ‘pixel filling algorithm’ for super resolution mapping has been proposed. The algorithm considers the information from the neighboring pixels of pixel to be super-resolved and treats all the classes equal to produce fine spatial resolution maps. The performance of the algorithm has been tested on a synthetic dataset as well as on a hyperspectral data. The datasets were reduced by Daubechies 4 wavelets and then a 3×3 filter was applied to make the datasets coarser. The overall accuracy of super resolution algorithm for synthetic data and hyperspectral data are calculated as 96.3 percent and 83.6 percent, respectively for the whole data and 86.3 percent and 70.8 percent, respectively for super resolved mixed pixels.
    Image Information Processing (ICIIP), 2013 IEEE Second International Conference on; 01/2013
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    Avnish Varshney, Manoj Kumar Arora, Jayanta Kumar Ghosh
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    ABSTRACT: Improved change vector analysis (ICVA) has recently been promoted as an effective algorithm for multi-class change detection. Unlike the conventional change vector analysis (CVA) that works on two-dimensional data, the ICVA works on multidimensional data. However, ICVA has limitations when the change vector is fraught with similar direction cosine values. In this article, a new algorithm, named median change vector analysis (MCVA) has been proposed for multi-class change detection. The algorithm is based on an enhanced 2n-dimensional feature space comprising direction cosine values of both the change vector and the median vector, which allows for more accurate detection of change classes than those obtained from ICVA. As a case study, the proposed algorithm has been implemented on Landsat-7 Enhanced Thematic Mapper Plus (ETM+) images of a typical Indian city and surrounding areas for land-cover change detection.
    Remote Sensing Letters 12/2012; 3(7):605-614. DOI:10.1080/01431161.2011.648281 · 1.43 Impact Factor
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    ABSTRACT: The Himalayan region has been studied extensively during the past few decades in terms of present ongoing deformations. Various models have been proposed for the evolution of the Himalaya to explain the cause of earthquake occurrences and to understand the seismotectonics of the Himalayan collision zone. However, the information on displacements from field geodetic surveys is still too scarce in time and spatial domains so as to provide convincing evidences. Moreover, classical Probabilistic Seismic Hazard Approaches also fail due to paucity of data in higher magnitude range, thus emphasizing the need of spatial level displacement measurements. It is in this context that the present study has been carried out to estimate the surface displacement in a seismically active region of the Himalaya between Ganga and Yamuna Tear using Differential SAR interferometry. Three single-look complex images, obtained from ASAR sensor onboard ENVISAT satellite, have been used. A displacement rate of 8–10 mm per year in N15°E direction of Indian plate has been obtained in this three-pass SAR interferometry study. It has been noted that the estimated convergence rate using Differential SAR interferometry technique is relatively low in comparison with those obtained from previous classical studies. The reported low convergence rate may be due to occurrence of silent/quite earthquakes, aseismic slip, differential movement of Delhi Hardwar ridge, etc. Therefore, in view of the contemporary seismicity and conspicuous displacements, a study of long-term observations of this surface movement has been recommended in future through a time-series SAR interferometry analysis.
    Natural Hazards 11/2012; 64(2). DOI:10.1007/s11069-012-0292-4 · 1.96 Impact Factor
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    ABSTRACT: The water cloud model is used to account for the effect of vegetation water content on radar backscatter data. The model generally comprises two parameters that characterize the vegetated terrain, A and B, and two bare soil parameters, C and D. In the present study, parameters A and B were estimated using a genetic algorithm (GA) optimization technique and compared with estimates obtained by the sequential unconstrained minimization technique (SUMT) from measured backscatter data. The parameter estimation was formulated as a least squares optimization problem by minimizing the deviations between the backscatter coefficients retrieved from the ENVISAT ASAR image and those predicted by the water cloud model. The bias induced by three different objective functions was statistically analysed by generating synthetic backscatter data. It was observed that, when the backscatter coefficient data contain no errors, the objective functions do not induce any bias in the parameter estimation and the true parameters are uniquely identified. However, in the presence of noise, these objective functions induce bias in the parameter estimates. For the cases considered, the objective function based on the sum of squares of normalized deviations with respect to the computed backscatter coefficient resulted in the best possible estimates. A comparison of the GA technique with the SUMT was undertaken in estimating the water cloud model parameters. For the case considered, the GA technique performed better than the SUMT in parameter estimation, where the root mean squared error obtained from the GA was about half of that obtained by the SUMT.Editor D. Koutsoyiannis; Associate editor L. SeeCitation Kumar, K., Hari Prasad, K.S. and Arora, M.K., 2012. Estimation of water cloud model vegetation parameters using a genetic algorithm. Hydrological Sciences Journal, 57(4), 1–15.
    Hydrological Sciences Journal/Journal des Sciences Hydrologiques 05/2012; DOI:10.1080/02626667.2012.678583 · 1.25 Impact Factor

Publication Stats

1k Citations
88.61 Total Impact Points

Institutions

  • 2014–2015
    • PEC University of Technology
      Chandigarh, Chandigarh, India
  • 2005–2014
    • Indian Institute of Technology Roorkee
      • Department of Civil Engineering
      Roorkee, Uttarakhand, India
  • 2010–2013
    • Uttar Pradesh Textile Technology Institute
      Cawnpore, Uttar Pradesh, India
  • 2002–2004
    • Syracuse University
      • Department of Electrical Engineering and Computer Science
      Syracuse, New York, United States