Faculty Publications
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Item Effectiveness of contrast limited adaptive histogram equalization technique on multispectral satellite imagery(Association for Computing Machinery acmhelp@acm.org, 2017) Ganesh, V.R.; Ramesh, H.Contrast Limited Adaptive Histogram Equalization technique (CLAHE) is a widely used form of contrast enhancement, used predominantly in enhancing medical imagery like X-rays and to enhance features in ordinary photographs. This paper aimed to understand the effectiveness of using this technique in multispectral satellite imagery and to study its effectiveness in different regions of the electromagnetic spectrum. This study also aimed at analyzing variations of spatial and spectral resolutions of a sensor affect the performance of the CLAHE technique by means of comparing quantitative parameters of the enhanced images between the sensors. A general idea of the feature that can be enhanced in each spectral region was also studied. The results showed that a comparative study between the CLAHE technique and the conventional global histogram equalization technique resulted in the former technique emerging superior of the two and thereby reconstructed images of better quality. © 2017 Association for Computing Machinery.Item Software Reliability Estimation of Schick-Wolverton Rayleigh Failure Time Model(International Society of Science and Applied Technologies, 2021) Tantri, R.; Murulidhar, N.It is well known that the users and the developers of the software often encounter the problem of its quality and durability. Software reliability is a measure of the quality of the software. By estimating the reliability of the software, the developers can ensure that the reliability objectives as specified by the user are met. On the other hand, the reliability estimate also enables the users to decide whether or not to accept the software. Thus, reliability estimates are the key factors in decision making problems for both the developers and the users of the software. Software reliability models with specified failure time distributions are considered and failure data obtained during testing are used to estimate their reliability. Herein, Rayleigh failure time distribution of Schick-Wolverton is considered and reliability estimates have been obtained by the methods of Minimum Variance Unbiased Estimation (MVUE) and Maximum Likelihood Estimation (MLE). The two estimates have been compared. Case studies have also been considered and the above two estimates of reliability have been compared. It is observed that the estimator obtained using the method of minimum variance unbiased estimation provides a better estimator than that obtained using the method of maximum likelihood estimation. © 2021 Proceedings - 26th ISSAT International Conference on Reliability and Quality in Design, RQD 2021. All rights reserved.Item Simulation of varada aquifer system for sustainable groundwater development(2008) Ramesh, H.; Mahesha, A.Groundwater flow modeling has been used extensively worldwide with varying degrees of success. The ability to predict the groundwater flow is critical in planning and implementing groundwater development projects under increasing demand for fresh water resources. This paper presents the simulation of the aquifer system for planning the groundwater development of Varada basin, Karnataka, India using the Galerkin finite-element method. The government of Karnataka State, India is implementing the World Bank assisted project, "Jal Nirmal" for a sustainable development of the region, thereby ensuring a safe supply of drinking water to the northern districts of the state. Varada basin is one of the beneficiaries of the project in Haveri district. Field tests carried out in the study area indicate that the region is predominantly a confined aquifer with transmissivity and storage coefficients ranging from 5.787×10-6m2/s (0.500 m2/day) to 4.213×10-3m2/s (3.640×102m2/day) and 0.011-0.001× 10-2, respectively. This study mainly emphasizes the spatial and temporal variability of groundwater potential under different developmental scenarios. The model predictions were reasonably good with correlation coefficients ranging from 0.78 to 0.91 with the root mean square error of about 0.46-0.78 during calibration and validation. The stated accuracies are based on comparisons between measured and calculated heads. The outcome of the study would be a useful input for the conjunctive use of surface water and groundwater planning for the sustainable development of the region. © 2008 ASCE.Item A comparative study on RBF and NARX based methods for forecasting of groundwater level(2011) Dandagala, D.; Deka, P.C.Evaluation and forecasting of groundwater levels through time series model (s) helps for the sustainable development of groundwater resources. The focus of the present study is on the application of Radial Basis Function (RBF) and Non Linear auto-regressive with exogenous variable (NARX) data driven models to forecast groundwater level for multiple input scenario's and also multiple lead time. Weekly time series groundwater level data has been used as input and the models are developed to forecast one, two, three, four, five and sixth week ahead. Root mean square error (RMSE) and correlation coefficient (Cc) are used for evaluating the accuracy of the models. Based on the comparison of results, it was found that the RBF models are superior to the NARX models in forecasting groundwater level considering RMSE and Cc. The obtained result indicates that the RBF has high performance and consistent upto fourth week lead time and decaying performance for NARX models. Hence, RBF and NARX have the potential in forecasting groundwater level efficiently for multi step lead time. © 2011 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.Item Genetic algorithm based support vector machine regression in predicting wave transmission of horizontally interlaced multi-layer moored floating pipe breakwater(Elsevier Ltd, 2012) Patil, S.G.; Mandal, S.; Hegde, A.V.Planning and design of coastal protection works like floating pipe breakwater require information about the performance characteristics of the structure in reducing the wave energy. Several researchers have carried out analytical and numerical studies on floating breakwaters in the past but failed to give a simple mathematical model to predict the wave transmission through floating breakwaters by considering all the boundary conditions. Computational intelligence techniques, such as, Artificial Neural Networks (ANN), fuzzy logic, genetic programming and Support Vector Machine (SVM) are successfully used to solve complex problems. In the present paper, a hybrid Genetic Algorithm Tuned Support Vector Machine Regression (GA-SVMR) model is developed to predict wave transmission of horizontally interlaced multilayer moored floating pipe breakwater (HIMMFPB). Furthermore, optimal SVM and kernel parameters of GA-SVMR models are determined by genetic algorithm. The GA-SVMR model is trained on the data set obtained from experimental wave transmission of HIMMFPB using regular wave flume at Marine Structure Laboratory, National Institute of Technology, Karnataka, Surathkal, Mangalore, India. The results are compared with ANN and Adaptive Neuro-Fuzzy Inference System (ANFIS) models in terms of correlation coefficient, root mean square error and scatter index. Performance of GA-SVMR is found to be reliably superior. b-spline kernel function performs better than other kernel functions for the given set of data. © 2011 Elsevier Ltd. All rights reserved.Item Discrete wavelet-Ann approach in time series flow forecasting-a case study of Brahmaputra river(2012) Deka, P.C.; Haque, L.; Banhatti, A.G.This paper deals with the prediction of hydrologic behavior of the runoff for the one of the largest discharge carrier International River, Brahmaputra, located in Assam (India) at the Pandu station, by using daily time unit. The flow regime dominated by high data non-stationary and seasonal irregularity due to Himalayan climate fallout. The influence of data preprocessing through wavelet transforms has been investigated. For this, the main time series of flow data were decomposed to multi resolution time series using discrete wavelet transformations. Then these decomposed data were used as input to Artificial Neural Network (ANN) for multiple lead time flow forecasting. Various types of wavelets were used to evaluate the optimal performance of models developed. The forecasting accuracy of the models has been tested for multiple lead time upto 4 days using different decomposition levels. The performance of the proposed hybrid model has been evaluated based on the performance indices such as root mean square error (RMSE), coefficient of efficiency (CE) and mean relative error (MRE).The results shows the better forecasting accuracy by the proposed combined hybrid model over the single ANN model in hydrological time series forecasting. © 2012 CAFET-INNOVA TECHNICAL SOCIETY.Item Study on soil moisture retention function for the indian forested hillslope soils(Taiwan Geotechnical Society 43, Sec. 4, Keelong Rd, Taipei 106,, 2013) Prasanna, P.; Varija, K.; Kumar, P.Pedotransfer functions (PTFs) are one of the widely used tools to predict the soil water retention curves (SWRC). The objective of this study was to develop and validate point and parametric PTF models based on nonlinear regression technique using the different set of predictors such as particle-size distribution, bulk density, porosity and organic matter content. Soil samples were collected from different elevations at different depths in forested hillslope area of Pavanje river basin that lies in coastal area of Karnataka, India. The point PTF models estimated retention points at 33, 100, 300, 500, 1000, and 1500 kPa pressure heads and the parametric PTF models estimated the van Genuchten and Brooks-Corey retention parameters. The data were evaluated with the root mean square error (RMSE), mean error (ME), and coefficient of determination (R2) between the measured and predicted water contents. The prediction of soil water retention curve using PTFs by point estimation method for the sampled soils was relatively successful (best case R2 = 0.862). Further, a critical comparative analysis on the performances of point and parametric methods was done. It can be suggested to use the developed PTFs for the prediction of soil water retention curve for the loamy sand and sandy loam textured soils in this forest area of the coastal region in south western portion of India.Item Latent heat flux estimation using trapezoidal relationship between MODIS land surface temperature and fraction of vegetation-application and validation in a humid tropical region(Taylor and Francis Ltd., 2014) Laxmi, K.; Nandagiri, L.The present study was taken up with the objective of developing a methodology for estimation of actual evapotranspiration (AET) using only satellite data. Accordingly, an algorithm based on the popular Priestley-Taylor method was developed. While previous studies have assumed a triangular relationship between land surface temperature (LST) and fraction of vegetation (FV) to calculate the Priestley-Taylor parameter (?), a trapezoidal relationship was adopted in the present study to enable applications in forested regions in the humid tropics. The developed algorithm was applied to the humid tropical Mae Klong region, Thailand, and latent heat flux (ET) estimates were validated with measurements made at a flux tower located at the centre of the region. Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing satellite data products corresponding to the study area were used to derive various inputs required by the algorithm. Comparison of estimated and measured fluxes on five cloud-free days in 2003 yielded root mean square error (RMSE) of 64.73 W m-2 which reduced to 18.65 W m-2 when one day was treated as an outlier. The methodology developed in this study derived inputs only from satellite imagery and provided reasonably accurate estimates of latent heat flux at a humid tropical location. © 2014 Taylor & Francis.Item Particle Swarm Optimization based support vector machine for damage level prediction of non-reshaped berm breakwater(Elsevier Ltd, 2015) Narayana, N.; Mandal, S.; Rao, S.; Patil, S.G.The damage analysis of coastal structure is very much essential for better and safe design of the structure. In the past, several researchers have carried out physical model studies on non-reshaped berm breakwaters, but failed to give a simple mathematical model to predict damage level for non-reshaped berm breakwaters by considering all the boundary conditions. This is due to the complexity and non-linearity associated with design parameters and damage level determination of non-reshaped berm breakwater. Soft computing tools like Artificial Neural Network, Fuzzy Logic, Support Vector Machine (SVM), etc, are successfully used to solve complex problems. In the present study, SVM and hybrid of Particle Swarm Optimization (PSO) with SVM (PSO-SVM) are developed to predict damage level of non-reshaped berm breakwaters. Optimal kernel parameters of PSO-SVM are determined by PSO algorithm. Both the models are trained on the data set obtained from experiments carried out in Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal, India. Results of both models are compared in terms of statistical measures, such as correlation coefficient, root mean square error and scatter index. The PSO-SVM model with polynomial kernel function outperformed other SVM models. © 2014 Elsevier B.V.Item Nonlocal linear minimum mean square error methods for denoising MRI(Elsevier Ltd, 2015) Sudeep, P.V.; Ponnusamy, P.; Kesavadas, C.; Rajan, J.The presence of noise results in quality deterioration of magnetic resonance (MR) images and thus limits the visual inspection and influence the quantitative measurements from the data. In this work, an efficient two stage linear minimum mean square error (LMMSE) method is proposed for the enhancement of magnitude MR images in which data in the presence of noise follows a Rician distribution. The conventional Rician LMMSE estimator determines a closed-form analytical solution to the aforementioned inverse problem. Even-though computationally efficient, this approach fails to take advantage of data redundancy in the 3D MR data and hence leads to a suboptimal filtering performance. Motivated by this observation, we put forward the concept of nonlocal implementation with LMMSE estimation method. To select appropriate samples for the nonlocal version of the LMMSE estimation, the similarity weights are computed using Euclidean distance between either the gray level values in the spatial domain or the coefficients in the transformed domain. Assuming that the signal dependent component of the noise is optimally suppressed by this filtering and the rest is a white and uncorrelated noise with the image, we adopt a second stage LMMSE filtering in the principal component analysis (PCA) domain to further enhance the image and the noise variance is adaptively adjusted. Experiments on both simulated and real data show that the proposed filters have excellent filtering performance over other state-of-the-art methods. © 2015 Elsevier Ltd. All rights reserved.