Faculty Publications
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Publications by NITK Faculty
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Item Improved phase estimation based on complete bispectrum and modified group delay(2008) Narasimhan, S.V.; Basumallick, N.; Chaitanya, R.In this paper, a new method for extracting the system phase from the bispectrum of the system output has been proposed. This is based on the complete bispectral data computed in the frequency domain and modified group delay. The frequency domain bispectrum computation improves the frequency resolution and the modified group delay reduces the variance preserving the frequency resolution. The use of full bispectral data also reduces the variance as it is used for averaging. For the proposed method at a signal to noise ratio of 5dB, the reduction in root mean square error is in the range of 1.5-7 times over the other methods considered. © 2008 Springer-Verlag London Limited.Item Fuzzy logic modeling for groundwater level forecasting of west coast region in India(2011) Dandagala, D.; Deka, P.C.Forecasting the groundwater table in unconfined aquifer is essential for efficient planning of conjunctive use in a basin. In this study, fuzzy logic (FL) models have been developed for groundwater level forecasting in west coast humid region of Karnataka state, India. The FL modeling was carried out to forecast the groundwater table by one week lead time at three different sites over the study area. Mamdani fuzzy inference system was adopted in the present study and finally centroid of area defuzzification method has been applied to obtain crisp output. The results concluded that the FL model performed quite satisfactorily as assessed by various performance indices such as Root mean square error, Coefficient of correlation, and Mean absolute error. © 2011 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.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 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 Soft and evolutionary computation based data association approaches for tracking multiple targets in the presence of ECM(Elsevier Ltd, 2017) Satapathi, G.S.; Srihari, P.This paper proposes two novel soft and evolutionary computing based hybrid data association techniques to track multiple targets in the presence of electronic countermeasures (ECM), clutter and false alarms. Joint probabilistic data association (JPDA) approach is generally used for tracking multiple targets. Fuzzy clustering means (FCM) technique was proposed earlier as an efficient method for data association, but its cluster centers may fall to local minima. Hence, new hybrid data association approaches based on fuzzy particle swarm optimization (Fuzzy-PSO) and fuzzy genetic algorithm (Fuzzy-GA) clustering techniques have been presented as robust methods to overcome local minima problem. The data association matrix is evaluated for all tracks using validated measurements obtained by phased array radar for four different cases applying four data association methods (JPDA, FCM, Fuzzy-PSO, and Fuzzy-GA). Therefore, two hybrid data association approaches are designed and tested for multi-target tracking using intelligent techniques. Experimental results indicate that Fuzzy-GA data association technique provides improved performance compared to all other methods in terms of position and velocity RMSE values (38.69% and 33.19% average improvement for target-1;31.17% and 9.68% average improvement for target-2) respectively for crossing linear targets case. However, FCM technique gives better performance in terms of execution time (94.88% less average execution time) in comparison with other three techniques(JPDA, Fuzzy-GA, and Fuzzy-PSO) for the case of linear crossing targets. Thus accomplishing efficient and alternative multiple target tracking algorithms based on expert systems. The results have been validated with 100 Monte Carlo runs. © 2017 Elsevier LtdItem Location prediction algorithm for a nonlinear vehicular movement in VANET using extended Kalman filter(Springer New York LLC barbara.b.bertram@gsk.com, 2017) Jaiswal, R.K.; Jaidhar, C.D.Vehicular ad-hoc network (VANET) is an essential component of the intelligent transportation system, that facilitates the road transportation by giving a prior alert on traffic condition, collision detection warning, automatic parking and cruise control using vehicle to vehicle (V2V) and vehicle to roadside unit (V2R) communication. The accuracy of location prediction of the vehicle is a prime concern in VANET which enhances the application performance such as automatic parking, cooperative driving, routing etc. to give some examples. Generally, in a developed country, vehicle speed varies between 0 and 60 km/h in a city due to traffic rules, driving skills and traffic density. Likewise, the movement of the vehicle with steady speed is highly impractical. Subsequently, the relationship between time and speed to reach the destination is nonlinear. With reference to the previous work on location prediction in VANET, nonlinear movement of the vehicle was not considered. Thus, a location prediction algorithm should be designed by considering nonlinear movement. This paper proposes a location prediction algorithm for a nonlinear vehicular movement using extended Kalman filter (EKF). EKF is more appropriate contrasted with the Kalman filter (KF), as it is designed to work with the nonlinear system. The proposed prediction algorithm performance is measured with the real and model based mobility traces for the city and highway scenarios. Also, EKF based prediction performance is compared with KF based prediction on average Euclidean distance error (AEDE), distance error (DE), root mean square error (RMSE) and velocity error (VE). © 2016, Springer Science+Business Media New York.Item Wavelet coupled MARS and M5 Model Tree approaches for groundwater level forecasting(Elsevier B.V., 2017) Rezaie-Balf, M.; Naganna, S.R.; Ghaemi, A.; Deka, P.C.In this study, two different machine learning models, Multivariate Adaptive Regression Splines (MARS) and M5 Model Trees (MT) have been applied to simulate the groundwater level (GWL) fluctuations of three shallow open wells within diverse unconfined aquifers. The Wavelet coupled MARS and MT hybrid models were developed in an attempt to further increase the GWL forecast accuracy. The Discrete Wavelet Transform (DWT) which is particularly effective in dealing with non-stationary time-series data was employed to decompose the input time series into various sub-series components. Historical data of 10 years (August-1996 to July-2006) comprising monthly groundwater level, rainfall, and temperature were used to calibrate and validate the models. The models were calibrated and tested for one, three and six months ahead forecast horizons. The wavelet coupled MARS and MT models were compared with their simple counterpart using standard statistical performance evaluation measures such as Root Mean Square Error (RMSE), Normalized Nash-Sutcliffe Efficiency (NNSE) and Coefficient of Determination (R2). The wavelet coupled MARS and MT models developed using multi-scale input data performed better compared to their simple counterpart and the forecast accuracy of W-MARS models were superior to that of W-MT models. Specifically, the DWT offered a better discrimination of non-linear and non-stationary trends that were present at various scales in the time series of the input variables thus crafting the W-MARS models to provide more accurate GWL forecasts. © 2017 Elsevier B.V.Item Estimation of saturated hydraulic conductivity using fuzzy neural network in a semi-arid basin scale for murum soils of India(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2018) More, S.B.; Deka, P.C.Saturated hydraulic conductivity, Ks is an important input parameter in modeling flow process in soil. Measurement of Ks in field is time consuming and costly. Also, due to inherent temporal and spatial variability of this parameter, large number of samples are required to characterize the areas of site. In this study, a hybrid approach consists of Fuzzy Neural Network (FNN), has been proposed to estimate Ks from limited number of field measurements using Guelph permeameter. The various soil properties such as bulk density, porosity, specific gravity, sand, clay, silt and organic matter were used as input variables and Ks was kept as output. In this study, 175 field measurements and soil samples were collected in a grid of 40 m × 200 m with uniform spacing along the slope of barren land in the site of Punanaka (Solapur city), India. To quantify the prediction accuracy, this FNN approach is compared with regression, Fuzzy Mamdani approach and artificial neural network with BP algorithm. The various statistical performance indices like root mean square error, coefficient of determination (R2), and Mean relative error were used for evaluation of model performance. It was found that the hybrid FNN approach in comparison with others could more accurately predict saturated hydraulic conductivity. © 2017 Indian Society for Hydraulics.