Faculty Publications
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Item Evaluating the Performance of CHIRPS Satellite Rainfall Data for Streamflow Forecasting(Springer Netherlands rbk@louisiana.edu, 2019) Sulugodu, B.; Deka, P.C.Streamflow forecasting can offer valuable information for optimal management of water resources, flood mitigation, and drought warning. This research aims in evaluating the effectiveness of CHIRPS satellite rainfall data in comparison with IMD gridded Rainfall Data and development of various flow forecasting models. Daily rainfall data for three decades (1983–2012) over the Nethravathi Basin, Karnataka, India is used for analysis. The analysis is carried out for the monsoon season (June–September), out of which 70% data considered for training the model and remaining for testing. Different input combinations are developed, and soft-computing methods like ANFIS, GRNN, PSO-ANN, and ELM are applied for flow forecasting on a temporal scale. The model performance is evaluated using various statistical indices like NNSE, RRMSE, and MAE. The results indicate that CHIRPS rainfall showed better performance in comparison with IMD data. ELM expressed an enhanced effect when compared to all other methods. The usefulness and effectiveness of CHIRPS data compared to IMD data has been explored. © 2019, Springer Nature B.V.Item Evaluation of satellite precipitation products in simulating streamflow in a humid tropical catchment of india using a semi-distributed hydrological model(MDPI, 2020) Sharannya, T.M.; Al-Ansari, N.; Deb Barma, S.; Mahesha, M.Precipitation obtained from rain gauges is an essential input for hydrological modelling. It is often sparse in highly topographically varying terrain, exhibiting a certain amount of uncertainty in hydrological modelling. Hence, satellite rainfall estimates have been used as an alternative or as a supplement to station observations. In this study, an attempt was made to evaluate the Tropical Rainfall Measuring Mission (TRMM) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), employing a semi-distributed hydrological model, i.e., Soil and Water Assessment Tool (SWAT), for simulating streamflow and validating them against the flows generated by the India Meteorological Department (IMD) rainfall dataset in the Gurupura river catchment of India. Distinct testing scenarios for simulating streamflow were made to check the suitability of these satellite precipitation data. The TRMM was able to better estimate rainfall than CHIRPS after performing categorical and continuous statistical results with respect to IMD rainfall data. While comparing the performance of model simulations, the IMD rainfall-driven streamflow emerged as the best followed by the TRMM, CHIRPS-0.05, and CHIRPS-0.25. The coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS) were in the range 0.63 to 0.86, 0.62 to 0.86, and ?14.98 to 0.87, respectively. Further, an attempt was made to examine the spatial distribution of key hydrological signature, i.e., flow duration curve (FDC) in the 30–95 percentile range of non-exceedance probability. It was observed that TRMM underestimated the flow for agricultural water availability corresponding to 30 percent, even though it showed a good performance compared to the other satellite rainfall-driven model outputs. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.Item Constrained radar waveform optimization for a cooperative radar-communication system(Elsevier B.V., 2023) Mahipathi, A.C.; Gunnery, S.; Srihari, P.; D'Souza, J.; Jena, P.The coexistence of radar-sensing and communication systems research has received a surge of interest in recent times to tackle the issue of spectrum inadequacy. Designing an optimized radar waveform for a coexistence scenario has been a challenging task for accomplishing the convergence of radar-sensing and communication functionalities, without degrading the performance at either end. This paper proposes a novel global optimization-based Spatial Branch and Bound (SBnB) approach to optimize the phase coefficients of a Non-Linear Frequency Modulated (NLFM) waveform in a CRCS framework. In addition, the Modified-Power Ratio Constraint-Cramér–Rao Lower Bound (M-PRC-CRLB), a local optimization-based approach is proposed to optimize the phase coefficients of an NLFM waveform. The spectral energy distribution and auto-correlation characteristics of an NLFM waveform are comprehensively investigated for various values of polynomial order (N) and at different threshold Signal-to-Noise-Ratio (SNR) values. To compare the proposed waveform design approaches (M-PRC-CRLB, SBnB) with the existing waveform design approaches namely, Minimum Estimation Error Variance (MEEV) and PRC- CRLB, a Peak-to-Side-Lobe-Ratio (PSLR), and Integrated-Side-Lobe-Ratio (ISLR) are evaluated at various polynomial orders and threshold SNR values. Furthermore, the performance of a CRCS is assessed using the radar estimation rate and communication data rate. The simulation results reveal that the proposed optimized radar waveform design approaches provide improved performance compared to the existing radar waveform design approaches in terms of radar estimation rate. Further, the proposed global optimization-based SBnB approach achieves a comparable performance of the communication data rate. In addition, the proposed approaches accomplish enhanced spectral utilization, controlled side-lobe energy levels, reduced range-domain ambiguities, and a higher information rate in a CRCS. © 2022 Elsevier B.V.Item Optimum Waveform Selection for Target State Estimation in the Joint Radar-Communication System(Institute of Electrical and Electronics Engineers Inc., 2024) Mahipathi, A.C.; Pardhasaradhi, B.P.; Gunnery, S.; Srihari, P.; D'Souza, J.; Jena, P.The widespread usage of the Radio Frequency (RF) spectrum for wireless and mobile communication systems generated a significant spectrum scarcity. The Joint Radar-Communication System (JRCS) provides a framework to simultaneously utilize the allocated radar spectrum for sensing and communication purposes. Generally, a Successive Interference Cancellation (SIC) based receiver is applied to mitigate mutual interference in the JRCS configuration. However, this SIC receiver model introduces a communication residual component. In response to this issue, the article presents a novel measurement model based on communication residual components for various radar waveforms. The radar system's performance within the JRCS framework is then evaluated using the Fisher Information Matrix (FIM). The radar waveforms considered in this investigation are rectangular pulse, triangular pulse, Gaussian pulse, Linear Frequency Modulated (LFM) pulse, LFM-Gaussian pulse, and Non-Linear Frequency Modulated (NLFM) pulse. After that, the Kalman filter is deployed to estimate the target kinematics (range and range rate) of a single linearly moving target for different waveforms. Additionally, range and range rate estimation errors are quantified using the Root Mean Square Error (RMSE) metric. Furthermore, the Posterior Cramer-Rao Lower Bound (PCRLB) is derived to validate the estimation accuracy of various waveforms. The simulation results show that the range and range rate estimation errors are within the PCRLB limit at all time instants for all the designated waveforms. The results further reveal that the NLFM pulse waveform provides improved range and range rate error performance compared to all other waveforms. © 2020 IEEE.Item Novel sorted PWM strategy and control for photovoltaic-based grid-connected cascaded H-bridge inverters(Springer, 2025) Maheswari, G.; Manjunatha Sharma, K.M.; Prabhakaran, P.This paper proposes a novel sorted level-shifted U-shaped carrier-based pulse width modulation (SLSUC PWM) strategy combined with an input power control approach for a 13-level cascaded H-bridge multi-level inverter designed for grid connection, specifically tailored for photovoltaic (PV) systems, which avoids a double-stage power conversion configuration. In this methodology, every inverter generates a quasi-square output voltage waveform with a width that is intricately linked to the output power of its corresponding PV panel. The application of this SLSUC pulse width modulation technique with input power control in a solar energy-based 13-level grid-tied inverter facilitates precise maximum power point (MPP) tracking for each of the PV panels under uniform and non-uniform irradiation conditions and ensures the consistent maintenance of capacitor voltage balance. Moreover, this novel SLSUC PWM method for 13-level inverters offers a range of benefits, including a low total harmonic distortion (THD) in the output voltage of the multi-level inverter and higher inverter and MPPT efficiencies over the existing PWM techniques. To verify the efficacy of the proposed control method over existing techniques, a PV-based grid-connected multi-level inverter with the proposed control strategy undergoes modeling and simulation using MATLAB/Simulink. Then, experimental hardware-in-the-loop (EHIL) testing is conducted to confirm and evaluate its effectiveness. © The Author(s) under exclusive licence to The Korean Institute of Power Electronics 2024.Item Dynamic mode decomposition based fault diagnosis in three-phase electrical machines(Elsevier B.V., 2025) RAJENDRAN, S.; Sreejesh, R.; Devi, V.S.; Jena, D.; Banjerdpongchai, D.Three-phase electrical machines are widely used in various industrial applications, and the mechanical fault in those machines leads to an oscillation in the load torque, which introduces an amplitude and/or phase modulation in the stator current. This work proposes a methodology to detect mechanical faults in rotating machines using dynamic mode decomposition (DMD). The DMD technique utilises singular value decomposition (SVD) to extract the dominant modulation frequencies and indexes by constructing a shifted-stack matrix of the three-phase current signal. Further, to validate the proposed methodology, an unbalance in the three-phase signal is examined, which includes both amplitude and phase unbalance with additive white Gaussian noise. The results demonstrated that the proposed DMD extracts the modulation frequencies and indexes with a maximum error of 2%. Additionally, different fault severities are considered to establish a real-time scenario, and the results show that the proposed DMD effectively identifies the faults with a maximum error of 1.3%. © 2024 The Author(s)