Faculty Publications
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Publications by NITK Faculty
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Item A novel strategy to save energy consumed in electric water lifting for multi storied buildings(2007) Kappali, M.; Kumar, U.Electric water pumps have become almost an essential commodity for urban households. Existing practice of water lifting in multi-storied buildings employs Top Floor Storage Method (TFSM). Here considerable amount of water is pumped to unnecessary heights, resulting in energy wastage. This paper presents a novel scheme "Individual Floor Storage Method" (IFSM). Here water is pumped to the required optimum heights thus saving energy. Proposed method is analyzed for two different cases: (i) A two-storied house and (ii) A three-storied student hostel. The energy saving is to the extent of 10% for the first case and 20% with the payback period of 3.5 years for the second case. As a concept, IFSM is very simple and economic. It yields greater benefits i.e. higher energy saving if implemented for multistoried buildings with large water consumption like hospitals, hostels, flat complexes etc. In the present day scenario of mushrooming multi storied buildings and escalating energy costs, IFSM is more appropriate compared to the existing TFSM.Item FMCW Radar-Based Detection and Tracking of Drones Using DBSCAN Clustering and Extended Kalman Filter for Anti-Drone Defense Systems(Institute of Electrical and Electronics Engineers Inc., 2024) Srihari, P.; Vandana, G.S.; Kumar, U.; Nandagiri, A.; Pardhasaradhi, B.P.; Cenkarmaddi, L.R.This paper aims to develop a radar-based detection and tracking system to mitigate the threats posed by drones, particularly those carrying malicious payloads. Due to the limitations of cameras in adverse weather and the high costs of LiDAR systems, radar technology is employed as a cost-effective alternative. The system utilizes 3D FFT followed by CA-CFAR for drone range-azimuth detections. The range-azimuth detections are clustered using DBSCAN. We simplified the extended target tracking problem into point target tracking based on the drone's size, with the dBSCAN cluster center acting as the measurement for the tracker. The tracking algorithm combines an Extended Kalman Filter (EKF) with Global Nearest Neighbor (GNN) data association. Experiments were conducted using a 77 GHz AWR1642 radar sensor to track a micro drone of hexacopter type within a range of 10m to 100m. The results demonstrated effective tracking capabilities with radar sensors successfully generating tracks. This study highlights the viability of radar-based systems for anti-drone applications, offering a practical solution for enhancing infrastructure security against potential drone threats. © 2024 IEEE.Item Real-Time UAV Altitude Estimation and Data Transmission via mmWave Radar and Edge Computing(Institute of Electrical and Electronics Engineers Inc., 2024) Vandana, G.S.; Srihari, P.; Kumar, U.; Nandagiri, A.; Pardhasaradhi, B.P.; Cenkarmaddi, L.R.This paper presents a novel approach for UAV altitude estimation and data transmission using a 60 GHz IWR6843 mmWave radar mounted on a micro-drone, coupled with a Raspberry Pi edge device. The radar, configured in a long-range mode, leverages its high accuracy in altitude measurement, surpassing the performance of traditional UAV altimeters. The radar altimeter data is processed on the Raspberry Pi and wirelessly transmitted to the cloud, from which it can be accessed by a ground station for real-time monitoring and analysis. To validate the accuracy of the radar-based altitude measurements, GPS data is simultaneously recorded on the UAV, serving as a ground truth reference. Experimental results demonstrate that the radar-based measurements closely match the GPS-derived altitudes, showcasing the effectiveness of the proposed system. This approach not only improves altitude estimation accuracy but also enhances the reliability of UAV operations in various environments. Potential applications of this system include precision agriculture, disaster management, and search and rescue operations, where accurate altitude data is critical for mission success. The integration of mmWave radar with edge computing and cloud-based data management opens new avenues for real-time UAV monitoring and autonomous navigation. © 2024 IEEE.