Faculty Publications
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Publications by NITK Faculty
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Item Development of a relay test bench and an arbitrary waveform generator in RTAI-Linux platform(2013) Hazarika, P.P.; Shubhanga, K.N.This paper presents the development of a PC-based relay test bench and an arbitrary waveform generator using an open-source Real-Time Application Interface (RTAI) in Linux environment. Such a setup not only permits the generation of any arbitrary waveforms: steady-state signals as well as transients, but also simplifies the testing of various single-phase or three-phase static relays such as over-voltage relay, directional power relay, negative sequence relay as well as numerical impedance relays. The actuating signal is fed to the relay under test using a set-up that consists of a PCI card with a built-in Digital to Analog Converter and a feedback circuit for detecting the status of the relay. Using any off-line simulation package, sampled data points of a desired waveform are obtained in the form of a data-file. The RTAI-Linux program generates this waveform in real-time which is directly fed to the relay under test. The relay test bench presented in this paper is a cost-effective set-up for laboratories to test and understand the behavior and characteristics of relaying systems. © 2013 IEEE.Item Development of low-cost real-time driver drowsiness detection system using eye centre tracking and dynamic thresholding(Springer Verlag service@springer.de, 2020) Khan, F.; Sharma, S.One in every five vehicle accidents on the road today is caused simply due to driver fatigue. Fatigue or otherwise drowsiness, significantly reduces the concentration and vigilance of the driver thereby increasing the risk of inherent human error leading to injuries and fatalities. Hence, our primary motive being - to reduce road accidents using a non-intrusive image processing based alert system. In this regard, we have built a system that detects driver drowsiness by real time tracking and monitoring the pattern of the driver’s eyes. The stand alone system consists of 3 interconnected components - a processor, a camera and an alarm. After initial facial detection, the eyes are located, extracted and continuously monitored to check whether they are open or closed on the basis of a pixel-by-pixel method. When the eyes are seen to be closed for a certain amount of time, drowsiness is said to be detected and an alarm is issued accordingly to alert the driver and hence, prevent a casualty. © Springer Nature Switzerland AG 2020.Item Dynamic performance of microturbine generation system connected to a grid(2008) Gaonkar, D.N.; Pillai, G.N.; Patel, R.N.The interconnection of distribution generation systems into distribution networks has great impact on real-time system operation, control, and planning. It is widely accepted that microturbine generation (MTG) systems are currently attracting a lot of attention to meet customers' needs in the distributed power generation market. In order to investigate the performance of MTG systems, their efficient modeling is required. This article presents the dynamic model of an MTG system, suitable for grid connection to study the performance of the MTG system. The presented model uses back-to-back power electronic converter topology for grid connection, which allows the bidirectional power flow between the grid and MTG system. Thus, the need of separate starting arrangements during launching of the microturbine is avoided. The components of the system are built from the dynamics of each part with their interconnections. The dynamics of the model have been studied under various grid disturbance conditions. The converter control strategies for MTG system operation in grid-connected mode are presented in this article. This article also compares the various grid connection topologies suitable for MTG system interconnection. The simulation results show that the developed model performance is not affected by the grid disturbances considered in the study, and that it has the ability to adjust the supply as per the power requirements of the load within the MTG system rating.Item Automated multi-level malware detection system based on reconstructed semantic view of executables using machine learning techniques at VMM(Elsevier B.V., 2018) M.a, A.K.; Jaidhar, C.D.In order to fulfill the requirements like stringent timing restraints and demand on resources, Cyber–Physical System (CPS) must deploy on the virtualized environment such as cloud computing. To protect Virtual Machines (VMs) in which CPSs are functioning against malware-based attacks, malware detection and mitigation technique is emerging as a highly crucial concern. The traditional VM-based anti-malware software themselves a potential target for malware-based attack since they are easily subverted by sophisticated malware. Thus, a reliable and robust malware monitoring and detection systems are needed to detect and mitigate rapidly the malware based cyber-attacks in real time particularly for virtualized environment. The Virtual Machine Introspection (VMI) has emerged as a fine-grained out-of-VM security solution to detect malware by introspecting and reconstructing the volatile memory state of the live guest Operating System (OS) by functioning at the Virtual Machine Monitor (VMM) or hypervisor. However, the reconstructed semantic details by the VMI are available in a combination of benign and malicious states at the hypervisor. In order to distinguish between these two states, extensive manual analysis is required by the existing out-of-VM security solutions. To address the foremost issue, in this paper, we propose an advanced VMM-based guest-assisted Automated Multilevel Malware Detection System (AMMDS) that leverages both VMI and Memory Forensic Analysis (MFA) techniques to predict early symptoms of malware execution by detecting stealthy hidden processes on a live guest OS. More specifically, the AMMDS system detects and classifies the actual running malicious executables from the semantically reconstructed process view of the guest OS. The two sub-components of the AMMDS are: Online Malware Detector (OMD) and Offline Malware Classifier (OFMC). The OMD recognizes whether the running processes are benign or malicious using its Local Malware Signature Database (LMSD) and online malware scanner and the OFMC classify unknown malware by adopting machine learning techniques at the hypervisor. The AMMDS has been evaluated by executing large real-world malware and benign executables on to the live guest OSs. The evaluation results achieved 100% of accuracy and zero False Positive Rate (FPR) on the 10-fold cross-validation in classifying unknown malware with maximum performance overhead of 5.8%. © 2017 Elsevier B.V.Item Coordinated volt-var control: Online voltage-profile estimation in smart distribution networks(Institute of Electrical and Electronics Engineers Inc., 2018) Raghavendra, P.; Gaonkar, D.N.The increasing penetration of intermitt ent and stochastic distributed generation (DG) sources in the existing power grid can lead to voltage rise problems. Meanwhile, the rapid development of smart grid technologies calls for effective solutions to realize the real-time measurements and coordinated volt-var control to improve the overall systems voltage profile (VP). This article presents online VP estimation and coordinated volt-var control in a smart distribution network with multiple DG systems. © 1975-2012 IEEE.Item Dynamic video anomaly detection and localization using sparse denoising autoencoders(Springer New York LLC barbara.b.bertram@gsk.com, 2018) Narasimhan, M.G.; Kamath S?, S.The emergence of novel techniques for automatic anomaly detection in surveillance videos has significantly reduced the burden of manual processing of large, continuous video streams. However, existing anomaly detection systems suffer from a high false-positive rate and also, are not real-time, which makes them practically redundant. Furthermore, their predefined feature selection techniques limit their application to specific cases. To overcome these shortcomings, a dynamic anomaly detection and localization system is proposed, which uses deep learning to automatically learn relevant features. In this technique, each video is represented as a group of cubic patches for identifying local and global anomalies. A unique sparse denoising autoencoder architecture is used, that significantly reduced the computation time and the number of false positives in frame-level anomaly detection by more than 2.5%. Experimental analysis on two benchmark data sets - UMN dataset and UCSD Pedestrian dataset, show that our algorithm outperforms the state-of-the-art models in terms of false positive rate, while also showing a significant reduction in computation time. © 2017, Springer Science+Business Media, LLC.Item A Google Glass Based Real-Time Scene Analysis for the Visually Impaired(Institute of Electrical and Electronics Engineers Inc., 2021) Ali A, H.; Rao, S.U.; Ranganath, S.; Ashwin, T.S.; Guddeti, G.R.M.Blind and Visually Impaired People (BVIP) are likely to experience difficulties with tasks that involve scene recognition. Wearable technology has played a significant role in researching and evaluating systems developed for and with the BVIP community. This paper presents a system based on Google Glass designed to assist BVIP with scene recognition tasks, thereby using it as a visual assistant. The camera embedded in the smart glasses is used to capture the image of the surroundings, which is analyzed using the Custom Vision Application Programming Interface (Vision API) from Azure Cognitive Services by Microsoft. The output of the Vision API is converted to speech, which is heard by the BVIP user wearing the Google Glass. A dataset of 5000 newly annotated images is created to improve the performance of the scene description task in Indian scenarios. The Vision API is trained and tested on this dataset, increasing the mean Average Precision (mAP) score from 63% to 84%, with an IoU > 0.5. The overall response time of the proposed application was measured to be less than 1 second, thereby providing accurate results in real-time. A Likert scale analysis was performed with the help of the BVIP teachers and students at the 'Roman Catherine Lobo School for the Visually Impaired' at Mangalore, Karnataka, India. From their response, it can be concluded that the application helps the BVIP better recognize their surrounding environment in real-time, proving the device effective as a potential assistant for the BVIP. © 2013 IEEE.Item Cardamom Plant Disease Detection Approach Using EfficientNetV2(Institute of Electrical and Electronics Engineers Inc., 2022) Sunil, C.K.; Jaidhar, C.D.; Patil, N.Cardamom is a queen of spices. It is indigenously grown in the evergreen forests of Karnataka, Kerala, Tamil Nadu, and the northeastern states of India. India is the third largest producer of cardamom. Plant diseases cause a catastrophic influence on food production safety; they reduce the eminence and quantum of agricultural products. Plant diseases may cause significantly high loss or no harvest in dreadful cases. Various diseases and pests affect the growth of cardamom plants at different stages and crop yields. This study concentrated on two diseases of cardamom plants, Colletotrichum Blight and Phyllosticta Leaf Spot of cardamom and three diseases of grape, Black Rot, ESCA, and Isariopsis Leaf Spot. Various methods have been proposed for plant disease detection, and deep learning has become the preferred method because of its spectacular accomplishment. In this study, U2-Net was used to remove the unwanted background of an input image by selecting multiscale features. This work proposes a cardamom plant disease detection approach using the EfficientNetV2 model. A comprehensive set of experiments was carried out to ascertain the performance of the proposed approach and compare it with other models such as EfficientNet and Convolutional Neural Network (CNN). The experimental results showed that the proposed approach achieved a detection accuracy of 98.26%. © 2013 IEEE.Item Voltage Profile Analysis in Smart Grids Using Online Estimation Algorithm(Hindawi Limited, 2022) Raghavendra, P.; Nuvvula, R.S.S.; P Kumar, P.P.; Gaonkar, D.N.; Sathoshakumar, A.; Khan, B.Voltage rise is the main obstacle to prevent the increase of distributed generators (DGs) in low-voltage (LV) distribution grids. In order to maintain the power quality and voltage levels within the tolerance limit, new measurement techniques and intelligent devices along with digital communications should be used for better utilization of the distribution grid. This paper presents a real-time sensor-based online voltage profile estimation technique and coordinated Volt/VAR control in smart grids with distributed generator interconnection. An algorithm is developed for voltage profile estimation using real-time sensor remote terminal unit (RTU) which takes into account topological characteristics, such as radial structure and high R/X ratio, of the smart distribution grid with DG systems. A coordinated operation of multiple generators with on-load tap changing (OLTC) transformer for Volt/VAR control in smart grids has been presented. Direct voltage sensitivity analysis is used to select a single DG system for reactive power support in multi-DG environment. The on-load tap changing transformer is employed for voltage regulation when generators' reactive power contributions are not enough to regulate the voltages. Simulation results show that the reported method is capable of maintaining voltage levels within the tolerance limit by coordinated operation of DG systems and on-load tap changing transformer. © 2022 P. Raghavendra et al.Item FLAG: fuzzy logic augmented game theoretic hybrid hierarchical clustering algorithm for wireless sensor networks(Springer, 2022) Naik, C.; Shetty D, P.D.Stability of the wireless sensor network (WSN) is the most critical factor in real-time and data-sensitive applications like military and surveillance systems. Many energy optimization techniques and algorithms have been proposed to extend the stability of a wireless sensor network. Clustering is a well regarded method in the research communities among them. Hence, this paper presents hybrid hierarchical artificial intelligence based clustering techniques, named FLAG and I-FLAG. The first phase of these algorithms use game-theoretic technique to elect suitable cluster heads (CHs) and later phase of the algorithms use fuzzy inference system to select appropriate super cluster heads (SCHs) among CHs. The I-FLAG is an improved version of FLAG where additional parameters like energy and distance are considered to elect CHs. Simulations are performed to check superiority of the proposed algorithms over the existing protocols like LEACH, CHEF, and CROSS. Simulation results show that the average stability period of WSN is better in FLAG and I-FLAG compared to other protocols, and so is the throughput of WSN during the stability period. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.