Conference Papers

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    Challenges in Developing Hardware-In-Loop Model of Cage Induction Motor with Eccentricity Fault
    (IEEE Computer Society help@computer.org, 2018) Ilamparithi, T.; Rangachar, B.; Rangineedi, T.; Nagendrappa, H.
    In this paper, an attempt is made to develop a MATLAB based Hardware-In-Loop compatible model of a three phase cage induction motor with a provision to accommodate eccentricity fault. The major benefits of the attempted work include compatibility with commercially available real-time simulators, facilitation of testing motor control units of electric vehicles when the motor is subjected to eccentricity fault, and ability to simulate in real-time varying severities of eccentricity fault. Modeling of the eccentricity fault is made by using the widely adopted modified winding function approach. Real-time simulation of the developed model is attempted by building a MATLAB model that utilizes pre-computed inductances for real-time execution. Finally, the major challenges encountered in this attempt are presented and discussed to pave way for further research. © 2018 IEEE.
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    Automated Parking System in Smart Campus Using Computer Vision Technique
    (Institute of Electrical and Electronics Engineers Inc., 2019) Banerjee, S.; Ashwin, T.S.; Guddeti, R.M.R.
    In today's world we need to maintain safety and security of the people around us. So we need to have a well connected surveillance system for keeping active information of various locations according to our needs. A real-time object detection is very important for many applications such as traffic monitoring, classroom monitoring, security rescue, and parking system. From past decade, Convolutional Neural Networks is evolved as a powerful models for recognizing images and videos and it is widely used in the computer vision related work for the best and most used approach for different problem scenario related to object detection and localization. In this work, we have proposed a deep convolutional network architecture to automate the parking system in smart campus with modified Single-shot Multibox Detector (SSD) approach. Further, we created our dataset to train and test the proposed computer vision technique. The experimental results demonstrated an accuracy of 71.2% for the created dataset. © 2019 IEEE.