Indukala P. K., currently serves as Research Associate at the Amrita Center for Wireless Networks & Applications (Amrita WNA), Amritapuri. She pursued her M. Tech. in Computational Engineering and Networking, from Amrita School of Engineering, Coimbatore in 2013 and B. Tech. in Information Technology from College of Engineering, Thalassery (CUSAT) in 2008. She is also pursuing Ph. D. in Wireless Sensor Networks and her research work is on design and development of context aware smart sensor networks for early warning and detection of landslides, under the guidance of Dr. Seshaiah Ponnekanti.
Research Group: Machine Learning & Artificial Intelligence, Communication Systems for Extreme Environments: Landslide & Oceannet, IoT & Bigdata
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2018 |
D. Arjun, Indukala, P. K., and K. A. Unnikrishna Menon, “Border surveillance and intruder detection using wireless sensor networks: A brief survey”, in 2017 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, 2018.[Abstract] Intrusion of terrorists and trespassers are adversely affecting the peace and harmony in the nation. The fatalities and disturbances caused by the latest Uri attack in Indian Army Camp show the necessity of an efficient border surveillance and intruder detection system for the effective monitoring and detecting the unauthorized movement of intruders across the national borders. Conventional border patrolling lacks an integrated multi-sensing system that coordinates various technologies for surveillance and detection of human intruder movement in the different border scenarios: flat surface movement, river/pond crossing and dry leaves movement. This paper describes the current Wireless Sensor Network (WSN) techniques related to intruder detection and border surveillance. Our future work focuses on delivering an improved multi-sensing system for detecting intrusion activities to secure the national borders. More »» |
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2012 |
P. K. Indukala, Lakshmi, K., Sowmya, and Dr. Soman K. P., “Implementation of ℓ 1 magic and one bit compressed sensing based on linear programming using excel”, International Conference on Advances in Computing and Communications, ICACC 2012. IEEE, Kochi, Kerala, pp. 69-72, 2012.[Abstract] Compressed sensing helps in the reconstruction of sparse or compressible signals from small number of measurements. The sparse representation has great importance in modern signal processing. The main objective is to provide a strong understanding of the concept behind the theory of compressed sensing by using the key ideas from linear algebra. In this paper, the concept of compressed sensing is explained through an experiment formulated based on linear programming and solved using l1 magic and One bit compressed sensing methods in Excel. © 2012 IEEE. More »» |