Faculty Publications

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Author: search.filters.author.Patel, S.Has files: No

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    Comparative analysis of Vertex Cover computation algorithms for varied graphs
    (Institute of Electrical and Electronics Engineers Inc., 2014) Patel, S.; Kamath S․, S.
    There are several vertex cover algorithms proposed for the solution of well-known NP-complete class problem of computing vertex cover. The Vertex Cover problem is important to address as it has various real world applications viz. Wireless Communication Network, Airline Communication Network, Terrorist Communication Network, etc. In this paper, we present a comparative evaluation of different existing algorithms like approximation, list, greedy and Alom's for most efficiently computing vertex cover over a variety of large graphs. Our empirical study found that Alom's algorithm performs consistently better than the other algorithms for all types of graphs, regardless of their class and number of vertices in the graph, while approximation algorithms show the worst performance for very large graphs. © 2014 IEEE.
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    Improved approximation algorithm for vertex cover problem using articulation points
    (Institute of Electrical and Electronics Engineers Inc., 2014) Patel, S.; Kamath S․, S.
    There has been many vertex cover algorithms proposed for the solution of well-known NP-complete class problem of vertex cover. The Vertex Cover problem is important to address in graphs as it has various real world applications viz. Wireless Communication Network, Airline Communication Network, Terrorist Communication Network etc. In this paper, we propose a new algorithm based on Articulation Point, which reduces the vertex cover computation problem in polynomial time and yield solution nearer to an optimal solution, better than the classical approach. We also present a Graphical Visualization Tool that allows the automatic application of the Improved Articulation Point based Approximation Algorithm to process large graphs and finds their articulation points for minimal vertex cover computation. The tool is currently under development.
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    Role of smart meters in smart city development in India
    (Institute of Electrical and Electronics Engineers Inc., 2017) Patel, S.; Uday Kumar, R.Y.; Prasanna Kumar, B.
    Day by day in India we are moving towards technologies so everything started becoming advance. The proposal of smart cities in India has already came which include better way of urbanization. Smart meters are the key component in smart cities. Here we are going to discuss about how smart meters will be helpful in making our energy consumption as well as metering system smart. Concepts of smart cities and smart grid and how they are dependent on smart meters will be discussed here. Smart meters has brought big revolution in the fields of energy and power measurement. Worldwide at so many places smart meters has been already deployed but in India it is just starting of deployment of smart meters here some reports regarding that are also discussed. © 2016 IEEE.
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    Automated Traffic Light Signal Violation Detection System Using Convolutional Neural Network
    (Springer, 2020) Bordia, B.; Nishanth, N.; Patel, S.; Anand Kumar, M.; Rudra, B.
    Automated traffic light violation detection system relies on the detection of traffic light color from the video captured with the CCTV camera, detection of the white safety line before the traffic signal and vehicles. Detection of the vehicles crossing traffic signals is generally done with the help of sensors which get triggered when the traffic signal turns red or yellow. Sometimes, these sensors get triggered even when the person crosses the line or some animal crossover or because of some bad weather that gives false results. In this paper, we present a software which will work on image processing and convolutional neural network to detect the traffic signals, vehicles and the white safety line present in front of the traffic signals. We present an efficient way to detect the white safety line in this paper combined with the detection of traffic lights trained on the Bosch dataset and vehicle detection using the TensorFlow object detection SSD model. © 2020, Springer Nature Singapore Pte Ltd.
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    Performance assessment of waste heat/solar driven membrane-based simultaneous desalination and liquid desiccant regeneration system using a thermal model and KNN machine learning tool
    (Elsevier B.V., 2021) Kiran Naik, B.; Chinthala, M.; Patel, S.; Ramesh, P.
    In this work, the waste heat/solar heat-driven membrane-based liquid desiccant regenerator performance, as well as desalinated water extraction rate, are predicted and analyzed by developing a thermal model and KNN–ML tool. In the membrane-based liquid desiccant regenerator, water is used as a working fluid instead of scavenging air for desalinated water extraction purpose. The proposed thermal model and KNN-ML tool are validated with the literature data and found in good agreement. Optimal inlet conditions were determined for the given operating range using the thermal model and KNN–ML tool. With vapor flux and energy exchange as the performance indicators, the water extraction rate and thermal performance of the membrane-based liquid desiccant regenerator are predicted for the optimal inlet condition using the KNN-ML tool. Also, the heat and mass transfer characteristics such as Lewis number and NTUm across the membrane in the liquid desiccant regenerator are assessed using the developed thermal model. Further, for the optimal inlet conditions, utilizing waste heat from thermal power plants (Method–I) and solar energy from solar heater (Method–II), the thermal performance and water extraction rate across the membrane in the liquid desiccant regenerator are assessed based on the developed thermal model. © 2021 Elsevier B.V.
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    Thermo-mechanical energy harvesting and storage analysis in 0.6BZT-0.4BCT ceramics
    (EDP Sciences, 2021) Patel, S.; Kumar, M.; Kashyap, Y.
    Present work shows waste energy (thermal/mechanical) harvesting and storage capacity in bulk lead-free ferroelectric 0.6Ba(Zr0.2Ti0.8)O3-0.4(Ba0.7Ca0.3)TiO3 (0.6BZT-0.4BCT) ceramics. The thermal energy harvesting is obtained by employing the Olsen cycle under different stress biasing, whereas mechanical energy harvesting calculated using the thermo-mechanical cycle at various temperature biasing. To estimate the energy harvesting polarization-electric field loops were measured as a function of stress and temperatures. The maximum thermal energy harvesting is obtained equal to 158 kJ/m3 when the Olsen cycle operated as 25-81 °C (at contact stress of 5 MPa) and 0.25-2 kV/mm. On the other hand, maximum mechanical energy harvesting is calculated as 158 kJ/m3 when the cycle operated as 5-160 MPa (at a constant temperature of 25 °C) and 0.25-2 kV/mm. It is found that the stress and temperature biasing are not beneficial for thermal and mechanical energy harvesting. Further, a hybrid cycle, where both stress and temperature are varied, is also studied to obtain enhanced energy harvesting. The improved energy conversion potential is equal to 221 kJ/m3 when the cycle operated as 25-81 °C, 5-160 MPa and 0.25-2 kV/mm. The energy storage density varies from 43 to 66 kJ/m3 (increase in temperature: 25-81 °C) and 43-80 kJ/m3 (increase in stress: 5-160 MPa). Also, the pre-stress can be easily implemented on the materials, which improves energy storage density almost 100% by stress induced domain switching. The results show that stress confinement can be used to enhance energy storage effectively. © EDP Sciences, 2021.