Faculty Publications

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Publications by NITK Faculty

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    High-speed and reliable Underwater Wireless Optical Communication system using Multiple-Input Multiple-Output and channel coding techniques for IoUT applications
    (Elsevier B.V., 2020) Naik, P.N.; Acharya Udupi, S.; Krishnan, P.
    In this paper, we investigate the performance of an Underwater Wireless Optical Communication (UWOC) system employing on–off keying modulation at a data-rate of 500 Mbps over a link-range of 30 m. Transmit/receive diversity schemes, namely Multiple-Input to Single-Output (MISO), Single-Input to Multiple-Output (SIMO) and Multiple-Input to Multiple-Output (MIMO) techniques with and without RS-coding have been employed to mitigate the effects of weak oceanic turbulence and beam attenuation. The novel closed-form analytical Bit Error Rate (BER) expressions of Single-Input to Single-Output (SISO), SIMO, MISO and MIMO links for un-coded and RS-coded cases have been computed using the hyperbolic tangent distribution and validated with Monte-Carlo simulation results. The obtained BER results show that the use of (63,51) RS-coded 4 × 5 MIMO UWOC system offers at-least 35 dB of transmit power gain compared with the un-coded SISO UWOC system at a BER of 10?5. Emerging technologies like the fifth-generation (5G) networks and the Internet of Underwater Things (IoUT) will have a high impact on UWOC as these systems require a high degree of information integrity, high data rates and energy efficiency when employed in conjunction with data transfer between underwater vehicles and objects. The proposed RS-coded MIMO UWOC system offers high reliability and power efficiency and it has the potential to be gainfully employed in IoUT applications. © 2020 Elsevier B.V.
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    Evaluation of artificial neural network in data reduction for a natural convection conjugate heat transfer problem in an inverse approach: experiments combined with CFD solutions
    (Springer, 2020) Kumar, M.K.H.; Vishweshwara, P.S.; Gnanasekaran, N.
    In this work, natural convection fin experiments are performed with mild steel as the fin and an aluminium plate as base. The dimension of the mild steel fin is 250 mm × 150 mm × 6 mm and the aluminium base plate is 250 mm × 150 mm × 8 mm. A heater is provided on one side of the aluminium base plate and the mild steel fin emerges on the other side of the plate. The heater provides required heat flux to the fin base; several steady-state natural convection experiments are performed for different heat fluxes and corresponding temperature distributions are recorded using thermocouples at different locations of the fin. In addition, a numerical model is developed that contains the dimensions of the fin set-up along with extended domain to capture the information of the fluid. Air is treated as a working fluid that enters the extended domain and absorbs heat from the heated fin. The temperature and the velocity of the fluid in the extended domain are obtained by solving the Navier–Stokes equation. The numerical model is now treated as a forward model that provides the temperature distribution of the fin for a given heat flux. An inverse problem is proposed to determine the heat flux that leads to the temperature distributions during experiments. The temperature distributions of the experiments and forward model are compared to identify the unknown heat flux. In order to reduce computational cost of the inverse problem the forward model is then replaced with artificial neural network (ANN) as data reduction, which is developed using several computational fluid dynamics solutions, and the inverse estimation is accomplished. The results indicate that a quick solution can be obtained using ANN with a limited number of experiments. © 2020, Indian Academy of Sciences.