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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Bulletin of Electrical Engineering and Informatics
Kannadhasan Suriyan
Cheran College of Engineering
Economics, Econometrics & Finance Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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Comparative study of BER With NOMA system in different fading channels Roselin Suganthi Jesudoss; Rajeswari Kaleeswaran; Manjunathan Alagarsamy; Dineshkumar Thangaraju; Dinesh Paramathi Mani; Kannadhasan Suriyan
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3311

Abstract

In today's world, cellular communication is rapidly expanding. One of the most common strategies for assigning the spectrum of users in cellular communication is the multiple access strategy. Because the number of people using cellular communication is continually expanding, spectrum allotment is an important factor to consider. To access the channel in fifth-generation mobile communication, a method known as non-orthogonal multiple access (NOMA) is used. NOMA is a promising method for improving sum rate and spectral efficiency. In this research, we used the NOMA approach to compare the bit error rate (BER) versus signal to noise ratio (SNR) of two users in rayleigh, rician, and nakagami fading channels. A single antenna with two users is used in this NOMA system. Two users can tolerate the same frequency with differing power levels in the power domain using 5G NOMA technology. Non-orthogonality ensures that NOMA users are treated equally to OMA users. According to the MATLAB simulation findings, the BER vs. SNR of two user NOMA in the Nakagami channel is substantially better than the rayleigh and rician channels.
A comprehensive analysis on IoT based smart farming solutions using machine learning algorithms Ahamed Ali Samsu Aliar; Justindhas Yesudhasan; Manjunathan Alagarsamy; Karthikram Anbalagan; Jeevitha Sakkarai; Kannadhasan Suriyan
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3310

Abstract

Agriculture and farming are the most important and basic industries that are very important to humanity and generate a considerable portion of any nation's GDP. For good agricultural and farming management, technological advancements and support are required. Smart agriculture (or) farming is a set of approaches that uses a variety of current information and communication technology to improve the production and quality of agricultural products with minimum human involvement and at a lower cost. Smart farming is mostly based on IoT technology, since there is a need to continually monitor numerous aspects in the agricultural field, such as water level, light, soil characteristics, plant development, and so on. Machine learning algorithms are used in smart farming to increase production and reduce the risk of crop damage. Data analytics has been shown through extensive study to improve the accuracy and predictability of smart agricultural systems. Data analytics is utilised in agricultural fields to make decisions and recommend acceptable crops for production. This study provides a comprehensive overview of the different methods and structures utilised in smart farming. It also provides a thorough analysis of different designs and recommends appropriate answers to today's smart farming problems.
Classification of covid patient image dataset using modified deep convolutional neural network system Manjunathan Alagarsamy; Karthikram Anbalagan; Yuvaraja Thangavel; Jeevitha Sakkarai; Jenopaul Pauliah; Kannadhasan Suriyan
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3290

Abstract

The number of people infected with the corona virus is steadily rising. Even after being treated and returned to normality, many who were impacted are still suffering from a variety of health problems. We suggest a new, more effective approach to dealing with this issue, as well as putting in place preventative measures to prevent the spread of disease. The modified convolutional neural networks (M-CNN) architecture is modified deepCNN architecture. Using existingcorona virus disease 2019(COVID-19) computerizedtomographyscan (CT scan) images, this suggested approach intends to develop a deep model for screening and forecasting the risk of disease propagation. The suggested model was trained using 1000 scan pictures from various sources, yielding a prediction accuracy of 93 percent, which is much greater than previous methods.
Co-Authors Ahamed Ali Samsu Aliar Dinesh Paramathi Mani Dineshkumar Thangaraju Geethalakshmi Manickam Jeevitha Sakkarai Jemin Vijayaselvan Mariyarose Jenopaul Pauliah Joseph Michael Jerard Vedam Justindhas Yesudhasan Kanagaraj Venusamy Karthikram Anbalagan Karthikram Anbalagan Manjunathan Alagarsamy Manjunathan Alagarsamy Manjunathan Alagarsamy Mary Dallfin Bruxella Joseph Nithya Devi Shanmugam Poongodi Jayaraman Prabakaran Kasinathan Prabu Ragavendiran Duruvarajan Rajagopal Sudarmani Rajeswari Kaleeswaran Roselin Suganthi Jesudoss Sathish Sivaraman Yuvaraja Thangavel