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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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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.
Machine to machine communication enabled internet of things: a review Rajagopal Sudarmani; Kanagaraj Venusamy; Sathish Sivaraman; Poongodi Jayaraman; Kannadhasan Suriyan; Manjunathan Alagarsamy
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 11, No 2: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v11.i2.pp126-134

Abstract

Internet of things (IoT) will be the main part in upcoming generation devices that would not simply sense and report, also will have the controlling capability. It may be a connected vehicle, connected devices, robot, a building automation system, a door lock or a thermostat, these connected machines or devices will provide greater impact on our daily lives. Control data and the operating instructions could be protected to ensure control and autonomy for our safety and security, this could be a critical task. Privacy and security are important consideration in designing the system. With the intense growth of devices or devices with facilities such as computing and communication are carried out using a profound technology known as machine to machine (M2M) communication, which is specially designed for cross‐platform integration. In many industries, smart homes, smart cities, smart agriculture, government, connected devices, security, healthcare, education, public safety, and supply chain management. Internet of things (IoT) and machine to machine communication have to be implemented in near future. Also, this paper gives an in depth view about the different M2M techniques with interconnected IoT for truly connected, smart, and sustainable world.
IoT based E-vehicle monitoring system using sensors and imaging processing algorithm Manjunathan Alagarsamy; Prabakaran Kasinathan; Geethalakshmi Manickam; Prabu Ragavendiran Duruvarajan; Jeevitha Sakkarai; Kannadhasan Suriyan
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 11, No 2: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v11.i2.pp196-204

Abstract

Human evolution has included the development of transportation systems. People are currently driving a significant number of fuel-powered automobiles. This resulted in an increase in the number of accidents as well as pollution in the environment. To address the disadvantages of gasolinebased vehicles, this study presents an IoT-based E-vehicle monitoring system (E-VMS) for early accident detection and to make the environment cleaner and greener by using alternative energy. E-VMS employs internet of things (IoT) technology to continuously monitor the vehicle as well as to access and control it remotely. The IoT devices installed in vehicles are built using an Arduino microcontroller and sensors to detect accidents quickly. When an accident occurs, the E-VMS recognizes it quickly and determines the severity of the incident. The machine will then promptly alert the authorities. The E-VMS is also familiar with the GPS system. This will allow the E-VMS to maintain track of the cars' location in real time. This information will be used to locate the car in the event of an accident or theft. The E-VMS system's results were promising in terms of accurately identifying accidents, determining the severity of the accident, and determining the position of the vehicle.
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.
Development of electrocardiogram intelligent and wearable monitoring system-assisting in care Manjunathan Alagarsamy; Jemin Vijayaselvan Mariyarose; Nithya Devi Shanmugam; Joseph Michael Jerard Vedam; Mary Dallfin Bruxella Joseph; Kannadhasan Suriyan
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i1.pp51-59

Abstract

The primary factor contributing to the high mortality rate in our country is coronary heart disease, which affects almost 50% of people from rural regions. Internet of things (IoT) contributes effectively to the development of point of care (POC) gadgets that support the medical upkeep of an expanding agricultural population. An electrocardiogram test is crucial for analysing cardiac disorders. Therefore, we must develop a POC piece of hardware to assess the health of the heart in an affordable manner and to design it for the patients without interfering with their daily regular procedure in order to monitor the patient's coronary heart disease. As a result, we must design an uninterrupted workbench, which consists of three main integrated parts. The first is a mobile Bluetooth low energy device that has 5-lead electrocardiogram (ECG) surveillance equipment and the smallest form factor. The smart phone Android application that inherits, resolves, and maps the data sent from the ECG device comes next. The patient's information and report details are then compiled on a cloud server for the doctor's future attribution needs.
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