A Computer Control System for Home Appliances

In this paper, we present a system to control home appliances from a computer. The system is designed for controlling the ON/OFF mode of different home appliances such as light, fan, TV, air-condition and so on. The appliances are connected to a computer through a programmed PIC16F73 microcontroller. An USB interface is used to connect the microcontroller with a computer. The program for the PIC16F73 has been written in micro C language. All the commands are carried out from a software layout running on a computer to control the home appliances

Blockchain based secure data handover scheme in non-orthogonal multiple access

Non-orthogonal multiple access (NOMA) with successive interference cancellation receiver is considered as one of the most potent multiple access techniques to be adopted in future wireless communication networks. Data security in the NOMA transmission scheme is on much attention drawing issue. Blockchain is a distributed peer-to-peer network enables a way of protecting information from unauthorized access, tempering etc. By utilizing encryption techniques of blockchain, a secured data communication scheme using blockchain in NOMA is proposed in this paper. A two-phase encryption technique with key generation using different parameter is proposed. In the first-phase data is encrypted by imposing users' public key and in the second phase, a private key of the base station (BS) is engaged for encryption. Finally, the superiority of the proposed scheme over existing scheme is proven through a comparative study based on the different features.Comment: Published in 2018 4th International Conference on Wireless and Telematics (ICWT

Development of duplex eye contact framework for human-robot inter communication

Funding Information: This work was supported in part by the National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science and ICT) under Grant NRF 2020R1A2B5B02002478, in part by the Sejong University through its Faculty Research Program, and in part by the Directorate of Research and Extension (DRE), Chittagong University of Engineering and Technology.Peer reviewedPublisher PD

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis : Principles and Recent Advances

This work was supported in part by the National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science and ICT) under Grant NRF 2020R1A2B5B02002478, and in part by Sejong University through its Faculty Research Program under Grant 20212023.Peer reviewedPublisher PD

Neural network-based english alphanumeric character recognition

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