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All Journal The Indonesian Journal of Computer Science
Lwin, Zin Mar
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Computer Science & IT Electrical & Electronics Engineering Engineering

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IoT Based Environmental Monitoring System for Residential Building with LoRa Technology Zin Mar; Lwin, Zin Mar; Hla, Tin Tin
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4745

Abstract

This paper introduces an environmental monitoring system for residential buildings that combines PIC microcontroller functionality with LoRa technology. The system integrates motion, flame, gas, vibration, temperature, and humidity sensors to provide real-time monitoring of environmental conditions and safety risks. The PIC microcontroller acts as the system's core, gathering and processing data from the sensors before transmitting it over a LoRa network to a remote station. LoRa technology is employed for its long-range and low-power communication capabilities, making the system energy-efficient and reliable for residential applications. By delivering timely alerts and insights into potential hazards, this system enhances safety and livability in residential settings. The results highlight the practicality of the proposed design and its potential for integration into smart building applications, offering a scalable and efficient solution for modern environmental monitoring challenges.
Deep Learning based Channel Estimation and Hybrid Beamforming for 5G Massive MIMO Wireless Communications Tun, Thwe Zin; Lwin, Zin Mar; Hla, Tin Tin
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3941

Abstract

Hybrid beamforming (BF), which divides beamforming operation into radio frequency (RF) and baseband (BB) domains, will play a critical role in MIMO communication at millimeter-wave(mmWave) frequencies. This paper also introduce offline training and prediction schemes for channel estimation and hybrid beamforming. The aim of this paper is that to increase spectral efficiency over more data streams by leveraging the deep learning based LSTM network. The LSTM network is used to train the numeric values from sequence data and predict on new sequence data. The performance is evaluated under different parameters including number of data streams (1, 2, 3 and 4) with different signal-to-noise ratio (SNR) for different carrier frequencies (28GHz, 38GHz, 60GHz and 73GHz) through computer simulation using MATLAB. The simulation results verified that the proposed method can achieve higher spectral efficiency when the number of data streams increases and the value of SNR-Test increases too.
Co-Authors Hla, Tin Tin Tun, Thwe Zin Zin Mar