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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Huu Ai, Duong
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Average symbol error rate analysis of reconfigurable intelligent surfaces based free-space optical link over Weibull distribution channels Huu Ai, Duong; Tho Dang, Dai; Dat Vuong, Cong; Loi Nguyen, Van; Ty Luong, Khanh
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp443-450

Abstract

Optical wireless communication (OWC) enables wireless connectivity using ultraviolet bands, infrared or visible. With its advantages features as high bandwidth, low cost, and operation in an unregulated spectrum. Free-space optical (FSO) communication systems are near terrestrial as a communication link between transceivers, the link is line-of-sight and successfully transmitted optical signals. Nevertheless, the optical signals transmissions over the FSO channels bring challenges to the system. To overcome the challenges posed by the FSO channels, the most common technique is to use relay stations, the most recent is the reconfigurable intelligent surfaces (RISs) technique. This study introduces a Weibull distribution model for a free-space optical communication link with RISs assisted, the parameter used to evaluate the performance of the system is the average symbol error rate (ASER). The RISs effect is examined by considering the influence of the transmitter beam waist radius, shape parameter, aperture radius, scale parameter, and signal-to-noise ratio on the ASER.
Applications of artificial intelligence in indoor fire prevention and fighting Huu Ai, Duong; Nguyen, Van Loi; Luong, Khanh Ty; Le, Viet Truong
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2646-2654

Abstract

In this study, we design and analysis of artificial intelligence (AI) in indoor fire prevention and fighting. The application of image recognition processing technology has progressed from the early stages using color recognition and feature extraction methods, a newer approach is optical flow using image sequence data to identify motion regions. Image recognition processing technology, a subset of computer vision and AI, has numerous applications across different industries. It allows machines to interpret and make decisions based on visual data, such as photos, videos, or live camera feeds. Recently, AI has many applications in the field of indoor fire prevention and firefighting, leveraging real-time data analysis, predictive modeling, and automation to enhance safety and efficiency. With the application of a neural network, the simulated flame features in the laboratory are used as the input; The image containing the flame from the animation and the features of the image are fed into the artificial neural network obtained from the image from the charge-coupled device camera.
Co-Authors Dat Vuong, Cong Le, Viet Truong Loi Nguyen, Van Luong, Khanh Ty Nguyen, Van Loi Tho Dang, Dai Ty Luong, Khanh