Yeong Min Jang
Kookmin University
355 Papers
1.6K Citations
Yeong Min Jang is an academic researcher from Kookmin University. The author has contributed to research in topics: Computer science & Visible light communication. The author has an hindex of 28, co-authored 308 publications.
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Papers
A Novel Neural Network-Based Method for Decoding and Detecting of the DS8-PSK Scheme in an OCC System
TL;DR: An OCC vehicular system architecture with artificial intelligence (AI) functionalities is proposed, where dimmable spatial 8-phase shift keying (DS8-PSK) is employed as one out of two modulation schemes to form a hybrid waveform.
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Design of MIMO C-OOK using Matched filter for Optical Camera Communication System
Huy Nguyen,Yeong Min Jang +1 more
- 13 Apr 2021
TL;DR: In this article, the MIMO C-OOK modulation, which is upgraded from camera on-off keying (one of scheme is applied in IEEE 802.15.7-2018 standard), with the matched filter technique in receiver side, the proposed scheme can improve the data rate and communication distance comparing with the conventional scheme.
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Frequency shift on-off keying for optical camera communication
Nam-Tuan Le,Trang Nguyen,Yeong Min Jang +2 more
- 08 Jul 2014
TL;DR: The proposed flickering control scheme, named frequency shift ON-OFF keying, is based on two efficient synchronization protocols and analysis of shutter speed on subcarrier frequency of modulated bit and will be a promising technique for future trend of visible light communication.
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Priority-based resource allocation scheme for visible light communication
Muhammad Shahin Uddin,Mostafa Zaman Chowdhury,Yeong Min Jang +2 more
- 16 Jun 2010
TL;DR: This paper supports priority-based resource allocation and shows that priority schemes have better performances than non-priority schemes and can be used in the promising communication technology, visible light communication (VLC), an encouraging green and energy-efficient communication technology.
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Data Augmentation Using Generative Adversarial Network for Automatic Machine Fault Detection Based on Vibration Signals
TL;DR: A method of using the generative adversarial network as the fault signal augmentation method to enrich the dataset and conclude that the generated data could be used to mix with original data and improve the model performance.
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