Proceedings Article10.1109/ICUFN.2019.8806169
An Artificial Intelligence-based Error Correction for Optical Camera Communication
Tung Lam Pham,Trang Nguyen,Minh Duc Thieu,Huy Nguyen,Hoan Nguyen,Yeong Min Jang +5 more
- 02 Jul 2019
- pp 137-140
9
TL;DR: This paper proposed a novel error correction method based on the most trending technology - artificial intelligence (AI) to deal with the various existing issues in the communication channel.
read more
Abstract: Optical Camera Communication (OCC) is promising to be the candidate for vehicular wireless communication due to its low cost, unlicensed spectrum and safe for human. Our most recent approach is to add region-of-interest (RoI) signaling functionality for cars via either headlight or taillight using hybrid of a low-rate waveform and high-rate waveform, which is already standardized in IEEE 802.15.7 standard. However, commercialize OCC for vehicular communication still be challenging work. In this paper, we proposed a novel error correction method based on the most trending technology - artificial intelligence (AI) to deal with the various existing issues in the communication channel. The simulation for analyzing the performance of a new method in enhancing the performance of the communication system also be provided
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A comprehensive survey on hybrid wireless networks: practical considerations, challenges, applications and research directions
TL;DR: In this paper, the authors provide a technological overview of optical wireless hybrid networks, including optical-based free-space optics (FSO), optical camera communication (OCC), light fidelity (LiFi), RF-based Bluetooth, wireless fidelity (WiFi), small cell, macrocell, mmWave and microwave.
46
Deep Learning for Optical Vehicular Communication
TL;DR: The novel neural-network-based decoder and AI-based error correction proved effective in improving the data decoding accuracy, resulting in a best-case reduction of 2.2 and 9.0 dB, respectively, in the signal-to-noise ratio needed to achieve the desired bit error rate in a vehicular OCC/VLC system.
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.
18
Object Detection Framework for High Mobility Vehicles Tracking in Night-Time
Tung Lam Pham,Huy Nguyen,Hoan Nguyen,Van Hoa Nguyen,Van Bui,Yeong Min Jang +5 more
- 01 Feb 2020
TL;DR: Recent work on applying a well-known object detection framework - You Only Look Once (YOLO) to detect and track a large number of high mobility vehicles, which is also considered as region of interests (RoIs) in vehicular OCC system is introduced.
6
Artificial Intelligence-Driven Techniques to Advanced Signals and Communication Systems
10 Mar 2022
TL;DR: In this paper , a time-domain-based artificial intelligence radar provision was established using gesture consciousness utilizing a 33 GS/s direct copy approach, where a 1-D convolutional neural community was used, with consciousness costs of 93.2 percent and 90.5 percent.
1
References
Deep Residual Learning for Image Recognition
Kaiming He,Xiangyu Zhang,Shaoqing Ren,Jian Sun +3 more
- 27 Jun 2016
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
•Posted Content
Deep Residual Learning for Image Recognition
TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
117.9K
•Proceedings Article
Sequence to Sequence Learning with Neural Networks
Ilya Sutskever,Oriol Vinyals,Quoc V. Le +2 more
- 08 Dec 2014
TL;DR: The authors used a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector.
DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving
Chenyi Chen,Ari Seff,Alain L. Kornhauser,Jianxiong Xiao +3 more
- 07 Dec 2015
TL;DR: This paper proposes to map an input image to a small number of key perception indicators that directly relate to the affordance of a road/traffic state for driving and argues that the direct perception representation provides the right level of abstraction.
Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems
TL;DR: The proposed deep learning-based approach to handle wireless OFDM channels in an end-to-end manner is more robust than conventional methods when fewer training pilots are used, the cyclic prefix is omitted, and nonlinear clipping noise exists.