Author ORCID Identifier
https://orcid.org/0000-0002-5647-4470
Date of Award
5-13-2021
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Ashwin Ashok
Second Advisor
Anu Bourgeois
Third Advisor
Xiaojun Cao
Abstract
This thesis studies and evaluates Deep Neural Network models for data demodulation and decoding in a camera-based Visible Light Communication system. Camera communication is an emerging technology that enables communication using light beams, where information is modulated through optical transmissions from light-emitting diodes. This work conducts empirical studies to identify the feasibility and effectiveness of using Deep Learning models to improve signal reception in camera communication. The key contributions of this work include the investigation of transfer learning and customization of existing models to demodulate transmitted signals at the receiver end. The work expounds from a binary quantized system to a 3-bit and 4-bit quantized system. In addition to leveraging Deep Learning methods for demodulating a single VLC transmission, this thesis has developed a pipeline for integration of Deep Learning in a visual multiple-input multiple-output system where transmissions from an LED array are decoded by a camera receiver.
DOI
https://doi.org/10.57709/22708502
Recommended Citation
Ahmed, AbdulHaseeb, "Feasibility and Effectiveness Analysis of Deep Learning Vision Classification Models for Camera Communication." Thesis, Georgia State University, 2021.
doi: https://doi.org/10.57709/22708502
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