A Novel Frame Identification and Synchronization Technique for Smartphone Visible Light Communication Systems Based on Convolutional Neural Networks

• URL: http://arxiv.org/abs/2506.23004v1
• Date: Sat, 28 Jun 2025 20:29:37 GMT
• Title: A Novel Frame Identification and Synchronization Technique for Smartphone Visible Light Communication Systems Based on Convolutional Neural Networks
• Authors: Vaigai Nayaki Yokar, Hoa Le-Minh, Xicong Li, Wai Lok Woo, Luis Nero Alves, Stanislav Zvanovec, Tran The Son, Zabih Ghassemlooy,
• Abstract summary: This paper proposes a novel, robust, and lightweight supervised Convolutional Neural Network (CNN)-based technique for frame identification and synchronization.<n>The proposed CNN model was trained through three real-time experimental investigations conducted in Jupyter Notebook.<n>Experiments incorporated a dataset created from scratch to address various real-time challenges in S2C communication, including blurring, cropping, and rotated images in mobility scenarios.
• Score: 3.8544293389005593
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes a novel, robust, and lightweight supervised Convolutional Neural Network (CNN)-based technique for frame identification and synchronization, designed to enhance short-link communication performance in a screen-to-camera (S2C) based visible light communication (VLC) system. Developed using Python and the TensorFlow Keras framework, the proposed CNN model was trained through three real-time experimental investigations conducted in Jupyter Notebook. These experiments incorporated a dataset created from scratch to address various real-time challenges in S2C communication, including blurring, cropping, and rotated images in mobility scenarios. Overhead frames were introduced for synchronization, which leads to enhanced system performance. The experimental results demonstrate that the proposed model achieves an overall accuracy of approximately 98.74%, highlighting its effectiveness in identifying and synchronizing frames in S2C VLC systems.

Related papers

• A study on audio synchronous steganography detection and distributed guide inference model based on sliding spectral features and intelligent inference drive [3.5516803380598074]
  This paper proposes a detection and distributed guidance reconstruction model based on short video "Yupan" samples released by China's South Sea Fleet on TikTok.<n>The proposed framework validates the effectiveness of sliding spectral features for synchronized steganography detection and builds an inference model for covert communication analysis and tactical guidance simulation on open platforms.
  arXiv  Detail & Related papers  (2025-05-06T05:24:11Z)
• Revelio: A Real-World Screen-Camera Communication System with Visually Imperceptible Data Embedding [1.9924262168387745]
  We present Revelio', a real-world screen-camera communication system leveraging temporal flicker fusion in the OKLAB color space.<n>Revelio achieves visually imperceptible data embedding while remaining robust against noise, asynchronicity, and distortions in screen-camera channels.
  arXiv  Detail & Related papers  (2025-01-04T18:05:08Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• CP-SLAM: Collaborative Neural Point-based SLAM System [54.916578456416204]
  This paper presents a collaborative implicit neural localization and mapping (SLAM) system with RGB-D image sequences. In order to enable all these modules in a unified framework, we propose a novel neural point based 3D scene representation. A distributed-to-centralized learning strategy is proposed for the collaborative implicit SLAM to improve consistency and cooperation.
  arXiv  Detail & Related papers  (2023-11-14T09:17:15Z)
• A Unified Framework for Event-based Frame Interpolation with Ad-hoc Deblurring in the Wild [72.0226493284814]
  We propose a unified framework for event-based frame that performs deblurring ad-hoc.<n>Our network consistently outperforms previous state-of-the-art methods on frame, single image deblurring, and the joint task of both.
  arXiv  Detail & Related papers  (2023-01-12T18:19:00Z)
• Streaming Audio-Visual Speech Recognition with Alignment Regularization [69.30185151873707]
  We propose a streaming AV-ASR system based on a hybrid connectionist temporal classification ( CTC)/attention neural network architecture. The proposed AV-ASR model achieves WERs of 2.0% and 2.6% on the Lip Reading Sentences 3 dataset in an offline and online setup.
  arXiv  Detail & Related papers  (2022-11-03T20:20:47Z)
• Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural Network [14.796204921975733]
  Dual-view snapshot compressive imaging (SCI) aims to capture videos from two field-of-views (FoVs) in a single snapshot. It is challenging for existing model-based decoding algorithms to reconstruct each individual scene. We propose an optical flow-aided recurrent neural network for dual video SCI systems, which provides high-quality decoding in seconds.
  arXiv  Detail & Related papers  (2021-09-11T14:24:44Z)
• Self-Supervised Multi-Frame Monocular Scene Flow [61.588808225321735]
  We introduce a multi-frame monocular scene flow network based on self-supervised learning. We observe state-of-the-art accuracy among monocular scene flow methods based on self-supervised learning.
  arXiv  Detail & Related papers  (2021-05-05T17:49:55Z)
• Event-based Asynchronous Sparse Convolutional Networks [54.094244806123235]
  Event cameras are bio-inspired sensors that respond to per-pixel brightness changes in the form of asynchronous and sparse "events" We present a general framework for converting models trained on synchronous image-like event representations into asynchronous models with identical output. We show both theoretically and experimentally that this drastically reduces the computational complexity and latency of high-capacity, synchronous neural networks.
  arXiv  Detail & Related papers  (2020-03-20T08:39:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.