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Related Concept Videos

Visual System01:26

Visual System

558
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
558

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Real-Time Indoor Visible Light Positioning (VLP) Using Long Short Term Memory Neural Network (LSTM-NN) with Principal

Yueh-Han Shu1, Yun-Han Chang1, Yuan-Zeng Lin1

  • 1Department of Photonics & Graduate Institute of Electro-Optical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.

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|August 29, 2024
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Summary
This summary is machine-generated.

This study enhances indoor positioning accuracy using visible light. Combining a long short-term memory neural network (LSTM-NN) with principal component analysis (PCA) significantly reduces positioning errors, improving reliability for applications like augmented reality.

Keywords:
long short-term memory neural network (LSTM-NN)principal component analysis (PCA)visible light communication (VLC)visible light positioning (VLP)

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Area of Science:

  • Indoor Positioning Systems
  • Optical Wireless Communications
  • Machine Learning for Localization

Background:

  • Emerging applications like AR/VR, IoT, and AMR demand high-accuracy indoor positioning.
  • Visible Light Positioning (VLP) offers a promising solution for real-time tracking.
  • Received Signal Strength (RSS) based VLP is simple but prone to errors, especially at cell boundaries.

Purpose of the Study:

  • To propose and demonstrate a real-time VLP system with enhanced accuracy.
  • To mitigate positioning errors using a combination of LSTM-NN and PCA.
  • To improve the reliability of indoor tracking for various applications.

Main Methods:

  • Implemented a real-time Visible Light Positioning (VLP) system.
  • Utilized a Long Short-Term Memory Neural Network (LSTM-NN) for positioning.
  • Integrated Principal Component Analysis (PCA) with LSTM-NN to reduce positioning errors.

Main Results:

  • Achieved an average positioning error of 5.912 cm using LSTM-NN alone.
  • Reduced average positioning error to 1.806 cm with LSTM-NN and PCA, a 69.45% improvement.
  • 95% of experimental data showed errors <5 cm with LSTM-NN and PCA, compared to >15 cm with LSTM-NN alone.
  • Demonstrated the system's capability to predict direction and trajectory of moving receivers.

Conclusions:

  • The proposed VLP system effectively enhances indoor positioning accuracy.
  • The combination of LSTM-NN and PCA significantly improves precision, particularly at unit cell boundaries.
  • The system shows potential for real-time tracking and trajectory prediction in demanding applications.