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Visual System

Visual System

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...

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Underwater localization system based on visible-light communications using neural networks.

Alzahraa M Ghonim, Wessam M Salama, Abd El-Rahman A El-Fikky

|May 13, 2021

Summary

This study introduces a novel underwater localization method using visible-light communications and neural networks (NNs) for received signal strength (RSS) estimation. The system achieves high accuracy, demonstrating its potential for precise underwater positioning.

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Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

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Published on: April 18, 2025

Area of Science:

Optical Communications
Underwater Navigation
Machine Learning

Background:

Underwater localization is crucial for various applications.
Visible-light communication (VLC) offers a promising alternative for underwater data transmission.
Accurate positioning in underwater environments remains a challenge.

Purpose of the Study:

To propose and evaluate a novel underwater localization system.
To leverage neural networks (NNs) for received signal strength (RSS) based positioning.
To assess the performance and robustness of the proposed NN model.

Main Methods:

Data collection using Zemax OpticStudio Monte Carlo ray tracing software with 40,000 receivers.
Channel gain measurement in seawater to create input datasets for NNs.
NN system development and training using Orange data mining software.

Main Results:

Achieved high performance metrics: 99.1% Area Under the Curve (AUC), 98.7% classification accuracy (CA), F1 score, precision, and recall.
Logloss of 7.3% and specificity of 99.3% indicate a robust and accurate model.
Optimized NN parameters including training algorithms, activation functions, and neuron count.

Conclusions:

The proposed NN-based underwater localization system demonstrates high accuracy and robustness.
Visible-light communication combined with NNs is effective for underwater positioning.
The study provides a validated methodology for improving underwater navigation systems.