Search research articles

Related Concept Videos

Uniform Depth Channel Flow: Problem Solving

Uniform Depth Channel Flow: Problem Solving

To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...

Modeling and Similitude

Modeling and Similitude

Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Decoding the human gut bacterial plasmids in colorectal cancer.

Communications biology·2026

Same author

High-Accuracy and Real-Time Fingerprint-Based Continual Learning Localization System in Dynamic Environment.

Sensors (Basel, Switzerland)·2025

Same author

Physical therapy options for knee osteoarthritis: A review.

Medicine·2024

Same author

On Improving 5G Internet of Radio Light Security Based on LED Fingerprint Identification Method.

Sensors (Basel, Switzerland)·2021

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jan 13, 2026

Characterization of Anisotropic Leaky Mode Modulators for Holovideo

Characterization of Anisotropic Leaky Mode Modulators for Holovideo

Published on: March 19, 2016

Adaptive Exposure Optimization for Underwater Optical Camera Communication via Multimodal Feature Learning and

Jiongnan Lou¹, Xun Zhang², Haifei Shen¹

¹School of Information Engineering, Huzhou University, Huzhou 313000, China.

Sensors (Basel, Switzerland)

|October 29, 2025

Summary

This study introduces an adaptive system for underwater optical camera communication (UOCC) that enhances reliability in variable conditions. The new method improves signal-to-noise ratio (SNR) for clearer underwater data transmission.

Keywords:

CMOS imaging adaptive exposure multimodal feature learning optical channel emulation underwater optical camera communication

More Related Videos

Simultaneously Capturing Real-time Images in Two Emission Channels Using a Dual Camera Emission Splitting System: Applications to Cell Adhesion

Simultaneously Capturing Real-time Images in Two Emission Channels Using a Dual Camera Emission Splitting System: Applications to Cell Adhesion

Published on: September 4, 2013

Related Experiment Videos

Last Updated: Jan 13, 2026

Characterization of Anisotropic Leaky Mode Modulators for Holovideo

Characterization of Anisotropic Leaky Mode Modulators for Holovideo

Published on: March 19, 2016

Simultaneously Capturing Real-time Images in Two Emission Channels Using a Dual Camera Emission Splitting System: Applications to Cell Adhesion

Simultaneously Capturing Real-time Images in Two Emission Channels Using a Dual Camera Emission Splitting System: Applications to Cell Adhesion

Published on: September 4, 2013

Area of Science:

Underwater optical communication
Autonomous underwater vehicles (AUVs)
Machine learning for environmental sensing

Background:

Underwater Optical Camera Communication (UOCC) is vital for AUVs but struggles with environmental variability (turbidity, scattering, illumination).
Fixed camera settings (exposure time, ISO sensitivity) limit UOCC reliability due to changing aquatic conditions.
Previous deep learning approaches for parameter prediction lacked environmental awareness and adaptability.

Purpose of the Study:

To develop an adaptive system for UOCC that overcomes limitations of fixed settings and improves communication reliability.
To introduce a Real-to-Sim-to-Deployment framework incorporating a Hybrid CNN-MLP Model (HCMM) for dynamic parameter prediction.
To enhance UOCC performance by enabling real-time adaptive reconfiguration based on environmental and camera states.

Main Methods:

Developed a physically calibrated emulation platform for reproducible optical channel simulation.
Introduced a Hybrid CNN-MLP Model (HCMM) that fuses optical images, environmental states, and camera configurations.
Validated the HCMM's performance through simulation and deployment on embedded hardware for real-time adaptation.

Main Results:

The HCMM achieved substantially improved parameter prediction accuracy, reducing RMSE to 0.23-0.33.
Real-time adaptive reconfiguration on embedded hardware resulted in up to 8.5 dB SNR gain.
The proposed system outperformed static-parameter systems and prior deep learning baselines in dynamic scenarios.

Conclusions:

Environment-aware multimodal learning, coupled with optical channel emulation, offers a scalable and robust solution for practical UOCC.
The adaptive system enhances UOCC reliability for applications like AUV positioning, inspection, and laser-based communication.
This framework paves the way for more dependable underwater data exchange in diverse and challenging aquatic environments.