Shoreline Wave Analysis & Generator Control

🌊 Project Overview

This repository houses the software suite developed for the NSF Urban Shorelines Reconfiguration Research project. The system is designed to simulate, track, and analyze ocean waves in a physical tank to optimize seawall architectures for coastal protection (specifically for NYC shorelines).

The project consists of three main pillars:

1. Computer Vision Analysis (MATLAB): Automated edge detection and tracking of wave amplitude and velocity.
2. Hardware Control (Python/Arduino): Wireless and manual control of the wave generation apparatus.
3. TPMS Optimization: The design and testing of Triply Periodic Minimal Surfaces as novel shoreline protection.

🏗️ Physical Engineering & TPMS

This software controls a custom-built six-foot physical wave tank, engineered to simulate realistic ocean conditions using high-torque stepper motors.

TPMS Seawall Design

A core focus of this research is the optimization of Triply Periodic Minimal Surfaces (TPMS) for shoreline protection. TPMS are complex, naturally occurring mathematical geometries that divide space with minimal surface area.

• The Goal: We utilized these geometries to design porous, structurally efficient seawalls.
• Optimization: By iterating on the TPMS parameters, we aimed to find the optimal geometry that maximizes wave energy dissipation while using minimal material.

Hardware-Software Integration

The setup requires precise calibration between the physical generator and the analysis software to ensure accurate environmental modeling. The WaveGeneratorApp drives the physical motors to create specific wave frequencies, allowing us to test how different TPMS designs perform under varying wave conditions.

STEM Workshop Mentorship

This research served as a foundation for the ERHS STEM Workshop. A dedicated session was led to mentor students on:

• CAD & Fabrication: Teaching students how to design and fabricate their own seawall prototypes.
• Experimental Design: Demonstrating how computer vision can quantify the energy dissipation of student-designed structures compared to the TPMS models.

📸 Experimental Setup

The data acquisition setup is designed to capture high-fidelity wave mechanics without interfering with the fluid dynamics.

• Wave Tank: A 6-foot custom acrylic tank featuring a stepper-motor-driven paddle for precise frequency generation.
• Imaging: A high-resolution camera is mounted parallel to the tank's cross-section. It captures the wave profile against a high-contrast background, allowing the computer vision algorithms to "see" the water-air interface.
• Lighting: Controlled lighting conditions ensure the HSV thresholding (used in createMask.m) operates consistently across different test runs.

📂 Repository Structure

1. Wave Analysis (MATLAB)

Located in the root and app directories, these tools handle the data processing pipeline.

• WaveTankAnalyzer.mlapp: The primary GUI application for importing video feeds and visualizing results.
• WaveTankFunction.m: The core backend function containing the mathematical logic for processing wave data and calculating metrics.
• createMask.m / createMask2.m: Auto-generated functions using HSV color space thresholding to mask the water against the tank background.
• CurveFittingTool.sfit: Saved sessions for fitting wave data to theoretical models.

2. Hardware Control (Python & Arduino)

Tools for communicating with the physical wave generator hardware.

Arduino Firmware:

• BlueTooth.ino: Establishes a BLE (Bluetooth Low Energy) peripheral service to expose control characteristics.
• switch.ino / switchdeploy.ino: Firmware for standalone operation using physical limit switches to safe-guard the motor assembly.

Python & Serial Control:

• bluetooth_bleak.py: A cross-platform Python script using the bleak library to discover and communicate with the Arduino.
• WaveGeneratorApp_serial.mlapp: A MATLAB interface for sending direct serial commands to the wave generator motor drivers.

🚀 Getting Started

Prerequisites

• MATLAB: R2022a+ (Image Processing & Curve Fitting Toolboxes).
• Python 3.x: pip install bleak pygatt bluepy tk.
• Hardware: Arduino Nano 33 BLE Sense / Nano RP2040 Connect & Stepper Motor setup.

Usage

Running the Analysis

1. Open MATLAB and run WaveTankAnalyzer.mlapp.
2. Import your experimental video file.
3. The app calls WaveTankFunction.m to isolate the wave edge and calculate velocity/amplitude metrics.

Controlling the Hardware

• Via Bluetooth: Upload BlueTooth.ino and run python bluetooth_bleak.py to toggle hardware states.
• Via Physical Switch: Upload switchdeploy.ino for standalone manual control in the lab.

👥 Contributors

• Daniel Halpern - Developer
  • Computer Vision Pipeline, MATLAB App Architecture, Curve Fitting Tools, and Data Analysis Algorithms.
• Ryan Zhang - Developer
  • MQTT Protocol Integration, Stepper Motor Control Logic, and Python/GUI Backend Structure.
• Michael Wen - Developer
  • Arduino Firmware Development, Switch/Limit Logic Implementation, and MQTT TUI.
• Gavin Brady - Developer
  • UI/UX Interface Optimization, Input Control Systems, and Wave Maker Speed Adjustments.
• Chin-Sung Lin - Research Advisor
  • Project Mentorship, Experimental Oversight, and Faculty Guidance.

📄 License

This project is open-source and available under the MIT License.

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Developed by ELROSTEM at Eleanor Roosevelt High School.