Finalized Project Plan: Raspberry Pi 5 STM32H7 Ultrasonic Data Logger - dbsandis/MyUTProject GitHub Wiki

📌 Finalized Project Plan: Raspberry Pi 5 + STM32H7 Ultrasonic Data Logger

Since you're a highly experienced developer with hardware and software expertise (from 8088 assembly to Django), I recommend a modular approach using Raspberry Pi 5 + STM32H7, which will allow easy migration to a more powerful system later.

---

1️⃣ Project Overview: Raspberry Pi 5 + STM32H7 Data Logger

✔ Raspberry Pi 5 → Handles heatmap visualization, AI processing, cloud tracking.
✔ STM32H7 MCU → Handles real-time ultrasonic signal capture & preprocessing.
✔ LTC2387-16 ADC → High-speed 16-bit ADC for ultrasonic wave capture.
✔ 5 MHz Ultrasonic Transducer → Used for pipe thickness measurement.
✔ Battery-Powered (Li-Ion 3.7V, 2500mAh) → Portable & compact form factor.
✔ Cloud Integration (WiFi/5G via Raspberry Pi) → Tracks data in real time.
✔ Future-Proofing → Easy to migrate from Raspberry Pi to Snapdragon when needed.

---

2️⃣ Updated Hardware Block Diagram

[BATTERY (Li-Ion 3.7V, 2500mAh)]
        ↓
[Power Management Circuit (Boost Converter, LDO)]
        ↓
[STM32H7 MCU] → [High-Speed ADC (LTC2387-16)]
        ↓
[Raspberry Pi 5 (AI Processing, Heatmap Display, Cloud Sync)]
        ↓
  ├── [Django-Based Web UI for Data Tracking]
  ├── [OpenCV for Real-Time Heatmaps]
  ├── [SQLite/PostgreSQL for Data Storage]
  ├── [5G/WiFi for Remote Monitoring]
  ├── [SSD for High-Speed Data Logging]

---

3️⃣ Recommended Parts List & Purchase Links

Component	Function	Estimated Cost	Purchase Link
Raspberry Pi 5 (8GB RAM)	Main processing unit (AI, cloud, UI)	$100-$150	Pi 5 @ PiShop
STM32H743ZI (NUCLEO-H743ZI2)	Real-time ultrasonic signal processing	$40-$60	Mouser
LTC2387-16 ADC (16-bit, 15 MSPS)	High-speed ultrasonic echo capture	$50-$80	Analog Devices
5 MHz Ultrasonic Transducer	Sends/receives ultrasonic waves	$50-$100	Olympus
Power Supply (Li-Ion 3.7V, 2500mAh + TP4056 Charger)	Portable power source	$10-$20	Amazon
MicroSD Card (SanDisk Extreme 64GB+ or SSD)	Storage for Raspberry Pi	$15-$40	Amazon
WiFi 6 / 5G Module (Sixfab LTE Hat for Pi)	Remote connectivity for cloud sync	$100-$200	Sixfab
TFT Display (3.5”-5”)	On-device visualization	$30-$70	Waveshare

---

4️⃣ Software Stack & Implementation Plan

✅ Step 1: STM32H7 Real-Time Data Acquisition

• Configure SPI DMA for LTC2387-16 (16-bit, 15 MSPS).
• Develop low-latency ADC capture firmware in STM32CubeIDE.
• Send data to Raspberry Pi via SPI/UART for analysis.

🔹 STM32 SPI ADC Logging Code Example

#include "stm32h7xx_hal.h"

#define ADC_BUF_SIZE 1024
uint16_t adc_buffer[ADC_BUF_SIZE];

void HAL_ADC_ConvCpltCallback(ADC_HandleTypeDef* hadc) {
    SendDataToRaspberryPi(adc_buffer, ADC_BUF_SIZE);
}

int main() {
    HAL_Init();
    ADC_Config();
    HAL_ADC_Start_DMA(&hadc1, (uint32_t*)adc_buffer, ADC_BUF_SIZE);
    
    while (1) {
        HAL_Delay(100);
    }
}

---

✅ Step 2: Raspberry Pi 5 AI Processing & Heatmap Display

• Use OpenCV & Matplotlib for ultrasonic signal visualization.
• Process ADC data into heatmaps using NumPy.
• Implement Django-based UI for real-time data tracking.

🔹 Heatmap Visualization Code (Python)

import numpy as np
import matplotlib.pyplot as plt

# Simulated ultrasonic data
data = np.random.randn(100, 100)

plt.imshow(data, cmap='hot', interpolation='nearest')
plt.colorbar()
plt.title("Ultrasonic Heatmap")
plt.show()

---

✅ Step 3: Cloud Data Sync & Web Dashboard (Django)

• SQLite/PostgreSQL for ultrasonic data storage.
• Django-based API to push logs to the cloud.
• Flask or Dash for a local web UI on Pi 5.

🔹 Django API for Data Logging

from django.shortcuts import render
from django.http import JsonResponse
from .models import UltrasonicData

def log_data(request):
    if request.method == 'POST':
        data = request.POST.get('adc_value')
        UltrasonicData.objects.create(value=data)
        return JsonResponse({'status': 'success'})

---

5️⃣ Final Migration Path: From Raspberry Pi 5 to More Powerful System

Phase	Hardware	Software	Estimated Timeline
Phase 1	Raspberry Pi 5 + STM32	Django + OpenCV + PostgreSQL	3-6 months
Phase 2	Upgrade to Snapdragon Dev Kit	Optimize AI, TensorFlow Edge	6-12 months
Phase 3	Custom PCB (Snapdragon + STM32)	Fully integrated solution	12-24 months

📌 Starting with Raspberry Pi 5 is the fastest and most practical way to get a working system before migrating to Snapdragon!

---

6️⃣ Next Steps

🔍 Would you like a PCB design for integrating STM32H7 + Power Management?
📡 Should I research best cloud storage options for ultrasonic tracking?
🎨 Do you need UI/UX wireframes for the Django-based heatmap interface?

🚀 Let me know how you'd like to proceed! Your ultrasonic data logger is now fully planned!