README.md

metadata

title: Sensor Placement Explorer
emoji: 🎯
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 6.2.0
app_file: app.py
pinned: false
license: mit

🎯 Risk-Aware Sensor Placement Explorer

An interactive educational tool to understand optimal sensor placement using Log-Gaussian Cox Process (LGCP) models.

What You'll Learn

1. Detection Formula: How sensors detect targets based on distance
2. Intensity Functions: Where targets are expected to appear
3. Uncertainty: How variance affects decision-making
4. Mean vs Conservative: When to use each placement strategy

Features

• 🎮 Interactive sliders to adjust all parameters
• 📊 Real-time visualizations of sensor coverage
• 🔬 Monte Carlo simulation to test placement strategies
• 📚 Educational summaries of key concepts

How to Use

1. Tab 1: Learn the detection probability formula
2. Tab 2: Play with a single sensor
3. Tab 3: Understand intensity and uncertainty
4. Tab 4: Run full analysis comparing strategies
5. Tab 5: Review key concepts

The Core Question

"Should we place sensors where we EXPECT the most intruders (mean-based) or prepare for WORSE than expected (conservative/Q90-based)?"

Spoiler: For high-stakes security, conservative placement wins! 🏆

License

MIT License - Feel free to use and modify!