---
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!