🌟 GlowBarn

High-Performance Paranormal Detection Suite

A cross-platform native application for paranormal investigation, environmental monitoring, and multi-modal anomaly detection. Built in Rust for maximum performance and safety.

✨ Features

🔬 50+ Sensor Types

Thermal: IR arrays, FLIR cameras, thermocouples
Electromagnetic: EMF probes, flux gates, gaussmeters
Audio: Infrasound, ultrasonic, EVP detection
Radiation: Geiger counters, scintillators
Environmental: Barometers, hygrometers, air quality
Quantum: QRNG, entanglement detectors
Optical: Spectrometers, laser grids, UV sensors

📊 Advanced Analysis

10+ Entropy Measures: Shannon, Rényi, Tsallis, Approximate, Sample, Permutation
Anomaly Detection: Z-score, MAD, CUSUM, Isolation Forest, Local Outlier Factor
Signal Processing: FFT, wavelets, cross-correlation
Pattern Recognition: Recurrence analysis, complexity measures

🎯 Multi-Sensor Fusion

Bayesian fusion with confidence weighting
Dempster-Shafer evidence theory
Cross-sensor correlation analysis
Temporal pattern detection

🔐 Security

AES-256-GCM encryption
ChaCha20-Poly1305 support
Argon2id key derivation
Secure memory handling (zeroize)

🖥️ Visual Console

Real-time waveform displays
Thermal heatmaps
Spectrum analyzers
Detection alerts
Dark/Light themes

🚀 Quick Start

Pre-built Binaries

Download from Releases.

Build from Source

# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Clone and build
git clone https://github.com/bad-antics/glowbarn-rs.git
cd glowbarn-rs
cargo build --release --features gui

# Run
./target/release/glowbarn --demo

System Dependencies (Linux)

sudo apt install libasound2-dev libudev-dev pkg-config libssl-dev \
  libxkbcommon-dev libwayland-dev

📖 Usage

# GUI mode with demo sensors
glowbarn --demo

# Headless server mode
glowbarn --headless --ws-port 8765

# With debug logging
glowbarn --demo --debug

# Custom config file
glowbarn -c /path/to/config.toml

🏗️ Architecture

glowbarn-rs/
├── src/
│   ├── core/          # Engine, event bus, scheduler
│   ├── sensors/       # 50+ sensor implementations
│   ├── analysis/      # Entropy, anomaly, signal processing
│   ├── detection/     # Fusion, classification, correlation
│   ├── security/      # Encryption, auth, secure memory
│   ├── streaming/     # MQTT, WebSocket, export
│   ├── gpu/           # wgpu compute shaders
│   ├── ui/            # egui visual console
│   ├── config/        # Configuration management
│   └── db/            # SQLite persistence

🔧 Configuration

Default config location: ~/.config/glowbarn/config.toml

[sensors]
sample_rate = 1000.0
buffer_size = 4096

[analysis]
entropy_window = 256
anomaly_threshold = 3.0

[detection]
fusion_method = "bayesian"
min_confidence = 0.7

[gui]
theme = "dark"
refresh_rate = 60

📡 Streaming

WebSocket API

Connect to ws://localhost:8765 for real-time data:

{
  "type": "reading",
  "sensor_id": "emf-probe-1",
  "timestamp": "2026-01-24T12:00:00Z",
  "values": [0.5, 0.7, 0.3],
  "quality": 0.95
}

MQTT

Publish to topics:

glowbarn/readings/{sensor_id}
glowbarn/detections
glowbarn/alerts

🤝 Contributing

Contributions welcome! Please read our Contributing Guide.

📜 License

MIT License - see LICENSE for details.

🔗 Related Projects

glowbarn-os - Custom Linux OS for GlowBarn
glowbarn - Original Python prototype

Made with 🔮 by bad-antics

glowbarn 2.0.0