NanoTech Signal Analysis Framework

Overview

NanoTech is a cutting-edge signal analysis platform designed for real-time monitoring and diagnostics at the nanoscale level. This platform integrates advanced AI models, state-of-the-art databases, and secure communication protocols to drive innovations in nanotechnology and diagnostics.

The system is DARPA-compliant and built for military-grade security and performance, ensuring robust functionality across diverse environments, including CBRN contexts.

Key Features

• EDA Framework: Powered by Kafka and RabbitMQ for efficient data stream processing.
• AI Engine: Utilizes OpenCV, ONNX, and NVIDIA Triton for high-performance AI-powered signal analysis.
• Secure Communication: Implements gRPC with Protobuf and Quiche/HTTP3 for low-latency, encrypted data transmission.
• Databases:
  • InfluxDB for time-series data.
  • Cloudflare D1/PostgreSQL for transactional storage.
  • Immutable storage via immudb with IPFS archival.
• Zero Trust Architecture: Ensured by Cloudflare Zero Trust and quantum-safe encryption (QKD + PQC).
• Edge Computing: Cloudflare Workers for edge-based data processing.
• Standards Compliance: ISO 27001/27701, GDPR, and military-grade security.

System Architecture

NanoTech follows a modular architecture:

1. Data Ingestion: High-speed ingestion with Kafka and RabbitMQ.
2. AI Processing: Inference models optimized via NVIDIA Triton and ONNX.
3. Data Storage: Redundant, decentralized storage through IPFS.
4. Secure Communication: End-to-end encryption and quantum resilience.
5. Real-Time Visualization: Centralized dashboards for monitoring.

Installation

Prerequisites

• Docker
• Node.js
• Python 3.9+
• Git

Steps

1. Clone the repository:

   git clone https://github.com/rfc391/NanoTech.git
   cd NanoTech

2. Install dependencies:

   npm install
   pip install -r requirements.txt

3. Configure environment variables:

   • Copy .env.example to .env:

     cp .env.example .env

   • Update the .env file with your configurations.

4. Start the services:

   docker-compose up

Usage

• Access the dashboard at http://localhost:3000.
• Use the provided SDKs for integrating with your applications.

Contributing

We welcome contributions! Please refer to the CONTRIBUTING.md file for guidelines.

License

This project is licensed under the MIT License. See the LICENSE file for details.