Altara

Abstract

1. Introduction

A drone ground station built from core + aerospace. Video not playing? Watch the GIF.

2. Objective

React components for real-time telemetry dashboards — robotics, aerospace, autonomous vehicles, industrial IoT. PFD, HSI, time-series, gauges, GPS maps, and ROS2/MQTT adapters at 60fps.

This project demonstrates how TypeScript can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Node.js (LTS) and npm / yarn / pnpm
• VS Code or any code editor
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/JayaSaiKishanChapparam/altara.git
cd altara

# Start with core — required for every package below.
npm install @altara/core

# Add the extras you need:
npm install @altara/aerospace        # flight instruments
npm install @altara/av three         # autonomous-vehicle UI (three is an optional peer dep)
npm install @altara/industrial       # SCADA / HMI / industrial-IoT
npm install @altara/ros              # ROS2 / rosbridge
npm install @altara/mqtt mqtt        # MQTT brokers (mqtt is an optional peer dep)

import '@altara/core/styles.css';
import { AltaraProvider, TimeSeries, Gauge } from '@altara/core';
import { PrimaryFlightDisplay } from '@altara/aerospace';

export function Dashboard() {
  return (
    <AltaraProvider theme="dark">
      <PrimaryFlightDisplay mockMode size="md" />
      <TimeSeries mockMode height={240} />
      <Gauge mockMode min={0} max={100} label="Battery" unit="%" />
    </AltaraProvider>
  );
}

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by JayaSaiKishanChapparam and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.