Projects

Engage Knowledge Network (EngageKTN)

Engage is managed by a consortium of academia and industry, with the support of the SESAR JU, to promote and facilitate the development of air traffic management research in Europe. The project forces on inspiring new researchers and helping to align exploratory and industrial research, through a wide range of activities and financial support actions. link

OpenAP: Open Aircraft Performance Model

This project is aimed to produce a fully open aircraft performance model. The OpenAP is about to model aircraft performance parameters involving kinematic, thrust, drag, and fuel flow. The repository contains all OpenAP databases and a Python implementation which facilitates the data access and aircraft performance computation. source code

pyModeS

The ADS-B decoder has evolved into a fully powered Mode-S decoder with contributions from the community. It decodes ADS-B messages (DF17, DF18) and Enhanced Mode-S (DF20, DF21) messages. Many more aircraft states can now be discovered through Mode-S, in addition to ADS-B. source code

Aircraft Database

A database to search for aircraft IDs (such as ICAO address, registrations ID, etc) and related information. It is built in Python / Flask and data is from Flight Radar 24. source code an database (not updating)

The 1090Mhz Riddle - Mode-S Decoding Guide

A guide to decode the ADS-B and Enhance Mode-S messages. Since aircraft surveillance data is one of the foundation of my PhD research, this one of the first project that I worked on during the PhD. read the book

BlueSky

BlueSky is the open source Air Traffic Simulation project started by prof. Hoekstra. I am contributing to its performance model, data feed, wind models, and bug fixings. source code

Memeit

This is a fun project created during one of the machine learning classes. It is not actively maintained. Creating an internet MEME is easy, but it is difficult for a computer to understand the accompanying text. We developed a machine learning system that can extract and understand the text within an internet MEME. The accuracy of the text recognition exceeds 95%. source code



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