About
Research
Collaborations
Publications
Our team
About
Welcome to the Aviation Center of Excellence! Our mission is to serve researchers at the University of Sharjah who are involved in promoting the adoption of UAV technology within the United Arab Emirates and to modernize the regulatory framework governing UAV operation and certification but has since expanded to cover the aviation field in general, encompassing both manned aircraft and UAVs, along with various related tasks within the aviation sector.
The Aviation Center of Excellence’s primary goal is to promote the integration of technology in aviation and elevate the UAE’s global role in shaping international standards and best practices. It is dedicated to pioneering innovative solutions for aviation-related challenges.
Objectives:
The objectives of the Aviation Center of Excellence are to:
- Contribute to the research field in aviation: The center is committed to conducting impactful research and disseminating its findings to enhance the aviation field's knowledge base through research publications in the form of conference and journal papers. This includes exploring state-of-the-art advancements, developing innovative solutions, and contributing to the evolution of standards and best practices in the field.
- Foster technological advancement and innovation in aviation: The primary aim of the center is to foster innovation and drive the advancement of cutting-edge technology within the aviation industry. It seeks to create solutions that will contribute to the improvement of aviation-related services in the UAE benefiting not only aviation professionals but also the general public.
- Promote senior projects and theses in the field of aviation within the academic community: The center provides support and mentorship to undergraduate, postgraduate, and doctoral students at the University of Sharjah with their senior projects and theses. It enables students to research, develop and publish aviation related solutions and corresponding papers which enriches their academic profile.
- Collaborate with external aviation entities: The center has also explored collaboration opportunities with national and international aviation entities for exchanging ideas to enrich the research and development of solutions in the aviation field through their support. It is also involved in talks with the GCAA and other external organizations to establish aviation related degrees at the university as the need for such programs is deemed necessary with the evolving needs of the aviation industry.
- Enrich the aviation workforce: It offers research opportunities, hands-on experience, and access to UAV and aviation technology to students and researchers at different educational levels. This provides an opportunity to enrich aviation knowledge in individuals and equip them with practical skills to prepare them for prospective careers in the aviation sector which is crucial as per the demands in the aviation field.
- Align with UAE’s vision in aviation: Through the promotion of innovation, driving advancements in technology, and disseminating knowledge, the center actively contributes to boosting the UAE’s position in cutting-edge aviation research, development, and the establishment of industry standards. This mission seamlessly aligns with the nation's aviation objectives and contributes to enhancing aviation operations in the industry.
The center continues to foster research, innovation, and collaboration in the aviation industry. Through their dedication and expertise, they strive to create a secure and technologically advanced future for aviation.
Research
The center is dedicated to developing innovative open-source solutions in the aviation industry and contributing to the field through research publications. It focuses on a wide range of research directions, including Robotic Process Automation (RPA), Artificial Intelligence (AI), Drone Forensics and Security, as well as UAV Systems R&D, while constantly exploring newer research directions to keep up with technological advancements and their beneficial contributions to the aviation field.
The Aviation Center of Excellence has state-of-the-art technology and a skilled workforce. It has actively engaged with graduate and undergraduate students, fostering a robust research environment. Furthermore, the Aviation Center of Excellence has established and nurtured connections and collaborations with industrial and educational entities across the UAE and internationally.
The center supports diverse research groups and entities interested in advancing and utilizing aviation technology. The following figure depicts the center’s research focus areas:
The below figure presents the number of ongoing and completed projects categorized by project type. The project types include senior projects, PhD thesis projects, and project that have been completed and handed over.
-
Event Management System
The center is involved in developing an Event Management System web application and mobile application for the General Civil Aviation Authority (GCAA) that aims to provide a centralized and innovative approach to managing significant events. The system intends to streamline the event management process and provide added value features that enhance the event experience for both attendees and organizers. The objective is to create a comprehensive and user-friendly platform that can manage all aspects of event planning, including registration, scheduling, communication, ticketing, and reporting. The system will be a valuable tool for GCAA to host successful events and improve overall efficiency in their event management process.
-
Drones Digital Forensics
Increased usage of UAVs for various applications by individuals and different industries has also brought forth risks and threats. This necessitates the forensic analysis of captured drones to reveal the suspicious events and crimes involving the use of UAVs and to understand the security vulnerabilities of UAVs to lead to better implementations of security mechanisms. This project aims to develop drone forensics testbeds and guidelines based on open-source technologies, test them with drones, and share our findings for UAV security threats with manufacturers to help them deploy potential security enhancements.
-
Incident Investigation Using Drones
Drones have become valuable tools in accident investigations because they can access difficult-to-reach locations and capture high-resolution imagery. This project focuses on utilizing drones equipped with advanced sensors and cameras that provide detailed, real-time information for performing comprehensive crash or incident analysis or investigations and ensuring site safety for investigators.
-
SDR Based Airplane Blackbox Localization System
The Software-Defined Radio (SDR)-Based Airplane Blackbox Localization System aims to assist the aviation accident investigation. The motivation behind this project originates from the critical need to enhance accident investigation to minimize the time to get the flight data. The objective of the project is to develop a handheld system that uses SDR to detect and localize signals from an aircraft's black box. enhanced sensitivity and range compared to traditional approaches, lowering the time and resources required for search operations. Additionally, the system seeks to provide more precise location data.
-
Applications on Local Large Language Model
This project aims to enhance GCAA’s document access process. The focus is to develop a chatbot based on a local data-trained model, answering queries from multiple PDFs. It prioritizes data privacy, offering immediate answers to save employees time and effort, ensuring a user-friendly experience.
-
RPA in Finance
Some flights can’t be processed automatically for billing purposes; hence, data must be dealt with manually to remove duplicates. This process consumes time and effort. This project aims to automate some manual tasks in the aircraft billing process for the GCAA finance department, significantly reducing employee efforts and minimizing time consumption.
-
Aircraft Type Recognition Using DL Techniques
This project aims to develop a novel system that can accurately identify and classify different aircraft types using deep learning methods. By leveraging the power of deep neural networks, the system will analyze and learn from large datasets of aircraft images, allowing it to recognize and categorize aircraft based on their visual characteristics. This technology has the potential to enhance various applications in aviation by providing automated and efficient aircraft-type recognition capabilities. This project aims to overcome the limitations of the alternative one.
-
Aircraft Conflict Detection & Resolution
This project aims to develop a support system for Air Traffic Controllers (ATCs) that detects potential conflicts ahead of time and recommends resolution measures to prevent them from occurring. This would allow sufficient time for ATCs to take appropriate actions and ensure a safe and orderly flow of air traffic. The system also aims to reduce the workload of ATCs by generating relevant alerts that focus only on high-priority potential conflicts, enabling them to focus on other crucial tasks and ultimately improving Air Traffic Management (ATM) efficiency.
-
Aircraft Type Recognition in Remote Sensing Satellite Images Using Deep Active Learning
Deep Learning for Aircraft Type Recognition (ATR) must meet the task's demands, as it requires hefty amounts of labeled & balanced data, making the ATR task very expensive and time-consuming. This master’s thesis project addresses these limitations by creating an automated solution for ATR using a Deep Active Learning Framework, the first of its kind in this field, to accurately classify up to 50 different types of aircraft.
-
RPA CORSIA
The goal of this project is to automate the process of aggregating the CO2 emission levels from multiple different operators using Robotic Process Automation (RPA). To achieve this, we developed a user-friendly interface where the operators can easily upload and validate their CO2 emission files, and the admin can access the files uploaded and request their aggregation. By automating this previously manual task, we aim to enhance efficiency and accuracy in monitoring and reporting CO2 emissions.
-
Aircraft Type Classification (military vs. non-military) using Deep Learning
In this project, a system that can accurately distinguish between military and civil aircraft using publicly available datasets was developed. This program will enable specialized authorities to prevent unauthorized aircraft entering restricted areas such as military campsites. To achieve this, artificial intelligence (AI) and image processing techniques were employed to perform binary classification on the constructed datasets. Various machine learning techniques, particularly Deep Learning, were utilized to achieve accurate and reliable classification results.
-
Transfer, Active, and Federated Learning for Supporting Air Traffic Control and Remote Towers
This is an ongoing PhD thesis project that aims to combine Deep Learning (DL) modules with new learning training methods such as federated, active, and transfer learning to support the operations of air traffic controllers (ATCOs) and introduce innovation in air traffic control (ATC) through the use of remote towers. Utilizing these advanced training techniques in solution development will increase the accuracy needed.
-
Quantum Radars for UAV Detection
This project explores quantum radar for improved drone detection and localization, capitalizing on quantum mechanics to enhance signal-to-noise separation.
-
Reporting of Safety Incidents (ROSI) Dashboard
The Reporting of Safety Incidents (ROSI) Systems Dashboard fetches live data from the GCAA database and visualizes it in the form of charts. The dashboard uses live data to offer real time and up-to-date analysis, summarizes report counts, and report types based on custom time intervals. Visualizing data collected over the span of 4 years as charts helps the aviation team identify and analyze trends quickly.
-
Drone Detection using Radio Frequency and Machine Learning
The rapid rise in the utilization of Unmanned Aerial Vehicles (UAVs) has raised significant concerns regarding security, privacy, and public safety. In response to these concerns, various anti-drone systems have been developed, including those employing radio frequency (RF) techniques for drone detection. RF detection involves monitoring the communication channel between a drone and its controller. This project investigates the application of artificial intelligence/machine learning in the detection of drones using RF techniques.
Collaboration
The ACoE actively pursues collaborations with national, regional, and international aviation-related entities that include, but are not limited to:
- General Civil Aviation Authority: With the research center being affiliated with GCAA, most of our services are developed for their use. Notably, our RPA automated solutions have been successfully integrated into their operations.
- Aviation Australia: Ongoing discussions with Aviation Australia, a world-class registered training organisation, aim to foster collaboration on projects and the establishment of aviation-related majors at the university.
- Matesol: A project for delivering medical supplies through efficient and smart long-range drones in Dubai, along with a blockchain-powered application to manage operations and keep track of every transaction, was developed through this collaboration.
- Dubai Police: In 2021, H.H Sheikh Mohammed Bin Rashid launched the 'Drone Boxes' project to reduce Dubai Police's response time from 4.4 minutes to 1 minute. The project developed through this collaboration utilizes a hybrid solution integrating AI algorithms with the ArcGIS environment to efficiently localize drone boxes across Dubai.
