Tutorials

Avionics and Space systems require the use of a real-time operating system (RTOS) in order to meet their timing constraints. This tutorial focuses on the RTEMS RTOS, a widely used RTOS in commercial, certified systems. RTEMS is a POSIX-compliant RTOS, developed by OAR for the US DoD in the ‘80s, and it is open source with a permissive license.

This course offers a detailed look at basic spacecraft avionics systems engineering and design processes and principals. All spacecraft avionics systems have similarities, but differ in many ways. This course addresses the up-front systems engineering process; requirement levels, trade studies, requirements allocation/linking requirements derivation, requirements verification, risk and risk assessment, safety, integration and test, costing, scheduling, and then applying all this to the avionics subsystem level design on a subsystem-by-subsystem basis

Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) is the first wireless communication research platform envisioned and built to allow studying the convergence of advanced wireless communication technologies (such as 5G) and autonomous drones.

This course provides a fundamental background in assured navigation for unmanned aircraft systems (UAS). It first introduces the various UAS/RPAS application domains and operational environments, UAS flight management and path planning, required performance parameters, and autonomy at the various levels of the Guidance, Navigation and Control function.

In the latest years, sense and avoid (SAA), or detect and avoid (DAA), has represented one of the main roadblocks to the integration of unmanned aircraft systems (UAS) operations. This course outlines and reviews architectures, technologies, and algorithms for SAA. First, starting from a discussion about what constitutes a UAS and how it is different than manned aircraft, basic SAA definitions and taxonomies are discussed.

The cyber threat landscape of aviation is increasing. Threats bring new security risks that are specific to aviation and impact public safety and well-being. This tutorial will introduce you to aviation cyber security, focusing on the aircraft at the center of an increasingly complex and technology-driven aviation ecosystem.

To securely build and defend your systems against attacks, it is essential to adopt the mindset of an adversary. This tutorial will enable you to do so and provide you a foundational understanding of cybersecurity principles and practices in the context of aviation.

The international standards D-326A (U.S.) and ED-202A (Europe) titled "Airworthiness Security Process Specification" are the cornerstones of the "DO-326/ED-202 Set": the only Acceptable Means of Compliance (AMC 20-42) by EASA for aviation cyber security airworthiness certification, as of Jan 1st, 2021, and enroute to becoming such by the FAA.

Aviation software guidelines/standards seem to require heavyweight processes. However, Agile software development CAN be successfully deployed for DO-178C software, DO-254 hardware, and ARP4754B Systems development. 

Aviation hardware and silicon-based logic is increasingly regulated like software: the new A(M)C 20-152A and the forthcoming DO-254A (ED-80A for Europe) are significantly more rigorous than the current DO-254/ED-80. This tutorial explains these new Guidelines (Standards!) and how to successfully transition to modern aviation hardware development enabling certification.

As airborne systems become more and more complex – partly in order to “off load” aircrews, the certification of such systems becomes even more challenging, as the emphasis shifts from the complexity of human behavior to the even greater complexities of systems and software.

There are avionics failures that most designers think can't happen, which actually can and do happen with probabilities far greater than requirements allow. This lack of understanding leads to designs with insufficient dependability, which then contributes to accidents and incidents.

This tutorial aims to provide participants with a comprehensive understanding of aircraft trajectory analysis using deterministic rule-based methods and machine learning techniques. Over the course of three hours, participants will learn how to access trajectory data, implement analysis techniques in Python, and design machine learning algorithms for more advanced studies. By the end of the tutorial, participants will acquire the necessary knowledge to analyse and interpret aircraft trajectories in diverse real-world scenarios.

SAE’s ARP4761: “Guidelines and Methods for Conducting the Safety Assessment Process on Civil Airborne Systems and Equipment” has been the “law of the land” for airborne systems certification for over a quarter of a century, since 1996. But now – a change is coming: the new ARP4761A: “Guidelines for Conducting the Safety Assessment Process on Civil Aircraft, Systems, and Equipment”, aligned with the new ARP4754B: “Guidelines for Development of Civil Aircraft and Systems” has finally been published on December 2023. A seemingly insignificant title change – but a huge upgrade to the document itself.

At the end of the day, we are only assessing, per project, one aircraft (OEM), one system (tier

1) or one item (tier 2). This state of affairs was natural 120 years ago, when aircraft were manually flown and included, at the most, mechanical equipment to assist pilots: neither the potential impact, nor the difficulty to assess safety were extremely challenging back then. Fast-forward to the 21st century, we now have complex aircraft and digital systems, developed and manufactured by a blend of humans and automated tools.

Due to rapid technological advancement, certification authorities frequently find themselves challenged to keep up with emerging innovations, with AI technologies standing out as a prime example of this struggle. The process of developing certification standards, rooted in the consensus of domain experts, is often slow and arduous, requiring years to finalize.

GPUs are currently considered from all safety critical industries, including avionics and aerospace to accelerate general purpose computations and meet performance requirements of new advanced functionalities, which are not possible with the legacy, single-core processors used in these domains, such as in the recent Airbus project Automatic Taxi, Take- off and Landing (ATTOL) project.

GPUs are currently considered from all safety critical industries, including avionics and aerospace to accelerate general purpose computations and meet performance requirements of new advanced functionalities, which are not possible with the legacy, single-core processors used in these domains, such as in the recent Airbus project Automatic Taxi, Take- off and Landing (ATTOL) project. However, most of the R&D is currently focused on proof of concepts, which demonstrate the capabilities of employing GPUs in avionics, ignoring the certification challenges introduced by GPUs.

Traditional digital avionics systems have included ground and flight segments where the most intensive processing is often done on the ground for applications like optimization compared to more immediate lower latency requirements for flight control and flight management.

Digital aviation systems designers are being asked to provide more sophisticated autonomous and semi-autonomous features for aerospace systems including general aviation and UAS much like automotive AV/ADAS (Autonomous Vehicle/Advanced Driver-Assistance Systems) is challenging automotive embedded systems.

In recent years, the market growth of the aviation sector has resumed its pre-pandemic trends, and a significant expansion of commercial space operations is being witnessed. The anticipated rise of commercial Unmanned Aircraft Systems (UAS) and Advanced Air Mobility (AAM) services at the lower end of the airspace, and of operations above Flight Level 600 and point-to-point high-speed transport at the other end are expected to compound these trends, challenging the viability of conventional Air Traffic Management (ATM) and airspace management paradigms.

The landscape of complex system design and engineering is rapidly evolving with digital engineering practice and integrated models across the system life cycle. Digital Engineering is a holistic approach to system design that replaces documents centric practices with digital models, artifacts, and data.