Tutorials

This tutorial explores architectures from numerous civil and military aircraft. Key architecture and design challenges are described for legacy as well as the newest aircraft types. Architectures are examined with comparisons of hardware and avionics functions of each are discussed in detail. Civil aircraft investigated include Boeing 787 and Airbus A350. Military aircraft include F-22 and Rafael. IMA 2G and other advanced concepts will be explored. 

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. Attendees will be exposed to avionics subsystem designs that are typically used on satellite buses and will learn the terms, nomenclature and rules of thumb used in the development process. Each avionics subsystem is explained in detail to gain insight into manpower and cost requirements. In addition to spacecraft avionics equipment, the design, fabrication, and qualification of the electrical ground support equipment required for satellites are discussed in detail.

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. You will learn about ethical hacking and penetration testing methodologies and explore how they are used to identify and exploit vulnerabilities in computer systems and networks. 

SAE-ARP4754B provides guidance for the development of aircraft and aircraft systems while taking into account the overall aircraft operating environment and functions. ARP4761A is the corresponding Safety Assessment for aircraft and electronic systems (“avionics”).  

Aircraft, systems, software, and hardware have one common Primary Ingredient:  “REQUIREMENTS”! But unlike other industries, aviation has very explicit rules and best-practices for developing and managing those “requirements”.  But can you answer these questions below?

This tutorial is designed to provide a hands-on, deep dive into Machine Learning and Deep Learning models by applying such models in a real-world use cases. 

The main goal of Air Traffic Management (ATM) is to enable safe operation of air traffic while accommodating the demand and doing it efficiently with minimum disruption to schedules. The impact of aircraft emissions and contrails on the environment adds an additional dimension to the planning of aircraft operations. 

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.

The tutorial introduces several Air Traffic Management (ATM) initiatives envisioned by the Federal Aviation Administration (FAA) for a bold future airspace that combines conventional traffic and new entrants (e.g., drones) without sacrificing safety. In this framework, we demonstrate the use of state-of-the-art AI/ML modeling and prediction tools that will enable efficient and safe traffic flow in the U.S. National Airspace System (NAS). 

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. 

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. Compounding this issue is the relentless pace of technological development, which can render the final standard outdated by the time it is implemented.
 

Neuromorphic computing is an interdisciplinary field that combines concepts from neuroscience, computer science, biology, and electrical and computer engineering. It is an exciting and innovative field miming the human brain's structure, function, and operation.

AI-based functions and, eventually, autonomy are getting an increasing interest in the airborne systems domain. They indeed challenge how we assure the system’s safety. Scenario- based testing approaches have recently emerged as an answer to this and have matured quite a bit, especially in the automotive industry in the Automated Vehicle domain. 

Multi-core processors (MCPs) have been readily available since 2005, but developers have been constrained to using a single core in real-time or safety-critical applications because of the non- deterministic nature of shared resources on a multi-core chip. 

For several decades, the aviation sector has demonstrated remarkable growth and resilience, weathering various socioeconomic and geopolitical crises. As a result, without substantial further interventions, this sector alone is projected to contribute significantly to climate change, accounting for 6-10% of human-induced climate impacts by 2050.