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This basic course introduces the intent of the DO-254 standard for commercial avionics hardware development. The content will cover many aspects of avionic hardware including, aircraft safety, systems, hardware planning, requirements, design, implementation, and testing. Participants will learn industry-best practices for real-world hardware development, common DO-254 mistakes and how to prevent them, and how to minimize risks and costs while maximizing hardware quality.

This two-day course will introduce participants to industry best practices for real-world software development and how to avoid common DO-178C mistakes. This course is intended to present the information necessary to help minimize DO-178C risks and costs, while also maximizing software quality during avionics development.  The instructor will guide participants through topics such as aircraft safety, systems, software planning, software requirements, and software design/code/test.  

Active safety and (ADAS) are now being introduced to the marketplace as they serve as key enablers for anticipated autonomous driving systems. Automatic emergency braking (AEB) is one ADAS application which is either in the marketplace presently or under development as nearly all automakers have pledged to offer this technology by the year 2022. This one-day course is designed to provide an overview of the typical ADAS AEB system from multiple perspectives.

This course is offered in China only and presented in Mandarin Chinese. The course materials are bilingual (English and Chinese). Participants in this course comprehensive understanding of the DO-254 standard including how the development of airborne electronic hardware (AEH) and the development of the aircraft system are related. It addresses the key objectives, activities, and generated data of the AEH lifecycle and presents common problems in the application of DO-254 and how to prevent them. This course also includes current AEH certification requirements and interprets some of the difficulties in gaining compliance approval.

This course will introduce participants to industry best practices for real-world aviation cyber-security risk-assessment, development & assurance. Participants will learn the information necessary to help minimize DO-326/ED-202-set compliance risks and costs, while also optimizing cyber-security levels for the development, deployment and in-service phases Topics such as aircraft security aspects of safety, systems-approach to security, security planning, the airworthiness security process, and security effectiveness assurance will be covered.

Convolutional neural networks are the de facto method of processing camera, radar, and lidar data for use in perception in ADAS and L4 vehicles, yet their operation is a black box to many engineers. Unlike traditional rules-based approaches to coding intelligent systems, networks are trained and the internal structure created during the training process is too complex to be understood by humans, yet in operation networks are able to classify objects of interest at error rates better than rates achieved by humans viewing the same input data.

The calibration of Engine Control Units (ECUs) for road vehicles is challenged by stringent legal and environmental regulations, coupled with short development cycles. The growing number of vehicle variants, although sharing similar engines and control algorithms, requires different calibrations. Additionally, modern engines feature increasingly number of adjustment variables, along with complex parallel and nested conditions within the software, demanding a significant amount of measurement data during development. The current state-of-the-art (White Box) model-based ECU calibration proves effective but involves considerable effort for model construction and validation. This is often hindered by limited function documentation, available measurements, and hardware representation capabilities. This article introduces a model-based calibration approach using Neural Networks (Black Box) for two distinct ECU functional structures with minimal software documentation.