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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.  

Photographs and video recordings of vehicle crashes and accident sites are more prevalent than ever, with dash mounted cameras, surveillance footage, and personal cell phones now ubiquitous. The information contained in these pictures and videos provide critical information to understanding how crashes occurred, and  analyze physical evidence. This course teaches the theory and techniques for getting the most out of digital media, including correctly processing raw video and photographs, correcting for lens distortion, and using photogrammetric techniques to convert the information in digital media to usable scaled three-dimensional data.

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.

The emergence of connected vehicles is driven by increasing customer and regulatory demands. To meet these, more complex software applications, some of which require service-based cloud and edge backends, are developed. Due to the short lifespan of software, it becomes necessary to keep these cloud environments and their applications up to date with security updates and new features. However, as new behavior is introduced to the system, the high complexity and interdependencies between components can lead to unforeseen side effects in other system parts. As such, it becomes more challenging to recognize whether deviations to the intended system behavior are occurring, ultimately resulting in higher monitoring efforts and slower responses to errors. To overcome this problem, a simulation of the cloud environment running in parallel to the system is proposed. This approach enables the live comparison between simulated and real cloud behavior.

Homologation is an important process in vehicle development and aerodynamics a main data contributor. The process is heavily interconnected: Production planning defines the available assemblies. Construction defines their parts and features. Sales defines the assemblies offered in different markets, where Legislation defines the rules applicable to homologation. Control engineers define the behavior of active, aerodynamically relevant components. Wind tunnels are the main test tool for the homologation, accompanied by surface-area measurement systems. Mechanics support these test operations. The prototype management provides test vehicles, while parts come from various production and prototyping sources and are stored and commissioned by logistics. Several phases of this complex process share the same context: Production timelines for assemblies and parts for each chassis-engine package define which drag coefficients or drag coefficient contributions shall be determined.

This paper proposes a novel approach to the design of a Hardware Abstraction Layer (HAL) specifically tailored to embedded systems, placing a significant emphasis on time-controlled hardware access. The general concept and utilization of a HAL in industrial projects are widespread, serving as a well-established method in embedded systems development. HALs enhance application software portability, simplify underlying hardware usage by abstracting its inherent complexity and reduce overall development costs through software reusability. Beyond these established advantages, this paper introduces a conceptual framework that addresses critical challenges related to debugging and mitigates input-related problems often encountered in embedded systems. This becomes particularly pertinent in the automotive context, where the intricate operational environment of embedded systems demands robust solutions. The HAL design presented in this paper mitigates these issues.

The issue of greenhouse gas (GHG) emissions from the transportation sector is widely acknowledged. Recent years have witnessed a push towards the electrification of cars, with many considering it the optimal solution to address this problem. However, the substantial battery packs utilized in electric vehicles contribute to a considerable embedded ecological footprint. Research has highlighted that, depending on the vehicle's size, tens or even hundreds of thousands of kilometers are required to offset this environmental burden. Human-powered vehicles (HPVs), thanks to their smaller size, are inherently much cleaner means of transportation, yet their limited speed impedes widespread adoption for mid-range and long-range trips, favoring cars, especially in rural areas. This paper addresses the challenge of HPV speed, limited by their low input power and non-optimal distribution of the resistive forces.

When travelling in an open-jet wind tunnel, the path of an acoustic wave is affected by the flow causing a shift of source positions in acoustical maps of phased arrays outside the flow. The well-known approach of Amiet attempts to correct for this effect by computing travel times between microphones and map points based on the assumption that the boundary layer of the flow, the so-called shear-layer, is infinitely thin and refracts the acoustical ray in a conceptually analogy to optics. However, in reality, the turbulent nature of both the not-so thin shear-layer and the acoustic emission process itself causes an additional smearing of sources in acoustic maps, which in turn causes deconvolution methods based on these maps - the most prominent example being CLEAN-SC - to produce certain ring effects, so-called halos, around sources.

This course is offered in China only and presented in Mandarin Chinese. The course materials are bilingual (English and Chinese). Participants deepen the understanding of the standard and build on how software engineers, inspectors, and DERs can comply with the standard's objectives. Practitioners engage with the implications and rationales of DO-178C in order to effectively tackle real worked applications.

Software certification guidelines, such as RTCA DO-178C, mandate the analysis of data and control coupling (DC/CC) in safety-critical avionics software using requirement-based testing. The intention of this analysis is to ensure correctness in the interactions and dependencies between software components. The shift from confirming the coupling (as in DO-178B) to verifying the exercising of the coupling (as introduced in DO-178C) transitions the DC/CC objective from an analytical exercise against the test design to a measurement exercise against the test execution. Current methodologies for measuring Data Coupling and Control Coupling (DC/CC) rely on source code instrumentation, which embeds code to record coverage information during requirements-based testing. However, this approach has significant drawbacks. Primarily, it necessitates executing tests on both the instrumented and non-instrumented versions of the code, ensuring their outputs match.

In any aerospace mission, after the vehicle has taken off, the visual is lost and the information about its current state is only through the sensor data telemetered in real-time. Very often, this data is difficult to perceive and analyze. In such cases, a 3D, near to real representation of the data can immensely improve the understanding of the current state of mission and can aid in real-time decision making if possible. Generally, any aerospace vehicle carries onboard an inertial system along with other sensors, which measures the position and attitude of the vehicle. This data is communicated to ground station. The received telemetry data is encoded as bytes and sent as packets through the network using the Universal Datagram Protocol (UDP).  The transmitted data is often available in a very low frequency, which is not desirable for a smooth display. It is therefore necessary to interpolate the data between intervals based on the time elapsed since last rendered frame.

In a typical Launch Vehicle (LV), dynamic responses due to various flight events are estimated through Coupled Load Analysis (CLA) where the launch vehicle is coupled with a spacecraft. A launch vehicle is subjected to various loads during its flight due to engine thrust depletion / shut-off, thrust oscillation, wind and gust, maneuvering loads. In aerospace industry a standard CLA is performed by generating the mathematical model of launch vehicle and coupling it with reduced mathematical model of satellite and applying the boundary conditions. A CLA is a time consuming process as several flight instances and load cases need to be considered along with generation of structural dynamic model at each time instants. For every new mission, the satellites are mission specific whereas the launch vehicle and the loads remain unchanged. To take advantage of this fact, a new method called “Fast CLA through Reanalysis technique” is proposed in the present paper.

It is important to accurately predict the aerodynamic properties for designing applications which involves fluid flows, particularly in the aerospace industry. Traditionally, this is done through complex numerical simulations, which are computationally expensive, resource-intensive and time-consuming, making them less than ideal for iterative design processes and rapid prototyping. Machine learning, powered by vast datasets and advanced algorithms, offers an innovative approach to predict airfoil characteristics with remarkable accuracy, speed, and cost-effectiveness. Machine learning techniques have been applied to fluid dynamics and have shown promising results. In this study, machine learning model called the back-propagation neural network (BPNN) is used to predict key aerodynamic coefficients of lift and drag for airfoils.

Launch vehicle structures in course of its flight will be subjected to dynamic forces over a range of frequencies up to 2000 Hz. These loads can be steady, transient or random in nature. The dynamic excitations like aerodynamic gust, motor oscillations and transients, sudden application of control force are capable of exciting the low frequency structural modes and cause significant responses at the interface of launch vehicle and satellite. The satellite interface responses to these low frequency excitations are estimated through Coupled Load Analysis (CLA). The analysis plays a crucial role in mission as the satellite design loads and Sine vibration test levels are defined based on this. The perquisite of CLA is to predict the responses with considerable accuracy so that the design loads are not exceeded in the flight. CLA validation is possible by simulating the flight experienced responses through the analysis.