Over-the-air Software Updates (OTASU) in the critical domain are already a reality. OTASU provide huge benefits in terms of user experience, security, and efficiency. However, due to involved risks, safety and security mechanisms and new regulations are needed for their adoption in the critical domain. The automotive industry is already in the race to adopt safe and secure OTASU, as by 2024, compliance to new UN regulations will become compulsory. However, the standards providing the specifications and requirements for OTASU are still in their infancy. Many other dependable system domains, that are now more digital and connected than ever, are following same trends towards OTASU. For instance, OTASU are very likely to be adopted in the railway domain in a near future, as the ability of remotely updating railway equipment considerably reduces maintenance costs and time, improving system availability. This paper describes how the UP2DATE framework adheres to existing and emerging regulations and standards and evaluates them through a railway case-study. Obtained results demonstrate that the proposed updating framework can provide great savings in the installation and maintenance phases of railway signalling devices by reducing the time required for the update and by removing the need for operator presence on-site.

The UP2DATE H2020 project focuses on highperformance heterogeneous embedded platforms for critical systems. We will develop observability and controllability solutions to support online updates while ensuring safety and security for mixed-criticality tasks. In this paper, we describe the rationale behind the selection of the baseline research platforms which will be used to develop and demonstrate the project concepts, including a performance comparison to identify the most efficient one.

Embedded GPUs have been identified from both private and government space agencies as promising hardware technologies to satisfy the increased needs of payload processing. The GPU4S (GPU for Space) project funded from the European Space Agency (ESA) has explored in detail the feasibility and the benefit of using them for space workloads. Currently at the closing phases of the project, in this paper we describe the main project outcomes and explain the lessons we learnt. In addition, we provide some guidelines for the next steps towards their adoption in space.

Enter the RobotriX, an extremely photorealistic indoor dataset designed to enable the application of deep learning techniques to a wide variety of robotic vision problems. The RobotriX consists of hyperrealistic indoor scenes which are explored by robot agents which also interact with objects in a visually realistic manner in that simulated world. Photorealistic scenes and robots are rendered by Unreal Engine into a virtual reality headset which captures gaze so that a human operator can move the robot and use controllers for the robotic hands; scene information is dumped on a per-frame basis so that it can be reproduced offline using UnrealCV to generate raw data and ground truth labels. By taking this approach, we were able to generate a dataset of 38 semantic classes across 512 sequences totaling 8M stills recorded at +60 frames per second with full HD resolution. For each frame, RGB-D and 3D information is provided with full annotations in both spaces. Thanks to the high quality and quantity of both raw information and annotations, the RobotriX will serve as a new milestone for investigating 2D and 3D robotic vision tasks with large-scale data-driven techniques.