Rapita Systems (United Kingdom)

The current generation of ground-based imaging atmospheric Cherenkov telescopes (IACTs) operate in the very-high-energy (VHE) domain from ~100 GeV to ~100 TeV. They use electronic digital trigger systems to discern the Cherenkov light flashes emitted by extensive air showers (EASs), from the overwhelming light of the night sky (LoNS) background. Near the telescope energy threshold, the number of emitted Cherenkov photons by gamma-ray-induced EASs is comparable to the fluctuations of the LoNS and the photon distribution at the Cherenkov-imaging camera plane becomes patchy. This results in a severe loss of effectiveness of the digital triggers based on combinatorial logic of thresholded signals. A stereoscopic analog trigger system has been developed for improving the detection capabilities of the Major Atmospheric Gamma-ray Imaging Cherenkov (MAGIC) telescopes at the lowest energies. It is based on the analog sum of the photosensor electrical signals. In this article, the architectural design, technical performances, and configuration of this stereoscopic analog trigger, dubbed “Sum-Trigger-II,” are described.

Properly allocating time budgets to applications during system's early design phases (EDP) prevents costly-to-handle time overruns in late design phases (LDP). Applications running in a multicore affect each other's behavior, which complicates reaching this goal. Further, in multi-provider software developments, software providers are reluctant to share their applications for IP reasons. Both factors prevent deriving tight bounds until LDP when applications are actually integrated. In this paper we propose a modeling approach, which we tailor for a processor in the space domain, that simplifies time budgeting in EDP by developing surrogate applications (SurApps) and an automatic framework to generate them. A SurApp copies the non-functional behavior of a given target application automatically. Each software provider generates, for an application AppA, a surrogate application SurAppA and gives it to other providers without the risk of revealing any IP. By running their applications against the SurAppA, other providers obtain a tight estimate of the slowdown their applications will suffer when run against AppA.

The complexity and size of Autonomous Driving (AD) software are comparably higher than that of software implementing other (standard) functionalities in the car. To make things worse, a big fraction of AD software is not specifically designed for the automotive (or any other critical) domain, but the mainstream market. This brings uncertainty on to which extent AD software adheres to guidelines in safety standards. In this paper, we present our experience in applying ISO 26262 -- the applicable functional safety standard for road vehicles -- software safety guidelines to industrial AD software, in particular, Apollo, a heterogeneous Autonomous Driving framework used extensively in industry. We provide quantitative and qualitative metrics of compliance for many ISO 26262 recommendations on software design, implementation, and testing.

Modern aeronautical telecommunication networks (ATN) make use of different simultaneous datalinks to deliver robust, secure, and efficient ATN services. This paper proposes a multiple attribute decision making based optimal datalink selection algorithm, which considers different attributes including safety, QoS, costs, and user/operator preferences. An intelligent TRigger-based aUtomatic Subjective weighTing ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> -TRUST) method is also proposed for computing subjective weights necessary to provide user flexibility. Simulation results demonstrate that the proposed algorithm significantly improves the performance of the ATN system.

Timing Verification is a fundamental step in real-time embedded systems, with measurement-based timing analysis (MBTA) being the most common approach used to that end. We present a Space case study on a real platform that has been modified to support a probabilistic variant of MBTA called MBPTA. Our platform provides the properties required by MBPTA with the predicted WCET estimates with MBPTA being competitive to those with current MBTA practice while providing more solid evidence on their correctness for certification.

The EC project parMERASA (Multicore Execution of Parallelized Hard Real-Time Applications Supporting Analyzability) investigated timing-analyzable parallel hard real-time applications running on a predictable multicore processor. A pattern-supported parallelization approach was developed to ease sequential to parallel program transformation based on parallel design patterns that are timing analyzable. The parallelization approach was applied to parallelize the following industrial hard real-time programs: 3D path planning and stereo navigation algorithms (Honeywell International s.r.o.), control algorithm for a dynamic compaction machine (BAUER Maschinen GmbH), and a diesel engine management system (DENSO AUTOMOTIVE Deutschland GmbH). This article focuses on the parallelization approach, experiences during parallelization with the applications, and quantitative results reached by simulation, by static WCET analysis with the OTAWA tool, and by measurement-based WCET analysis with the RapiTime tool.

In the last three decades a number of methods have been devised to find upper-bounds for the execution time of critical tasks in time-critical systems. Most of such methods aim to compute Worst-Case Execution Time (WCET) estimates, which can be used as trustworthy upper-bounds for the execution time that the analysed programs will ever take during operation. The range of analysis approaches used include static, measurement-based and probabilistic methods, as well as hybrid combinations of them. Each of those approaches delivers its results on the assumption that certain hypotheses hold on the timing behaviour of the system as well that the user is able to provide the needed input information. Often enough the trustworthiness of those methods is only adjudged on the basis of the soundness of the method itself. However, trustworthiness rests a great deal also on the viability of the assumptions that the method makes on the system and on the user's ability, and on the extent to which those assumptions hold in practice. This paper discusses the hypotheses on which the major state-of-the-art timing analyses methods rely, identifying pitfalls and challenges that cause uncertainty and reduce confidence on the computed WCET estimates. While identifying weaknesses, this paper does not wish to discredit any method but rather to increase awareness on their limitations and enable an informed selection of the technique that best fits the user needs.

Many scenarios require that robots work together as a team in order to effectively accomplish their tasks. However, pre-coordinating these teams may not always be possible given the growing number of companies and research labs creating these robots. Therefore, it is desirable for robots to be able to reason about ad hoc teamwork and adapt to new teammates on the fly. Past research on ad hoc teamwork has focused on relatively simple domains, but this paper demonstrates that agents can reason about ad hoc teamwork in complex scenarios. To handle these complex scenarios, we introduce a new algorithm, PLASTIC–Policy, that builds on an existing ad hoc teamwork approach. Specifically, PLASTIC– Policy learns policies to cooperate with past teammates and reuses these policies to quickly adapt to new teammates. This approach is tested in the 2D simulation soccer league of RoboCup using the half field offense task.

Hard real-time applications in safety-critical domains - namely avionics, automotive, and machinery - require high-performance and timing analysability. We present research results of the parallelisation and WCET analysis of an industrial hard real-time application, i.e. the control code of a large drilling machine from BAUER Maschinen GmbH. We reached a quad-core speedup of 2.62 for the maximum observed execution time (MOET) and 1.93 on the WCET compared to the sequential version. For the WCET analysis we used the measurement-based WCET analysis tool RapiTime.

In this paper, we propose a saturation computation method of neural networks for efficiently solving combinatorial optimization problems. In this computation method, once the neuron is in excitatory state, then its input potential is considered to be in positive saturation where the input potential can only be reduced but cannot be increased, and once the neuron is in inhibitory state, then its input potential is considered to be in negative saturation where the input potential can only be increased but cannot be reduced. The proposed method is applied to N-Queens problem. The performance is evaluated through simulations where the results show that the saturation method improves the searching capability of neural networks and shortens the computation time. Particularly, the simulation results show that the performance of the proposed method surpasses the exiting methods for N-queens problem in synchronous parallel model.