Real-time systems require high confidence in execution times. Static analysis techniques provide this confidence by accounting for the specific execution platform and code being executed to provide an upper bound on the worst-case execution time. To make analysis easier and more accurate, it is imperative to design the platform to be predictable. One such design is the Patmos processor's split caches: The traditional data cache is split into a stack cache and a data cache. The stack cache stores data from the program stack. This data is highly predictable and easy to reason about for a static analyzer. The current implementation of the Patmos compiler does not use the stack cache optimally; caching only spilled registers as part of register allocation. This paper presents the local variable promotion optimization, which enables the Patmos compiler to store function-local variables on the stack cache. Our results show that this optimization is critical for any platform using a stack cache. While not all benchmark programs see a significant performance benefit, those that did saw increases mostly between 50% and 60%.

The wiretap channel is a well-studied problem in the physical layer security literature. Although it is proven that the decoding error probability and information leakage can be made arbitrarily small in the asymptotic regime, further research on finite-blocklength codes is required on the path towards practical, secure communication systems. This work provides the first experimental characterization of a deep learning-based, finite-blocklength code construction for multi-tap fading wiretap channels without channel state information. In addition to the evaluation of the average probability of error and information leakage, we examine the designed codes in the presence of fading in terms of the equivocation rate and illustrate the influence of (i) the number of fading taps, (ii) differing variances of the fading coefficients, and (iii) the seed selection for the hash function-based security layer.

This paper presents a system which constantly monitors the level of attention of a driver in traffic. The vehicle is instrumented and can identify the state of traffic-lights, as well as obstacles on the road. If the driver is inattentive and fails to recognize a threat, the assistance system produces a warning. Therefore, the system helps the driver to focus on crucial traffic situations. Our system consists of three components: computer vision detection of traffic-lights and other traffic participants, an eye tracking device used also for head localization, and finally, a human machine interface consisting of a head-up display and an acoustic module used to provide warnings to the driver. The orientation of the driver's head is detected using fiducial markers visible in video frames. We describe how the system was integrated using an autonomous car as experimental ADAS platform.