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A cause-effect chain is used to define the logical order of data dependent tasks, which is independent from the execution order of the jobs of the (periodic/sporadic) tasks. Analyzing the worst-case End-to-End timing behavior, associated to a cause-effect chain, is an important problem in embedded control systems. For example, the detailed timing properties of modern automotive systems are specified in the AUTOSAR Timing Extensions. In this paper, we present a formal End-to-End timing analysis for distributed systems. We consider the two most important End-to-End timing semantics, i.e., the button-to-action delay (termed as the maximum reaction time ) and the worst-case data freshness (termed as the maximum data age ). Our contribution is significant due to the consideration of the sporadic behavior of job activations, whilst the results in the literature have been mostly limited to periodic activations. The proof strategy shows the (previously unexplored) connection between the reaction time (data age, respectively) and immediate forward (backward, respectively) job chains. Our analytical results dominate the state of the art for sporadic task activations in distributed systems and the evaluations show a clear improvement for synthesized task systems as well as for a real world automotive benchmark setting.

Energy-efficiency has been an important system issue in hardware and software designs to extend operation duration or cut power bills. This research explores systems with probabilistic distribution on the execution time of real-time tasks for systems with discrete frequencies. Most previous studies consider DVS systems with continuous frequencies for the minimization of expected energy consumption under timing constraints. However, these approaches cannot guarantee the minimization of expected energy consumption when only discrete frequencies are available. This paper presents new approaches to minimize the expected energy consumption. By applying intra-task frequency scheduling, we develop an efficient algorithm to derive optimal frequency scheduling for a single task. The algorithm is then extended to cope with periodic real-time tasks with different power characteristics. With inter-task and intra-task frequency scheduling, we present a linear-programming approach to derive optimal solutions for frame-based real-time tasks and an on-line algorithm for periodic real-time tasks. Experimental results show that the proposed algorithms can effectively reduce the expected energy consumption.

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A mobile system that can detect viruses in real time is urgently needed, due to the combination of virus emergence and evolution with increasing global travel and transport. A biosensor called PAMONO (for Plasmon Assisted Microscopy of Nano-sized Objects) represents a viable technology for mobile real-time detection of viruses and virus-like particles. It could be used for fast and reliable diagnoses in hospitals, airports, the open air, or other settings. For analysis of the images provided by the sensor, state-of-the-art methods based on convolutional neural networks (CNNs) can achieve high accuracy. However, such computationally intensive methods may not be suitable on most mobile systems. In this work, we propose nanoparticle classification approaches based on frequency domain analysis, which are less resource-intensive. We observe that on average the classification takes 29 μs per image for the Fourier features and 17 μs for the Haar wavelet features. Although the CNN-based method scores 1–2.5 percentage points higher in classification accuracy, it takes 3370 μs per image on the same platform. With these results, we identify and explore the trade-off between resource efficiency and classification performance for nanoparticle classification of images provided by the PAMONO sensor.

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