Search

Virtualization plays a crucial role in successfully integrating multiple real-time applications on a single platform. Through virtualization, different applications can run in independent virtual machines, isolated from each other to prevent mutual interference. This paper considers partitioned fixed priority (P-FP) scheduling for tasks and servers on the multi-core virtualization platform with non-preemptive resource sharing. The solution for P-FP scheduling on multi-core virtualization environments typically uses vMPCP, an extension protocol of the well-known Multiprocessor Priority Ceiling Protocol (MPCP), to address the problem of resource sharing among real-time application tasks. However, the existing analysis of Worst-Case Blocking Time (WCBT) for the P-FP+vMPCP scheduling tends to be overly conservative, resulting in many schedulable task sets being incorrectly classified as unschedulable. Consequently, it leads to wasted system resources and degraded performance. To mitigate this issue and reduce the pessimism of existing analysis, we propose an improved WCBT analysis. This analysis bounds the maximum blocking time that tasks experience when each job from the same task requires to access the same type of non-preemptive global resource multiple times. As a result, it enhances the accuracy over prior work and obtains more precise Worst-Case Response Time (WCRT) for tasks. In our experiments, the WCRT-based schedulability test is adopted to evaluate the schedulability of tasks. Additionally, we do not consider additional scheduling costs, as no online decisions are required. Experimental results demonstrate that our proposed method outperforms the latest advanced methods in terms of the schedulability rate of tasks. Specifically, the schedulable task sets are increased by approximately 47.7% on average.

Mechatronic systems, like mobile robots, are fairly complex. They are composed of electromechanical actuation components and sensing elements supervised by microcontrollers running complex embedded software. This paper proposes a novel approach to aid mobile robotics developers in adopting a rigorous development process to design and verify the robot’s detection and mitigation capabilities against random hardware failures affecting its sensors or actuators. Unfortunately, assessing the interactions between the various safety/mission-critical subsystem is quite complex. The failure mode effect analysis (FMEA) alongside an analysis of the failure detection capabilities (FMEDA) are the state-of-the-art methodologies for performing such an analysis. Various guidelines are available, and the authors decided to follow the one released by AIAG&VDA in June 2019. Since the robot’s behavior is based on embedded software, the FMEA has been integrated with the hardware/software interaction analysis described in the ECSS-Q-ST-30-02C manual. The core of this proposal is to show how a simulation-based approach, where the mechanical and electrical/electronic components are simulated alongside the embedded software, can effectively support FMEA. As a benchmark application, we considered the mobility system of a proof-of-concept assistance rover for Mars exploration designed by the D.I.A.N.A. student team at Politecnico di Torino. Thanks to the adopted approach, we described how to develop the detection and mitigation strategies and how to determine their effectiveness, with a particular focus on those affecting the sensors.

Embedded systems consist of one or more processing units which are completely encapsulated by the devices under their control, and they often have stringent timing constraints associated with their functional specification. Previous research has considered the performance of different types of task scheduling algorithm and developed associated timing analysis techniques for such systems. Although preemptive scheduling techniques have traditionally been favored, rapid increases in processor speeds combined with improved insights into the behavior of non-preemptive scheduling techniques have seen an increased interest in their use for real-time applications such as multimedia, automation and control. However when non-preemptive scheduling techniques are employed there is a potential lack of error confinement should any timing errors occur in individual software tasks. In this paper, the focus is upon adding fault tolerance in systems using non-preemptive deadline-driven scheduling. Schedulability conditions are derived for fault-tolerant periodic and sporadic task sets experiencing bounded error arrivals under non-preemptive deadline scheduling. A timing analysis algorithm is presented based upon these conditions and its run-time properties are studied. Computational experiments show it to be highly efficient in terms of run-time complexity and competitive ratio when compared to previous approaches.

Computer vision and deep learning are clearly demonstrating a capability to create engaging cognitive applications and services. However, these applications have been mostly confined to powerful Graphic Processing Units (GPUs) or the cloud due to their demanding computational requirements. Cloud processing has obvious bandwidth, energy consumption and privacy issues. The Eyes of Things (EoT) is a powerful and versatile embedded computer vision platform which allows the user to develop artificial vision and deep learning applications that analyse images locally. In this article, we use the deep learning capabilities of an EoT device for a real-life facial informatics application: a doll capable of recognizing emotions, using deep learning techniques, and acting accordingly. The main impact and significance of the presented application is in showing that a toy can now do advanced processing locally, without the need of further computation in the cloud, thus reducing latency and removing most of the ethical issues involved. Finally, the performance of the convolutional neural network developed for that purpose is studied and a pilot was conducted on a panel of 12 children aged between four and ten years old to test the doll.