Computer systems are permanently present in our daily basis in a wide range of applications. In systems with mixedcriticality requirements, e.g., autonomous driving or aerospace applications, devices are expected to continue operating properly even in the event of a failure. An approach to improve the robustness of the device's operation lies in enabling faulttolerant mechanisms during the system's design. This article proposes Lock-V, a heterogeneous architecture that explores a Dual-Core Lockstep (DCLS) fault-tolerance technique in two different processing units: a hard-core Arm Cortex-A9 and a softcore RISC-V-based processor. It resorts a System-on-Chip (SoC) solution with software programmability (available trough the hard-core Arm Cortex-A9) and field-programmable gate array (FPGA) technology, taking advantages from the latter to support the deployment of the RISC-V soft-core along with dedicated hardware accelerators towards the realization of the DCLS.
With the current technological transformation in the automotive industry, autonomous vehicles are getting closer to the Society of Automative Engineers (SAE) automation level 5. This level corresponds to the full vehicle automation, where the driving system autonomously monitors and navigates the environment. With SAE-level 5, the concept of a Shared Autonomous Vehicle (SAV) will soon become a reality and mainstream. The main purpose of an SAV is to allow unrelated passengers to share an autonomous vehicle without a driver/moderator inside the shared space. However, to ensure their safety and well-being until they reach their final destination, active monitoring of all passengers is required. In this context, this article presents a microphone-based sensor system that is able to localize sound events inside an SAV. The solution is composed of a Micro-Electro-Mechanical System (MEMS) microphone array with a circular geometry connected to an embedded processing platform that resorts to Field-Programmable Gate Array (FPGA) technology to successfully process in the hardware the sound localization algorithms.
Maintaining high levels of employee's performance at work is a major concern among managers. Employees' characteristics and individual performance contribute to the competitiveness of organisations. The current work environment is highly sedentary, which influences employees' performance. This quantitative study investigates the moderating effect of physical exercise on the relationships between job satisfaction, motivation, and organisational performance. Data was collected using an online questionnaire and it was analysed using structural equation modelling and moderation analysis. The results show the existing relationships between job satisfaction, intrinsic motivation and performance regarding both dimensions of organisational performance: creativity and dealing with emergencies. Such relationships are moderated by the level of exercise performed by the employees.
With the current technological transformation in the automotive industry, autonomous vehicles are getting closer to the Society of Automative Engineers (SAE) automation level 5. This level corresponds to the full vehicle automation, where the driving system autonomously monitors and navigates the environment. With SAE-level 5, the concept of a Shared Autonomous Vehicle (SAV) will soon become a reality and mainstream. The main purpose of an SAV is to allow unrelated passengers to share an autonomous vehicle without a driver/moderator inside the shared space. However, to ensure their safety and well-being until they reach their final destination, it is required an active monitoring of all passengers. In this context, this article presents a microphone-based sensor system that is able to localize sound events inside an SAV. The solution is composed of a Micro-Electro-Mechanical System (MEMS) microphone array with a circular geometry connected to an embedded processing platform that resorts to Field-Programmable Gate Array (FPGA) technology to successfully process in hardware the sound localization algorithms.
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