The operating system (OS) is a major part of Embedded software systems and its robustness has considerable influence on the robustness of the entire system. Thus, its robustness testing is critical for assessing the dependability of the system. In this paper, a state-aware approach is proposed to evaluate the robustness of components of Embedded real-time OSs in the presence of different types of faulty inputs. This approach leads to identifying critical OS states, their criticality level, and the maximum and minimum level of the OS robustness. It also facilitates comparing the robustness level of OS’ s components and helps the system developers to select the most appropriate fault tolerance techniques by considering the robustness level and timing limitations. The experimental results demonstrate the ability of the proposed approach in providing more information about the robustness vulnerabilities in the states of the system.

Chaos is Embedded to the he Charged system Search (CSS) to solve practical optimization problems. To improve the ability of global search, different chaotic maps are introduced and three chaotic-CSS methods are developed. A comparison of these variants and the standard CSS demonstrates the superiority and suitability of the selected variants for practical civil optimization problems.

Embedded processors are now widely used in system-on-chips. The computational power of such processors and their ease of access to/from other Embedded cores can be utilized to test SoCs (system-on-Chips). The first step, naturally, is to test the processor it self and its memory partner, as all other SoC test activities require both. In this paper, efficient test architecture is presented to achieve high quality testing of Embedded processor and memory cores. Especially, in testing the memory core, a test algorithm is presented for bit-oriented memories and its enhanced version for word-oriented memories. The method requires slow overhead but provides significant flexibility in terms of fault model, test mechanism and future reuse.

Background: Diagnosing emotional states would improve human-computer interaction (HCI) systems to be more effective in practice. Correlations between Electroencephalography (EEG) signals and emotions have been shown in various research,therefore, EEG signal-based methods are the most accurate and informative. Methods: In this study, three Convolutional Neural Network (CNN) models, EEGNet, ShallowConvNet and DeepConvNet, which are appropriate for processing EEG signals, are applied to diagnose emotions. We use baseline removal preprocessing to improve classification accuracy. Each network is assessed in two setting ways: subject-dependent and subject-independent. We improve the selected CNN model to be lightweight and implementable on a Raspberry Pi processor. The emotional states are recognized for every three-second epoch of received signals on the Embedded system, which can be applied in real-time usage in practice. Results: Average classification accuracies of 99. 10% in the valence and 99. 20% in the arousal for subject-dependent and 90. 76% in the valence and 90. 94% in the arousal for subject independent were achieved on the well-known DEAP dataset. Conclusion: Comparison of the results with the related works shows that a highly accurate and implementable model has been achieved for practice.

The Health system Impact (HSI) Fellowship program in Canada offers a transformative approach to health services and policy research (HSPR) training, preparing PhD graduates for diverse career pathways and leadership roles within learning health systems. This commentary builds on Kasaai and colleagues’ evaluation of the HSI Fellowship to discuss the diverse career paths of alumni and highlight the multifaceted benefits of the program. Further, we emphasize the need for future research and knowledge mobilization to better understand and evaluate Embedded research roles. Developing a robust evaluation framework is essential to capture the unique impacts of Embedded research, fostering a culture that prioritizes and integrates it, thereby driving the transformation towards learning health systems.