This research presents a machine learning modeling process for detecting mental fatigue using three physiological signals: electrodermal activity, electrocardiogram, and respiration. It follows the conventional machine learning modeling pipeline, while emphasizing the significant contribution of the feature selection process, resulting in, not only a high-performance model, but also a relevant one. The employed feature selection process considers both statistical and contextual aspects of feature relevance. Statistical relevance was assessed through variance and correlation analyses between independent features and the dependent variable (fatigue state). A contextual analysis was based on insights derived from the experimental design and feature characteristics. Additionally, feature sequencing and set conversion techniques were employed to incorporate the temporal aspects of physiological signals into the training of machine learning models based on random forest, decision tree, support vector machine, k-nearest neighbors, and gradient boosting. An evaluation was conducted using a dataset acquired from a wearable electronic system (in third-party research) with physiological data from three subjects undergoing a series of tests and fatigue stages. A total of 18 tests were performed by the 3 subjects in 3 mental fatigue states. Fatigue assessment was based on subjective measures and reaction time tests, and fatigue induction was performed through mental arithmetic operations. The results showed the highest performance when using random forest, achieving an average accuracy and F1-score of 96% in classifying three levels of mental fatigue.

Avionics Full Duplex switched Ethernet (AFDX) is the de facto standard for the transmission of critical avionics flows. It is a specific switched Ethernet solution based on First-in First-out (FIFO) scheduling. Worst-case traversal time (WCTT) analysis is mandatory for such flows, since timing constraints have to be guaranteed. A classical approach in this context is Network Calculus (NC). However, NC introduces some pessimism in the WCTT computation. Moreover, the worst-case often corresponds to very rare scenarios. Thus, the network architecture is most of the time lightly loaded. Typically, less than 10 % of the available bandwidth is used for the transmission of avionics lows on an AFDX network embedded in an aircraft. One solution to improve the utilization of the network is to introduce Quality of Service (QoS) mechanisms. Deficit Round Robin (DRR) is such a mechanism and it is envisioned for future avionics networks. A WCTT analysis has been proposed for DRR. It is based on NC. It doesn't make any assumption on the scheduling of flows by end systems. The first contribution of this paper is to identify sources of pessimism of this approach and to propose an improved solution which removes part of this pessimism. The second contribution is to show how the scheduling of flows can be integrated in this optimized DRR approach, thanks to offsets. An evaluation on a realistic case study shows that both contributions bring significantly tighter bounds on worst-case latencies.

This research proposes a unique platform for energy management optimization in smart grids, based on 6G technologies. The proposed platform, applied on a virtual power plant, includes algorithms that take into account different profiles of loads and fairly schedules energy according to loads priorities and compensates for the intermittent nature of renewable energy sources. Moreover, we develop a bidirectional energy transition mechanism towards a fleet of intelligent vehicles by adopting vehicle-to-grid technology and peak clipping. Performance analysis shows that the proposed energy provides fairness to electrical vehicles, satisfies urgent loads, and optimizes smart grids energy.

Worst-case delay analysis of real-time networks is mandatory, since distributed real-time applications require bounded end-to-end delays. Switched Ethernet technologies have become popular solutions in the context of real-time systems. Several approaches, based on Network Calculus, trajectories, ..., have been proposed for the worst-case analysis of such technologies. They compute pessimistic upper bounds of end-toend delays. Since this pessimism leads to an over-dimensioning of the network, it is important to quantify the pessimism of the computed upper bounds. In this paper, we propose such a pessimism analysis, based on Network Calculus. In a first step we focus on avionics switched Ethernet network (AFDX) with Fixed Priority/First In First Out (FP/FIFO) scheduling.