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Cadre de simulation multi-véhicules pour les MAV non conventionnels
No full textInternational audienceThe growing interest in using aerial vehicles for diverse missions creates the need for higher autonomy and dexterity. Unconventional configurations can potentially satisfy such requirements, at a cost of new challenges in design and control. Simulation environments are often employed to tackle such challenges while minimizing resources expenditure. In this paper, an existing simulation framework has been extended to allow for the modeling of new UAV configurations. Aerodynamic forces and moments generated by propellers and airframes are defined in separate functions to ease the understanding and allow future addition of new vehicle configurations. Incremental nonlinear dynamic inversion and vector field guidance methods are implemented as vehicle agnostic control and guidance solutions. Vehicle models are extracted and implemented into Paparazzi open-source autopilot system, which makes a seamless switch towards real flights and hardware in the loop tests, facilitating control law development. The simulator can also be used within vehicle and mission design environments. With that, the entire closed loop behavior of the system can be addressed from the design stage, allowing for the vehicle and fleet optimization while considering control and operation constraints. Finally, the whole codebase is made available for the community and can be reached at: github.com/enac-drones/dronesi
Adopting and diffusing hydrogen technology in air transport: An evolutionary game theory approach
No full textInternational audienceThis paper uses an evolutionary game theory approach to assess the diffusion of different hydrogen technologies in the air transport system. Our model is extended to consider uncertainty, as well as the impact of the “Flight Shame” movement and different policies such as subsidies or taxes. A sufficiently high initial percentage of hydrogen adopters is required for full adoption following the market social learning mechanism. This fixed internal point is determined by the benefits of switching technologies, the extra costs, and the ground handling fee. We complement our theoretical analysis with a well-calibrated simulation using industry data to study the diffusion of different hydrogen innovations. Without any government intervention, carbon neutrality at the 2050 Horizon is only feasible with the most disruptive technologies. Other intermediate technologies are not profitable enough to meet the Net Zero CO2 emissions goal
Metaheuristic for Optimal Dynamic K-Coloring Application on Band Sharing for Automotive Radars
No full textInternational audienceMetaheuristic for Optimal Dynamic K-Coloring Application on Band Sharing for Automotive Radars
RFC 9407 Tetrys: An On-the-Fly Network Coding Protocol
No full textThis document describes Tetrys, which is an on-the-fly network coding protocol that can be used to transport delay-sensitive and loss-sensitive data over a lossy network. Tetrys may recover from erasures within an RTT-independent delay thanks to the transmission of coded packets. This document is a record of the experience gained by the authors while developing and testing the Tetrys protocol in real conditions. This document is a product of the Coding for Efficient NetWork Communications Research Group (NWCRG). It conforms to the NWCRG taxonomy described in RFC 8406
Fair Network Division of Nano-satellite Swarms
No full textInternational audienceWe address the problem of partitioning a network of nano-satellites to distribute fairly the network load under energy consumption constraints. The study takes place in a context where this swarm of nano-satellites orbits the Moon and works as, but not limited to, a distributed radio-telescope for low-frequency radio interferometry. During an interferometry mission, each nano-satellite collects observation data, then shares them with the other swarm members to compute a global image of space. However, the simultaneous transmission of large volumes of data can cause communication issues by overloading the radio channel, leading to potential packet loss. In this context, we investigate three division algorithms based on graph sampling techniques. We prove that random walk-based algorithms overall perform the best in terms of conservation of graph properties and fairness for group sizes down to 10% of the original graph
Mental strategies and resting state EEG :: Effect on high alpha amplitude modulation by neurofeedback in healthy young adults
No full textInternational audienceNeurofeedback (NFB) is a brain-computer interface which allows individuals to modulate their brain activity. Despite the self-regulatory nature of NFB, the effectiveness of strategies used during NFB training has been little investigated. In a single session of NFB training (6*3 min training blocks) with healthy young participants, we experimentally tested if providing a list of mental strategies (list group, N = 46), compared with a group receiving no strategies (no list group, N = 39), affected participants’ neuromodulation ability of high alpha (10–12 Hz) amplitude. We additionally asked participants to verbally report the mental strategies used to enhance high alpha amplitude. The verbatim was then classified in pre-established categories in order to examine the effect of type of mental strategy on high alpha amplitude. First, we found that giving a list to the participants did not promote the ability to neuromodulate high alpha activity. However, our analysis of the specific strategies reported by learners during training blocks revealed that cognitive effort and recalling memories were associated with higher high alpha amplitude. Furthermore, the resting amplitude of trained high alpha frequency predicted an amplitude increase during training, a factor that may optimize inclusion in NFB protocols. The present results also corroborate the interrelation with other frequency bands during NFB training. Although these findings are based on a single NFB session, our study represents a further step towards developing effective protocols for high alpha neuromodulation by NFB
Computers as interactive machines: Can we build an explanatory abstraction?
No full textInternational audienceIn this paper, we address the question of what current computers are from the point of view of human-computer interaction. In the early days of computing, the Turing machine (TM) has been the cornerstone of the understanding of computers. The TM defines what can be computed and how computation can be carried out. However, in the last decades, computers have evolved and increasingly become interactive systems, reacting in real-time to external events in an ongoing loop. We argue that the TM does not provide a mechanistic explanation for interactive computing. The reason is that the fundamental phenomena relevant to interactive computing are out of the scope of classical computability theory. Part of the explanatory power of the TM relies on what we propose to call an execution model. An execution model belongs to a level of abstraction where it is possible to describe both the functional architecture and the execution in mechanistic terms. An updated execution model is warranted to provide the minimal mechanistic description for interactive computation as a counterpart of what the TM could explain regarding Church-Turing computation. It would support an explanation of the ubiquitous computing devices we know-those interacting with humans, e.g., through digital interfaces. We show that such a model is not available within interactive models of computation and that relevant abstractions and concerns are available in computer engineering but need to be identified and gathered. To fill this void, we propose to reflect on the level of abstractionrequired to support the mechanistic description of an interactive execution and propose some preliminary requirements
CC : Eye-Interaction : Une étude multimodale de l'incapacitation cognitive chez les pilotes de ligne
No full textInternational audienceEn aviation civile, l'incapacitation cognitive désigne l'altération temporaire des facultés cognitives du pilote en réaction à un contexte de stress intense. Elle se traduit par des phénomènes variés tels que la persévération cognitive, des actions de pilotage risquées, etc. Il apparaît important de mieux comprendre l'émergence de l'incapacitation cognitive afin de pouvoir l'anticiper, la détecter, et la traiter. Cette communication fait état d'une expérimentation en cours visant à identifier des marqueurs physiologiques, comportementaux et de personnalité possiblement associés à l'incapacitation cognitive. Pour cela, des pilotes réalisent des tests de personnalité et des tâches cognitives avec manipulation de la charge mentale. Ils réalisent ensuite une session de pilotage en simulateur de vol mettant en scène des évènements intenses et surprenants pouvant générer un sursaut (startle reflex en anglais). Les résultats préliminaires montrent que la majorité des pilotes a eu un réflexe de sursaut suite à un stimulus intense (fort bruit de tonnerre). L'impact sur la performance de pilotage est en cours d'analyse
RCS prediction and optimization for anomalous reflection metasurfaces using Floquet analysis: measurements
No full textInternational audienceThis letter proposes the design and measurement of a periodic metasurface that achieves anomalous reflection with reduced RCS in a given direction. A previous study proposed a semi-analytical model to predict the RCS behaviour of such a metasurface. This work focuses the discussion on manufacturing and measurement issues. The synthesis, design specifications, fabrication method and experimental setup are presented and discussed. Measurement results are also examined in detail, highlighting some limitations in metasurfaces RCS measurements. The proposed metasurface effectively achieves the predicted RCS level reduction in the Floquet direction considered. The agreement between simulation and experimental results demonstrates the accuracy of the modelling and the efficiency of the optimisation procedure
AI driven TEM configuration for better data acquisition
No full textInternational audienceRecent advances in artificial intelligence are paving the way for the automation of data acquisition across the scientific landscape. Ongoing research in transmission electron microscopy (TEM) is primarily focused on image processing for super resolution and feature extraction using Deep Convolutional Neural Networks (CNN). Similar methods could use image information and a microscopist-defined goal (such as drift correction or automatic alignment) to automatically adjust the TEM configuration. To assess the feasibility of such an endeavour, we first tested a model's ability to predict the resulting image solely based on the microscope configuration on a modified HF3300 and an in-house paraxial simulation. Then we did the opposite: we predicted a configuration from an image that would result in a microscope image similar to the given image. The ultimate goal is to be able to predict a configuration in which the resulting image meets user-defined properties such as being in-focus, centred, and properly illuminated.As a result, a dataset containing configurations and their associated images was required. We chose to align the microscope first, then slightly modify some parameters while preserving a visible beam because completely randomizing those parameters would not produce a visible beam in most images. We generated 9 datasets with 450-900 configuration/image tuples each with the TEM. The deep learning models were then trained to predict one using the other. This validated the models' ability to simulate the entire microscope and reverse engineer its configuration from an image. Because direct prediction is impossible for the latter since multiple configurations produce similar images, we trained it to produce a configuration whose resulting image is similar to the given one. The simulation was not accurate enough to use the microscope's initial configuration. To address the issue of automation using user-defined goals, we are developing a workflow based on Reinforcement Learning (RL) that will allow the models to train in real time by modifying the microscope configuration directly