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AudioXtend: Assisted Reality Visual Accompaniments for Audiobook Storytelling During Everyday Routine Tasks
No full textInternational audienceThe rise of multitasking in contemporary lifestyles has positioned audio-first content as an essential medium for information consumption. We present AudioXtend, an approach to augment audiobook experiences during daily tasks by integrating glanceable, AI-generated visuals through optical see-through head-mounted displays (OHMDs). Our initial study showed that these visual augmentations not only preserved users’ primary task efficiency but also dramatically enhanced immediate auditory content recall by 33.3% and 7-day recall by 32.7%, alongside a marked improvement in narrative engagement. Through participatory design workshops involving digital arts designers, we crafted a set of design principles for visual augmentations that are attuned to the requirements of multitaskers. Finally, a 3-day take-home field study further revealed new insights for everyday use, underscoring the potential of assiste
Effective Angle of Attack Measurements in Active Control for Vortex Gust Mitigation
No full textInternational audienceGust encounters can have adverse effects on the flight stability and trajectories of air vehicles. These effects are particularly pronounced for Micro Air Vehicles (MAVs) which generally fly at velocities of the same order of magnitude with the flow velocity changes induced by atmospheric gusts. This study proposes and experimentally demonstrates the use of effective angle of attack measurements taken from upstream of a NACA0012 wing that is exposed to a continuous vortex gust generated by the wake of an upstream cylinder for the mitigation of gust-induced loads by feed-forwarding the measurements to actuate a trailing edge flap to exploit the temporal gap between the measurement and the force development in the air. Follow-up water channel experiments have been performed to verify the validity of the measurements performed in the air. It was seen that feed-forwarding the angle of attack measurements in water fails due to the added mass effects present in the medium. An ODE solution was implemented to work around the issue and, through a few iterations, a force mitigation of 74.40% was achieved
McEliece Parameter Sets Optimized for Processing in Memory Architectures
No full textInternational audienceParameter sets for post-quantum primitives have to be designed with their whole lifecycle in mind. Computers will likely evolve to include larger computing spaces, for instance, with processing in memory. This architecture change is already known to improve cryptographic performance. In this paper, we propose new parameter sets for Classic McEliece to benefit further from this change
Absolute Environmental Sustainability Assessment of aviation transition scenarios
No full textInternational audienc
Designing the invisible reactive BCI: the STAR-Burst paradigm
No full textInternational audienceReactive Brain-Computer Interfaces (rBCIs) hold the potential to revolutionize human-machine interaction. Despite their promise, widespread adoption of rBCIs is hindered by several usercentric challenges. Prominent among these are attentional fatigue, along with the burdens of repetitive and lengthy calibration processes and a lack of user control. These issues largely stem from the field's focus on advancing algorithmic and technological aspects without considering user experience. To address these challenges, we propose a novel approach rooted in cognitive neuroscience to improve the design of the stimuli and their presentation. This groundbreaking approach not only mitigates visual and attention-related issues but also enables the development of highly mobile BCIs characterized by self-paced operation, swift calibration, and rapid decoding capabilities, making BCIs more accessible and efficient.</div
ORCA-A* : A Hybrid Reciprocal Collision Avoidance and Route Planning Algorithm for UAS in Dense Urban Areas
No full textInternational audienceThe rapid development of drones (or Unmanned Aerial Systems) and their potential deployment in urban areas poses a number of safety issues. Some degree of automation is most probably necessary to ensure that the UAS missions are safely and efficiently performed in urban environments. In a context where a large number of non-cooperative, noncommunicative UAS would fly in dense urban areas, decentralized and autonomous approaches naturally come to mind. In such approaches, each agent would navigate among the buildings while avoiding the other traffic. ORCA (Optimal Reciprocal Collision Avoidance) is a state-of-the art geometric method for robot collision avoidance that could be used as a Detect & Avoid logic on-board UAS. It was initially designed for the 2D-motion of holonomic robots and requires some adaptation in order to be applied to flying objects in an urban environment. In particular, ORCA is a short-term collision avoidance that is not designed for path planning in a complex urban environment.In this study, we introduce a hybrid method combining ORCA with an A * path-planning algorithm and show that ORCA-A * significantly reduces the separation losses when compared with the baseline ORCA in artificial scenarios of dense UAS traffic
Examining Decision-Making in Air Traffic Control: Enhancing Transparency and Decision Support Through Machine Learning, Explanation, and Visualization: A Case Study
No full textInternational audienceArtificial Intelligence (AI) has recently made significant advancements and is now pervasive across various application domains. This holds true for Air Transportation as well, where AI is increasingly involved in decision-making processes. While these algorithms are designed to assist users in their daily tasks, they still face challenges related to acceptance and trustworthiness. Users often harbor doubts about the decisions proposed by AI, and in some cases, they may even oppose them. This is primarily because AI-generated decisions are often opaque, non-intuitive, and incompatible with human reasoning. Moreover, when AI is deployed in safety-critical contexts like Air Traffic Management (ATM), the individual decisions generated by AI models must be highly reliable for human operators. Understanding the behavior of the model and providing explanations for its results are essential requirements in every life-critical domain. In this scope, this project aimed to enhance transparency and explainability in AI algorithms within the Air Traffic Management domain. This article presents the results of the project’s validation conducted for a Conflict Detection and Resolution task involving 21 air traffic controllers (10 experts and 11 students) in En-Route position (i.e. hight altitude flight management). Through a controlled study incorporating three levels of explanation, we offer initial insights into the impact of providing additional explanations alongside a conflict resolution algorithm to improve decision-making. At a high level, our findings indicate that providing explanations is not always necessary, and our project sheds light on potential research directions for education and training purpose
A comparative study of gravity models to assess the evolution of urban bicycle mobility
No full textGravity models are extensively used tools to understand mobility, providing an approximation of the volume of a flow as a function of distance and local population density. Specific gravity models have been designed for studying bike sharing systems (BSS) in urban areas. In this paper, we show how robustness to scale change in data pre-processing can be used as a criterion to select the best model for BSS usage, in addition to the usual criteria of quality of fit. In a second stage, the study the recent evolution of BSS use in large cities via a dynamical application of gravity models over rolling time windows. Exploiting the good interpretability of gravity models, this methodology produces a time series for each parameter, that can be analysed to study the evolution of local policies and users habits
Simultaneous Trajectory and Design Optimization of Small VTOL UAVs With Controllability Considerations
No full textInternational audienceHybrid vertical takeoff and landing unmanned aerial vehicles are attractive for missions where flight dexterity and long ranges are needed. However, designing such kind of vehicle is troublesome, considering their broad flight envelope and controllability challenges. This paper focuses on the problem of simultaneously designing the vehicle and optimizing its flight trajectory. It continues from a previous work, which evaluated the insertion of a full control system with attitude control, velocity control, and guidance law in an MDO process. It starts with the description of the flight mechanics model, the two-fidelity aerodynamic tool used for integrated aero propulsive analysis, and the 3D modeling strategy that allows for weight and inertia prediction using the Engineering Sketch Pad. The trajectory optimization problem, that accounts for a symbolic mission with hover, transition hover-cruise, cruise, climb, and transition cruise-hover, is then presented. Optimizations with two different objective functions are performed: minimum time and minimum energy consumption. Two different results for each objective function are shown: one considering a fixed design and the other that is obtained with the full design-trajectory optimization. Simultaneous design and trajectory optimization leads to a better performance for the evaluated cases. We then discuss the closed loop feasibility of optimal trajectories, and perform a preliminary study to address the control law tuning for them. The paper finishes with conclusions and ideas for a future work with full integration of trajectory optimization and control law tuning
A Performant Quantum-Resistant KEM for Constrained Hardware: Optimized HQC
No full textInternational audienceSecure Key Encapsulation Mechanisms (KEMs) are necessary for providing authentication and confidentialitythrough symmetrical encryption. The emergence of quantum computers is a threat to current KEM standards,therefore new quantum-resistant algorithms have been developed in recent years. One of these propositionsis the code-based Hamming Quasi-Cyclic (HQC) algorithm. However, a lightweight version of this algorithmis required to run on low-performance systems such as Internet of Things (IoT) devices or small UnmannedAerial Vehicles (UAVs). This article presents an algorithmic optimization of the HQC algorithm applied onconstrained hardware. The goal is to improve the performance for real-life applications, and thus the testbed uses a Real-Time Operating System (RTOS) to emulate a system able to complete complex tasks. Thisoptimization reduces the completion time of key generation, encapsulation, and decapsulation by a factor of 10,and reduces significantly the Random Access Memory (RAM) usage for the algorithm. These improvementsmake HQC viable for real-life applications on constrained hardware, and the performance could be furtherimproved by using hardware-specific optimizations