Extended, Virtual, Mixed and Augmented reality (XR, VR, MR, AR) devices, allow users to interact with computer-generated simulations that mimic or enhance the perception of the real world. AR overlays digital information onto the actual environment, while VR immerses users entirely in a virtual environment. MR combines elements of both to seamlessly blend the virtual and real worlds. XR technology provides immersive experiences, creating a sense of presence in the virtual realm. XR applications span various domains, such as training, education, medical, digital health, and aviation, benefiting pilots and air traffic controllers (ATCOs). In this chapter, we share our...

Interferometric measurements correspond to sample covariance matrices of signals received by multiple sensors. In dynamic scenarios, such as radio astronomy imaging, the properties of these signals can vary over time, posing a significant challenge for study. This work addresses the issue of estimating the stochastic power and steering vector of signal sources from sample covariance measurements. A novel approach is proposed, introducing a non-standard Kalman filter designed to accommodate any noise and signal distribution, thereby broadening the Kalman filter's applicability to situations with unknown measurement models. The effectiveness of this method is highlighted in...

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Magnitude is a simulation tool prototype, designed by Laetitia Bornes, as part of her thesis, and developed by Laetitia Bornes, Mathieu Magnaudet, Stéphane Conversy, and Mathieu Poirier. This tool allows, among other things, the modelling of direct and indirect environmental impacts of digital products or services. It was designed to enable collaborative construction of a simplified model of direct and indirect effects of an intervention (product, service, regulation, etc.) based on mixed data (system data, user surveys, expert assumptions). The objective is to enable involved stakeholders (decision-makers, designers, policymakers, citizens, etc.) to better understand the...

Electrophysiological signals offer invaluable insights for human monitoring, providing real-time, objective measures of mental states. Among the sensors used for signal acquisition, electrocardiography (ECG) and electrodermal activity (EDA) sensors stand out for their affordability and wearability. Electroencephalography (EEG) is also gaining popularity, particularly with the advent of dry electrode devices that reduce setup times, albeit at the expense of signal quality. These signals are increasingly employed to close the loop and develop adaptive systems, a discipline known as physiological computing (Fairclough, Interact Comput 21(1–2):133–145, 2009). For example...

We take up the idea of Nelson’s stochastic processes, the aim of which was to deduce Schrödinger’s equation. We make two major changes here. The first one is to consider deterministic processes which are pseudo-random but which have the same characteristics as Nelson’s stochastic processes. The second is to consider an extended particle and to represent it by a set of interacting vibrating points. In a first step, we represent the particle and its evolution by four points that define the structure of a small elastic string that vibrates, alternating at each period a creative process followed by a process of annihilation. We then show how Heisenberg’s spin and relations of...

This paper presents a test bench for the emulation of a satellite-ground laser link, from the atmospheric propagation to its coherent detection. The scheme involves two projects. The EPLO project (Free-space Optical Propagation Emulator) aims at modeling and mitigating atmospheric impairments. The wave front deformation is emulated by time series with a DMD (Digital Micro-mirror Device), using Lee holographic methods and the Rytov approximation in the Kolmogorov theory. The signal then undergoes a correction in the TILBA-ATMO device, based on Cailabs’ Multi-Plane Light Conversion (MPLC). Alongside this approach, the CALICO project (Compensation of Atmospheric losses on a...

The transition from fossil fuels to sustainable energy sources is an important challenge for current aviation industries. One of the critical questions in this transition is how much electricity is needed to accommodate future air transport demand and how to supply it. The answer depends on the type of energy carriers, their production process, and the source of the electricity. This paper provides a methodology to estimate the annual required electricity to produce energy carriers (particularly electrofuel and liquid hydrogen) with a given energy production process for an airport. The methodology also computes the surface of solar panels and/or the number of wind...

In the field of Intelligent Transportation Systems (ITS), trajectory data processing is essential yet challenging, as road and aerial trajectories are often high-dimensional. This complexity can hinder inference tasks such as prediction, compression, filter- ing, and classification. To address this issue, this dissertation is structured into two parts that explore different statistical and unsupervised machine learning methods applied to trajectory data. Applications covered include anomaly detection in streaming data, real-time compression, and the smoothing and filtering of road vehicle trajectories. These techniques have been used in contexts such as compressing data...