Scientific direction Development of key enabling technologies
Transfer of knowledge to industry

PhD : selection by topics

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Millimeter wave MIMO propagation modeling for mobile-to-mobile and V2X communications

Département Systèmes (LETI)

Laboratoire Antennes, Propagation, Couplage Inductif

01-09-2020

SL-DRT-20-0977

gloria.makhoul@cea.fr

Communication networks, IOT, radiofrequencies and antennas (.pdf)

Self-driving cars and Vehicle-to-Everything (V2X) communications are envisioned as vertical use cases by 5G and beyond technologies. Traditionally, V2X communications were supported by the dedicated short-range communication (DSRC) at sub-6 GHz bands. However, the increasing needs in terms of high data rate would require the use of higher bandwidths, which are available at millimeter wave (mm-wave) frequencies. To this purpose, prior knowledge of the propagation environment and precise modeling of the multipath characteristics are essentials to assess future system performance and incorporate beam-forming antennas. The aim of this thesis is to characterize the mm-wave MIMO V2X dynamic channel using the CEA-LETI sounder. Based on the experimental data a channel model will be proposed, focusing on temporal angular statistics to enable novel beam-forming approaches. The PhD student will be part of the Wireless Technologies division at CEA-LETI, in Grenoble (France). He/she will benefit of the state of the art facilities, including channel sounders, anechoic chambers and simulator. The position is open to outstanding students with Master of Science, ?école d'ingénieur? or equivalent. The student should have specialization in the field of telecommunications, microwave and/or signal processing. A predisposition to teamwork, organization and reporting skills are required. The application must necessarily include a CV, cover letter and grades for the last two years of study.

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Machine learning methods using uncertain labels, human stress estimation application

Département Systèmes (LETI)

Laboratoire Signaux et Systèmes de Capteurs

01-10-2019

SL-DRT-20-0981

christelle.godin@cea.fr

Artificial intelligence & Data intelligence (.pdf)

With wearable sensors development, it is now possible to monitor physiological parameters and activity. Many studies show that stress level assessment can be done by using those measurements. Supervised machine learning methods used for it are flourishing. They suppose that for each measurement, a ?ground truth? stress level is available. However, while doing experiments for database construction it is not possible to attribute an exact stress label to each event but subjective values are easily available. The goal of the PhD is to take into account data with uncertain, fuzzy, redundant, contradictory or missing values in order to obtain a better stress estimator. This kind of approach should be useful for a lot of applications including other mental state estimation like drowsiness detection, mental disease diagnosis, emotion estimation.

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Modelling and data assimilation for pre-stresses recovery in the context of structured health monitoring

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Simulation et Modélisation en Acoustique

01-09-2020

SL-DRT-20-0982

alexandre.imperiale@cea.fr

Numerical simulation (.pdf)

The context of this thesis is the structural health monitoring (SHM) of materials using ultrasonic guided waves. SHM is spreading across numerous industrial fields, aeronautics being one important example. This technique aims at using one or an array of actuators and sensors, embedded or onto the specimen, in order to perform in situ controls. Collected data are used to feed fully -- or partially -- automated analysis tool chains. This analysis is intended to ascertain the integrity of the structure, to evaluate its life expectancy or to adjust its maintenance cycle. Among various possible ways to implement SHM, guided waves based SHM is receiving a significant amount of interest. The main sought advantage of this approach is to rely on the capacity of guided waves to propagate over large distances in order to broaden the size of the inspected region and to reduce the number of sensors. However, by nature, SHM is performed during exploitation, i.e. while the specimen under inspection is online. Thus, it is subject to important internal stresses, potentially inducing modifications to the way waves propagate. The general positioning of the thesis is the modeling of wave propagation in the aforementioned context, aiming at facilitating the interpretation and analysis of the experimental data. In particular, two main issues are addressed: (1) building an efficient numerical method able to represent the complexity of guided waves propagation in pre-stressed materials; (2) proposing an inverse method based upon a data assimilation approach leading to the reconstruction of the internal stress within the specimen.

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Waveform optimization for 6G communication systems in the sub-THz bands

Département Systèmes (LETI)

Laboratoire Sans fils Haut Débit

01-10-2020

SL-DRT-20-0984

jean-baptiste.dore@cea.fr

Communication networks, IOT, radiofrequencies and antennas (.pdf)

The race to terabit per second wireless services has begun and is a new challenge for the future wireless systems (6G). The aim of this work is to develop and propose new waveforms adapted and optimized for very high data rates on millimeter and micrometer waves (> 90GHz). The use of these bands currently reserved for astronomy equipments will be discussed at an international level by regulators. Despite the evolution of semiconductor technologies, it is hard to achieve Tb/s using classical transmission technologies due to the RF impairments, the analog to digital (resp. digital to analog) constraints, the power consumption of the device and the high frequency digital processing. A joint optimization of the waveforms and the architecture (analog and digital) will be studied. We address in this work challenges both at digital and analog levels. It is an exploratory study that required strong knowledge on wireless system, digital modulation as well as signal processing. The research plan will be divided into four tasks, bibliography work (20%), analog impairments modelling (30%), optimization of the waveform (40%) and dissemination (10% - including PhD report).

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Dynamic simulation and control of Continious solar fuel gazeification process

Département Thermique Biomasse et Hydrogène (LITEN)

Laboratoire des Systèmes Solaires et Thermodynamiques

01-10-2020

SL-DRT-20-0989

nathalie.dupassieux@cea.fr

Solar energy for energy transition (.pdf)

The topic of this thesis studies deals with the valorisation of solar energy as a storable and/or transportable energy vector. For this purpose, the so-called solar thermochemical processes, combining thermal solar technologies and thermochemical conversions of renewable or waste carbonaceous materials have been selected. The reactor studied previously implements endothermic reactions. Those reactions carry out under solar thermal input, generate gaseous products in which solar energy is stored in chemical form. For the scale-up of the SOLAR-FUEL reactors studied in previous work (theses, Carnot and European projects), a major obstacle to industrial deployment remains : the variability of the solar resource does not allow continuous operation. The objective of the research project is to propose a hybrid process (carbon/solar resource) able of continuously produce a renewable solar fuel. The research work will be based both on dynamic simulation and on experimental validation, in order to ensure optimal control of the process according to the available solar resource. The energy and environmental balance will also be studied in order to compare this solar energy storage pathway with other technologies.

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Thermal networks fault detection and localization on a citywide scale: an approach combining artificial intelligence and physical simulation

Département Thermique Biomasse et Hydrogène (LITEN)

Laboratoire des Systèmes Energétiques et Démonstrateurs Territoriaux

01-10-2020

SL-DRT-20-0990

yacine.gaoua@cea.fr

Smart Energy grids (.pdf)

Detection and localization of faults in thermal networks on a citywide scale: an approach combining artificial intelligence and physical simulation Over time, thermal networks (heating and cooling networks) age differently. Damages to underground pipelines, which can be invisible and not repaired, endanger not only the financial equilibrium of the network operator, but also the quality of heat supply to users, especially in winter periods. Thus, detecting hydraulic leaks and other anomalies remains one of the network operator's top priorities, as it requires a significant share of investment related to civil engineering works for locating and repairing leaks. The objective of this thesis is to implement an innovative diagnostic approach for the detection and localization of anomalies on the pipes of a heating network by exploiting network measurement data (data at the substation level and data from detectors positioned in the gutters) and the capabilities that artificial intelligence offers. The main objectives of this thesis are the following: - Generate database of normal and abnormal network operating modes using numerical simulation. This will be based on existing numerical models in the host laboratory. - Use numerical models for data validation and reconciliation to improve the quality of measurement data from a real network. - Identify algorithms for the detection and the localisation of anomalies based on machine learning algorithms (AI), data generated by simulation and measurement data from a real network. - Validate the performance of the algorithms for detecting anomalies on various simulated operating scenarios and then in real-life situations. - Implement a decision support tool for the detection and localization of anomalies for thermal networks One of the originalities of this work lies in the complementarity between the use of AI methods (supervised and unsupervised learning and classification) and detailed thermo-hydraulic models of networks. The latter should make it possible to compensate for two of the main pitfalls currently encountered by the use of AI methods: the strong dependence on the quality of measurement data, and the need to have very large databases with a high number of anomaly occurrences.

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