Quantum Control attempts to apply and extend the principles already used for classical control systems to the quantum domain. In this way we hope to establish a control theory specifically dedicated to regulating quantum systems. This proposal addresses some key problems related to the control of open quantum systems by applying quantum feedback control. Open quantum systems are quantum systems in interaction with an environment. This interaction perturbs the system states and causes loss of information from the system to the environment. However by applying quantum feedback control, the system can “fight” against this loss of information. The main obstacle is that standard strategies from classical control are not immediately applicable to quantum systems. While there has been much development on the theoretical side, there remain key open questions concerning optimality, robustness, and best design methods for dealing with generic quantum models which can be implemented in concrete experiments with less difficulties. The first objective of Q-COAST is to develop more efficient and robust strategies for quantum feedback design applied to open quantum systems. As a second objective, we investigate the situation where the inputs are in non-classical states, the case where the generalization from the classical to the quantum case becomes more difficult. Such states are critically important for scalable quantum information processing. Our third objective is to go beyond the existing tools to design estimators and controllers. This will be achieved by introducing new pathways through the interaction between fields of quantum statistical mechanics, quantum information geometry, quantum filtering, and quantum feedback control. The final goal is to develop further numerical simulations of quantum components as well as implementing our proposed strategies in real experiments. The experimental implementations can be realized as the project will involve collaboration with leading experimental groups who have been successfully applying feedback control theoretic principles to actual quantum systems.

5G networks are expected to revolution our living environments, our cities and our industry by connecting everything. 5G design has, thus, to meet the requirements of two “new” mobile services: massive Machine-Type Communications (mMTC), and Ultra Reliable Low Latency Communications (URLLC). Slicing concept facilitates serving these services with very heterogeneous requirements on a unique infrastructure. Indeed, slicing allows logically-isolated network partitioning with a slice representing a unit of programmable resources such as networking, computation and storage. Slicing was originally proposed for core networks, but is now being discussed for the Radio Access Network (RAN) owing to the evolution of technologies which now enable its implementation. These technologies include mainly the tendency for virtualizing the RAN equipment and its programmable control, the advent of Mobile Edge Computing (MEC) and the flexible design of 5G on the physical and MAC layers. However, the complete implementation of slicing in the RAN faces several challenges, in particular to manage the slices and associated control and data planes and for scheduling and resources allocation mechanisms. MAESTRO-5G project develops enablers for implementing and managing slices in the 5G radio access network, not only for the purpose of serving heterogeneous services, but also for dynamic sharing of infrastructure between operators. For this aim the project puts together exerts on performance evaluation, queuing theory, network economy, game theory and operations research. MAESTRO-5G is expected to provide: •A resource allocation framework for slices, integrating heterogeneous QoS requirements and spanning on multiple resources including radio, backhauling/fronthauling and processing resources in the RAN. •A complete slice management architecture including provisioning and re-optimization modules and their integration with NFV and SDN strata. •A business layer for slicing in 5G, enabling win-win situations between players from the telecommunications industry and the verticals, ensuring that the 5G services are commercially viable and gain acceptance in the market. •A demonstrator showing the practical feasibility as well as integration of the major functions and mechanisms proposed by the project, on a 5G Cloud RAN platform. The enhanced platform is expected to support the different 5G services (eMBB and IoT) and to demonstrate key aspects of slicing, such as: - Ability to create and operate in parallel multiple slices, on the same infrastructure and sharing the same radio resources (e.g. spectrum), each having different service requirements. - Ability to create and operate in parallel and independently different slices, sharing the same infrastructure/spectrum, belonging to different business actors, such as different operators. - Demonstrate inter-slice control ensuring respect of SLAs and a fair resource sharing.

Project HERMIN aims to develop shared control strategies between a robotic prosthesis and its user. We will investigate the impact of augmenting a bidirectional neuroprosthesis with autonomous motor routines that implement safety-related reflexes and perform touch exploration for obstacle detection and avoidance. We will implement such routines in our 4 degrees-of-freedom, miniature mouse forelimb neuroprosthesis, in addition to the volitional movements controlled by chronic recordings of the firing rate of neurons in the motor cortex. The neuroprosthesis will provide real-time feedback to the mouse using spatio-temporally patterned optogenetic neuronal activation tools that are unique to the mouse model. We hypothesise that, despite the mismatch between neuronal commands and the effective movements during the execution of the autonomous motor routines, a rich touch and nociceptive-like feedback to the mouse cortex will ensure that (1) prosthesis control and embodiment is preserved during reflex action, and that (2) the enhanced tactile exploration provided by autonomous haptic routines will augment the prosthesis embodiment and/or performance during behavioural tests. This interdisciplinary project requires the complementary expertise of the 3 partners in robotics modelling and control, brain-machine interfacing and animal behaviour. The development of shared control strategies between robot and user constitutes a pre-clinical trial of robotic prostheses with brain-machine interface for human users, and might also be translated in the non invasive context of physical human-robot interaction with exoskeletons.

Ultrafast 3D ultrasound (US) imaging is being developed in research laboratories. However, its clinical application is hindered by insufficient image quality. This project addresses this limitation by proposing various signal and image processing methods for functional 3D US imaging applied to the cardiac muscle. During ischemia, the tissue structure undergoes various changes, such as oedema and reperfusion, and patient management remains under study. Thus, obtaining a local tissue marker related to the orientation of the cardiac tissue is crucial for improving patient management and assessing treatment success. High-quality real-time 3D imaging is necessary to allow practitioners to visualize the heart and perform local measurements. Deep learning algorithms will generate a high-resolution 3D volume from a limited number of low-resolution volumes. This learning process will involve synthetic and experimental acquisitions. Once localized, the anisotropy of the tissue will be measured using the coherence of ultrasound signals. This approach consists of calculating the 3D spatial covariance matrix. Estimating this matrix with limited sample supports compared to the probe elements’ dimension requires specific signal processing techniques to help compute it efficiently, understand it smartly and propose a dedicated estimator of the local anisotropy. Furthermore, the spatial structure of the coherence matrix, derived from the covariance matrix, will be explored. Integrating the evaluation of this coherence matrix as a parameter of a matrix-variable distribution will be studied to adapt to biological signals and ensure stable anisotropy estimation. The project will also address out-of-plane estimation of anisotropy and the extension of the field of view, which is essential for future clinical applications. Currently, measurement is restricted to planes parallel to the probe, limiting its use. In cardiac imaging, a wider field of view is necessary, and the validity of anisotropy measurement with this type of acquisition needs verification. These methodologies will be validated using different in vitro models and an animal ischemia model before evaluation on human subjects. The proposed imaging will be compared with diffusion magnetic resonance imaging, a state-of-the-art technique for estimating cardiac muscle anisotropy but unsuitable for routine clinical use. In conclusion, this project aims to validate the entire acquisition and post-processing chain to develop a new marker of cardiac muscle anisotropy in ultrasound imaging. This marker will be compared with reference imaging techniques to assess its potential for future human clinical applications.

When it comes to sensing the environment (RADAR, imaging, seismic, ...), the current trend is to develop acquisition systems that are more and more sophisticated. For example, we can point out an increase in the number of sensors, the use of multiple arrays for either emission or reception, as well as the integration of several modalities like polarization, interferometry, temporal, spatial and spectral information, or waveforms diversity. Obviously, this sophistication is made to enrich the obtained information and to reach better performances compared to classical systems, such as improving the resolution, improving detection performance (especially for low SNR settings), or allowing a better discrimination between physical phenomena. However, the simple transposition of classical process/algorithms in these new systems does not necessary led to the expected improved performances. Indeed, several effects impose to deeply re-derive the modelizations and the processes: - the answer of the sensed environment becomes complex and heterogeneous, - the size of the data is increased, so the estimation of statistical parameters may become difficult, - in systems with multiple modalities, the construction of the data vector is nontrivial, - there are more uncertainties on the model of the useful signal (therefore on its parameterization) The MARGARITA project aims at solving the aforementioned issues by developing new estimation/detection processes for multi-sensors/multi-modal systems operating in a complex heterogeneous environment. These new methods will be based upon the combination of recent tools and advances in signal processing: robust estimation, optimization methods, differential geometry and large random matrices theory. Hence, the project aims at: + integrating an accurate statistical modeling (i.e. handling non Gaussianity and heterogeneity) for estimation/detection problems in large dimension settings. + integrating prior information and model uncertainties in a modern robust estimation/detection framework. + accurately characterizing the theoretical performances of the developed processes. Apart from providing theoretical guarantees, this characterization will also offer tools for system design and specification. + Demonstrating that the proposed tools can be applied in fields that involve modern acquisition systems. We propose to adapt these processes to specific radar applications (STAP, MIMO-STAP, SAR) as well as other civilian applications (Hyperspectral imaging, radio-astronomy and GPR) From a scientifical and technical perspective, this project will: - use tools from the robust estimation framework and the optimization framework (majorization-minimization and optimization on manifolds) to propose new estimators (notably for covariance matrices) that exploit available prior information to counter the large dimension problem. - extend the Bayesian subspace estimation methods to a robust estimation/detection framework in order to integrate uncertainties on the signal model. - exploit the misspecified performance bounds framework to solve the problem of multi-sensors/multi-modal systems calibration. - use recent theoretical tools (large random matrices theory and intrinsic bounds) to characterize the performances of the developed processes.