Telecommunications have experienced a paradigm shift in the past decades with the advent of Internet or things (IoT). With this new huge market, a lot of several interesting challenges have emerged, particularly with the potential inclusion of the IoT world in the future fifth generation of cellular mobile communications (5G) and beyond. Indeed, wireless networks have been supporting unprecedented traffic due to the drastic growth of mobile devices, the development of various applications and the implementation of IoT. Consequently, beyond 5G (B5G) systems spotlighted diverse and often contradicting key performance indicators, such as high capacity, low latency, high reliability/user-rates, ubiquitous coverage, high mobility, massive connectivity. Since the available frequency spectrum is absolutely limited, next generations of wireless communications should be accompanied with the adoption of non-orthogonal frequency sharing. Non-orthogonal multiple access (NOMA) and successive interference cancellation (SIC) have been conceived as a breakthrough technology in the B5G networks because of its superior spectral efficiency. Indeed, even if spectrum sharing and non-orthogonal transmissions have been deeply investigated during the past decades, they are considered as the most promising multiple access techniques to be adopted since the BS cannot serve UEs in an orthogonal manner anymore. Moreover, energy efficiency at both network and terminal sides has to be enhanced for economical and ecological reasons. Indeed, energy efficiency has now become a key pillar in the design of communication networks. With the advent of the fifth generation of wireless networks, expecting millions more base stations and billions of connected devices, the need for energy-efficient system design and operation will be even more compelling. As network architecture becomes complex and the user requirement gets diverse, the role of efficient resource management has come to be a crucial task. Besides, the data protection and privacy of Machine Type Devices (MTDs) remain major challenges, mainly due to the massive scale and the distributed nature of IoT networks. Lack of security measures will result in decreased adoption among users and therefore is one of the driving factors in the success of the IoT. Note that the proposed solutions should not involve significant energy, delay, and computational overhead since they will be implemented in resource-constrained devices. The main objective of the project MOMENT is to explore innovative network architectures/topologies and investigate new multiple access techniques to ensure the fulfillment of these heterogeneous constraints and a high-level security in order to cope with the aforementioned challenges. Particularly, we will investigate some of the practical challenges of large-scale deployment of NOMA such as the inter-NOMA- interference (INI), inter-cell interference, and implementation complexity. Distributed security management protocols will be investigated to ensure the scalabity of the proposed solutions and enable the deployment of the IoT. The proposed solutions will be based on sophisticated coding and sparse signal processing concepts, advanced game theoretical tools, artificial intelligence and machine learning.

An in-depth knowledge of the microscopic fine ultrastructure of muscles is imperative for the accurate diagnosis of various diseases characterized by muscle dysfunction. At present, a biopsy is the only reliable clinical method that offers a conclusive diagnosis of the pathology. Duchenne dystrophy, inclusion body myositis or peripheral neuropathies i.e. Charcot-Marie-Tooth disease are few examples of muscle pathologies that require a muscle biopsy. But, a muscle biopsy is an invasive procedure that can be distressing for both the muscle tissue and the patient. Optical solutions offer label-free information compatible with in vivo clinical imaging. But the current solutions probe a limited number of biomedical substances of interest, not enough for a reliable diagnostic of muscle pathologies. The χ-MAlMa project introduces an innovative instrumental approach for characterizing biomedical targets based on their contrast in non-linearities characterized by four nonlinear parameters. A supercontinuum device serves as laser excitation, allowing the selective stimulation of each them. The emitted spectra are recorded across five spectral zones, constituting the χ-matrix of the sample under test. Hyperspectral data processing is undertaken to identify distinguishing features within this χ-matrix (15 spectra for each image pixel) utilizing first artificial intelligence (AI) and then chemometric methods. The solution of AI aims to identify the presence of discriminating biomarkers using a training database. The chemometrics solution aims to identify the discriminating spectral signatures from one or two χij parameters. This step plays a significant role for the test of the use of an optical fibre for the delivery of excitation and emission beams. This innovative approach would lead to a new medical device and practice that could improve the diagnostic reliability, orientation of therapy, patients care and healthy life expectancy.

Exposure to toxic gases or volatile organic compounds (VOCs) affects safety and public health. More than 6.5 million deaths per year worldwide are attributed to environmental pollution (indoor and outdoor air quality). With the Internet of Things, large-scale deployment of gas sensors becomes accessible to monitor and analyze the environment of individuals, in public or private spaces. This market, driven in particular by the construction, medical industry or consumer applications markets, requires low-energy, low-cost monitoring devices. Portable sensors alone represent a market estimated at 3 billion units in 2025, of which more than 30% are emerging communicating sensors, including chemical sensors, growing exponentially over the next 10 years. In these new markets, research and development of innovative tools is becoming an exciting new field for electronics. In this context, we propose to address some of the issues related to monitoring the quality of polluted air related to exhaust gases due, for instance, to transports and industrial activity. This has led to specifications in particular for certain harmful vapors such as nitrogen dioxide (NO2), sulfur dioxide (SO2) and ozone (O3) as highlighted in the French regulations. The objective of CARDIF is to respond in particular to the challenge of selectivity, identified as a bottleneck limiting the contribution of conventional metrology in observing or diagnostic systems in a heterogeneous medium. This will be done using functionalized polymers with specific groups. The current innovative sensors generally suffer from high consumption and / or bulky instrumentation due to low frequency operation, and are mainly based on expensive solutions. As part of the CARDIF project, we propose another type of sensors based on microwave transducers operating at ambient temperature. In addition to being a passive device and therefore not consuming power, they could also operate wirelessly. They are thus suitable for networking and high-frequency communications, usable for real-time detection and providing directly exploitable information. In addition, because of its planar structure, the device can be manufactured on a flexible substrate by low-cost printing technologies. This multidisciplinary study is made possible thanks to the close collaboration between two industrial (ISORG, Efficacity) and three academic partners (LCPO, IMS, XLIM). Partners have the experience to meet these scientific and technical challenges. To our knowledge, no internationally referenced work has focused on such printed RF gas sensors with optimized selectivity, meeting the following key features: 1 - real-time monitoring of the quality of the outside air, 2 - high sensitivity at room temperature and therefore low energy consumption, 3 - the selective detection of NO2, SO2 and O3 at levels of a few ppb to ppm using functionalized polymers, 4 - low cost manufacturing processes based on collective printing technologies, 5 - new autonomous and wireless solutions, operating in real time thanks to the passive microwave transduction, 6 - outdoor tests following a realistic deployment scenario. By this way, CARDIF clearly responds to the priorities of the call, in particular the development of sensors for environmental monitoring (smart monitoring)

A real polynomial is hyperbolic whenever all its roots are real or, equivalently, if it is the characteristic polynomial of a real symmetric matrix. Testing efficiently this property, that can be extended to the multivariate case, is an open problem in computer science and mathematics. Whereas hyperbolic polynomials constitute nowadays a central topic in the applications, the computational/complexity aspects have a limited role in this theory. The general goal of HYPERSPACE is to develop an effective approach to hyperbolic polynomials. On the one hand, we propose to improve existing algorithms for the computation of determinantal representations (a hyperbolicity certificate that exists in some cases) or independent of classical representations. On the other hand, the project will be consecrated to the implementation of new algorithms in a free software dedicated to hyperbolic polynomials.

This research project focuses on the development of a bio-inspired procedural generation technique for electromagnetic components. This work allows for the automated synthesis of structures capable of controlled interactions with incident waves, guiding the growth of components to meet multiple coupling and radiation constraints. The computerization of the means of measurement and production are indeed in several aspects based on the development of dedicated electromagnetic components. Whether they allow these systems to communicate, to interrogate their environment or to direct data flows between their various sub-parts, these components must face the definition of increasingly specific and constrained specifications. They must also respond to the problems of increasing carrier frequency, which facilitates higher data rates and the integration of these solutions. However, such case-by-case optimizations are accompanied by prohibitive engineering costs, justifying that many applications are limited to the use of generic components, which are in essence less well adapted to these environmental and field specifications. This project proposes to develop a technique capable of addressing this issue. This technique will be exploited first to ensure the procedural synthesis of radiating metasurfaces optimized for the realization of multiple polarization constrained point-to-point links and for computational imaging activities. The versatility of the developed generative models will also allow for the procedural synthesis of functionalized materials, suitable for free-form waveguide design and constrained generation of aniostropy properties. This research project will focus on the realization of proofs of concept in K and W bands, demonstrating through the automated design of numerous components the diversity of the potentially impacted domains.