YAHYA-6G aims to propose new signal processing solutions doped with machine-learning. We will focus on the detection and compensation of RF imperfections in mMIMO (massive Multiple input Multiple output) based NOMA (Non-orthogonal multiple access) pair . In other hand, YAHYA-6G target is to minimize the long-term power consumption based on the stochastic optimization theory for mMIMO-NOMA IoT networks with EH (Energy Harvesting) in presence of RF imperfections. Thus the objectives of the YAHYA-6G project are: 1- Identify major RF imperfections that may occur in a multi-access / multi-antenna broadband system. 2- Propose new solutions to optimize the energy efficiency at the RF transmitters. This solution will focus on the power amplifier that represents 60 at 70% of the energy consumed in an RF transmitter. 3- Analyze the impact of these RF imperfections on mobile radio systems exploiting NOMA technologies. 4- Propose a Deep Learning online learning process to detect the NOMA channel characteristics and compensate the effect of HPA nonlinearity. A joint detection of the NOMA interference and HPA (High Power Amplifier) nonlinearity will be studied in mMIMO-NOMA system. 5- Resolve a non convex based problem coping with the expected 6G requirements, with a particular focus on optimal resource scheduling and computation capacity allocation and reducing energy consumption of wireless devices, through a set of new algorithms . 6- Realize a demonstrator based on the SDR (Software Defined Radio) USRP cards on which some algorithms developed in the project will be implemented.

YAHYA-6G aims to propose new signal processing solutions doped with machine-learning. We will focus on the detection and compensation of RF imperfections in mMIMO (massive Multiple input Multiple output) based NOMA (Non-orthogonal multiple access) pair . In other hand, YAHYA-6G target is to minimize the long-term power consumption based on the stochastic optimization theory for mMIMO-NOMA IoT networks with EH (Energy Harvesting) in presence of RF imperfections. Thus the objectives of the YAHYA-6G project are: 1- Identify major RF imperfections that may occur in a multi-access / multi-antenna broadband system. 2- Propose new solutions to optimize the energy efficiency at the RF transmitters. This solution will focus on the power amplifier that represents 60 at 70% of the energy consumed in an RF transmitter. 3- Analyze the impact of these RF imperfections on mobile radio systems exploiting NOMA technologies. 4- Propose a Deep Learning online learning process to detect the NOMA channel characteristics and compensate the effect of HPA nonlinearity. A joint detection of the NOMA interference and HPA (High Power Amplifier) nonlinearity will be studied in mMIMO-NOMA system. 5- Resolve a non convex based problem coping with the expected 6G requirements, with a particular focus on optimal resource scheduling and computation capacity allocation and reducing energy consumption of wireless devices, through a set of new algorithms . 6- Realize a demonstrator based on the SDR (Software Defined Radio) USRP cards on which some algorithms developed in the project will be implemented.

In recent years, transformer-based deep learning models such as BERT or GPT-3 have led to impressive results in many natural language processing (NLP) tasks, exhibiting transfer and few-shot learning capabilities. However, despite faring well in benchmarks, current deep learning models for NLP often fail badly in the wild: they are bad at out-of-domain generalization, they do not exploit contextual information, they are poorly calibrated, and their memory is not traceable. These limitations stem from their monolithic architectures, which are good for perception, but unsuitable for tasks requiring higher-level cognition. In this project, I attack these fundamental problems by bringing together tools and ideas from machine learning, sparse modeling, information theory, and cognitive science, in an interdisciplinary approach. First, I will use uncertainty and quality estimates for utility-guided controlled generation, combining this control mechanism with the efficient encoding of contextual information and integration of multiple modalities. Second, I will develop sparse and structured memory models, together with attention descriptive representations towards conscious processing. Third, I will build mathematical models for sparse communication (reconciling discrete and continuous domains), supporting end-to-end differentiability and enabling a shared workspace where multiple modules and agents can communicate. I will apply the innovations above to highly challenging language generation tasks, including machine translation, open dialogue, and story generation. To reinforce interdisciplinarity and maximize technological impact, collaborations are planned with cognitive scientists and with a scale-up company in the crowd-sourcing translation industry.

The population in Europe is living longer and healthier, and that is a great achievement. On the other hand, an ageing population raises major financial and social challenges. One in four (25%) persons living in Europe could be aged 65+ by 2050. The greater expectancy of life in Europe is posing serious challenges to healthcare, namely through the associated increasing incidence of various diseases, as well as health conditions, which the elderly are mostly prone. One of the latter conditions is bone fractures, which can typically occur as a consequence of osteoporosis. Furthermore, the consequences of associated complications in fracture recovering include further costs, not only for the patient but also for the European society in general. To address such growing issue, the multidisciplinary consortium of ROBUST takes this as a challenging use case for demonstrating the relevance and proficiency of smart mobile eHealth systems as innovative solutions to address surging issues in our ageing society. ROBUST targets developing a new concept and platform for remote monitoring of patients’ healing process, in the eHealth domain. An integrated mobile eHealth system will be devised exploiting recent advances in RF-based sensing technologies, which are being investigated in this consortium. The system will be able to respond promptly to dynamic and complex situations, while preserving control, safety and privacy, in a reliable and energy efficiency manner. The ROBUST system will include a fast feedback loop that dynamically processes sensing information to generate, accordingly, instructions to the patient, encompassing cognitive and learning capabilities as well. ROBUST is committed to create an exceptional network, which is multidisciplinary and intersectoral in nature, for staff exchange in the mobile eHealth field, namely targeting structure training and knowledge sharing towards enhancing the European innovation capacity in relevant eHealth systems and applications.

We envision a radically new technology for in-vivo bioresorbable chemical sensing, where optical devices, power and light sources, synthetic receptors - made out of materials that completely dissolve with biologically benign byproducts in biofluids - will be developed and integrated together. The sensing system, the size of 1 EuroCent, will be coated by a long-lived biocompatible polymer designed with on-demand degradation, then implanted in the body to monitor in-vivo, in-situ, and in real-time a chemotherapeutic drug, doxorubicin, commonly used to treat cancer; the system is then fully and safely RESORBed once no more needed using an external temperature-trigger that initiates the dissolution of the protecting coating and, in turn, of the system, avoiding device-retrieval surgery that may cause tissue lesion/infection. The general objective is to demonstrate fabrication, operation (2 months) in-vivo and in real-time - then dissolution - of such a bioresorbable chemical sensing system for the detection of doxorubicin in an animal model. This will break a new ground in in-situ monitoring of chemotherapeutic drug enabling for the first time a fine tuning of the drug dose at the tumor site, increasing patient survival rate. Being aware of the project risks, we have broken down the general into different specific objectives, identified a set of Key Performance Indicators, alternative material synthesis/device fabrication techniques, mitigation measures to tackle major risks. The RESORB technology truly represents the foundation of a future technology for personalized medicine, enabling to address a number of medical issues for which continuous and localized monitoring of specific analytes (i.e., biomarkers and drugs) in-vivo for a prescribed time is of chief importance, e.g., acute trauma treatment, post-surgery sepsis, drug therapeutic profiling, and other, all examples for which ex-situ analysis of biofluids has proved to be not fully adequate for clinical needs.