Autonomous and intelligent embedded solutions are mainly designed as cognitive systems composed of a three step process: perception, decision and action, periodically invoked in a closed-loop manner in order to detect changes in the environment and appropriately choose the actions to be performed according to the mission to be achieved. In an autonomous agent such as a robot, a drone or a vehicle, these 3 stages are quite naturally instantiated in the form of i) the fusion of information from different sensors, ii) then the scene analysis typically performed by artificial neural networks, and iii) finally the selection of an action to be operated on actuators such as engines, mechanical arms or any mean to interact with the environment. In that context, the growing maturity of the complementary technologies of Event-Based Sensors (EBS) and Spiking Neural Networks (SNN) is proven by recent results. The nature of these sensors questions the very way in which autonomous systems interact with their environment. Indeed, an Event-Based Sensor reverses the perception paradigm currently adopted by Frame-Based Sensors (FBS) from systematic and periodical sampling (whether an event has happened or not) to an approach reflecting the true causal relationship where the event triggers the sampling of the information. We propose to study the disruptive change of the perception stage and how event-based processing can cooperate with the current frame-based approach to make the system more reactive and robust. Hence, SNN models have been studied for several years as an interesting alternative to Formal Neural Networks (FNN) both for their reduction of computational complexity in deep network topology, but also for their natural ability to support unsupervised and bio-inspired learning rules. The most recent results show that these methods are becoming more and more mature and are almost catching up with the performance of formal networks, even though most of the learning is done without data labels. But should we compare the two approaches when the very nature of their input-data is different? In the context of interest of image processing, one (FNN) deals with whole frames and categorizes objects, the other (SNN) is particularly suitable for event-based sensors and is therefore more suited to capture spatio-temporal regularities in a constant flow of events. The approach we propose to follow in the DeepSee project is to associate spiking networks with formal networks rather than putting them in competition.

The objectives of the project "Smart IoT for Mobility" (SIM) are to work on IoT-type architectures - so low computing capacity, very low power, small footprint – and to have access to blockchains, Smart Contracts while at the same time being very understandable by the users, accepted by these same users, which are both legally plausible and therefore usable by everyone. The use case that will be taken into account by the project "Smart IoT for Mobility" (SIM) will be the "Smart Services Book" or "advanced maintenance book" of Renault with the main use cases of vehicle accidents. These use cases are extremely studied by all insurers to try to make accident reports tamper-proof. The project "Smart IoT for Mobility" (SIM) will bring together researchers in law, economics, experimental economics, computer science, electronics and will ensure the soundness of its experiences through the partnership with the company Renault Software Labs and the Symag company, subsidiary of BNP Parisbas.

Full duplex consists in transmitting and receiving simultaneously in the same frequency band, which in theory allows to increase the capacity of communication. DUPLEX project objectives are: - to study the theoretical limits (throughput) of full duplex communication equipment - to develop antenna techniques, analog and digital processing for the cancellation of the transmitted signal at the receiver - to develop a full duplex communication equipment (prototype) for the next generation of communications. The project will address two scenarios: - full-duplex communication between two communication nodes. In this case, the signal cancellation device uses the knowledge of signal, assumed to be known - full-duplex relaying, in which the relay processes, amplifies and retransmits the received signal in the same band. In this scenario, several cases will be considered depending on the information to be relayed (i.e. decodable or not). The project is divided into 5 tasks or work packages: • The first task will consider the overall system aspects of the DUPLEX project. The refinement of the target scenario and the system requirements specification, taking into account real use cases and constraints, will be the starting point for the project implementation. Led by industrial partners, this task will ensure a consistent project development under common scope, requirements and working assumptions. The target system definition will indeed impose important constraints like for example the radio environment, the radio access technologies characteristics, the amount of available spectrum, the power available for transmissions, the required sensitivity and dynamics of the equipment, and any other relevant parameters • Task 2 (digital cancellation) aims to go beyond the state of the in terms of digital techniques for self-interference cancellation. • Task 3 (analog techniques) is devoted to the development of analog techniques, including the design and implementation of antennas and circuits. • Task 4 (prototyping) is dedicated to the integration of all the hardware and software parts developed (in Tasks 2 and 3) in the hardware platform OpenAirInterface (www.openairinterface.org), which is an existing Software Defined Radio platform developed by EURECOM. Furthermore, this task also aims to test and validate the system in laboratory conditions. • Finally, task 5 (dissemination, communications) aims to promote the scientific results of the project and to disseminate the reusable results for future industrialization.

The 3DRAW project takes place in the current context of a sharp increase in the integration density of electronic systems for communication, tracking or surveillance equipment. This requires the design of antenna systems with wide spatial coverages and wide frequency bands in order to provide high performance as well as multi-functionality capabilities. The dual issues are evident. On the one hand, increasing the bandwidths of multi-function radar antennas, which exploit beamforming over a large angular sector, is a major challenge for them in order to improve their spatial resolutions and their probability of detection. On the other hand, in the civilian sector, the new cellular coverage takes advantage both of the increase in spatial diversity provided by the active electronic scanning antenna arrays but also of the extension of the bandwidths to increase the rates. The state of the art shows that it is very difficult to design a broadband antenna capable of scanning a directional beam in a large angular sector. The high operating frequency of an antenna array is naturally limited by the inter-element spacing, itself constrained by technology, while the low frequency is conventionally defined by the level of coupling accepted. For a fxied inter-element spacing, allowing for the integration of the distribution and / or digitization network, the solution to extend the minimum frequency of the network and thus obtaining a wideband network is to reduce the mutual coupling between the radiating elements. A promising solution to address this issue is the deployment of antenna array based on wideband 3D dielectric resonators with low mutual coupling as well as reduced truncation effects, thanks to the high concentration of electromagnetic fields inside the resonators. Thus the 3DRAW project aims to develop an antenna array providing a high angular coverage (up to 60 °) over a wide frequency band (40%) while preserving the efficiency of the network and the purity of polarization. More specifically, the 3DRAW project aims to demonstrate the targeted performance on an antenna arraymade up of 64 radiating elements. The breakthrough compared to the state of the art are twofold since new antenna concepts will be developed as well as new approaches of manufacturin ceramic materials. Indeed, an innovative technique of additive manufacturing of ceramics allowing the control of the porosity density will be exploited in order to offer more degrees of freedom on the shape but also on the permittivity of the antennas. Thus, multi-permittivity resonators will be considered in order to improve the performance of antenna systems. The complementary and multidisciplinary skills of the partners will be essential to overcome these many challenges

The tremendous increase of transistors integration during the last few years has reached the limits of classic Von Neuman architectures. This has enabled a wide adoption of parallel processors by the industry, enabling many-core processing architectures as a natural trend for the next generation of computing devices. Nonetheless, one major issue of such massively parallel processors is the design and the deployment of applications that cannot make an optimal use of the available hardware resources. This limit is even more acute when we consider application domains where the system evolves under unknown and uncertain conditions such as mobile robotics, IoT, autonomous vehicles or drones. In the end, it is impossible to foresee every possible context that the system will face during its lifetime, making thus impossible to identify the optimal hardware substrate to be used. Interestingly enough, the biological brain has ”solved” this problem using a dedicated architecture and mechanisms that offer both adaptive and dynamic computations, namely, self-organization. However, even if neuro-biological systems have often been a source of inspiration for computer science (as recently demonstrated by the renewed interest in deep-learning), the transcription of self-organization at the hardware level is not straightforward and requires a number of challenges to be taken-up. The first challenge is to extend the usual self-organization mechanisms to account for the dual levels of computation and communication in a hardware neuromorphic architecture. From a biological point of view, this corresponds to a combination of the so-called synaptic and structural plasticities. We intend to define computational models able to simultaneously self-organize at both levels, and we want these models to be hardware-compliant, fault tolerant and scalable by means of a neuro-cellular structure. The second challenge is to prove the feasibility of a self-organizing hardware structure. Considering that these properties emerge from large scale and fully connected neural maps, we will focus on the definition of a self-organizing hardware architecture based on digital spiking neurons that offer hardware efficiency. The third challenge consists in coupling this new computation paradigm with an underlying conventional manycore architecture. This will require the specification of a Network-on-Chip that adapts to self-organizing hardware resources, as well as the definition of a programming model using the learning of input data to better and automatically divide and allocate functional elements. Hence, this project is a convergence point between past research approaches toward new computation paradigms: adaptive reconfigurable architecture, cellular computing, computational neuroscience, and neuromorphic hardware. 1. SOMA is an adaptive reconfigurable architecture to the extent that it will dynamically reorganize both its computation and its communication by adapting itself to the data to process. 2. SOMA is based on cellular computing since it targets a massively parallel, distributed and decentralized neuromorphic architecture. 3. SOMA is based on computational neuroscience since its self-organization capabilities are inspired from neural mechanisms. 4. SOMA is a neuromorphic hardware system since its organization emerges from the interactions between neural maps transposed into hardware from brain observation. This project represents a significant step toward the definition of a true fine-grained distributed, adaptive and decentralized neural computation framework. This new computing framework may indeed represent a viable integration of neuromorphic computing into the classical Von Neumann architecture and could endow these hardware systems with novel adaptive properties.