More than one billion homes worldwide still lack a broadband Internet connection. In addition, power consumption related to telecommunication network is constantly increasing following data traffic exponential growth. EEMW4FIX ambition is to offer reliable, high data rate and low-power access to end-users by using advanced antenna architectures for future wireless backhauls and Fixed Wireless Access (FWA). To this end, EEMW4FIX aims at developing innovative low-profile, high-gain, and steerable beam smart antenna, using 3D-printed flat lens. EEMW4FIX will address 3 main unresolved challenges needed for mmW FWA: - Drastically improving system energy efficiency of antenna system, RF front-end and beamforming algorithms. Back of the envelope calculations suggest that the EEMW4FIX approach can achieve a factor 10 of reduction in power consumption by combining 4 ingredients. The collimating gain provided by lens approach allows to reduce transmit power and increase reception sensitivity proportionally. The Massive MIMO system is realized via a lens antenna and beam space processing, which leads to beamforming algorithms with highly reduced computational complexity (which is normally cubic in the number of antennas). In addition, the number of activated antennas at any time in the feeding array is small compared to a classical antenna array in which all antenna elements are activated, leading to a significant reduction in the number of RF front-ends. Finally, the RF front-end thermal power will be harvested using integrated Peltier cells, further increasing the global system power efficiency. - Design of low-profile highly-directive steerable beam antenna. Most solutions available today exhibit a limited number of switched beam angles, using transmitarray or conventional bulk lenses without any fine beam tuning capability. In EEMW4FIX, a flat full dielectric multifocal lens will be optimized to spatially couple with a steerable phased array to obtain a high and quasi-constant directivity for all steered angles while ensuring extremely low spillover loss. This lens will be monolithically integrated inside a radome by additive manufacturing. Such concept has never been studied. - Extension for dual-band operation. Using multiple frequency bands enables operators to capitalize on the massive bandwidth available in mmW (37.75-40 GHz and 58-64 GHz) for upgrading the last kilometers access network. As a proof-of-concept, the 3D-printed lens of EEMW4FIX antenna will be designed for dual-band operation. Such capabilities are currently not available. EEMW4FIX gathers two academic partners (LEAT and Eurecom), one innovative SME (EV-Technologies) and two large companies (Orange and Thales), all selected for their complementary expertise.

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 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.

The project aims to develop smart textiles with integrated radiofrequency technologies (RF) and electronic components for on-body communications in Wireless Body Area Networks (WBAN). Based on existing technologies in smartphones, two issues will be treated in the project: 1 - The first one is an energy transfer using the Near Field Communication technology (NFC) at 13.56 MHz with the goal to supply a sensor away from the smartphone. This part of the project answers the issues of the use of battery in smart textiles and the washability. For energy transfer, the project will develop antennas, transmission lines, and integrated organic components into a textile technology. In the basic principle, antenna will couple the smartphone and textile, and it will be connected to a sensor by a transmission line made of textile. Sensor will be supplied by a textile-integrated RF/DC rectifying circuit in organic technology. Such an organic technology components is chosen because it is appropriate for a textile integration purpose. Every RF and electronic element will be studied and characterized with the final goal to produce a prototype validating the battery-less supplying of a wireless textile-integrated sensor by a smartphone. For the final application, a wireless electrocardiogram sensor could be worn in the underwear of the subject and the smartphone on a pocket of a garment. The proposed system should be completely transparent for the subject and no modification of subject’s way of using his underwear should be undertaken. It is important to note that this system will be a life style device and not a medical one, it could just suggest consulting a general practitioner in the case of troubles. 2 - The second issue of the project will be to transmit data between a sensor and a smartphone. Depending on the previous technology for energy transfer, the NFC band will be also considered in the project, provided that the link-budget favorably demonstrates the feasibility of data transmission. However, data transmission by using the Industrial, Scientific, and Medical band (ISM) at 2.4 GHz is expected to be more efficient especially in terms of high flow rate. By taking inspiration from metamaterials, the project will intend to use textile as a waveguide for surface waves. Three technological ways will be explored to produce surfaces with reactive impedance: (i) an Artificial Magnetic Conductor (AMC) type structure; (ii) a corrugated ground plane by using woven or sewn metallic yarns; and (iii) woven metasurfaces with phase-advance. Each technological route will need to produce specific textiles by lamination, weaving or knitting, and using enameled or bare metallic yarns. Such reactive impedance surfaces can guide surface waves with a Transverse Magnetic (TM) polarization. To launch such surface waves, textile antennas operating at 2.4 GHz will be developed and produced. Besides the wave polarization, textile antennas will have to lower any interactions with human body for complying with the medical standards about the wave absorption. In the project, such interactions will be not precisely studied; particularly the Specific Absorption Rate (SAR) from antennas and waveguides will not be measured. However, RF components will be designed to reduce body interactions to facilitate future transfer of developed technologies to industrial applications. At last, each RF structures in the ISM band will be studied and optimized to produce a final prototype validating a RF data transmission by a guided surface wave onto a textile between two antennas. On the whole, exotic electromagnetic properties emerging from metamaterials will be considered as a potential solution for reducing the footprint of antennas and of the system, or to engineer waveguides of surface waves.

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.