CITI is hiring two PhD Students

PhD Student – Powering neural network based wake-up radio with radio-frequency energy harvesting


Supervision: Matthieu Gautier and Olivier Berder (IRISA), Guillaume Villemaud and Florin Hutu (CITI)
Keywords: Internet of things, Wake-up radio, Energy harvesting
Location: Shared between Lannion and Lyon (to be discussed)
Candidate skills: Signal processing and electronics are mandatory, backgrounds in digital communication, IoT, microcontroller programming are welcome
Application: Send CV, marks and motivation letter to and guillaume.villemaud@

Internet of Things (IoT) is becoming a reality. It will greatly impact our daily lives (city, housing, transportation, health, environment) and many economic sectors (agriculture, industry…). Unlicensed bands (868 MHz, 2.4 GHz) play an important role in this evolution with technologies like LoRa, SigFox or IEEE 802.15.4. However, energy consumption remains a major bottleneck, with many applications requiring the lifespan of objects to reach several years, even decades, without changing the batteries. Many efforts have been deployed to push the boundaries of energy autonomy, without however a full success.

The radio transceiver often turns out to be the most energy consuming part of a wireless node, due to both the transmitting and also receiving phases. For instance, initiating a communication requires that the source and the destination are awake at the same time. It can be difficult to plan and usually requires some highly penalizing signalling protocols. In short range multi-hop networks, ernergy consuming MAC strategies are implemented in order to synchronize the source and the destination. Low Power Wide Area Networks solved this issue by having always turned-on base stations using single hop communications and a simple ALOHA protocol, but this only works for the uplink. Wake-up receivers form an emerging technology, which allows continuous channel monitoring, while consuming orders of magnitude less power than traditional receivers. These receivers wake up a main transceiver using interrupts only when a specific signal is detected. Thus, fully asynchronous communication can be achieved, resulting in a huge decrease of energy waste. However, most wake-up receivers are still relying on low power microcontrollers that perform signal recognition but consume peak powers higher than 200 μW, making IoT nodes unable to reach their ultimate energy efficiency.

ANR U-Wake project aims to achieve a breakthrough in the field of IoT by developing a disruptive wake-up receiver solution based on (1) a bioinspired architecture achieved with an industrial CMOS technology (with transistors operating in deep sub-threshold regime) and (2) Electro Magnetic energy harvesting. The originality lies in the association of a Radio Frequency (RF) demodulator to a neuro-inspired detector and data- processing through a spiking neural network (SNN), resulting in a complete ultra-low power wake-up radio supplied with a voltage of a few 100 mV.

Objective of the PhD
The proposed receiver will be woken up when detecting a dedicated off-line learned sequence and implemented in a hardware fashion using an ultra-low power SNN. The main advantage of such a design is that it requires a few mW or less for the whole wake-up receiver. Furthermore, it can work in the 868 MHz or 2.4 GHz bands and has the ability to recognize different types of signals (on-off keying, BPSK or chirp spread spectrum modulation for instance). Requiring such a low consumption opens up the possibility to be powered using RF energy harvesting or Wireless Power Transfer, and opens the way to a wide range of application.

This PhD will focus on the energy efficiency of the proposed solution at both hardware and software levels. It will address the global node design, including RF energy harvesting unit, the integration of neuro-inspired circuits and related wake-up mechanisms, and will propose adequate power management policies.

More information here


PhD Student – Nouvelles stratégies de télé alimentation d’objets communicants et de drones en utilisant des techniques de formation de faisceau distribuée


Supervision: Guillaume Villemaud et Florin Hutu (CITI)
Keywords: radiocommunications, téléalimentation, IoT, drone, beamforming
Location: CITI laboratory, INSA Lyon, France
Application: Send CV, marks and motivation letter to and guillaume.villemaud@

Avec l’avancement des technologies semiconducteur et la réduction de la taille et du cout de fabrication des objets communicants, des conditions favorables ont été créées pour produire des capteurs communicants performants qui permettent le monitoring de différents phénomènes physiques, qui ont une autonomie accrue et qui impliquent une intervention humaine limitée voire inexistante. L’autonomie reste néanmoins un problème majeur pour ce type de capteurs, la durée de vie du réseau qui le forme lui étant étroitement liée. Plusieurs techniques ont été proposées et validées en ce sens, en partant de l’optimisation de la consommation énergétique au niveau bloc fonctionnel jusqu’à une optimisation au niveau protocole de communication. Le déploiement de tels capteurs est un enjeu majeur pour les systèmes civils et militaires et des techniques permettant la réduction de l’énergie consommée par ces objets se combinent avec des techniques de récupération d’énergie ambiante pour envisager des capteurs communicants sans source d’alimentation locale. De même la téléalimentation de micro-drones est un sujet en pleine émergence qui peut bénéficier des mêmes approches.

Objective of the PhD
Cette thèse adresse le problème de la synchronisation et de la mise en cohérence de phase des sources distribuées géographiquement. L’application envisagée est celle de la transmission de puissance sans fil en mettant en place des stratégies de type formation de faisceau distribuée. Ce projet souhaite aborder la problématique de la transmission de puissance sans fil vers un objet communicant à faible ressources énergétiques aussi bien de point de vue système de communication mais aussi du point de vue automatique ou le potentiel d’alimentation de micro-drones avec suivi de leur trajectoire. En automatique, le problème traité ici s’apparente à celui de la synchronisation d’un réseau de systèmes à retard ou bien à celui du suivi de trajectoire de référence. Les outils théoriques appliquées à ce scénario concret seront la commande de systèmes multi-agents, la commande de système à retard et l’observation de retard. Ces stratégies seront adaptées pour répondre aux contraintes matérielles des instruments de génération et d’analyse des signaux du laboratoire CITI. En effet, les « transcepteurs » vectoriels de signaux (VST) PXI-5646 de Nationals Instruments sont ciblés dans un premier temps pour ensuite passer à une échelle supérieure en utilisant la plateforme CorteXlab.

More information here



CITI seminar – El Hourcine Bergou (INRAE) – 09/04 at 15:00

Title:  Stochastic Three Points Method For Unconstrained Smooth Minimization

Date and Place: 9th April 2021 15:00 – link

Speaker: Dr El Hourcine Bergou (INRAE)



In this work, we consider the unconstrained minimization problem of a smooth function in a setting where only function evaluations are possible. We design a novel randomized derivative-free algorithm—the stochastic three points (STP) method—and analyze its iteration complexity. At each iteration, STP generates a random search direction according to a certain fixed probability law. Our assumptions on this law are very mild: roughly speaking, all laws which do not concentrate all measure on any halfspace passing through the origin will work. Although, our approach is designed to not explicitly use derivatives, it covers some first order methods. For instance, if the probability law is chosen to be the Dirac distribution concentrated on the sign of the gradient, then STP recovers the Signed Gradient Descent method. If the probability law is the uniform distribution on the coordinates of the gradient, then STP recovers the Randomized Coordinate Descent Method.
The complexity of STP depends on the probability law via a simple characteristic closely related to the cosine measure which is used in the analysis of deterministic direct search (DDS) methods. Unlike in DDS, where $O(n)$ ($n$ is the dimension of the problem) function evaluations must be performed in each iteration in the worst case, our method only requires two new function evaluations per iteration. Consequently, while the complexity of DDS depends quadratically on $n$, our method depends linearly on $n$.



Dr l Hourcine Bergou is a research scientist at INRAE. My research interests are in all areas that intersect with optimization, including algorithms, machine learning, statistics, and operations research. I am particularly interested in algorithms for large scale optimization including randomized and distributed optimization methods.


CITI seminar – Michael Barros (University of Essex, UK) – 01/04 at 14:00

Title:  Molecular Communications using Astrocytes for Boolean logic gates implementation in mammalian cells

Date and Place: 1st April 2021 14:00 – link

Speaker: Dr Michael Barros (University of Essex, UK)



In this talk we will show the use of astrocytes to realize Boolean logic gates, through manipulation of the threshold of Ca2+ ion fows between the cells based on the input signals. Through wet-lab experiments that engineer the astrocytes cells with pcDNA3.1-hGPR17 genes as well as chemical compounds, we show that both AND and OR gates can be implemented by controlling Ca2+ signals that fow through the population. A reinforced learning platform is also presented in the paper to optimize the Ca2+ activated level and time slot of input signals Tb into the gate. This design platform caters for any size and connectivity of the cell population, by taking into consideration the delay and noise produced from the signalling between the cells. To validate the efectiveness of the reinforced learning platform, a Ca2+ signalling simulator was used to simulate the signalling between the astrocyte cells. The results from the simulation show that an optimum value for both the Ca2+ activated level and time slot of input signals Tb is required to achieve up to 90% accuracy for both the AND and OR gates. Our method can be used as the basis for future Neural–Molecular Computing chips, constructed from engineered astrocyte cells, which can form the basis for a new generation of brain implants.



Dr Barros is an Assistant Professor (Lecturer) since June 2020 in the School of Computer Science and Electronic Engineering at the University of Essex, UK. He is also a MSCA-IF Research Fellow (part-time) at the Tampere University, Finland. He received the PhD in Computer Science at the Waterford Institute of Technology in 2016. He previously held multiple academic positions as a Research Fellow in the Waterford Institute of Technology, Ireland.
He has over 70 research peer-reviewed scientific publications in top journals and conferences such as Nature Scientific Reports, IEEE Transactions on Communications, IEEE Transactions on Vehicular Technology, in the areas of molecular and unconventional communications, biomedical engineering, bionano science and 6G. Since 2020, he is a review editor for the Frontiers in Communications and Networks journal in the area of unconventional communications. He also served as guest editor for the IEEE Transactions on Molecular, Biological and Multi-Scale Communications and Digital Communications Networks journals. He received the CONNECT Prof. Tom Brazil Excellence in Research Award in 2020. Dr Barros was awarded also the Irish Research Council’s (IRC) Government of Ireland Post-doc Fellow from 2016-2018 and the Enterprise Ireland’s (EI) Commercialization Funding from 2018-2019.