Title

IF Neuron: theoretical study and application to digital communication

Abstract

In the context of digital communication, one main mechanism proposed in the literature to overcome the large consumption of MAC layers when establishing communications is called wake-up radio: The main processor is only waking up when receiving a specific signal, as for instance the node ID in the network. Unfortunately, since most of the wake-up receivers rely on standard micro-controller, they suffer a large decrease of energy efficiency. Nevertheless, if the wake-up receivers was designed with neuromorphic circuits, one could achieve high energy efficiency for IoT and ad hoc networks.

The main question that is tackled in this presentation is whether a neuro-inspired detection scheme using an Integrate-and-Fire neuron is reliable enough when one needs to detect a weak signal surrounded by noise.

Biography

Anne Savard received the Eng. degree in Electrical Engineering with specialization in Multimedia Systems from the Ecole Nationale Supérieure de l’Electronique et de ses Applications (ENSEA), Cergy-Pontoise, France, and the M.Sc. degree in Intelligent and Communicating Systems from Univeristé Cergy-Pontoise, both in 2012.

From October 2012to September 2015,she was a PhD student at ETIS Laboratory/ENSEA, under the supervision of Claudio Weidmann and David Declercq. Her research interests include modern channel coding, cooperative communication and multi-user information theory.

She defended her PhD entitled ‘Coding for cooperative communications: Topics in distributed source coding and relay channels’ on September, 22th, 2015.

Title

Eye Tracking Algorithms

Abstract

In recent years, the interest in eye detection applications has increased considerably. There are a lot of eye detection methods used in different applications such as neuroscience, psychology, assistive technologies, in order to communicate with disabled patients, computer gaming, monitoring technologies for driver’s fatigue (in commercial and public transport), advertising industry, people identification based on face recognition and eye (iris) detection and in different military applications to help pilots to aim weapons just by looking at a target. A head-mounted eye tracking interface consists of an infrared video camera mounted on a frame glasses right underneath the eye, connected to a PC (or laptop), for eye pupil image acquisition and processing. This device is used to measure the point of gaze or the motion of an eye relative to the head. The presentation will focus on the software component used in eye tracking interfaces for real-time applications, which includes the algorithms for eye image binarization, pupil center detection, system calibration, mapping and ideogram selections. Different types of pupil detection algorithms are comparatively presented: the least squares fitting of ellipse, the RANdom SAmple Consensus (RANSAC) paradigm, the circular/elliptical Hough transform- based approaches, the projection method algorithm, the detection of the maximum dark area centroid in the eye image and the STARBURST algorithm.

Biography

Radu Gabriel Bozomitu received the degree in communications and electronic engineering; the master degree in the field of digital radio-communications; and the Ph.D. degree from the “Gheorghe Asachi” Technical University of Iaşi, Faculty of Electronics, Telecommunications and Information Technology in 1995, 1996 an  2005, respectively. R. G. Bozomitu obtained the PhD advisor position in 2017 and works as professor at the Department of Telecommunications and Information Technologies from Faculty of Electronics, Telecommunications and Information Technology from the “Gheorghe Asachi” Technical University of Iaşi. His present interests are in the areas of radio communications, analog integrated circuit design and assistive technology. Courses taught at “Gheorghe Asachi” Technical University of Iasi: “Radio communications“, “VLSI implementation of the radiofrequency circuits” and “Advanced radio communications”. He has edited or co-authored five books on analog VLSI circuits design, radiocommunications and assistive technology.

Title

Maximising the Utility of Virtually Sliced Millimetre-Wave Backhauls via a Deep Learning Approach

Abstract

Advances in network programmability enable operators to ‘slice’ the physical infrastructure into independent logical networks. By this approach, each network slice aims to accommodate the demands of increasingly diverse services. Precise allocation of resources to slices across future 5G millimetre-wave backhaul networks, so as to optimise their utility, is however challenging. This is because the performance of different services often depends on conflicting requirements, including bandwidth, sensitivity to delay, or the monetary value of the traffic incurred. In this talk, I will present our recent work in which we propose a general rate utility framework for slicing mm-wave backhaul links, which encompasses all known types of service utilities, i.e. logarithmic, sigmoid, polynomial, and linear. We then employ a deep learning solution to tackle the complexity of optimising non-convex objective functions built upon arbitrary combinations of such utilities. Specifically, using a stack of convolutional blocks, our approach can learn correlations between traffic demands and achievable optimal rate assignments. The proposed solution can be trained within minutes, following which it computes rate allocations that match those obtained with state-of-the-art global optimisation algorithms, yet orders of magnitude faster. This confirms applicability to highly dynamic traffic regimes and we demonstrate up to 62% network utility gains over a baseline greedy approach.

Titre

Recycler les ondes radio ambiantes pour connecter les objets

Description

o   Lors de l’édition 2017 du Salon de la recherche Orange, du 5 au 7 décembre (https://hellofuture.orange.com/fr/lenergy-free-communication-donne-des-ailes-aux-objets-connectes/), Orange a réalisé, pour la première fois, une transmission de données sans fil,  effectuée grâce aux seules ondes déjà diffusées par… la tour Eiffel ! Aucune onde supplémentaire n’a été émise. Cette technologie dite de rétro-diffusion ambiente découverte par l’Université de Washington en 2013, a une sobriété énergétique exceptionnelle. Elle permet de fournir de nouveaux services sans dépenser plus en spectre et en puissance rayonnée, ouvre d’énormes possibilités en termes d’utilisation massive d’objets connectés pour les villes, les maisons et les usines intelligentes.

o   Aujourd’hui, pour la  première fois, le projet ANR SpatialModulation (https://spatmodulation.cms.orange-labs.fr/) dirigé par Orange, tentera une démonstration en temps réel d’une communication utilisant les ondes TV de la Fourvière, entre un « émetteur » (qui n’émet pas) développé par Orange et un récepteur développé par l’Institut Langevin sur GNU Radio.

Title

Optimization Algorithms for Solving Problems Arising from Large Scale Machine Learning

Abstract

In the contemporary “big data” age, the use of Machine Learning models for analyzing large volumes of data has been instrumental in a lot of current technological development. These models necessitate solving very large scale optimization problems, presenting challenges in terms of developing appropriate solvers. In addition, especially for problems arising from Deep Neural Network architectures, the resulting problems are often nonconvex, and sometimes nonsmooth, giving additional difficulty.

In this talk I present the standard structural elements of this class of problems, and how these structures can be handled with appropriate parallel architectures. I discuss the state of the art in terms of optimization algorithms for this setting and summarize the prognosis for ongoing and future research.

Title

MRAM-based architectures in Spintec

Speaker

François Duhem

Date

25th April 2018

Time/Place

10am,Claude Chappe (room TBC)

Abstract

Non-Volatile Memories (NVMs) have gained traction in the last few years as they are expected to help mitigating the ever growing energy consumption due to leakage in advanced technology nodes. Among emerging NVM technologies, Magnetoresistive Random-Access Memory (MRAM) is considered to be one of the most promising as it reaches performance levels close to those of Static RAM (SRAM) with very high endurance, intrinsic immunity to radiations and good downsize scalability.
Spintec is a laboratory fully dedicated to spintronics research, aiming at bridging the gap between fundamental research and applications with expertise in fundamental physics as well as in device-oriented technologies. In particular, the design team focuses on the development of design tools for the hybrid CMOS/magnetic technology and the evaluation of hybrid non-volatile circuits (FPGA, processors, etc.).
This seminar will discuss ongoing research activities in Spintec with a focus on architecture and IC design.

Passive RADAR measurement using DVB-T receivers and Software Defined Radio processing

Jean-Michel Friedt (Université de Besançon, FEMTO-ST lab)

Abstract
We demonstrate the use of affordable DVB-T receivers used as general purpose software defined radio interfaces for collecting signals from a non-cooperative reference emitter on the one hand, and signals reflected from non-cooperative targets on the other hand, to map the range and velocity in a passive radar application. Issues include frequency and time synchronization of the DVB-T receivers, mitigated by appropriate digital signal processing relying heavily on cross-correlations.

Passive radar uses existing non-cooperative emitters as signal sources for mapping non-cooperative target range and possibly velocity. The attractive features of this strategy is the lack of dedicated broadband source for RADAR application, low cost from the use of existing emitters, and stealth since the operator is undetectable. This measurement technique has become accessible to the amateur with the availability of low cost receivers ideally suited for software defined radio processing. In the framework of passive radar applications, two receivers must be synchronized to record simultaneously the reference channel and the signal reflected by the targets: cross correlation will then finely identify the reference signal delay in the measurement signal and allow for target identification. In the case of moving targets, a brute force approach similar to Doppler compensation in GPS acquisition is applied for the cross correlation to coherently accumulate energy: the range-Doppler maps hint at the distance to the target and its velocity. Most interestingly, in the latter context, clutter (signals reflected from static targets) is separated from the moving target which becomes well visible even in a complex environment. In this presentation, we discuss the details of real time acquisition and signal post-processing for passive radar application, while addressing some of the challenges of diverting DVB-T receivers from their original application. While passive radar has been demonstrated with FM broadcast emitters, analog television emitters, or wifi, we shall here consider the broadband signal provided by digital terrestrial television broadcast signal.

Webpage

http://jmfriedt.free.fr/

Title

Heavy tailed distributions characterisations and examples of applications in channel modeling

Speaker

Associate Professor Nourddine Azzaoui

Date

16th March 2018

Time/Place

14h00 in TD-C.

Abstract

Currently, we are witnessing the proliferation of wireless sensor networks and the superposition of several communicating objects which have an heterogeneous nature. The advent of Internet of Things networks as well as the increasing demand for improved quality and services will increase the complexity of communications and puts a strain on current techniques and models. Indeed, they must firstly adapt to the temporal and spatial evolution and secondly, they must take into account the rare and unpredictable events that can have disastrous consequences for decision-making. This talk provides an overview of the various spectral techniques used in litterature describe a communication channel having an impulsive behavior. This is mainly motivated by the historical success of interactions between probabilities, statistics and the world of communications, information theory and signal processing. The presentation will be divided into two parts: the first is devoted to the synthesis of various developments on alpha-stable variables and processes in a purely mathematical mind. The second part will be devoted to applications in the context of communications. The two sides will combine two fundamentally linked aspects: first, a theoretical approach, necessary for a good formalization of problems and identifying the best solutions. Secondly, the use of these models in real work of channel modelling.

Title

Deep Learning: history, models & challenges, with an application in signal processing and mobile authentification

Abstract

Representation Learning (also known with its more popular title « Deep Learning ») consists in automatically learning layered and hierarchical representation with various layers abstraction from large amounts of data. This presentation will review the history of the field, the main actors and the major scientific challenges. We will first present a brief introduction into common deep models like convolutional neural networks and recurrent networks, before going more into detail of some selected applications in signal processing.

In particular, we present a large-scale study, exploring the capability of temporal deep neural networks in interpreting natural human kinematics and introduce the first method for active biometric authentication with mobile inertial sensors. This work has been done in collaboration with Google, where the first-of-its-kind dataset of human movements has been passively collected by 1500 volunteers using their smartphones daily over several months. We propose an optimized shift-invariant dense convolutional mechanism (DCWRNN) and incorporate the discriminatively-trained dynamic features in a probabilistic generative framework taking into account temporal characteristics. Our results demonstrate, that human kinematics convey important information about user identity and can serve as a valuable component of multi-modal authentication systems.

Biography

Christian WOLF is associate professor (Maitre de Conférences, HDR) at INSA de Lyon and LIRIS UMR 5205, a CNRS laboratory, since 2005. He is interested in computer vision and machine learning, deep learning, especially in the visual analysis of complex scenes in motion: gesture and activity recognition and pose estimation. In his work he puts an emphasis on models of complex interactions, on structured models, graphical models and on deep learning. He received his MSc in computer science from Vienna University of Technology (TU Wien) in 2000, and a PhD in computer science from INSA de Lyon, France, in 2003. In 2012 he obtained the habilitation diploma, also from INSA de Lyon. Since September 2017 Christian is on leave at INRIA, at the chroma work group at the CITI laboratory (“délégation INRIA”).