Scientific direction Development of key enabling technologies
Transfer of knowledge to industry

PhD : selection by topics

Protecting binary elliptic curve cryptography against Template atttacks and Horizontal attacks

Département Systèmes

Laboratoire Sécurité des Objets et des Systèmes Physiques

01-09-2019

SL-DRT-19-0385

antoine.loiseau@cea.fr

This study takes place in the field of embedded systems security and the asymetric cryptography against Template and Horizontal side channel attacks. Recent studies, applied to the symetric cryptography, have yielded new side channel attacks : by improving the efficiency of Template attacks, these new attacks allow to bypass countermeasures based on desynchronisation and masking. It is time now to study the relevance of those new Machine Learning-based Template and Horizontal attacks to asymetric cryptography, especially for binary elliptic curves. This thesis follows the work of Antoine Loiseau on Binary Edwards Curves (BEC). Those BECs have been proven to have some intrisic properties of security against side channel attacks. However, latest results have shown that the resistance of BECs against the new ML-based Template and Horizontal attacks have yet to be studied. This thesis aims at qualifying the degree of resistance of those BECs to ML-based Template and Horizontal Attacks and at devising, implementing and testing new countermeasures to twarth those lastest générations of side channel attacks.

Compact and ultra-wideband antenna arrays in Ka-band

Département Systèmes

Laboratoire Antennes, Propagation, Couplage Inductif

01-09-2019

SL-DRT-19-0386

loic.marnat@cea.fr

Millimeter-wave communication (e.g. 5G) or radar (e.g. automobile) systems require directive antennas to compensate for transmission losses and ultra-wideband antennas to ensure, depending on the targeted application, a high data rate or a fine resolution. Agility of radiation pattern is thus a key point. Array antennas offer undeniable advantages and come with a tradeoff between the number of radiating elements and the number of active circuit to achieve the performance required in terms of beam focalization and radiated power (for a form factor defined by the targeted system). However, classical design rules associated to typical array elements arrangement can be show stopper for the antenna integration in some applications and typically lead to limited operating band and scanning range. The aim of the thesis is to get rid of these limitations and to design a seamless compact phased array antenna while ensuring outstanding performance in terms of band of operation and scanning capabilities. To do so, studies will focus on tightly coupled miniature elements put in an array fashion. Four main steps will be needed to understand and model such compact array antennas: - State of the art on tightly coupled and ultra-wideband antenna arrays, - Theoretical study describing the coupled elements behavior and the law governing the coupling between them in dense arrays, - Design if a compact array based on miniature and ultra-wideband coupled elements. Technology compatible with seamless and low cost solutions will be preferred. - Active Ka-band prototype will be realized and measured. The thesis will lead to a compact active millimeter-wave phased array prototype competitive as compared to actual state of the art. These studies will pave the way to the use of phased array in applications with complex and compact environments such as 5G terminal and access point or automotive radars and even for advanced satellite antennas.

Study and implementation of non-recurrent deep learning algorithms for temporal sequences processing

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Calcul Embarqué

01-01-2019

SL-DRT-19-0393

david.briand@cea.fr

Recurrent neural networks - and notably the Long-Short Term Memory (LSTM) variant - are today at the state of the art for solving many temporal sequence classification problems and in particular used in speech recognition applications (from 2015 for Android) and automatic translation (from 2016 at Google, Apple and Facebook). This type of algorithm is also successfully applied in various applications such as audio event recognition, denoising, language modeling, sequences generations, etc. The success of these approaches comes however with the cost of huge computing power requirements. This is why most of this algorithms are run on the Cloud, and not on the Edge. Moreover, recurrent neural networks are very sensitive to training parameters and can be difficult to converge because gradients internal to their recurrent structure can easily explode or vanish to zero. The adaptation of these algorithms for an embedded implementation is therefore not straightforward, because the recurrence requires a high precision and partially sequential (large latency) computing. Some technics for overcoming these difficulties are starting to appear, but are still in their infancy. Among them, a non-recurrent technic allowing sequence processing with less constrain than LSTM seems promising: hierarchical networks. Temporal convolution networks (TCN) are one of their application. The advantages and drawbacks of this model are studied notably in "An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling" (Shaojie Bai, J. Zico Kolter, Vladlen Koltun). A basic implementation of each structure showed that TCN are more efficient in almost every test cases. Internal gradients are much more stable and computation can be easily parallelized thanks to the elimination of the recurrence.

Blending intuition with reasoning ? Deep learning augmented with algorithmic logic and abstraction

Département Ingénierie Logiciels et Systèmes (LIST)

Labo.conception des système embarqués et autonomes

01-01-2019

SL-DRT-19-0401

shuai.li@cea.fr

Within machine learning, deep learning, based on neural networks, is a subfield that has gained much traction since several high-profile success stories. Unlike classical computer reasoning, the statistical method by which a neural network solves a problem can be seen as a very primitive form of intuition, as opposite to classical computer reasoning. However, so far the only real success of deep learning has been its ability to self-tune its geometric logic that lets it transform data represented as points in n-dimension, to data represented as points in m-dimension, if we provide enough training data. Unlike a human being, a neural network does not have the ability to reason through algorithmic logic. Furthermore, although neural networks are tremendously powerful for a given task, since they have no ability to achieve global generalization, any deviation in the input data may give unpredicted results, which limits their reusability. Considering the significant cost associated with neural network development, integrating such systems is not always economically viable. It is therefore necessary to abstract, encapsulate, reuse and compose neural networks. Although lacking in deep learning, algorithmic logic and abstraction are today innate to classical software engineering, through programming primitives, software architecture paradigms, and mature methodological patterns like Model-Driven Engineering. Therefore, in this thesis, we propose to blend reusable algorithmic intelligence, providing the ability to reason, with reusable geometric intelligence, providing the ability of intuition. To achieve such an objective, we can explore some ideas like integrating programming control primitives in neural networks, applying software architecture paradigms in neural networks models, and assembling modular systems using libraries containing both algorithmic modules and geometric modules. The results of this thesis will be a stepping stone towards helping companies assemble AI systems for their specific problems, by limiting the costs in expertise, effort, time, and data necessary to integrate neural networks.

Tunable Metasurface

Département Systèmes

Laboratoire Antennes, Propagation, Couplage Inductif

01-06-2019

SL-DRT-19-0406

jean-francois.pintos@cea.fr

Metamaterials have been studied by the scientific community for several years with a particular focus on 2D or 3D meta shapes. In the antenna field, these structured materials have been mainly used as magnetic surfaces, filtering surfaces for surface waves or the antenna itself. The main disadvantage of these materials is its narrow band behaviour. Recent research has shown that it is possible to modify the response of metasurfaces by adding a film sensitive to a control voltage to the patterns or by arranging the active components between them. More recently, CEA Leti has developed a new approach, through a thesis, to modify the performance of a metasurface, by inserting control devices on its rear surface as well as on the feeder. The proposal, made here, is in line with the continuity of this work, initiated within the LAPCI laboratory, with a specific development around massively tunable metasurfaces. Indeed, it has been demonstrated that the metasurface/feeder pair should be jointly designed/optimized when the metasurface and/or feeder were compact or even miniature. The purpose of this thesis is to study this interaction through the notion of load impedance and to realize a final demonstrator of a reconfigurable metasurface of several hundred active elements. The main interest is to consider the use of ultra-compact adjustable metamaterials in order to miniaturize the size of an antenna placed near a reflecting plane. The second major point concerns the possibility of frequency-dependent control of the complete device (by nature very narrow band) over a frequency band of several tens of percent. During this thesis, the candidate will develop the theoretical modeling of the proposed device and validate the expected performances through 2D and/or 3D electromagnetic simulation campaigns. He/she will be in charge of having the selected demonstrators carried out and will carry out measurements of the devices in the test facilities of CEA-Leti and/or CNES (anechoic chamber). The candidate will be integrated into the Antenna, Propagation and Inductive Coupling Laboratory in Grenoble. He/she will be part of the research team (permanent, doctoral and non-permanent) and will be supervised by a research engineer from the laboratory. The candidate will be required to present his or her work at national and international conferences and symposiums.

Heat and moisture storage for optimization of heat sinks using dry cooling

Département Thermique Biomasse et Hydrogène (LITEN)

Laboratoire Stockage Thermique

01-10-2019

SL-DRT-19-0408

arnaud.bruch@cea.fr

Efficiency of thermodynamic systems of electricity production is strongly related to cooling temperature level. Performances of such system using dry cooling are direclty related to ambient temperature. Systems using wet cooling are less impacted but at the expense of large water consumption. Due to environmental reglementation about water consumption, there is also a real need for efficient cooling system wihtout or low water consumption. Thesis proposal here presented is based on an innovative storage technology, based on combined heat and humidity storage, to perform efficient cooling system with no water consumption. Freshness and humidity of the night are stored inside a solid matrix, allowing a cooling of hot air during the day by cumulative of convection and sorption. Thesis work will be divided in experimental work, trough the functional cool-sto experimental installation, and numerical simulation using a home-made model coupling sensible and thermochemical heat storages. Prospective work on integration of such system on real installations, and related gains are also considered.

134 Results found (Page 4 of 23)
first   previous  2 - 3 - 4 - 5 - 6  next   last

Voir toutes nos offres