WorldCat Identities

Guyeux, Christophe

Overview
Works: 39 works in 85 publications in 3 languages and 646 library holdings
Roles: Opponent, Other, Thesis advisor, Author, htt, Contributor
Publication Timeline
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Most widely held works by Christophe Guyeux
Discrete dynamical systems and chaotic machines : theory and applications by Jacques Mohcine Bahi( )

18 editions published between 2013 and 2019 in English and held by 359 WorldCat member libraries worldwide

"For computer scientists, especially those in the security field, the use of chaos has been limited to the computation of a small collection of famous but unsuitable maps that offer no explanation of why chaos is relevant in the considered contexts. Discrete Dynamical Systems and Chaotic Machines: Theory and Applications shows how to make finite machines, such as computers, neural networks, and wireless sensor networks, work chaotically as defined in a rigorous mathematical framework. Taking into account that these machines must interact in the real world, the authors share their research results on the behaviors of discrete dynamical systems and their use in computer science. Covering both theoretical and practical aspects, the book presents: Key mathematical and physical ideas in chaos theory Computer science fundamentals, clearly establishing that chaos properties can be satisfied by finite state machines Concrete applications of chaotic machines in computer security, including pseudorandom number generators, hash functions, digital watermarking, and steganography Concrete applications of chaotic machines in wireless sensor networks, including secure data aggregation and video surveillance. Until the authors' recent research, the practical implementation of the mathematical theory of chaos on finite machines raised several issues. This self-contained book illustrates how chaos theory enables the study of computer security problems, such as steganalysis, that otherwise could not be tackled. It also explains how the theory reinforces existing cryptographically secure tools and schemes"--
Design of digital chaotic systems updated by random iterations by Qianxue Wang( )

9 editions published in 2018 in English and held by 214 WorldCat member libraries worldwide

This brief studies the general problem of constructing digital chaotic systems in devices with finite precision from low-dimensional to high-dimensional settings, and establishes a general framework for composing them. The contributors demonstrate that the associated state networks of digital chaotic systems are strongly connected. They then further prove that digital chaotic systems satisfy Devaney's definition of chaos on the domain of finite precision. The book presents Lyapunov exponents, as well as implementations to show the potential application of digital chaotic systems in the real world; the authors also discuss the basic advantages and practical benefits of this approach. The authors explore the solutions to dynamic degradation (including short cycle length, decayed distribution and low linear complexity) by proposing novel modelling methods and hardware designs for two different one-dimensional chaotic systems, which satisfy Devaney's definition of chaos. They then extend it to a higher-dimensional digital-domain chaotic system, which has been used in image-encryption technology. This ensures readers do not encounter large differences between actual and theoretical chaotic orbits through small errors. Digital Chaotic Systems serves as an up-to-date reference on an important research topic for researchers and students in control science and engineering, computing, mathematics and other related fields of study
Discrete dynamical systems, chaotic machine, and applications to information security by Jacques Mohcine Bahi( Book )

1 edition published in 2013 in English and held by 5 WorldCat member libraries worldwide

Le désordre des itérations chaotiques Applications aux réseaux de capteurs, à la dissimulation d'information, et aux fonctions de hachage by Christophe Guyeux( )

1 edition published in 2012 in German and held by 3 WorldCat member libraries worldwide

Diversité et caractérisation fonctionnelle des communautés microbiennes inféodées au peuplier et issues d'une friche industrielle enrichie en mercure by Alexis Durand( Book )

2 editions published in 2017 in French and held by 2 WorldCat member libraries worldwide

Le sol possède un capital naturel qui lui confère la capacité à produire des services écosystémiques aussi bien culturel que de régulation ou d'approvisionnement, il est indispensable à la Vie telle que nous la connaissons et au développement des activités humaines. Cependant les activités anthropiques et les pollutions, notamment par les éléments traces métalliques (ETMs) tel que le mercure (Hg), perturbent les sols et modifient en profondeur l'organisation des écosystèmes. Face à ces enjeux, des projets de remédiation et de gestion des sites et sols pollués se sont multipliés durant les dernières décennies en vue de futures ré-exploitations de ces sols. Cette thèse s'inscrit dans le cadre des projets ANR-BIOFILTREE et EC2CO FREIDI-Hg gérés par le laboratoire Chrono-Environnement. Mes travaux ont permis l'exploration de la diversité des communautés de microorganismes associées à une plantation de peuplier sur un site contaminé par le Hg et géré par phytomanagement, via les approches combinées de séquençage à très haut débit et par l'approche culture dépendante. Ces méthodes combinées ont permis de révéler i) la diversité des communautés bactériennes et fongiques de la peupleraie ; ii) les groupes de microorganismes particulièrement résistant au Hg (Trichoderma et Pseudomonas) ; et iii) des bactéries promotrices de croissance des plantes (PGPB). Par ailleurs, la compréhension des mécanismes cellulaires liés à l'accumulation de Hg par les microorganismes a été un de mes sujets d'étude en partenariat avec le LIEC (Université de Lorraine). Les modèles eucaryotes Saccharomyces cerevisiae et Podospora anserina ont été utilisés pour tester le rôle potentiel de certains transporteurs d'ions dans l'entrée du Hg dans les cellules fongiques. Les résultats ont montré que le transporteur de magnésium Alr1 situé sur la membrane plasmique pourrait participer au transport du Hg. En outre, une approche de transcriptomique chez Saccharomyces cerevisiae après une courte exposition au Hg des souches mutantes et sauvages a été mise en œuvre. Pour conclure, ce travail de thèse ambitionne d'être un travail de référence pour les futurs projets de phytomanagement en milieux contaminé par le Hg, qui met en avant les communautés de microorganismes et leurs rôles fondamentaux
A Bregman-proximal point algorithm for robust non-negative matrix factorization with possible missing values and outliers - application to gene expression analysis by Stéphane Chrétien( )

1 edition published in 2016 in English and held by 2 WorldCat member libraries worldwide

Comparison of metaheuristics to measure gene effects on phylogenetic supports and topologies by Régis Garnier( )

1 edition published in 2018 in English and held by 2 WorldCat member libraries worldwide

Data Reduction based energy-efficient approaches for secure priority-based managed wireless video sensor networks by Christian Salim( Book )

2 editions published in 2018 in English and held by 2 WorldCat member libraries worldwide

The huge amount of data in Wireless Video Sensor Networks (WVSNs) for tiny limited resources sensor nodes increases the energy and bandwidth consumption challenges. Controlling the network is one of the challenges in WMSN due to the huge amount of images sent at the same time from the sensors to the coordinator. In this thesis, to overcome these problems, several contributions have been made. Each contribution concentrates on one or two challenges as follows: In the first contribution, to reduce the energy consumption a new approach for data aggregation in WVSN based on shot similarity functions is proposed. It is deployed on two levels: the video-sensor node level and the coordinator level. At the sensor node level, we propose a frame rate adaptation technique and a similarity function to reduce the number of frames sensed by the sensor nodes and sent to the coordinator. At the coordinator level, after receiving shots from different neighboring sensor nodes, the similarity between these shots is computed to eliminate redundancies. In the second contribution, some processing and analysis are added based on the similarity between frames on the sensor-node level to send only the important frames to the coordinator. Kinematic functions are defined to predict the next step of the intrusion and to schedule the monitoring system accordingly. In the third contribution, on the transmission phase, on the sensor-node level, a new algorithm to extract the differences between two images is proposed. This contribution also takes into account the security challenge by adapting an efficient ciphering algorithm on the sensor node level. In the last contribution, to avoid slower detection of intrusions leading to slower reactions from the coordinator, a mac-layer protocol based on S-MAC protocol has been proposed to control the network. This solution consists in adding a priority bit to the S-MAC protocol to give priority to critical data
Efficient and cryptographically secure generation of chaotic pseudorandom numbers on GPU by Christophe Guyeux( )

1 edition published in 2015 in English and held by 2 WorldCat member libraries worldwide

Wireless sensor networks for Industrial health assessment based on a random forest approach by Wiem Elghazel( )

2 editions published in 2015 in English and held by 2 WorldCat member libraries worldwide

Une maintenance prédictive efficace se base essentiellement sur la fiabilité des données de surveillance.Dans certains cas, la surveillance des systèmes industriels ne peut pas être assurée à l'aide de capteurs individuels ou filaires. Les Réseaux de Capteurs Sans Fil (RCSF) sont alors une alternative. Vu la nature de communication dans ces réseaux, la perte de données est très probable. Nous proposons un algorithme distribué pour la survie des données dans le réseau. Cet algorithme réduit le risque d'une perte totale des paquets de données et assure la continuité du fonctionnement du réseau. Nous avons aussi simulé de différentes topologies du réseau pour évaluer leur impact sur la complétude des données au niveau du nœud puits. Par la suite, nous avons proposé une démarche d'évaluation de l'état de santé de systèmes physiques basée sur l'algorithme des forêts aléatoires. Cette démarche repose sur deux phases : une phase hors ligne et une phase en ligne. Dans la phase hors ligne, l'algorithme des forêts aléatoires sélectionne les paramètres qui contiennent le plus d'information sur l'état du système. Ces paramètres sont utilisés pour construire les arbres décisionnels qui constituent la forêt. Dans la phase en ligne, l'algorithme évalue l'état actuel du système en utilisant les données capteurs pour parcourir les arbres construits. Chaque arbre dans la forêt fournit une décision, et la classe finale est le résultat d'un vote majoritaire sur l'ensemble de la forêt. Quand les capteurs commencent à tomber en panne, les données décrivant un indicateur de santé deviennent incomplètes ou perdues. En injectant de l'aléatoire dans la base d'apprentissage, l'algorithme aura des points de départ différents, et par la suite les arbres aussi. Ainsi, l'absence des mesures d'un indicateur de santé ne conduit pas nécessairement à l'interruption du processus de prédiction de l'état de santé
On the use of chaotic iterations to design keyed hash function by Zhuosheng Lin( )

1 edition published in 2017 in English and held by 2 WorldCat member libraries worldwide

Performance of low level protocols in high traffic wireless body sensor networks by Nadine Boudargham( )

1 edition published in 2019 in English and held by 2 WorldCat member libraries worldwide

Competent QoS-aware and energy efficient protocols for body sensor networks by Nadine Boudargham( Book )

2 editions published in 2020 in English and held by 2 WorldCat member libraries worldwide

Body Sensor Networks (BSNs) are formed of medical sensors that gather physiological and activity data from the human body and its environment, and send them wirelessly to a personal device like Personal Digital Assistant (PDA) or a smartphone that acts as a gateway to health care. Collaborative Body Sensor Networks (CBSNs) are collection of BSNs that move in a given area and collaborate, interact and exchange data between each other to identify group activity, and monitor the status of single and multiple persons.In both BSN and CBNS networks, sending data with the highest Quality of Service (QoS) and performance metrics is crucial since the data sent affects people's life. For instance, the sensed physiological data should be sent reliably and with minimal delay to take appropriate actions before it is too late, and the energy consumption of nodes should be preserved as they have limited capacities and they are expected to serve for a long period of time. The QoS in BSNs and CBSNs largely depends on the choice of the Medium Access Control (MAC) protocols, the adopted routing schemes, and the efficient and accuracy of anomaly detection.The current MAC, routing and anomaly detection schemes proposed for BSNs and CBSNs in the literature present many limitations and open the door toward more research and propositions in these areas. Thus this thesis work focuses on three main axes. The first axe consists in studying and designing new and robust MAC algorithms able to address BSNs and CBSNs' challenges. Standard MAC protocols are compared in high traffic BSNs and a new MAC protocol is proposed for such environments; then an emergency aware MAC scheme is presented to address the dynamic traffic requirements of BSN in ensuring delivery of emergency data within strict delay requirements, and energy efficiency of nodes during regular observations; moreover, a traffic and mobility aware MAC scheme is proposed for CBSNs to address both traffic and mobility requirements for these networks.The second axe consists in proposing a thorough and efficient routing scheme suitable for BSNs and CBSNs. First, different routing models are compared for CBSNs and a new routing scheme is proposed in the aim of reducing the delay of data delivery, and increasing the network throughput and the energy efficiency of nodes. The proposed scheme is then adapted to BSN's requirements to become a solid solution for the challenges faced by this network. The third axe involves proposing an adaptive sampling approach that guarantees high accuracy in the detection of emergency cases, while ensuring at the same time high energy efficiency of the sensors.In the three axes, the performance of the proposed schemes is qualitatively compared to existing algorithms in the literature; then simulations are carried a posteriori with respect to different performance metrics and under different scenarios to assess their efficiency and ability to face BSNs and CBSNs' challenges.Simulation results demonstrate that the proposed MAC, routing and anomaly detection schemes outperform the existing algorithms, and present strong solutions that satisfy BSNs and CBSNs' requirements
Désordre des itérations chaotiques et leur utilité en sécurité informatique by Christophe Guyeux( Book )

2 editions published in 2010 in French and held by 2 WorldCat member libraries worldwide

Les itérations chaotiques, un outil issu des mathématiques discrètes, sont pour la première fois étudiées pour obtenir de la divergence et du désordre. Après avoir utilisé les mathématiques discrètes pour en déduire des situations de non convergence, ces itérations sont modélisées sous la forme d'un système dynamique et sont étudiées topologiquement dans le cadre de la théorie mathématique du chaos. Nous prouvons que leur adjectif « chaotique » a été bien choisi : ces itérations sont du chaos aux sens de Devaney, Li-Yorke, l'expansivité, l'entropie topologique et l'exposant de Lyapunov, etc. Ces propriétés ayant été établies pour une topologie autre que la topologie de l'ordre, les conséquences de ce choix sont discutées. Nous montrons alors que ces itérations chaotiques peuvent être portées telles quelles sur ordinateur, sans perte de propriétés, et qu'il est possible de contourner le problème de la finitude des ordinateurs pour obtenir des programmes aux comportements prouvés chaotiques selon Devaney, etc. Cette manière de faire est respectée pour générer un algorithme de tatouage numérique et une fonction de hachage chaotiques au sens le plus fort qui soit. À chaque fois, l'intérêt d'être dans le cadre de la théorie mathématique du chaos est justifié, les propriétés à respecter sont choisies suivant les objectifs visés, et l'objet ainsi construit est évalué. Une notion de sécurité pour la stéganographie est introduite, pour combler l'absence d'outil permettant d'estimer la résistance d'un schéma de dissimulation d'information face à certaines catégories d'attaques. Enfin, deux solutions au problème de l'agrégation sécurisée des données dans les réseaux de capteurs sans fil sont proposées
Analyse d'image hyperspectrale by Adrien Faivre( )

2 editions published in 2017 in French and held by 2 WorldCat member libraries worldwide

This dissertation addresses hyperspectral image analysis, a set of techniques enabling exploitation of micro-spectroscopy images. Images produced by these sensors constitute cubic arrays, meaning that every pixel in the image is actually a spectrum.The size of these images, which is often quite large, calls for an upgrade for classical image analysis algorithms.We start out our investigation with clustering techniques. The main idea is to regroup every spectrum contained in a hyperspectralimage into homogeneous clusters. Spectrums taken across the image can indeed be generated by similar materials, and hence display spectral signatures resembling each other. Clustering is a commonly used method in data analysis. It belongs nonetheless to a class of particularly hard problems to solve, named NP-hard problems. The efficiency of a few heuristics used in practicewere poorly understood until recently. We give theoretical arguments guaranteeing success when the groups studied displaysome statistical property.We then study unmixing techniques. The objective is no longer to decide to which class a pixel belongs, but to understandeach pixel as a mix of basic signatures supposed to arise from pure materials. The mathematical underlying problem is again NP-hard.After studying its complexity, and suggesting two lengthy relaxations, we describe a more practical way to constrain the problemas to obtain regularized solutions.We finally give an overview of other hyperspectral image analysis methods encountered during this thesis, amongst whomare independent component analysis, non-linear dimension reduction, and regression against a spectrum library
Data compression and deep learning for IoT healthcare applications based on physiological signals by Joseph Azar( Book )

2 editions published in 2020 in English and held by 2 WorldCat member libraries worldwide

In recent years, Internet of Things (IoT) technology has gained tremendous attention for its ability to relieve the burden on healthcare caused by an aging population and the increase in chronic disease. IoT technology facilitates the tracking of patients with different conditions and the processing of vast volumes of data, of which a substantial part of this data are physiological signals. Physiological signals are an invaluable source of data which helps to diagnose, rehabilitate, and treat diseases. The signals come from a Wireless Body Sensor Network or wearable devices placed on a patient's body. There are many difficulties in IoT-healthcare systems, such as data collection and processing especially that (1) wireless sensor nodes have limited energy, processing and memory resources, (2) the quantity of data collected periodically is enormous, (3) the quality of the data collected is not always satisfactory, and that such data are highly likely to include noisy or unreliable areas, and (4) the manual feature extraction process from the physiological signals requires significant human intervention and medical expertise.Firstly, an energy-efficient data compression technique is being proposed in this dissertation. The proposed scheme is based on an error-bound lossy compressor originally designed for high-performance computing applications and has been adapted for IoT devices that are resource constrained. The proposed solution is an easy-to-implement algorithm, which could reduce energy consumption by as much as 2.5 times. It also reduces the processing/transmission time by compressing large batches of data before their transfer from the IoT to the edge. Moreover, an empirical analysis was carried out to study the effect of lossy time series compression on the classification task. In addition to different variations of compression methods, various deep neural networks for time series classification were considered to identify the appropriate trade-off between the compression ratio and classification performance.Second, the problem of data distortion due to lossy compression was addressed. The reconstructed data may not always be satisfactory, given that the physiological signals collected are multivariate time series that are highly compressed with a lossy compressor prior to transmission. To solve this limitation, a convolutional autoencoder-based deep learning model was introduced. The proposed model was able to enhance the compressed data after reconstruction, thus fixing the shape of the physiological signal and allowing more accurate feature extraction.Then, the photoplethysmogram signals were given particular attention. Photoplethysmography (PPG) is used to measure the skin blood flow using infrared light. Photoplethysmography is a promising technique because of the capability of the new wrist-worn devices to provide the signal. The existence of motion artifacts and meaningless areas in the signal is a major challenge faced when working with this signal. A deep learning model for automatic motion artifacts detection based on a CNN-LSTM autoencoder architecture has been proposed to detect and discard irrelevant zones in photoplethysmogram signals to avoid analyzing and processing meaningless data.Finally, a deep learning model has been proposed for time series classification. Given that hand-designing features from physiological signals is a challenging task, the representation learning approach is a potential solution for automatically learning features from raw physiological data. A classification model based on the DenseNet architecture was proposed for the classification of multivariate time series. The results show that the proposed approach is capable of achieving and in several cases bypassing the performance of the state-of-the-art models on a benchmark dataset
Rigorous computational methods for understanding the statistical behavior of random dynamical systems by Luigi Marangio( Book )

2 editions published in 2021 in English and held by 2 WorldCat member libraries worldwide

In 2019 it has been estimated that the amount of digital data in the world is 40 zettabytes, 40 times the number of observable stars in the universe. Obviously providing an accurate estimate of this amount is impossible, but looking at our society, it is not difficult to imagine that it is so. Managing this amount of data and digital devices is a huge challenge for mankind, involving virtually every area of knowledge, from metallurgy to ethics. It arises spontaneously then, to wonder if, despite their intrinsic complexity, it is possible to understand, in some sense, the evolution of this type of complex systems. This question is naturally placed in the mathematical field, and one of the techniques currently used to model an observable phenomenon is to describe the evolution of a system through the evolution of a certain stochastic process, a random dynamical system. The purpose of this thesis is to try to answer the following question: given a random dynamical system, are we able to predict (not just simulate) its behavior in the long term? If I put a drop of black ink in a glass of water, the short term interactions between these two liquids are unpredictable. However, we can all say with certainty that after enough time, the water and ink mix completely, the glass is filled with a grey liquid, the system will have reached its equilibrium state.The example just exposed, can be formalized in many ways, one of these is to consider a certain operator defined on the appropriate spaces, which maps measures to measures, and prove for example that this operator admits as fixed point the Lebesgue's measure properly normalized (the state of equilibrium). This type of operators, are called Transfer Operators, and are linear operators defined on appropriate Banach spaces, and are the object of study of this thesis.A transfer operator associated with a dynamical system describes the evolution of the densities according to the dynamics (in the previous example, the drop of ink that we can model with a delta of Dirac, will evolve in the measure of Lebesgue). Under appropriate assumptions, these transfer operators admit fixed points, which are the stationary measures of the system, and our goal is to calculate these fixed points, which represent the statistic behavior of the system under consideration after a long time.Unfortunately, in many cases, even simple ones, the calculation of the stationary measure, or better, of its density with respect to the Lebesgue measure, cannot be addressed analytically. The strategy we follow is therefore to rigorously approximate these densities; we remark on the importance of the fact that we are interested in having rigorous quantitative estimates, and not in numerically simulating a system. The central core of this interdisciplinary thesis is to use mathematical ideas that allow us to have an explicit estimation of the errors related to these rigorous approximations, combined with an accurate computer science implementation, which leads to have quantitative theorems proved with the aid of a computer
Bioinformatic analysis of the genomes of epidemic pseudomonas aeruginosa by Panisa Treepong( )

2 editions published in 2017 in English and held by 2 WorldCat member libraries worldwide

Pseudomonas aeruginosa is a major nosocomial pathogen with ST235 being the most prevalent of the so-called 'international' or 'high-risk' clones. This clone is associated with poor clinical outcomes in part due to multi- and high-level antibiotic resistance. Despite its clinical importance, the molecular basis for the success of the ST235 clone is poorly understood. Thus this thesis aimed to understand the origin of ST235 and the molecular basis for its success, including the design of bioinformatics tools for finding insertion sequences (IS) of bacterial genomes.To fulfill these objectives, this thesis was divided into 2 parts.First, the genomes of 79 P. aeruginosa ST235 isolates collected worldwide over a 27-year period were examined. A phylogenetic network was built using Hamming distance-based method, namely the NeighborNet. Then we have found the Time to the Most Recent Common Ancestor (TMRCA) by applying a Bayesian approach. Additionally, we have identified antibiotic resistance determinants, CRISPR-Cas systems, and ST235-specific genes profiles. The results suggested that the ST235 sublineage emerged in Europe around 1984, coinciding with the introduction of fluoroquinolones as an antipseudomonal treatment. The ST235 sublineage seemingly spreads from Europe via two independent clones. ST235 isolates then appeared to acquire resistance determinants to aminoglycosides, [beta]-lactams, and carbapenems locally. Additionally, all the ST235 genomes contained the exoU-encoded exotoxin and identified 22 ST235-specific genes clustering in blocks and implicated in transmembrane efflux, DNA processing and bacterial transformation. These unique genes may have contributed to the poor outcome associated with P. aeruginosa ST235 infections and increased the ability of this international clone to acquire mobile resistance elements.The second part was to design a new Insertion Sequence (IS) searching tool on next-generation sequencing data, named panISa. This tool identifies the IS position, direct target repeats (DR) and inverted repeats (IR) from short read data (.bam/.sam) by investigating only the reference genome (without any IS database). To validate our proposal, we used simulated reads from 5 species: Escherichia coli, Mycobacterium tuberculosis, Pseudomonas aeruginosa, Staphylococcus aureus, and Vibrio cholerae with 30 random ISs. The experiment set is constituted by reads of various lengths (100, 150, and 300 nucleotides) and coverage of simulated reads at 20x, 40x, 60x, 80x, and 100x. We performed sensitivity and precision analyses to evaluate panISa and found that the sensitivity of IS position is not significantly different when the read length is changed, while the modifications become significant depending on species and read coverage. When focusing on the different read coverage, we found a significant difference only at 20x. For the other situations (40x-100x) we obtained a very good mean of sensitivity equal to 98% (95%CI: 97.9%-98.2%). Similarly, the mean of DR sensitivity of DR identification is high: 99.98% (95%CI: 99.957%-99.998%), but the mean of IR sensitivity is 73.99% (95%CI: 71.162%-76.826%), which should be improved. Focusing on precision instead of sensibility, the precision of IS position is significantly different when changing the species, read coverage, or read length. However, the mean of each precision value is larger than 95%, which is very good.In conclusion, P. aeruginosa ST235 (i) has become prevalent across the globe potentially due to the selective pressure of fluoroquinolones and (ii) readily became resistant to aminoglycosides, [beta]-lactams, and carbapenems through mutation and acquisition of resistance elements among local populations. Concerning the second point, our panISa proposal is a sensitive and highly precise tool for identifying insertion sequences from short reads of bacterial data, which will be useful to study the epidemiology or bacterial evolution
Ancestral Reconstruction and Investigations of Genomics Recombination on Chloroplasts Genomes by Bashar Al-Nuaimi( )

2 editions published in 2017 in English and held by 2 WorldCat member libraries worldwide

The theory of evolution is based on modern biology. All new species emerge of an existing species. As a result, different species share common ancestry,as represented in the phylogenetic classification. Common ancestry may explainthe similarities between all living organisms, such as general chemistry, cell structure,DNA as genetic material and genetic code. Individuals of one species share the same genes but (usually) different allele sequences of these genes. An individual inheritsalleles of their ancestry or their parents. The goal of phylogenetic studies is to analyzethe changes that occur in different organisms during evolution by identifying therelationships between genomic sequences and determining the ancestral sequences and theirdescendants. A phylogeny study can also estimate the time of divergence betweengroups of organisms that share a common ancestor. Phylogenetic trees are usefulin the fields of biology, such as bioinformatics, for systematic phylogeneticsand comparative. The evolutionary tree or the phylogenetic tree is a branched exposure the relationsevolutionary between various biological organisms or other existence depending on the differences andsimilarities in their genetic characteristics. Phylogenetic trees are built infrom molecular data such as DNA sequences and protein sequences. Ina phylogenetic tree, the nodes represent genomic sequences and are calledtaxonomic units. Each branch connects two adjacent nodes. Each similar sequencewill be a neighbor on the outer branches, and a common internal branch will link them to acommon ancestor. Internal branches are called hypothetical taxonomic units. Thus,Taxonomic units gathered in the tree involve being descended from a common ancestor. Ourresearch conducted in this dissertation focuses on improving evolutionary prototypesappropriate and robust algorithms to solve phylogenetic inference problems andancestral information about the order of genes and DNA data in the evolution of the complete genome, as well astheir applications
Towards smart firefighting using the internet of things and machine learning by Gaby Bou Tayeh( Book )

2 editions published in 2020 in English and held by 2 WorldCat member libraries worldwide

L'objectif de cette de thèse est d'étudier à la fois des solutions matérielles et logicielles pour améliorer les conditions de travail des sapeurs-pompiers. Il s'agit de développer un système intelligent basé sur l'internet des objets pour surveiller l'état de santé des pompiers et aider à les localiser lors des interventions. Dans la première partie de la thèse, nous avons étudié et proposé plusieurs approches permettant de réduire la consommation d'énergie du système afin de maximiser sa durée de vie. La première approche présente un modèle de prédiction basé sur la corrélation temporelle entre les mesures collectées par le même capteur. Il permet de réduire la quantité de données collectées et transmises au centre de contrôle. Ce modèle est exécuté à la fois par le capteur et le centre et qui s'auto-adapte en fonction de l'écart constaté entre les mesures réelles collectées et les mesures prédites. Une deuxième version de cette approche a été étudiée pour prendre en considération la perte de message et la synchronisation entre le capteur et le centre de contrôle. D'un autre côté et pour réduire davantage la consommation d'énergie, nous avons couplé l'approche de prédiction avec un algorithme de collecte de données adaptatif permettant de réduire l'activité du capteur et le taux d'échantillonnage. Toutes ces approches ont été testées via des simulations et de l'implémentation réelle. Les résultats obtenus montrent l'efficacité de ces approches en termes de réduction de la consommation d'énergie tout en gardant l'intégrité de données. La deuxième partie de cette thèse est dédiée au traitement des données issues des interventions des sapeurs-pompiers. Nous avons étudié plusieurs méthodes de clustérisation permettant un prétraitement de données avant l'extraction des connaissances. D'un autre côté, nous avons appliqué des méthodes d'apprentissage profond sur un grand ensemble de données concernant 200.000 interventions qui ont eu lieu pendant une période de 6 ans dans le département du Doubs, en France. Le but de cette partie était de prédire le nombre d'interventions futures en fonction de variables explicatives externes, pour aider les pompiers à bien gérer leurs ressources
 
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Discrete dynamical systems and chaotic machines : theory and applications
Covers
Design of digital chaotic systems updated by random iterationsDiscrete dynamical systems, chaotic machine, and applications to information securityLe désordre des itérations chaotiques Applications aux réseaux de capteurs, à la dissimulation d'information, et aux fonctions de hachage
Alternative Names
Christophe Guyeux wetenschapper

Languages
English (49)

French (6)

German (1)