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International audience; The aim of this work is to develop a completely integrated Lab-On-Chip (LOC) for easy, rapid and cost-effective immunoassays. The pathogen sensing system is composed of a microfluidic channel surrounded by planar microcoils which are responsible for the emission and the detection of magnetic fields. The system allows the detection and quantification of superparamagnetic beads used for immunoassays in a " sandwich " antigen-antibody configuration. Multiphysics simulations have been achieved and preliminary experimental results have allowed to validate the structure.

Forward osmosis is a water separation process that uses the natural energy of osmotic pressure to separate water from dissolved solutes through a semipermeable membrane. One of the major challenges using this process is the rejection water which contains high content of pollutants, hindering its practical application. Herein, for the first time, this work introduces a coupled electrochemical-physical process including iron-electrocoagulation/filtration/sedimentation as a cost-effective treatment to the forward osmosis reject water containing hexavalent chromium to be reclaimed. The synergistic treatment was optimized through a central composite design and response surface methodology to enhance hexavalent Cr removal and minimize operating costs, electrical energy consumption, and settled sludge volume. A 90.0% chromium removal was achieved under optimized conditions: electrolysis time of 59.7 min and current of 1.24 A (J = 6.32 mA cm−2). In addition, operating costs of 0.014 USD m−3, electrical energy consumption of 0.005 kWh m−3, and settled sludge volume of 445 mL L−1 were obtained.

Internet traffic recognition is an essential tool for access providers since recognizing traffic categories related to different data packets transmitted on a network help them define adapted priorities. That means, for instance, high priority requirements for an audio conference and low ones for a file transfer, to enhance user experience. As internet traffic becomes increasingly encrypted, the mainstream classic traffic recognition technique, payload inspection, is rendered ineffective. This paper uses machine learning techniques for encrypted traffic classification, looking only at packet size and time of arrival. Spiking neural networks (SNN), largely inspired by how biological neurons operate, were used for two reasons. Firstly, they are able to recognize time-related data packet features. Secondly, they can be implemented efficiently on neuromorphic hardware with a low energy footprint. Here we used a very simple feedforward SNN, with only one fully-connected hidden layer, and trained in a supervised manner using the newly introduced method known as Surrogate Gradient Learning. Surprisingly, such a simple SNN reached an accuracy of 95.9% on ISCX datasets, outperforming previous approaches. Besides better accuracy, there is also a very significant improvement on simplicity: input size, number of neurons, trainable parameters are all reduced by one to four orders of magnitude. Next, we analyzed the reasons for this good accuracy. It turns out that, beyond spatial (i.e. packet size) features, the SNN also exploits temporal ones, mostly the nearly synchronous (within a 200ms range) arrival times of packets with certain sizes. Taken together, these results show that SNNs are an excellent fit for encrypted internet traffic classification: they can be more accurate than conventional artificial neural networks (ANN), and they could be implemented efficiently on low power embedded systems., Comment: 22 pages, 8 figures. Neurocomputing (2022)

We have investigated the relationship between the excitation frequency and the depth of the flaws in our SQUID based NDE system to achieve optimum frequency for the detection of each particular flaw depth in the metallic samples. Also, the system resolution defined as the gap's distance between the two edges of a crack in the samples, have been experimentally investigated and the dependency to the frequency and the input excitation voltage amplitude was found. Afterwards, we have simulated the system in COMSOL Multiphysics using a basic model for the coils, and examined some special flaws to obtain their effects on the magnetic flux density caused by the eddy current distribution.

International audience; Deep learning architectures employ various regularization terms to handle different types of priors. Non-smooth regularization terms have shown promising performance in the deep learning architectures and a learning framework has recently been proposed to train autoencoders with such regularization terms. While this framework efficiently manages the non-smooth term during training through proximal operators, it is limited to autoencoders and suffers from low convergence speed due to several optimization sub-problems that must be solved in a row. In this paper, we address these issues by extending the framework to general feed-forward neural networks and introducing variable extrapolation which can dramatically increase the convergence speed in each sub-problem. We show that the proposed update rules converge to a critical point of the objective function under mild conditions. To compare the resulting framework with the previously proposed one, we consider the problem of training sparse autoencoders and robustifying deep neural architectures against both targeted and untargeted attacks. Simulations show superior performance in both convergence speed and final objective function value.

International audience; Thermal methods, particularly the in situ steam-based practices, used for oil production from oil sands and heavy oil reservoirs are the leading technology for enhanced oil recovery (EOR) in such reservoirs. However, a majority of these techniques, such as steam-assisted gravity drainage, cyclic steam stimulation, and steam flooding, are on the verge of exhaustion. Yet, the enduring interactions between rock and steam, bitumen recovery, and improving heavy oil recovery are the main challenges in the poststeam injection era. Here, we reviewed the experimental and field applications of the EOR methods and the recovery mechanisms behind each method in the poststeam injection era, in heavy oil reservoirs with different lithologies. Furthermore, several other thermal techniques, their applications, and their attributed pros and cons are discussed in this chapter.

International audience; Brain is the most complicated organ of body. It controls the activity of all other organs. Understanding its function and its language could give us a direct communication pathway for connecting with injured motor organ and it could be the core of functional repairing. Neurons are the vertices of a vast network that generates the brain signals. Neuronal recordings capture brain activity signatures. The processing of these signals can help to translate brain's language. Usually it follows three main stages: spike detection and extraction, spike sorting, and intention extraction from the encoded signal. In this work, we introduce an original idea based on Hidden Markov Models (HMM) which helps to improve the spike sorting stage. Our idea is a fast and simple method which uses Inter Spike Interval information besides spike waveforms to define a Hidden Markov Model that consecutive spikes should track.

Publisher Summary The linear mixing model, either without memory (instantaneous mixtures) or with memory (convolutive mixtures), is an approximated model, which is valid provided that the nonlinearities in the mixing system are weak, or the amplitudes of the signals are limited. In various situations, this approximation does not hold, for instance when one uses sensors with hard nonlinearities, or when signal levels lead to saturation of conditioning electronic circuits. Thus, it is relevant to consider the blind source separation problem in the more general framework of nonlinear mixtures. A natural extension of the linear independent component analysis method to the nonlinear case consists in estimating, by using only observations, a nonlinear transform such that the random vector has mutually independent components. Conformal mappings are often considered in the framework of functions of complex-valued variables, which are restricted to plane (two-dimensional) mappings. The postnonlinear mixtures are inspired by devices for which the sensors and their amplifiers used for signal conditioning are assumed to be nonlinear, for example due to saturation.

Signal machines form an abstract and idealized model of collision computing. Based on dimensionless signals moving on the real line, they model particle/signal dynamics in Cellular Automata. Each particle, or signal , moves at constant speed in continuous time and space. When signals meet, they get replaced by other signals. A signal machine defines the types of available signals, their speeds, and the rules for replacement in collision. A signal machine A simulates another one B if all the space-time diagrams of B can be generated from space-time diagrams of A by removing some signals and renaming other signals according to local information. Given any finite set of speeds S we construct a signal machine that is able to simulate any signal machine whose speeds belong to S . Each signal is simulated by a macro-signal , a ray of parallel signals. Each macro-signal has a main signal located exactly where the simulated signal would be, as well as auxiliary signals that encode its id and the collision rules of the simulated machine. The simulation of a collision, a macro-collision , consists of two phases. In the first phase, macro-signals are shrunk, and then the macro-signals involved in the collision are identified and it is ensured that no other macro-signal comes too close. If some do, the process is aborted and the macro-signals are shrunk, so that the correct macro-collision will eventually be restarted and successfully initiated. Otherwise, the second phase starts: the appropriate collision rule is found and new macro-signals are generated accordingly. Considering all finite sets of speeds S and their corresponding simulators provides an intrinsically universal family of signal machines.

International audience; In this paper, we have studied the effect of annealing under slightly oxidizing ambient (N2 + O2) on the structural and electrical characteristics of a limited number of silicon nanoparticles embedded in an ultra-thin SiO2 layer. These nanoparticles were synthesized by ultra-low energy (1 keV) ion implantation and annealing. Material characterization techniques including transmission electron microscopy (TEM), Fresnel imaging and spatially resolved electron energy loss spectroscopy (EELS) have been used to evaluate the effects of oxidation on structural characteristics of nanocrystal layer. Electrical transport characteristics have been measured on less than one hundred nanoparticles by exploiting a nanoscale MOS capacitor as a probe. Top electrode of this nanoscale capacitor (100 nm × 100 nm) was patterned over the samples by electron-beam nanolithography. Room temperature I–V characteristics of these structures exhibit discrete current peaks, which have been interpreted by quantized charging of the nanoparticles and electrostatic interaction between the trapped charges and the tunneling current. The effects of progressive oxidation on these current features has been studied and discussed.

International audience; In this paper, we have studied the effect of annealing under slightly oxidizing ambient (N2+O2) on the structural and electrical characteristics of a limited number of silicon nanoparticles embedded in an ultra-thin SiO2 layer. These nanoparticles were synthesized by ultra-low-energy (1 keV) ion implantation and annealing. Specific experimental methods have been used to characterize the ncs populations. They include transmission electron microscopy (TEM) Fresnel imaging for evaluating the distances and widths of interest and spatially resolved electron energy loss spectroscopy (EELS) using the spectrum-imaging mode of a scanning transmission electron microscope (STEM) to measure the size distribution and density of the ncs population. To perform electrical measurements of these particles, a nanoscale contact (100 nm × 100 nm) was patterned over the samples by electron-beam nanolithography. Room-temperature I–V characteristics of these nano-MOS structures exhibit discrete current peaks which have been associated with single-electron charging of the nanoparticles and electrostatic interaction of the trapped charges with the tunnelling current. The effect of the progressive oxidation of the Si nanoparticles on these I(V) characteristics has been studied and related to the nanocrystal characteristics.

The Doeblin Graph of a countable state space Markov chain describes the joint pathwise evolutions of the Markov dynamics starting from all possible initial conditions, with two paths coalescing when they reach the same point of the state space at the same time. Its Bridge Doeblin subgraph only contains the paths starting from a tagged point of the state space at all possible times. In the irreducible, aperiodic, and positive recurrent case, the following results are known: the Bridge Doeblin Graph is an infinite tree that is unimodularizable. Moreover, it contains a single bi-infinite path, which allows one to build a perfect sample of the stationary state of the Markov chain. The present paper is focused on the null recurrent case. It is shown that when assuming irreducibility and aperiodicity again, the Bridge Doeblin Graph is either an infinite tree or a forest made of a countable collection of infinite trees. In the first case, the infinite tree in question has a single end, is not unimodularizable in general, but is always locally unimodular. These key properties are used to study the stationary regime of several measure-valued random dynamics on this Bridge Doeblin Tree. The most important ones are the taboo random dynamics, which admits as steady state a random measure with mean measure equal to the invariant measure of the Markov chain, and the potential random dynamics which is a random extension of the classical potential measure, with a mean measure equal to infinity at every point of the state space., 31 Pages, 8 figures

This study addresses a many-to-many hub location-routing problem where the best-found locations of hubs and the best-found tours for each hub are determined with simultaneous pickup and delivery within the hard time window. To find practical solutions, the hubs and transportation fleet have constrained capacity, in which every node can be serviced by multiple allocations with the hard time window and limited tour length. First, a bi-objective optimization model is proposed to balance travel costs among different routes and to minimize the total sum of fixed costs of locating hubs, the costs of handling, traveling, assigning, and transportation costs. The problem is then solved using an augmented e-constraint technique for small to medium size instances of the problem. Due to the NP-hardness nature of the problem, the proposed multi-objective optimization model is solved by a multi-objective imperialist competitive algorithm (MOICA). To show the superior performance of the MOICA, the solutions are compared with those obtained by the non-dominated sorting genetic algorithm (NSGA-II). For the large-scale problem instances, the comparative results indicate that the MOICA can indeed provide better Pareto optimal solutions compared to NSGA-II for the large-scale problem instances.

Permutation and scale ambiguity are relevant issues in tensor decomposition and source separation algorithms. Although these ambiguities are inevitable when working on real data sets, it is preferred to eliminate these uncertainties for evaluating algorithms on synthetic data sets. The existing methods and measures for this purpose are either greedy and unreliable or computationally costly. In this paper, we propose a new performance index, called CorrIndex, whose reliability can be proved theoretically. Moreover, compared to the previous methods and measures, it has the lowest computational cost. By providing two theorems and a table of comparisons, we will show these advantages of CorrIndex compared to other measures.

Maryam Zarghami Dehaghani,1,* Babak Bagheri,2,* Farrokh Yousefi,3 Abbasali Nasiriasayesh,4 Amin Hamed Mashhadzadeh,5 Payam Zarrintaj,6 Navid Rabiee,7 Mojtaba Bagherzadeh,7 Vanessa Fierro,8 Alain Celzard,8 Mohammad Reza Saeb,5 Ebrahim Mostafavi9,10 1School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran; 2Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea; 3Department of Physics, University of Zanjan, Zanjan, 45195-313, Iran; 4Industrial Management Institute, Tehran, Iran; 5Center of Excellence in Electrochemistry, School of Chemistry, College of Science, University of Tehran, Tehran, 14155-6455, Iran; 6School of Chemical Engineering, Oklahoma State University, Stillwater, OK, 74078, USA; 7Department of Chemistry, Sharif University of Technology, Tehran, Iran; 8Université De Lorraine, CNRS, IJL, Epinal, 88000, France; 9Stanford Cardiovascular Institute, Stanford, CA, USA; 10Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA*These authors contributed equally to this workCorrespondence: Ebrahim MostafaviStanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USAEmail ebimsv@stanford.eduAmin Hamed MashhadzadehCenter of Excellence in Electrochemistry, School of Chemistry, College of Science, University of Tehran, Tehran, 14155-6455, IranEmail amin.hamed.m@gmail.comIntroduction: Nanotube-based drug delivery systems have received considerable attention because of their large internal volume to encapsulate the drug and the ability to penetrate tissues, cells, and bacteria. In this regard, understanding the interaction between the drug and the nanotube to evaluate the encapsulation behavior of the drug in the nanotube is of crucial importance.Methods: In this work, the encapsulation process of the cationic antimicrobial peptide named cRW3 in the biocompatible boron nitride nanotube (BNNT) was investigated under the Canonical ensemble (NVT) by molecular dynamics (MD) simulation.Results: The peptide was absorbed into the BNNT by van der Waals (vdW) interaction between cRW3 and the BNNT, in which the vdW interaction decreased during the simulation process and reached the value of − 142.7 kcal·mol− 1 at 4 ns.Discussion: The increase in the potential mean force profile of the encapsulated peptide during the pulling process of cRW3 out of the nanotube showed that its insertion into the BNNT occurred spontaneously and that the inserted peptide had the desired stability. The energy barrier at the entrance of the BNNT caused a pause of 0.45 ns when half of the peptide was inside the BNNT during the encapsulation process. Therefore, during this period, the peptide experienced the weakest movement and the smallest conformational changes.Keywords: boron nitride nanotube, drug delivery, antimicrobial peptide, molecular dynamic simulation, encapsulation

In this paper, a method for apnea bradycardia detection in preterm infants is presented based on coupled hidden semi Markov model (CHSMM). CHSMM is a generalization of hidden Markov models (HMM) used for modeling mutual interactions among different observations of a stochastic process through using finite number of hidden states with corresponding resting time. We introduce a new set of equations for CHSMM to be integrated in a detection algorithm. The detection algorithm was evaluated on a simulated data to detect a specific dynamic and on a clinical dataset of electrocardiogram signals collected from preterm infants for early detection of apnea bradycardia episodes. For simulated data, the proposed algorithm was able to detect the desired dynamic with sensitivity of 96.67% and specificity of 98.98%. Furthermore, the method detected the apnea bradycardia episodes with 94.87% sensitivity and 96.52% specificity with mean time delay of 0.73 s. The results show that the algorithm based on CHSMM is a robust tool for monitoring of preterm infants in detecting apnea bradycardia episodes. Graphical Abstract Apnea Bradycardia detection using Coupled hidden semi Markov Model from electrocardiography. In this model, a sequence of hidden states is assigned to each observation based on the effects of previous states of all observations.

This work introduces two new notions of dimension, namely the unimodular Minkowski and Hausdorff dimensions, which are inspired from the classical analogous notions. These dimensions are defined for unimodular discrete spaces, introduced in this work, which provide a common generalization to stationary point processes under their Palm version and unimodular random rooted graphs. The use of unimodularity in the definitions of dimension is novel. Also, a toolbox of results is presented for the analysis of these dimensions. In particular, analogues of Billingsley's lemma and Frostman's lemma are presented. These last lemmas are instrumental in deriving upper bounds on dimensions, whereas lower bounds are obtained from specific coverings. The notions of unimodular Hausdorff size, which is a discrete analogue of the Hausdorff measure, and unimodular dimension function are also introduced. This toolbox allows one to connect the unimodular dimensions to other notions such as volume growth rate, discrete dimension and scaling limits. It is also used to analyze the dimensions of a set of examples pertaining to point processes, branching processes, random graphs, random walks, and self-similar discrete random spaces. Further results of independent interest are also presented, like a version of the max-flow min-cut theorem for unimodular one-ended trees and a weak form of pointwise ergodic theorems for all unimodular discrete spaces., Comment: 89 pages, 1 figure. This version of the paper is a merging of the previous version with arXiv:1808.02551. Earlier versions of this paper were titled `On the Dimension of Unimodular Discrete Spaces, Part I: Definitions and Basic Properties'

International audience; Microchannels created in poly-2-hydroxyethyl methacrylate (poly(HEMA)) hydrogels have the potential to prevent dry-eye disease by facilitating tear exchange. Biosensors are further demonstrated for wearable biosensing applications.Microchannels in hydrogels play an essential role in enabling a smart contact lens. However, microchannels have rarely been created in commercial hydrogel contact lenses due to their sensitivity to conventional microfabrication techniques. Here, we report the fabrication of microchannels in poly(2-hydroxyethyl methacrylate) (poly(HEMA)) hydrogels that are used in commercial contact lenses with a three-dimensional (3D) printed mold. We investigated the corresponding capillary flow behaviors in these microchannels. We observed different capillary flow regimes in these microchannels, depending on their hydration level. In particular, we found that a peristaltic pressure could reinstate flow in a dehydrated channel, indicating that the motion of eye-blinking may help tears flow in a microchannel-containing contact lens. Colorimetric pH and electrochemical Na+ sensing capabilities were demonstrated in these microchannels. This work paves the way for the development of microengineered poly(HEMA) hydrogels for various biomedical applications such as eye-care and wearable biosensing.

International audience; In this study, the effect of distance-dependent dispersion coefficients on density-driven flow is investigated. The linear asymptotic model, which assumes that dispersivities increase linearly with distance from the source of contamination and reach asymptotic values at a large asymptotic distance, is employed. An in-house numerical model is adapted to handle distance-dependent dispersion. The effect of asymptotic-dispersion on aquifer contamination is analyzed for two tests: (i) a seawater intrusion problem in a coastal aquifer and (ii) a leachate transport problem from a surface deposit site. Global Sensitivity Analysis (GSA) combined with the Polynomial Chaos Expansion (PCE) surrogate modeling is conducted to assess the influence of the dispersion coefficients on the contamination plume for both configurations. For the seawater intrusion problem, the results show that the length of the toe is mainly controlled by the asymptotic transverse dispersivity whereas the spread of the concentration is sensitive to the asymptotic longitudinal dispersivity and the asymptotic dispersivity distance. The latter is the most important parameter controlling the amount of salt which intrudes into the aquifer. For the leachate transport problem, the results show that the asymptotic longitudinal dispersivity coefficient does not affect the concentration distribution. The asymptotic dispersivity distance has a strong effect on the total amount of contaminant that enters the aquifer. This effect can be three times more important than the effect of the asymptotic transverse dispersivity. These findings are likely to be helpful for the investigation and management of density-driven flow problems.

Cell survival during the early stages of transplantation and before new blood vessels formation is a major challenge in translational applications of three-dimensional (3D) bioprinted tissues. Supplementing oxygen (O(2)) to transplanted cells via an O(2) generating source such as calcium peroxide (CPO) is an attractive approach to ensure cell viability. Calcium peroxide also produces calcium hydroxide which reduces the viscosity of bioinks, which is a limiting factor for bioprinting. Therefore, adapting this solution into 3D bioprinting is of significant challenge. In this study, we developed a gelatin methacryloyl (GelMA) bioink that was optimized in terms of pH and viscosity. The improved rheological properties led to the production of a robust bioink suitable for 3D bioprinting and controlled O(2) release. In addition, we characterized O(2) release, bioprinting conditions and mechanical performance of hydrogels having different CPO concentrations. As a proof of concept study, fibroblasts and cardiomyocytes were bioprinted using GelMA bioink containing CPO. Viability and metabolic activity of printed cells were checked after seven days of culture under hypoxic condition. The results showed that the addition of CPO improves the metabolic activity and viability of cells in bioprinted constructs under hypoxic condition.

International audience; The efficient planning of search and rescue (SAR) operations is highly impactful in the disaster response phase, which offers a limited time window with a declining chance for saving trapped people. The present paper introduces a new robust decision support framework for planning SAR resource deployment in post-disaster districts. A two-stage decomposition approach is applied to formulate the problem as iterative interrelated stages of mixed-integer programming (MIP) models. The first stage presents a robust multi-period allocation model for maximizing fair and effective demand coverage in the affected districts during the entire planning horizon. It takes into account the time-sensitiveness of the operations via a time-dependent demand satisfaction measure and incorporates resource transshipment optimization. The second stage optimizes the routing of the resources allocated in the first stage for each district during the upcoming period. It aims to minimize the weighted sum of SAR demand fulfillment times under consideration of secondary destruction risk, resource collaboration, and rest time requirements. At the end of each period, the proposed framework can be re-executed to capture updated resource, demand, and travel time parameters. To tackle the environment’s inherent uncertainty, an interval-based robust optimization approach is adopted. The proposed framework is solved and analyzed for an urban zone in Iran under an earthquake scenario. Results show that the proposed robust models have superior performance compared to a deterministic approach for adaptation to an uncertain disaster environment. More importantly, they prove to be a strong analysis tool for providing helpful managerial insights for the mitigation and preparedness phases.

International audience; Apnea-bradycardia (AB) is a common complication in prematurely born infants, which is associated with reduced survival and neurodevelopmental outcomes. Thus, early detection or predication of AB episodes is critical for initiating preventive interventions. To develop automatic real-time operating systems for early detection of AB, recent advances in signal processing can be employed. Hidden Markov Models (HMM) are probabilistic models with the ability of learning different dynamics of the real time-series such as clinical recordings. In this study, a hierarchy of HMMs named as layered HMM was presented to detect AB episodes from pre-processed single-channel Electrocardiography (ECG). For training the hierarchical structure, RR interval, and width of QRS complex were extracted from ECG as observations. The recordings of 32 premature infants with median 31.2 (29.7, 31.9) weeks of gestation were used for this study. The performance of the proposed layered HMM was evaluated in detecting AB. The best average accuracy of 97.14 +/- 0.31% with detection delay of - 5.05 +/- 0.41 s was achieved. The results show that layered structure can improve the performance of the detection system in early detecting of AB episodes. Such system can be incorporated for more robust long-term monitoring of preterm infants.

International audience; Visibility graph of a simple polygon is a graph with the same vertex set in which there is an edge between a pair of vertices if and only if the segment through them lies completely inside the polygon. Each pair of adjacent vertices on the boundary of the polygon are assumed to be visible. Therefore, the visibility graph of each polygon always contains its boundary edges. This implies that we have always a Hamiltonian cycle in a visibility graph which determines the order of vertices on the boundary of the corresponding polygon. In this paper, we propose a polynomial time algorithm for determining such a Hamiltonian cycle for a pseudo-triangle polygon from its visibility graph.

International audience; We study a weighted balanced version of the k-center problem, where each center has a fixed capacity, and each element has an arbitrary demand. The objective is to assign demands of the elements to the centers, so as the total demand assigned to each center does not exceed its capacity, while the maximum distance between centers and their assigned elements is minimized. We present a deterministic O(1)-approximation algorithm for this generalized version of the k-center problem in the distributed setting, where data is partitioned among a number of machines. Our algorithm substantially improves the approximation factor of the current best randomized algorithm available for the problem. We also show that the approximation factor of our algorithm can be improved to $$5+\varepsilon $$, when the underlying metric space has a bounded doubling dimension.

In this work, the optical responses of Fe3O4@Au and Fe3O4@Ag are comprehensively investigated using the discrete dipole approximation. It is found that the resonance wavelength and absorption efficiency strongly depend on the composition of the core and shell, geometry of the nanoparticles, core to particle volume ratio, core radius and shell thickness. The strongest impact is due to the shell material, the shape of the nanoparticles and their combination. When the composition of the shell is changed from gold to silver, instead of one fundamental resonance peak the absorption spectrum shows two, corresponding to the bonding plasmon mode at the nanoparticle–environment interface and antibonding mode at the core–shell interface. The results also reveal a much higher tunability of the resonance wavelength as well as larger enhancement of the absorption efficiency as the spherical nanoparticle stretches to a prolate ellipsoidal shape. Furthermore, higher order plasmon modes appear in the absorption spectrum of prolate nanoparticles with a large aspect ratio. The existence of several plasmon modes together with wide tunability makes these nanoparticles good candidates for applications where two or more simultaneous absorption bands at different frequencies are required. These results might motivate experimentalists to optimize the synthesis of magnetic-plasmonic core–shell NPs in different applications as far as absorption is concerned.

International audience; In dictionary learning, a desirable property for the dictionary is to be of low mutual and average coherences. Mutual coherence is defined as the maximum absolute correlation between distinct atoms of the dictionary, whereas the average coherence is a measure of the average correlations. In this paper, we consider a dictionary learning problem regularized with the average coherence and constrained by an upper-bound on the mutual coherence of the dictionary. Our main contribution is then to propose an algorithm for solving the resulting problem based on convexly approximating the cost function over the dictionary. Experimental results demonstrate that the proposed approach has higher convergence rate and lower representation error (with a fixed sparsity parameter) than other methods, while yielding similar mutual and average coherence values.

International audience; Book Front Matter of LNCS 12281

Gallium nitride (GaN) transistors have demonstrated their potential as a high switching frequency, high power density, and high-efficiency power device. There has been substantial research undertaken on the switching and conduction losses calculation, where most studies have calculated them using an analytical approach. This paper presents an interpolation-based method to calculate switching and conduction losses. This interpolation method employs a combination of the GaN transistor and driving parameters such as parasitic capacitances, parasitic inductances, gate resistors, drain-to-source voltage, instantaneous current, and temperature. A value for switching loss is considered as a base value and the different combinations of circuits will be reached by using interpolated equations. The second aim of this study is to investigate the temperature-dependency of the ON state resistance: two values at different temperatures are considered and ON resistance will be obtained by linear interpolation. Finally, the accuracy of the proposed model is validated by using some scenarios on LTSPICE simulations.

In this work, we consider the fundamental problem of reachability analysis over imperative programs with real variables. The reachability property requires that a program can reach certain target states during its execution. Previous works that tackle reachability analysis are either unable to handle programs consisting of general loops (e.g. symbolic execution), or lack completeness guarantees (e.g. abstract interpretation), or are not automated (e.g. incorrectness logic/reverse Hoare logic). In contrast, we propose a novel approach for reachability analysis that can handle general programs, is (semi-)complete, and can be entirely automated for a wide family of programs. Our approach extends techniques from both invariant generation and ranking-function synthesis to reachability analysis through the notion of (Universal) Inductive Reachability Witnesses (IRWs/UIRWs). While traditional invariant generation uses over-approximations of reachable states, we consider the natural dual problem of under-approximating the set of program states that can reach a target state. We then apply an argument similar to ranking functions to ensure that all states in our under-approximation can indeed reach the target set in finitely many steps. On the theoretical level, we first show that our IRW/UIRW-based approach is sound and complete for reachability analysis of imperative programs. Then, we focus on linear and polynomial programs and develop automated methods for synthesizing linear and polynomial IRWs/UIRWs. In the linear case, our techniques are based on Farkas' lemma. For the polynomial case, our approach utilizes three classical theorems in polyhedral geometry and real algebraic geometry, namely Handelman's Theorem, Hilbert's Nullstellensatz and Putinar's Positivstellensatz. To the best of our knowledge, such a combination of these theorems to obtain algorithms for reachability analysis in programs is a novel contribution. On the practical side, our experimental results show that our automated approaches can efficiently prove complex reachability objectives over various benchmarks.

Transits in the planetary system WASP-4 were recently found to occur 80s earlier than expected in observations from the TESS satellite. We present 22 new times of mid-transit that confirm the existence of transit timing variations, and are well fitted by a quadratic ephemeris with period decay dP/dt = -9.2 +/- 1.1 ms/yr. We rule out instrumental issues, stellar activity and the Applegate mechanism as possible causes. The light-time effect is also not favoured due to the non-detection of changes in the systemic velocity. Orbital decay and apsidal precession are plausible but unproven. WASP-4b is only the third hot Jupiter known to show transit timing variations to high confidence. We discuss a variety of observations of this and other planetary systems that would be useful in improving our understanding of WASP-4 in particular and orbital decay in general., Comment: Accepted for publication to MNRAS Main Journal. 7 pages, 2 colour figures, 1 table. Version 2 is the revised version after the refereeing process, which includes changes to some of the conclusions of the paper

International audience; In this paper, we propose a novel method for extracting fiducial points (FPs) of the beats in electrocardiogram (ECG) signals using switching Kalman filter (SKF). In this method, according to McSharry's model, ECG waveforms (P-wave, QRS complex and T-wave) are modeled with Gaussian functions and ECG baselines are modeled with first order auto regressive models. In the proposed method, a discrete state variable called " switch " is considered that affects only the observation equations. We denote a mode as a specific observation equation and switch changes between 7 modes and corresponds to different segments of an ECG beat. At each time instant, the probability of each mode is calculated and compared among two consecutive modes and a path is estimated, which shows the relation of each part of the ECG signal to the mode with the maximum probability. ECG FPs are found from the estimated path. For performance evaluation, the Physionet QT database is used and the proposed method is compared with methods based on wavelet transform, partially collapsed Gibbs sampler (PCGS) and extended Kalman filter. For our proposed method, the mean error and the root mean square error across all FPs are 2 ms (i.e. less than one sample) and 14 ms, respectively. These errors are significantly smaller than those obtained using other methods. The proposed method achieves lesser RMSE and smaller variability with respect to others.

International audience; —This paper is concerned with designing efficient algorithms for recovering sparse signals from noisy underdeter-mined measurements. More precisely, we consider minimization of a non-smooth and non-convex sparsity promoting function subject to an error constraint. To solve this problem, we use an alternating minimization penalty method, which ends up with an iterative proximal-projection approach. Furthermore, inspired by accelerated gradient schemes for solving convex problems, we equip the obtained algorithm with a so-called extrapolation step to boost its performance. Additionally, we prove its convergence to a critical point. Our extensive simulations on synthetic as well as real data verify that the proposed algorithm considerably outperforms some well-known and recently proposed algorithms.

Nanofluids-dispersions of nanometer-sized particles in a liquid medium-have been proposed for a wide variety of thermal management applications. It is known that a solid-like nanolayer of liquid of typical thicknesses of 0.5-1 nm surrounding the colloidal nanoparticles can act as a thermal bridge between the nanoparticle and the bulk liquid. Yet, its effect on the nanofluid viscosity has not been elucidated so far. In this article, we compute the local viscosity of the nanolayer using equilibrium molecular dynamics based on the Green-Kubo formula. We first assess the validity of the method to predict the viscosity locally. We apply this methodology to the calculation of the local viscosity in the immediate vicinity of a metallic nanoparticle for a wide range of solid-liquid interaction strength, where a nanolayer of thickness 1 nm is observed as a result of the interaction with the nanoparticle. The viscosity of the nanolayer, which is found to be higher than its corresponding bulk value, is directly dependent on the solid-liquid interaction strength. We discuss the origin of this viscosity enhancement and show that the liquid density increment alone cannot explain the values of the viscosity observed. Rather, we suggest that the solid-like structure of the distribution of the liquid atoms in the vicinity of the nanoparticle contributes to the nanolayer viscosity enhancement. Finally, we observe a failure of the Stokes-Einstein relation between viscosity and diffusion close to the wall, depending on the liquid-solid interaction strength, which we rationalize in terms of the hydrodynamic slip.

International audience; 1-D compound metallic grating (CMG) is a periodic structure with more than one slit in each period. When CMG is combined with a graphene sheet as its cover, the incident light is effectively coupled to the plasmons in graphene which in turn can result in strong manipulation of light for both major polarizations. We show that tunable phase resonance and perfect absorption of the incident light are interesting outcomes of this manipulation. In this paper, we demonstrate that fano-like phase resonances which can be observed in CMGs under transverse magnetic polarized incident wave are tuned by changing the Fermi level of graphene. It is shown that while the spectral position of the phase resonances can be shifted up to several gigahertzes, their peak to peak amplitudes are tuned from ~0.9 to ~0.1. On the other hand, we design a graphene-covered CMG, which is able to perfectly absorb both major polarizations of incident wave in two separate bands; hence, providing the opportunity for designing multi-band/wide-band absorbers. We have developed a circuit model for the analysis of the structure. Parameters of the model are derived explicitly and analytically for both major polarizations. Our results are verified through comparison against results of the full-wave simulations.

In this paper, we propose a novel method for extracting fiducial points (FPs) of electrocardiogram (ECG) signals. We propose the use of multi hidden Markov model (MultiHMM) as opposed to the traditional use of Classic HMM. In the MultiHMM method, each segment of an ECG beat is represented by a separate ergodic continuous density HMM. Each HMM has different state number and is trained separately. In the test step, the log-likelihood of two consecutive HMMs is compared and a path is estimated, which shows the correspondence of each part of the ECG signal to the HMM with the maximum log-likelihood. Fiducial points are estimated from the obtained path. For performance evaluation, the Physionet QT database and a Swine ECG database are used and the proposed method is compared with the Classic HMM and a method based on partially collapsed Gibbs sampler (PCGS). In our evaluation using the QT database, we also compare the results with low-pass differentiation, hybrid feature extraction algorithm, a method based on the wavelet transform and three HMM-based approaches. For the Swine database, the root mean square error (RMSE) values, across all FPs for MultiHMM, Classic HMM and PCGS methods are 13, 21 and 40ms, respectively and the MultiHMM exhibits smaller error variability than other methods. For the QT database, RMSE values for MultiHMM, Classic HMM, Wavelet and PCGS methods are 10, 17, 26 and 38ms, respectively. Our results demonstrate that our proposed MultiHMM approach outperforms other benchmark methods that exist in the literature; therefore can be used in practical ECG fiducial point extraction. HighlightsA method is proposed to extract fiducial points of ECG signals.Each segment of an ECG beat is represented by a separate ergodic continuous density HMM.It compares the log-likelihood of two consecutive HMMs and estimate a multi-level path.Fiducial points are estimated from this path.The method is applied to the Physionet QT database and a Swine ECG database.The method outperforms other benchmark methods.

There has recently been an increasing desire to evaluate neural networks locally on computationally-limited devices in order to exploit their recent effectiveness for several applications; such effectiveness has nevertheless come together with a considerable increase in the size of modern neural networks, which constitute a major downside in several of the aforementioned computationally-limited settings. There has thus been a demand of compression techniques for neural networks. Several proposal in this direction have been made, which famously include hashing-based methods and pruning-based ones. However, the evaluation of the efficacy of these techniques has so far been heterogeneous, with no clear evidence in favor of any of them over the others. The goal of this work is to address this latter issue by providing a comparative study. While most previous studies test the capability of a technique in reducing the number of parameters of state-of-the-art networks , we follow [CWT + 15] in evaluating their performance on basic ar-chitectures on the MNIST dataset and variants of it, which allows for a clearer analysis of some aspects of their behavior. To the best of our knowledge, we are the first to directly compare famous approaches such as HashedNet, Optimal Brain Damage (OBD), and magnitude-based pruning with L1 and L2 regularization among them and against equivalent-size feed-forward neural networks with simple (fully-connected) and structural (convolutional) neural networks. Rather surprisingly, our experiments show that (iterative) pruning-based methods are substantially better than the HashedNet architecture, whose compression doesn't appear advantageous to a carefully chosen convolutional network. We also show that, when the compression level is high, the famous OBD pruning heuristics deteriorates to the point of being less efficient than simple magnitude-based techniques.

We consider the Relative-Majority Problem (also known as Plurality), in which, given a multi-agent system where each agent is initially provided an input value out of a set of $k$ possible ones, each agent is required to eventually compute the input value with the highest frequency in the initial configuration. We consider the problem in the general Population Protocols model in which, given an underlying undirected connected graph whose nodes represent the agents, edges are selected by a globally fair scheduler. The state complexity that is required for solving the Plurality Problem (i.e., the minimum number of memory states that each agent needs to have in order to solve the problem), has been a long-standing open problem. The best protocol so far for the general multi-valued case requires polynomial memory: Salehkaleybar et al. (2015) devised a protocol that solves the problem by employing $O(k 2^k)$ states per agent, and they conjectured their upper bound to be optimal. On the other hand, under the strong assumption that agents initially agree on a total ordering of the initial input values, Gasieniec et al. (2017), provided an elegant logarithmic-memory plurality protocol. In this work, we refute Salehkaleybar et al.'s conjecture, by providing a plurality protocol which employs $O(k^{11})$ states per agent. Central to our result is an ordering protocol which allows to leverage on the plurality protocol by Gasieniec et al., of independent interest. We also provide a $\Omega(k^2)$-state lower bound on the necessary memory to solve the problem, proving that the Plurality Problem cannot be solved within the mere memory necessary to encode the output., Comment: 14 pages, accepted at CIAC 2019

The notions of unimodular Minkowski and Hausdorff dimensions are defined in [5] for unimodular random discrete metric spaces. The present paper is focused on the connections between these notions and the polynomial growth rate of the underlying space. It is shown that bounding the dimension is closely related to finding suitable equivariant weight functions (i.e., measures) on the underlying discrete space. The main results are unimodular versions of the mass distribution principle and Billingsley's lemma, which allow one to derive upper bounds on the unimodular Hausdorff dimension from the growth rate of suitable equivariant weight functions. Also, a unimodular version of Frostman's lemma is provided, which shows that the upper bound given by the unimodular Billingsley lemma is sharp. These results allow one to compute or bound both types of unimodular dimensions in a large set of examples in the theory of point processes, unimodular random graphs, and self-similarity. Further results of independent interest are also presented, like a version of the max-flow min-cut theorem for unimodular one-ended trees.

This work is focused on large scale properties of infinite graphs and discrete subsets of the Euclidean space. It presents two new notions of dimension, namely the unimodular Minkowski and Hausdorff dimensions, which are inspired by the classical Minkowski and Hausdorff dimensions. These dimensions are defined for unimodular discrete spaces, which are defined in this work as a class of random discrete metric spaces with a distinguished point called the origin. These spaces provide a common generalization to stationary point processes under their Palm version and unimodular random rooted graphs. The main novelty is the use of unimodularity in the definitions where it suggests replacing the infinite sums pertaining to coverings by large balls by the expectation of certain random variables at the origin. In addition, the main manifestation of unimodularity, that is the mass transport principle, is the key element in the proofs and dimension evaluations. The work is structured in three companion papers which are called Part I-III. Part I (the current paper) introduces unimodular discrete spaces, the new notions of dimensions, and some of their basic properties. Part II is focused on the connection between these dimensions and the growth rate of balls. In particular, it gives versions of the mass distribution principle, Billingsley's lemma, and Frostman's lemma for unimodular discrete spaces. Part III establishes connections with other notions of dimension. It also discusses ergodicity and the non-ergodic cases in more detail than the first two parts. Each part contains a comprehensive set of examples pertaining to the theory of point processes, unimodular random graphs, and self-similarity, where the dimensions in question are explicitly evaluated or conjectured.

National audience; ext mining is addressed by identifying multiview models. Following the recent works of A.Anandkumar, we show that the algorithms previously proposed exhibit important drawbacks. The algorithm subsequently proposed permits to decompose a nonnegative tensor built with sample joint probabilities. It compares favorably to the former on synthetically generated data sets.

There has recently been an increasing desire to evaluate neural networks locally on computationally-limited devices in order to exploit their recent effectiveness for several applications; such effectiveness has nevertheless come together with a considerable increase in the size of modern neural networks, which constitute a major downside in several of the aforementioned computationally-limited settings. There has thus been a demand of compression techniques for neural networks. Several proposal in this direction have been made, which famously include hashing-based methods and pruning-based ones. However, the evaluation of the efficacy of these techniques has so far been heterogeneous, with no clear evidence in favor of any of them over the others. The goal of this work is to address this latter issue by providing a comparative study. While most previous studies test the capability of a technique in reducing the number of parameters of state-of-the-art networks , we follow [CWT + 15] in evaluating their performance on basic ar-chitectures on the MNIST dataset and variants of it, which allows for a clearer analysis of some aspects of their behavior. To the best of our knowledge, we are the first to directly compare famous approaches such as HashedNet, Optimal Brain Damage (OBD), and magnitude-based pruning with L1 and L2 regularization among them and against equivalent-size feed-forward neural networks with simple (fully-connected) and structural (convolutional) neural networks. Rather surprisingly, our experiments show that (iterative) pruning-based methods are substantially better than the HashedNet architecture, whose compression doesn't appear advantageous to a carefully chosen convolutional network. We also show that, when the compression level is high, the famous OBD pruning heuristics deteriorates to the point of being less efficient than simple magnitude-based techniques.

© 2019 Elsevier B.V. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This author accepted manuscript is made available following 24 month embargo from date of publication (February 2019) in accordance with the publisher’s archiving policy, In this study we use polynomial chaos expansion (PCE) to perform uncertainty analysis for seawater intrusion (SWI) in fractured coastal aquifers (FCAs) which is simulated using the coupled discrete fracture network (DFN) and variable-density flow (VDF) models. The DFN-VDF model requires detailed discontinuous analysis of the fractures. In real field applications, these characteristics are usually uncertain which may have a major effect on the predictive capability of the model. Thus, we perform global sensitivity analysis (GSA) to provide a preliminary assessment on how these uncertainties can affect the model outputs. As our conceptual model, we consider fractured configurations of the Henry Problem which is widely used to understand SWI processes. A finite element DFN-VDF model is developed in the framework of COMSOL Multiphysics®. We examine the uncertainty of several SWI metrics and salinity distribution due to the incomplete knowledge of fracture characteristics. PCE is used as a surrogate model to reduce the computational burden. A new sparse PCE technique is used to allow for high polynomial orders at low computational cost. The Sobol’ indices (SIs) are used as sensitivity measures to identify the key variables driving the model outputs uncertainties. The proposed GSA methodology based on PCE and SIs is useful for identifying the source of uncertainties on the model outputs with an affordable computational cost and an acceptable accuracy. It shows that fracture hydraulic conductivity is the first source of uncertainty on the salinity distribution. The imperfect knowledge of fracture location and density affects mainly the toe position and the total flux of saltwater entering the aquifer. Marginal effects based on the PCE are used to understand the effects of fracture characteristics on SWI. The findings provide a technical support for monitoring, controlling and preventing SWI in FCAs.

International audience; Text mining, as a special case of data mining, refers to the estimation of knowledge or parameters necessary for certain purposes, such as unsupervised clustering by observing various documents. In this context, the topic of a document can be seen as a hidden variable, and words are multi-view variables related to each other by a topic. The main goal in this paper is to estimate the probability of topics, and conditional probability of words given topics. To this end, we use non negative Canonical Polyadic (CP) decomposition of a third order moment tensor of observed words. Our computer simulations show that the proposed algorithm has better performance compared to a previously proposed algorithm, which utilizes the Robust tensor power method after whitening by second order moment. Moreover, as our cost function includes the non negativity constraint on estimated probabilities, we never obtain negative values in our estimated probabilities , whereas it is often the case with the power method combined with deflation. In addition, our algorithm is capable of handling over-complete cases, where the number of hidden variables is larger than that of multi-view variables, contrary to deflation-based techniques. Further, the method proposed therein supports a larger over-completeness compared to modified versions of the tensor power method, which has been customized to handle over-complete case.

In this study, a new series of assessments and evaluations of the Dual-Miller cycle is performed. Furthermore, the specified output power and the thermal performance associated with the engine are determined. Besides, multi-objective optimization of thermal efficiency, ecological coefficient of performance (ECOP) and ecological function ( E u n ) by means of NSGA-II technique and thermodynamic analysis are presented. The Pareto optimal frontier obtaining the best optimum solution is identified by fuzzy Bellman-Zadeh, Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision-making techniques. Based on the results, performances of dual-Miller cycles and their optimization are improved. For the results of the condition that (n < k) the best point has been LINMAP answer. The thermal efficiency for this point has been 0.5388. In addition, ECOP and E u n have been 1.6899 and 279.221, respectively. For the results of the condition that (n > k) the best point has been LINMAP and TOPSIS answer. The thermal efficiency for this point has been 0.5385. Also, ECOP and E u n have been 1.6875 and 279.7315, respectively. Furthermore, the errors are examined through comparison of the average and maximum errors of the two scenarios.

International audience; In this paper, an FPGA-controlled fault tolerant back-to-back converter for DFIG-based wind energy conversion application is studied. Before an open-circuit failure in one of the semiconductors, the fault tolerant converter operates as a conventional back-to-back six-leg one. After the fault occurrence in one of the switches, the converter will continue its operation with the remaining five healthy legs. Design, implementation, simulation and experimental verification of a reconfigurable control strategy for the fault tolerant six/five leg converter used in wind energy conversion are discussed. The proposed reconfigurable control strategy allows the uninterrupted operation of the converter with minimum affection from an open-circuit switch failure in one of the semiconductors. Software reconfiguration is also necessary in PWM signal generation unit, to assure that proper gate signals are calculated and generated based on the actual five-leg structure Simulations and experimental tests are carried out and the results are presented and compared. They all confirm the capability of the studied reconfigurable control and proposed fault tolerant architecture in ensuring the system's service continuity.

In this paper, we present a novel framework for the coupled hidden Markov model (CHMM), based on the forward and backward recursions and conditional probabilities, given a multidimensional observation. In the proposed framework, the interdependencies of states networks are modeled with Markovian-like transition laws that influence the evolution of hidden states in all channels. Moreover, an offline inference approach by maximum likelihood estimation is proposed for the learning procedure of model parameters. To evaluate its performance, we first apply the CHMM model to classify and detect disturbances using synthetic data generated by the FitzHugh–Nagumo model. The average sensitivity and specificity of the classification are above $93.98\%$ and $95.38\%$ and those of the detection reach $94.49\%$ and $99.34\%$ , respectively. The method is also evaluated using a clinical database composed of annotated physiological signal recordings of neonates suffering from apnea-bradycardia. Different combinations of beat-to-beat features extracted from electrocardiographic signals constitute the multidimensional observations for which the proposed CHMM model is applied, to detect each apnea bradycardia episode. The proposed approach is finally compared to other previously proposed HMM-based detection methods. Our CHMM provides the best performance on this clinical database, presenting an average sensitivity of $95.74\%$ and specificity of $91.88\%$ while it reduces the detection delay by $-$ 0.59 s.

International audience; This paper considers Pressure Oscillation (PO) experiments for which we find the minimum experiment time that guarantees user-imposed parameter variance upper bounds and honours actuator limits. The parameters permeability and porosity are estimated with a classical least-squares estimation method for which an expression of the covariance matrix of the estimates is calculated. This expression is used to tackle the optimization problem. We study the Dynamic Darcy Cell experiment set-up (Heller et al., 2002) and focus on data generation using square wave actuator signals, which, as we shall prove, deliver shorter experiment times than sinusoidal ones. Parameter identification is achieved using either inlet pressure/outlet pressure measurements (Heller et al., 2002) or actuator position/outlet pressure measurements, where the latter is a novel approach. The solution to the optimization problem reveals that for both measurement methods an optimal excitation frequency, an optimal inlet volume, and an optimal outlet volume exist. We find that under the same parameter variance bounds and actuator constraints, actuator position/outlet pressure measurements result in required experiment times that are a factor fourteen smaller compared to inlet pressure/outlet pressure measurements. This result is analysed in detail and we find that the dominant effect driving this difference originates from an identifiability problem when using inlet–outlet pressure measurements for joint estimation of permeability and porosity. We illustrate our results with numerical simulations, and show excellent agreement with theoretical expectations.

This paper is centered on the random graph generated by a Doeblin-type coupling of discrete time processes on a countable state space whereby when two paths meet, they merge. This random graph is studied through a novel subgraph, called a bridge graph, generated by paths started in a fixed state at any time. The bridge graph is made into a unimodular network by marking it and selecting a root in a specified fashion. The unimodularity of this network is leveraged to discern global properties of the larger Doeblin graph. Bi-recurrence, i.e., recurrence both forwards and backwards in time, is introduced and shown to be a key property in uniquely distinguishing paths in the Doeblin graph, and also a decisive property for Markov chains indexed by $\mathbb{Z}$. Properties related to simulating the bridge graph are also studied., 44 pages, 4 figures

International audience; This paper is focused on the development and verification of a heat attitude model (HAM) for satellite attitude determination. Within this context, the Sun and the Earth are considered as the main external sources of radiation that could effect the satellite surface temperature changes. Assuming that the satellite orbital position (navigational data) is known, the proposed HAM provides the satellite surface temperature with acceptable accuracy and also relates the net heat flux (NHF) of three orthogonal satellite surfaces to its attitude via the inertial to satellite transformation matrix. The proposed HAM simulation results are verified through comparison with commercial thermal analysis tools. The proposed HAM has been successfully utilized in some researches for attitude estimation, and further studies for practical implementations are still ongoing.