COURS DE SPECTROSCOPIE S3 PDF

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19 févr. inoubliable, votre précieuse aide au cours de mon doctorat, notamment pendant mon instruments basés sur la spectroscopie en champ proche (Tip Figure 4 and Figure S3 (Supporting Information) show curve-fitting. Résume du Spectroscopie SMC S5. طريق النجاح كلية العلوم بن المسيك s1 s2 s3 s4 s5 s6 SMPC) added 16 new photos. December 17, ·. Résume du. par spectroscopie infrarouge sur des amas de fibres de polystyrène. S3. Representative series of the four polarized Raman spectra recorded for PS Au cours des dernières années, plusieurs études ont porté sur la température de .

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The goal of this seminar is to welcome recognized researchers, but also PhD students and post-docs, around the field of signal processing and its applications. It is open to everyone free and will be host every Friday mornig We will have coffee and croissants before each seminar. Bilevel optimisation approaches for learning the optimal noise model in mixed and non-standard image denoising applications.

The regularised formulation of a general ill-posed inverse coours in imaging typically combines an edge-preserving regularisation term like the Total Variation semi-norm and a data fitting function encoding noise statistics balanced against each other by a positive – possibly space-variant – weight. The optimal choice of such parameter is crucial to improve the image quality while avoiding overfitting, and it is a very challenging problem among the inverse problem community.

When the noise level is known, classical approaches provide an estimate of such parameter based on discrepancy principles, but in many situations an accurate estimate of the noise intensity cannot be provided. In this talk we review the framework of bilevel optimisation as a powerful tool to estimate the optimal weighting where a training set spectroscpie examples is provided and no prior assumption on the noise level is made.

For the couurs of efficient optimisation techniques we employ second-order large-scale and sampling techniques.

S³ : Séminaire Signal de l’Université Paris-Saclay

The applications will consider at first standard noise scenarios such as Gaussian, impulsive and Poisson distributions, which are very common in medical, microscopy and astronomy imaging. Finally, we will present more recent developments in the case of noise mixtures and of Cauchy and Rician noise settings, which are very typical, for instance, in SAR and MRI imaging problems. His research interests lie in the fields of mathematical image processing, variational modelling, non-smooth optimisation with applications to real-world applications such as cultural heritage imaging or neuroscience.

During his PhD he has been invited for a research collaboration with J. Many problems in machine learning and imaging can be framed as an infinite dimensional Lasso problem to estimate a sparse measure. This includes for instance regression s33 a continuously parameterized dictionary, mixture model estimation and super-resolution of images. To make the problem tractable, one typically sketches the observations often called compressive-sensing xours imaging using randomized projections.

In this work, we provide a comprehensive treatment of the recovery performances of this class of approaches, proving that up to log factors spectroscoie number of sketches proportional to the sparsity is enough to identify the sought after measure with robustness to noise. We prove both exact support stability the number of recovered atoms matches that of the measure of interest and approximate stability localization of the atoms by extending two classical proof techniques minimal norm dual certificate and golfing scheme certificate.

He organizes the Laplace reading group and his research interests are compressive sensing, dimensionality reduction, learning, big data, and small data.

In the process of verifying entries of the classical table of integrals by Gradshteyn and Ryzhik, the author observed that entry 3.

This talk will discuss how was this discovered, the correct solution obtained this year by Arias de Reyna and the typo in the table, discovered by Petr Blaschke. From tohe was postdoctoral researcher at the Temple University, Philadelphia, Pennsylvania.

High-dimensional covariance matrix estimation with applications to microarray studies and portfolio optimization. We consider the problem of estimating a high-dimensional HD covariance matrix that can be applied in commonly occurring sparse data problems, i.

We develop a well-conditioned regularized sample covariance matrix RSCM estimator that is asymptotically optimal in the minimum mean squared error sense w. Frobenius metric under the assumption that the data samples follow an unspecified elliptically symmetric distribution. Asymptotically means that the number of observations and the number of variables grow large together.

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The proposed RSCM estimator has a simple explicit formula that is easy to compute and to interpret. The proposed covariance estimator is then used in microarray data analysis MDA and portfolio optimization problem in finance.

Microarray technology is a powerful approach for genomics research that allows monitoring the expression levels of tens of thousands of genes simultaneously. In MDA the task is to select differentially expressed genes, i. In portfolio optimization problem we use our estimator for optimally allocating the total wealth to a large number of assets, where optimality means that the risk i.

Our analysis results on real microarray data and stock market data illustrate that the proposed approach is able to outperform the benchmark methods. Esa Ollila M’03 received the M. Tech degree with honors in signal processing from Aalto University, in He has also been a Senior Lecturer at the University of Oulu. He is also an adjunct Professor statistics of Oulu University. His research interests focus on theory and methods of statistical signal processing, multivariate statistics and data science.

Independent component analysis ICA is a widely used signal processing technique in extracting unobserved independent source signals from their observed multivariate mixture recordings. In this talk, we develop low-complexity and stable bootstrap procedures for FastICA estimators. Such methods enable reliable bootstrap-based statistical inference in large-scale real-world ICA problems.

For example testing statistical significance of mixing coefficients in the ICA model can be used to identify the contribution of a specific source signal-of-interest onto the observed mixture variables.

An application of the proposed bootstrapping technique in Electroencephalogram EEG signal processing is presented. We also provide an alternative derivation of FastICA. The algorithm has been originally derived and motivated as being an approximate Newton-Raphson NR algorithm. Furthermore, the new derivation does not require assumptions and approximations that were used in the original derivation.

He received his M.

Symposium annuel PROTEO

His research interests focus on methods and theory of statistical signal processing, Big Data analytics, Independent Component Analysis, and blind source separation. The concept of a “flow network” — a set of nodes connected by flow paths — unites many different disciplines, including electrical, pipe flow, transportation, chemical reaction, ecological, epidemiological and human social networks.

Traditionally, flow networks have been analysed by conservation Kirchhoff’s laws, and more recently by dynamical simulation and optimisation methods. A less well explored approach, however, is to maximise an entropy defined over the uncertainty in the system, subject to its physical constraints, to infer the state of the network. We present a generalised maximum entropy MaxEnt framework for this purpose, which can be adapted both for undirected flow networks such as pipe flow or electrical networks, and directed flow networks such as transport networks.

This method is then demonstrated by application to a variety of systems: The connections between the MaxEnt formulation and one derived using Bayesian methods is also discussed. Other methods for probabilistic inference are also discussed. This method leads to a new information-theoretic objective function for optimisation of the order reduction method, based on the costs and benefits of the algorithm. The first includes research on the theory and applications of the maximum entropy methods, to dissipative systems, turbulent fluid flow and networked flow systems.

Groupwise registration of cardiac perfusion MRI sequences using mutual information in high dimension. Compensating cardio-thoracic motions is a requirement for enabling computer-aided quantitative assessment of myocardial ischaemia from contrast-enhanced p-MRI sequences. The classical paradigm consists of registering each sequence frame on a reference image using some intensity-based matching criterion.

In this work, we present an unsupervised method for the spatio-temporal groupwise registration of cardiac p-MRI exams based on mutual information MI between high-dimensional feature distributions. Here, local contrast enhancement curves are used as a dense set of spatio-temporal features, and statistically matched through variational optimization to a target feature distribution derived from a registered reference template.

The hard issue of probability density estimation in high-dimensional state spaces is bypassed by using consistent geometric entropy estimators, allowing MI to be computed directly from feature samples. Sameh Hamrouni received her MSc in computer vision from National computer science engineering school Tunis in Her research interests include image processing and medical physics. Divergent-beam backprojection-filtration formula with applications to region-of-interest imaging.

Interventional neuroradiology treats vascular pathologies of the brain through minimally invasive, endovascular procedures. These treatments are performed under the control of two-dimensional, real-time, projective X-ray imaging using interventional C-arm systems. Such systems can perform tomographic acquisitions which are further used to reconstruct a three-dimensional image by rotating the C-arm around the patient; however, C-arm cone-beam computed tomography CBCT achieves a lower contrast resolution which is necessary to recover the clinical information of soft tissues in the brain than diagnostic CT, mostly because of dose thus noise issues.

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In this talk, we revisit the classical direct filtered backprojection FBP reconstruction algorithm and propose a new alternative, backprojection-filtration BPF formula, that is exact in planar geometries and approximate in the cone-beam geometry. We then apply this result to the reconstruction of dual-rotation acquisitions, consisting of a truncated low-noise acquisition with dense angular sampling, and of additional non-truncated views that are either high-noise or angularly undersampled.

In both cases, the method successfully improves contrast resolution on digital phantoms and on real dual-rotation acquisitions of a quality assurance phantom Catphan His research interests include image processing, medical physics, tomographic reconstruction and inverse problems.

The challenges of the Synthetic Aperture Radar SAR image formation principles, the high data volume and the very high acquisition rate stimulated from the very beginning the elaborations of sophisticated techniques. Meanwhile the SAR technologies have immensely evolved. The state of the art sensors deliver widely different imaging modes, and have made considerable progress in spatial and radiometric resolution, target acquisition strategies, or geographical coverage and data rates.

Generally imaging sensors generate an isomorphic representation of the observed scene. This is not the case for SAR, the observations are a doppelganger of the scattered field, an indirect signature of the imaged object. The presentation reviews and analyses the new approaches of SAR imaging leveraging the recent advances in physical process based ML and AI methods and signal processing.

This is leading to Computational Imaging paradigms where intelligence is the analytical component of the end-to-end sensor and Data Science chain design. A particular focus is on the scientific methods of Deep Learning and an information theoretical model of the SAR information extraction process.

Mihai Datcu received the M. Valderio Reisen and Marton Ispany. The model is applied to a real data set with the aim of quantifying the association between the number of hospital admissions for respiratory diseases as response variable and air pollution concentrations, especially, PM10, SO2, NO2, CO and O3, as covariates. We introduce the topic of the Fourier transform of a Euclidean polytope, first by examples and then by more general formulations. Then we point out how we can use this transform and the frequency space to analyze the following problems: Compute lattice point enumeration formulas for polytopes 2.

Relate the transforms of polytopes to tilings of Euclidean space by translations of a polytope We will give a flavor of how such applications arise, and we point to some conjectures and applications. We prove an explicit formula for the polynomial part of a restricted partition function, also known as the first Sylvester wave.

This is achieved by way of some identities for higher-order Bernoulli polynomials, one of which is analogous to Raabe’s well-known multiplication formula for the ordinary Bernoulli polynomials. As a consequence of our main result we obtain an asymptotic expression of the first Sylvester wave as the coefficients of the restricted partition grow arbitrarily large.

Joint work with Christophe Vignat. Sparse approximation under non-negativity constraints naturally arises in several applications.

Many sparse solvers can be directly extended to the non-negative setting. It is not the case of Orthogonal Matching Pursuit OMPa well-known sparse solver, which gradually updates the sparse solution support by selecting a new dictionary atom at each iteration.

When dealing with non-negative constraints, the orthogonal projection computed at each OMP iteration is replaced by a non-negative least-squares NNLS subproblem whose solution is not explicit. Therefore, the usual recursive fast implementations of OMP do not apply. In my talk, I will first recall the principle of greedy algorithms, in particular NNOMP, and then, I will introduce our proposed improvements, based on the use of the active-set algorithm to address the NNLS subproblems.

The structure of the active-set algorithm is indeed intrisically greedy.