Multiscale and multimodal spectral Imaging for mapping cell wall polymers in plant organs

Multiscale and Multimodal Spectral Imaging for Mapping Cell Wall Polymers in Plant Organs. 2nd International Plant Spectroscopy Conferenc

COMPUTATION OF MINKOWSKI MEASURES ON 2D AND 3D BINARY IMAGES

Minkowski functionals encompass standard geometric parameters such as volume, area, length and the Euler-Poincaré characteristic. Software tools for computing approximations of Minkowski functionals on binary 2D or 3D images are now available based on mathematical methods due to Serra, Lang and Ohser. Minkowski functionals can not be used to describe spatial heterogeneity of structures. This description can be performed by using Minkowski measures, which are local versions of Minkowski functionals. In this paper, we discuss how to extend the computation of Minkowski functionals to the computation of Minkowski measures. Approximations of Minkowski measures are computed using fltering and look-up table transformations. The final result is represented as a grey-level image. Approximation errors are investigated based on numerical examples. Convergence and non convergence of the measure approximations are discussed. The measure of surface area is used to describe spatial heterogeneity of a synthetic structure, and of an image of tomato pericarp

Caractérisation par acoustique non linéaire des effets de vieillissement dans les milieux granulaires non cohésifs et désordonnés

Ce travail de thèse contribue à l'étude acoustique expérimentale des propriétés élastiques des milieux granulaires et de leurs évolutions lentes avec le temps faisant suite à des sollicitations mécaniques. Grâce à un montage expérimental et des protocoles expérimentaux précisément décrits, une étude des caractéristiques acoustiques linéaires et non linéaires d'une tranche de milieu granulaire est réalisée. Des zones fréquentielles caractéristiques de la fonction de transfert acoustique sont identifiées. Dans une zone basse fréquence, les propriétés élastiques du squelette solide (les billes et leurs contacts) régissent le comportement de la fonction de transfert acoustique. A plus hautes fréquences, seuls les paramètres acoustiques du fluide équivalent jouent un rôle. Il est montré que le couplage des modes de propagation au niveau de la détection du signal se manifeste à la transition entre ces zones caractéristiques. Les effets non linéaires d'auto-action et génération d'harmonique 2 dans cette tranche sont analysés pour différentes compacités. L'étude des effets de mémoire d'un échantillon granulaires est réalisée lors de la modification du protocole de compaction (l'amplitude de la sollicitation mécanique change de valeur). Cette modification entraîne des variations soudaines des paramètres élastiques et dissipatifs, linéaires et non linéaires, qui sont interprétées en termes de forces de contacts et distribution de forces de contacts. Pour la première fois, les effets de mémoire sont analysés via l'élasticité du milieu granulaire et non simplement via sa géométrie. Enfin, la relaxation lente des propriétés élastiques, linéaires et non linéaires, est observée suite à une unique sollicitation mécanique, un "tap". L'augmentation du paramètre élastique linéaire, la diminution de l'atténuation linéaire et les diminutions des paramètres non linéaires sont recueillies avec une résolution en temps de l'ordre de la seconde. Les rôles de la température et de l'hygrométrie de l'air ambiant dans les temps caractéristiques de relaxation qui sont de l'ordre de plusieurs minutes sont analysés.This work is a contribution to the experimental acoustic study of the elastic properties of granular media and their slow evolutions during time after mechanical solicitations. With an experimental setup and experimental methods specially adapted, a study of the linear and nonlinear acoustic characteristics of a granular slab is done. Characteristic frequency regions of the acoustic transfer function are identified. In a low frequency band (<10 kHz) the elastic properties of the solid skeleton (beads and theirs contacts) govern the behavior of the acoustic transfer function. In a higher frequency range (15 - 30 kHz), only the acoustic parameters of the equivalent fluid play a role. It is shown that the coupling of propagation modes in the detected signal is visible at the transition between these characteristic frequency regions. The nonlinear effects of self-action and harmonic generation in this slab are analyzed for several compacities. Then the study of the memory effects in a granular sample is performed during the modification of the compaction process (the amplitude of the mechanical solicitation is changed). This modification makes sudden variations of the linear and nonlinear elastic and dissipative parameters. These modifications are interpreted in terms of contact forces and contact force distribution. For the first time, the memory effects are not simply analyzed via packing geometry effects but via elasticity of the granular packing. Finally, the slow relaxation of the linear and nonlinear elastic properties is observed after one single mechanical solicitation called "tap". The increase in the linear elastic parameter, the decrease in the linear attenuation and the decrease in the nonlinear parameters are observed with a less than one second time resolution. Roles of the temperature and the hygrometry of the ambient air in the characteristic relaxation times which are lasting several minutes are analyzed.LE MANS-BU Sciences (721812109) / SudocSudocFranceF

Uniform Time Average Consistency of Monte Carlo Particle Filters

We prove that bootstrap type Monte Carlo particle filters approximate the optimal nonlinear filter in a time average sense uniformly with respect to the time horizon when the signal is ergodic and the particle system satisfies a tightness property. The latter is satisfied without further assumptions when the signal state space is compact, as well as in the noncompact setting when the signal is geometrically ergodic and the observations satisfy additional regularity assumptions.Comment: 21 pages, 1 figur

Beating of hemp bast fibres: an examination of a hydro-mechanical treatment on chemical, structural, and nanomechanical property evolutions

In this study, a gradually increased hydro-mechanical treatments duration were applied to native hemp bast fibres with a traditional pulp and paper beating device (laboratory Valley beater). There is often a trade-off between the treatment applied to the fibres and the effect on their integrity. The multimodal analysis provided an understanding of the beating impact on the fibres at multiple scales and the experimental design made it possible to distinguish the effects of hydro- and hydro-mechanical treatment. Porosity analyses showed that beating treatment doubled the macroporosity and possibly reduced nanoporosity between the cellulose microfibrils. The beating irregularly extracted the amorphous components known to be preferentially located in the middle lamellae and the primary cell walls rather than in the secondary walls, the overall increasing the crystallinity of cellulose from 49.3 % to 59.1 %, but a non-significant change in the indentation moduli of the cell wall was observed. In addition, beating treatments with two distinct mechanical severities showed a disorganization of the cellulose conformation, which significant dropped the indention moduli by 11.2 GPa and 8.4 GPa for 10 and 20 minutes of Valley beater hydro-mechanical treatment, respectively, compared to hydro-treated hemp fibres (16.6 GPa). Pearson’s correlation coefficients between physicochemical features and the final indentation moduli were calculated. Strong positive correlations were highlighted between the cellulose crystallinity and rhamnose, galactose and mannose as non-cellulosic polysaccharide components of the cell wall

A Hitchhiker's guide through the bio-image analysis software universe

Modern research in the life sciences is unthinkable without computational methods for extracting, quantifying and visualising information derived from microscopy imaging data of biological samples. In the past decade, we observed a dramatic increase in available software packages for these purposes. As it is increasingly difficult to keep track of the number of available image analysis platforms, tool collections, components and emerging technologies, we provide a conservative overview of software that we use in daily routine and give insights into emerging new tools. We give guidance on which aspects to consider when choosing the platform that best suits the user's needs, including aspects such as image data type, skills of the team, infrastructure and community at the institute and availability of time and budget.Peer reviewe

Reconstruction of three-dimensional porous media using generative adversarial neural networks

To evaluate the variability of multi-phase flow properties of porous media at the pore scale, it is necessary to acquire a number of representative samples of the void-solid structure. While modern x-ray computer tomography has made it possible to extract three-dimensional images of the pore space, assessment of the variability in the inherent material properties is often experimentally not feasible. We present a novel method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image datasets. We show, by using an adversarial learning approach for neural networks, that this method of unsupervised learning is able to generate representative samples of porous media that honor their statistics. We successfully compare measures of pore morphology, such as the Euler characteristic, two-point statistics and directional single-phase permeability of synthetic realizations with the calculated properties of a bead pack, Berea sandstone, and Ketton limestone. Results show that GANs can be used to reconstruct high-resolution three-dimensional images of porous media at different scales that are representative of the morphology of the images used to train the neural network. The fully convolutional nature of the trained neural network allows the generation of large samples while maintaining computational efficiency. Compared to classical stochastic methods of image reconstruction, the implicit representation of the learned data distribution can be stored and reused to generate multiple realizations of the pore structure very rapidly.Comment: 21 pages, 20 figure