Cette thèse vise à montrer rapport d'un lidar multispectral, en particulier en ajoutant des longueurs d'onde dans le proche infrarouge proche, pour la caractérisation des aérosols. En effet par rapport à un lidar mono-longueur d'onde, l'information contenue dans les profils multispectraux permet de remonter aux propriétés microphysiques des aérosols (distribution en aille et composition). Pour cela un simulateur de signaux lidar multispectraux a été adapté à notre étude afin de pouvoir développer et tester deux méthodes permettant de retrouver les propriétés microphysiques des aérosols le long de la ligne e visée à partir de signaux lidar synthétiques. La première méthode, basée sur l'inversion des signaux lidar, permet de retrouver la répartition en taille des aérosols et donc d'en déduire notamment leur concentration et leur rayon modal. Cette méthode nécessite des informations a priori sur les aérosols. Un bilan d'erreur a été réalisé en introduisant des incertitudes sur ces paramètres a priori et montre que les résultats obtenus sur la concentration et le rayon modal sont précis (respectivement 16% et 17% d'erreur). Cette méthode présente l'avantage de ne pas nécessiter d'étalonnage absolu de l'instrument. La deuxième méthode est basée sur la minimisation de l'écart entre des signaux simulés et les signaux que l'on étudie. Même si la précision obtenue sur la répartition en taille retrouvée est plus faible (35% et 40 % d'erreur sur la concentration t le rayon modal) et que la constante d'étalonnage de l'instrument doit être connue, cette méthode a l'avantage de retrouver la composition des aérosols dans 74 % des cas. / The purpose of this thesis is to show the contribution of a multispectral Iidar for the characterisation of aerosols, in particular hen wavelengths in near infrared are added. Indeed, compared with a mono-wavelength Iidar, the information contained in multispectral profiles allow to retrieve the microphysical properties of aerosols (particule size distribution and composition). To this end, we adapted a multispectral Iidar signal simulator to our study in order to develop and test two methods which objective is to obtain the microphysical properties of aerosol along the line-of-sight from synthetic lidar signals. The first method, based on the inversion of lidar signals, enables to find the length distribution of aerosols and therefore to educe their concentration and their modal radius. This method requires a priori information about the aerosols. An error budget was made by introducing uncertainties on the a priori parameters. It shows that the results obtained regarding the concentration and modal radius are accurate (respectively 16% and 17% uncertainty). The advantage of this method is that it does not require absolute calibration of the instrument. The principle of the second method is to minimize the difference between the studied and the simulated signals. Even if the accuracy on the size distribution is lower (35% and 40% on the concentration and modal radius) and the calibration constant of the instrument has to be known, this method has the advantage to find the concentration of the aerosols in 74% of the cases. Finally, the first method was validated on real data, coming from a collaboration with the RSLab (Barcelona), by comparing ur results with those obtained by this team (7% difference on the modal radius).

A new pulse-retrieval software for Frequency-Resolved Optical Gating (FROG) technique has been developed. The new software extends the capacity of the original FROG algorithm in two major categories. First is a new method to determine the uncertainty of the retrieved pulse field in FROG technique. I proposed a simple, robust, and general technique?tstrap method?ch places error bars on the intensity and phase of the retrieved pulse field. The bootstrap method was also extended to automatically detect ambiguities in the FROG pulse retrieval. The second improvement deals with the spatiotemporal effect of the input laser beam on the measured GRENOUILLE trace. I developed a new algorithm to retrieve the pulse information, which includes both pulse temporal field and the spatiotemporal parameters, from the spatiotemporal distorted GRENOUILLE trace. It is now possible to have a more complete view of an ultrashort pulse. I also proposed a simple method to remove the spatial profile influence of the input laser beam on the GRENOUILLE trace. The new method extends the capacity of GRENOUILLE technique to measure the beams with irregular spatial profiles.

Formulation of polyurethanes is a complex problem poorly understood as it has developed more as an art rather than a science. Only a few experts have mastered polyurethane (PU) formulation after years of experience and the major raw material manufacturers largely hold such expertise. Understanding of PU formulation is at present insufficient to be developed from first principles. The first principle approach requires time and a detailed understanding of the underlying principles that govern the formulation process (e.g. PU chemistry, kinetics) and a number of measurements of process conditions. Even in the simplest formulations, there are more that 20 variables often interacting with each other in very intricate ways. In this doctoral thesis the use of the Case-Based Reasoning and Artificial Neural Network paradigm is proposed to enable support for PUs formulation tasks by providing a framework for the collection, structure, and representation of real formulating knowledge. The framework is also aimed at facilitating the sharing and deployment of solutions in a consistent and referable way, when appropriate, for future problem solving. Two basic problems in the development of a Case-Based Reasoning tool that uses past flexible PU foam formulation recipes or cases to solve new problems were studied. A PU case was divided into a problem description (i. e. PU measured mechanical properties) and a solution description (i. e. the ingredients and their quantities to produce a PU). The problems investigated are related to the retrieval of former PU cases that are similar to a new problem description, and the adaptation of the retrieved case to meet the problem constraints. For retrieval, an alternative similarity measure based on the moment's description of a case when it is represented as a two dimensional image was studied. The retrieval using geometric, central and Legendre moments was also studied and compared with a standard nearest neighbour algorithm using nine different distance functions (e.g. Euclidean, Canberra, City Block, among others). It was concluded that when cases were represented as 2D images and matching is performed by using moment functions in a similar fashion to the approaches studied in image analysis in pattern recognition, low order geometric and Legendre moments and central moments of any order retrieve the same case as the Euclidean distance does when used in a nearest neighbour algorithm. This means that the Euclidean distance acts a low moment function that represents gross level case features. Higher order (moment's order>3) geometric and Legendre moments while enabling finer details about an image to be represented had no standard distance function counterpart. For the adaptation of retrieved cases, a feed-forward back-propagation artificial neural network was proposed to reduce the adaptation knowledge acquisition effort that has prevented building complete CBR systems and to generate a mapping between change in mechanical properties and formulation ingredients. The proposed network was trained with the differences between problem descriptions (i.e. mechanical properties of a pair of foams) as input patterns and the differences between solution descriptions (i.e. formulation ingredients) as the output patterns. A complete data set was used based on 34 initial formulations and a 16950 epochs trained network with 1102 training exemplars, produced from the case differences, gave only 4% error. However, further work with a data set consisting of a training set and a small validation set failed to generalise returning a high percentage of errors. Further tests on different training/test splits of the data also failed to generalise. The conclusion reached is that the data as such has insufficient common structure to form any general conclusions. Other evidence to suggest that the data does not contain generalisable structure includes the large number of hidden nodes necessary to achieve convergence on the complete data set.

The availability of spatial light modulators as standard turnkey components and their ongoing development makes them attractive for a huge variety of optical measurement systems in industry and research. Here, we outline two examples of how optical measurements can benefit from spatial light modulators. Ultrasound testing has become an indispensable tool for industrial inspection. Contact-free measurements can be achieved by laser-induced ultrasound. One disadvantage is that due to the highly divergent sound field of the generated shear waves for a point-wise thermoelastic excitation, only a poor spatial selectivity can be achieved. This problem can be solved by creating an ultrasound focus by means of a ring-like laser intensity distribution, but standard fixed-form optical components used for their generation are always optimised to a fixed set of parameters. Here, we demonstrate, how a predefined intensity pattern as e.g. a ring can be created from an arbitrary input laser beam using a phase-retrieval algorithm to shape an ultrasound focus in the sample. By displaying different patterns on the spatial light modulator, the focus can be traversed in all three directions through the object allowing a fast and highly spatially resolving scanning of the sample. Optical measurements take often place under difficult conditions. They are affected by variations of the refractive index, caused e.g. by phase boundaries between two media of different optical density. This will result in an increased measurement uncertainty or, in the worst case, will cause the measurement to fail. To overcome these limitations, we propose the application of adaptive optics. Optical flow velocity measurements based on image correlation in water that are performed through optical distortions are discussed. We demonstrate how the measurement error induced by refractive index variations can be reduced if a spatial light modulator is used in the measurement setup to compensate for the wavefront distortions.

<p>Multiple Signals of Opportunity (multi-SoOp) across different frequencies and polarizations</p> <p>offer a potential breakthrough for remote sensing of root-zone soil moisture (RZSM). Deeper penetration depths of existing communication transmissions in the frequency ranges of 137–138, 240–270, and 360–380 MHz enable the estimation of RZSM by complementing global navigation satellite system reflectometry (GNSS-R) in L-band. The small form factor of the multi-SoOp observatory allows for high spatiotemporal coverage of RSZM by a satellite constellation in a cost-effective manner. This study aims to develop models and tools to define mission requirements for various system parameters that affect observation accuracy and coverage, for the advancement of spaceborne multi-SoOp remote sensing. These parameters include frequency and polarization combinations, observation error, inter-frequency temporal coincidence, and configuration of the satellite constellation. We present the development of a retrieval algorithm and the sensitivity analysis of retrieval accuracy. The retrieval algorithm was evaluated using synthetic observations generated from multiyear time series of in-situ soil moisture (SM) and satellite-based vegetation data. The combined use of both high and low frequencies improves retrieval accuracy by limiting uncertainties from vegetation and surface SM and providing sensitivity to deeper layers. A bivariate model, derived from the sensitivity analysis, facilitates error prediction for future science missions. We introduce a framework for tradespace exploration of the multi-SoOp satellite constellation. A constellation design study indicates that a Walker constellation comprising 24 satellites with 3 orbital planes at 500 km and 50° inclination optimizes the coverage and mission cost under mission requirements. A tower-based field experiment validated the performance of a prototype antenna for multi-SoOp using the interference pattern technique. More field experiments with improved instruments are required to further advance the multi-SoOp technique.</p>