Ensaios avançados com fibras ópticas = advanced characterization of optical fibers

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações

Advanced Kernel Methods For Remote Sensing Image Classification

Résumé Suite aux recentes avancées technologiques, les archives d'images digitales ont connu une croissance qualitative et quantitative sans précédent. Malgré les énormes possibilités qu'elles offrent, ces avancées posent de nouvelles questions quant au traitement des masses de données saisies. Cette question est à la base de cette Thèse: les problèmes de traitement d'information digitale à très haute résolution spatiale et/ou spectrale y sont considérés en recourant à des approches d'apprentissage statistique, les méthodes à noyau. Cette Thèse étudie des problèmes de classification d'images, c'est à dire de catégorisation de pixels en un nombre réduit de classes refletant les propriétés spectrales et contextuelles des objets qu'elles représentent. L'accent est mis sur l'efficience des algorithmes, ainsi que sur leur simplicité, de manière à augmenter leur potentiel d'implementation pour les utilisateurs. De plus, le défi de cette Thèse est de rester proche des problèmes concrets des utilisateurs d'images satellite sans pour autant perdre de vue l'intéret des méthodes proposées pour le milieu du machine learning dont elles sont issues. En ce sens, ce travail joue la carte de la transdisciplinarité en maintenant un lien fort entre les deux sciences dans tous les développements proposés. Quatre modèles sont proposés: le premier répond au problème de la haute dimensionalité et de la redondance des données par un modèle optimisant les performances en classification en s'adaptant aux particularités de l'image. Ceci est rendu possible par un système de ranking des variables (les bandes) qui est optimisé en même temps que le modèle de base: ce faisant, seules les variables importantes pour résoudre le problème sont utilisées par le classifieur. Le manque d'information étiquétée et l'incertitude quant à sa pertinence pour le problème sont à la source des deux modèles suivants, basés respectivement sur l'apprentissage actif et les méthodes semi-supervisées: le premier permet d'améliorer la qualité d'un ensemble d'entraînement par interaction directe entre l'utilisateur et la machine, alors que le deuxième utilise les pixels non étiquetés pour améliorer la description des données disponibles et la robustesse du modèle. Enfin, le dernier modèle proposé considère la question plus théorique de la structure entre les outputs: l'intègration de cette source d'information, jusqu'à présent jamais considérée en télédétection, ouvre des nouveaux défis de recherche. Advanced kernel methods for remote sensing image classification Devis Tuia Institut de Géomatique et d'Analyse du Risque September 2009 Abstract The technical developments in recent years have brought the quantity and quality of digital information to an unprecedented level, as enormous archives of satellite images are available to the users. However, even if these advances open more and more possibilities in the use of digital imagery, they also rise several problems of storage and treatment. The latter is considered in this Thesis: the processing of very high spatial and spectral resolution images is treated with approaches based on data-driven algorithms relying on kernel methods. In particular, the problem of image classification, i.e. the categorization of the image's pixels into a reduced number of classes reflecting spectral and contextual properties, is studied through the different models presented. The accent is put on algorithmic efficiency and the simplicity of the approaches proposed, to avoid too complex models that would not be used by users. The major challenge of the Thesis is to remain close to concrete remote sensing problems, without losing the methodological interest from the machine learning viewpoint: in this sense, this work aims at building a bridge between the machine learning and remote sensing communities and all the models proposed have been developed keeping in mind the need for such a synergy. Four models are proposed: first, an adaptive model learning the relevant image features has been proposed to solve the problem of high dimensionality and collinearity of the image features. This model provides automatically an accurate classifier and a ranking of the relevance of the single features. The scarcity and unreliability of labeled. information were the common root of the second and third models proposed: when confronted to such problems, the user can either construct the labeled set iteratively by direct interaction with the machine or use the unlabeled data to increase robustness and quality of the description of data. Both solutions have been explored resulting into two methodological contributions, based respectively on active learning and semisupervised learning. Finally, the more theoretical issue of structured outputs has been considered in the last model, which, by integrating outputs similarity into a model, opens new challenges and opportunities for remote sensing image processing.

Fluid flow and dispersion predictions in porous media by utilizing advanced numerical methods

Diplomityön tavoitteena oli tarkastella numeerisen virtauslaskennan avulla virtaukseen liittyviä ilmiöitä ja kaasun dispersiota. Diplomityön sisältö on jaettu viiteen osaan; johdantoon, teoriaan, katsaukseen virtauksen mallinnukseen huokoisessa materiaalissa liittyviin tutkimusselvityksiin, numeeriseen mallinnukseen sekä tulosten esittämiseen ja johtopäätöksiin. Diplomityön alussa kiinnitettiin huomiota erilaisiin kokeellisiin, numeerisiin ja teoreettisiin mallinnusmenetelmiin, joilla voidaan mallintaa virtausta huokoisessa materiaalissa. Kirjallisuusosassa tehtiin katsaus aikaisemmin julkaistuihin puoliempiirisiin ja empiirisiin tutkimusselvityksiin, jotka liittyvät huokoisen materiaalin aiheuttamaan painehäviöön. Numeerisessa virtauslaskenta osassa rakennettiin ja esitettiin huokoista materiaalia kuvaavat numeeriset mallit käyttäen kaupallista FLUENT -ohjelmistoa. Työn lopussa arvioitiin teorian, numeerisen virtauslaskennan ja kokeellisten tutkimusselvitysten tuloksia. Kolmiulotteisen huokoisen materiaalinnumeerisessa mallinnuksesta saadut tulokset vaikuttivat lupaavilta. Näiden tulosten perusteella tehtiin suosituksia ajatellen tulevaa virtauksen mallinnusta huokoisessa materiaalissa. Osa tässä diplomityössä esitetyistä tuloksista tullaan esittämään 55. Kanadan Kemiantekniikan konferenssissa Torontossa 1619 Lokakuussa 2005. ASME :n kansainvälisessä tekniikan alan julkaisussa. Työ on hyväksytty esitettäväksi esitettäväksi laskennallisen virtausmekaniikan (CFD) aihealueessa 'Peruskäsitteet'. Lisäksi työn yksityiskohtaiset tulokset tullaan lähettämään myös CES:n julkaisuun.

ADVANCED NUMERICAL METHODS FOR SIMULATING MICROBUBBLE IN AN AERATED MIXING TANK

Fluid mixing in mechanically agitated tanks is one of the major unit operations in many industries. Bubbly flows have been of interest among researchers in physics, medicine, chemistry and technology over the centuries. The aim of this thesis is to use advanced numerical methods for simulating microbubble in an aerated mixing tank. Main components of the mixing tank are a cylindrical vessel, a rotating Rushton turbine and the air nozzle. The objective of Computational Fluid Dynamics (CFD) is to predict fluid flow, heat transfer, mass transfer and chemical reactions. The CFD simulations of a turbulent bubbly flow are carried out in a cylindrical mixing tank using large eddy simulation (LES) and volume of fluid (VOF) method. The Rushton turbine induced flow is modeled by using a sliding mesh method. Numerical results are used to describe the bubbly flows in highly complex liquid flow. Some of the experimental works related to turbulent bubbly flow in a mixing tank are briefly reported. Numerical simulations are needed to complete and interpret the results of the experimental work. Information given by numerical simulations has a major role in designing and scaling-up mixing tanks. The results of this work have been reported in the following scientific articles: ·Honkanen M., Koohestany A., Hatunen T., Saarenrinne P., Zamankhan P., Large eddy simulations and PIV experiments of a two-phase air-water mixer, in Proceedings of ASME Fluids Engineering Summer Conference (2005). ·Honkanen M., Koohestany A., Hatunen T., Saarenrinne P., Zamankhan P., Dynamical States of Bubbling in an Aerated Stirring Tank, submitted to J. Computational Physics.

Advanced control methods for reducing nitrogen oxides in a fluidized bed boiler

Tämän työn tarkoituksena oli löytää keinoja erään leijukerroskattilan typenoksidipäästöjen vähentämiseksi. Koska päästöt olivat jo alunperin alhaiset leijukerrostekniikan ja hybridin SNCR/SCR –typenpoistolaitteiston ansiosta, päätettiin päästöjä lähteä vähentämään parantamalla ammoniakkiruiskutuksen säätöä. Alkuperäinen ammoniakkiruiskutuksen säätö oli liian hidas, jotta satunnaisten häiriöiden aiheuttamat typenoksidipiikit olisi pystytty poistamaan. Ammoniakkiruiskutusta parannettiin lisäämällä jokaiseen ammoniakkilinjaan mäntäpumput, joiden avulla ammoniakkia voidaan syöttää sinne, missä sitä eniten tarvitaan. Ammoniakkiruiskutuksen säätöön kehitettiin uusi sumeaan logiikkaan perustuva säätäjä. Myös muita kehittyneitä säätömenetelmiä kuten neuroverkkoa hyödynnettiin säätäjän kehityksessä. Ammoniakkiruiskutuksen säätäjää testattiin menestyksekkäästi Ruotsissa Brista Kraftin Märstassa sijaitsevalla voimalaitoksella

Combining Advanced Forecasting Methods with System Dynamics - the Case of Finnish Seaports

Seaports play an important part in the wellbeing of a nation. Many nations are highly dependent on foreign trade and most trade is done using sea vessels. This study is part of a larger research project, where a simulation model is required in order to create further analyses on Finnish macro logistical networks. The objective of this study is to create a system dynamic simulation model, which gives an accurate forecast for the development of demand of Finnish seaports up to 2030. The emphasis on this study is to show how it is possible to create a detailed harbor demand System Dynamic model with the help of statistical methods. The used forecasting methods were ARIMA (autoregressive integrated moving average) and regression models. The created simulation model gives a forecast with confidence intervals and allows studying different scenarios. The building process was found to be a useful one and the built model can be expanded to be more detailed. Required capacity for other parts of the Finnish logistical system could easily be included in the model.

Advanced chemometric methods: Applicability on industrial data

Recent years have produced great advances in the instrumentation technology. The amount of available data has been increasing due to the simplicity, speed and accuracy of current spectroscopic instruments. Most of these data are, however, meaningless without a proper analysis. This has been one of the reasons for the overgrowing success of multivariate handling of such data. Industrial data is commonly not designed data; in other words, there is no exact experimental design, but rather the data have been collected as a routine procedure during an industrial process. This makes certain demands on the multivariate modeling, as the selection of samples and variables can have an enormous effect. Common approaches in the modeling of industrial data are PCA (principal component analysis) and PLS (projection to latent structures or partial least squares) but there are also other methods that should be considered. The more advanced methods include multi block modeling and nonlinear modeling. In this thesis it is shown that the results of data analysis vary according to the modeling approach used, thus making the selection of the modeling approach dependent on the purpose of the model. If the model is intended to provide accurate predictions, the approach should be different than in the case where the purpose of modeling is mostly to obtain information about the variables and the process. For industrial applicability it is essential that the methods are robust and sufficiently simple to apply. In this way the methods and the results can be compared and an approach selected that is suitable for the intended purpose. Differences in data analysis methods are compared with data from different fields of industry in this thesis. In the first two papers, the multi block method is considered for data originating from the oil and fertilizer industries. The results are compared to those from PLS and priority PLS. The third paper considers applicability of multivariate models to process control for a reactive crystallization process. In the fourth paper, nonlinear modeling is examined with a data set from the oil industry. The response has a nonlinear relation to the descriptor matrix, and the results are compared between linear modeling, polynomial PLS and nonlinear modeling using nonlinear score vectors.

Advanced characterization of a coffee fermenting tank by multi-distributed wireless sensors: spatial interpolation and phase diagrams.

In coffee processing the fermentation stage is considered one of the critical operations by its impact on the final quality of the product. However, the level of control of the fermentation process on each farm is often not adequate; the use of sensorics for controlling coffee fermentation is not common. The objective of this work is to characterize the fermentation temperature in a fermentation tank by applying spatial interpolation and a new methodology of data analysis based on phase space diagrams of temperature data, collected by means of multi-distributed, low cost and autonomous wireless sensors. A real coffee fermentation was supervised in the Cauca region (Colombia) with a network of 24 semi-passive TurboTag RFID temperature loggers with vacuum plastic cover, submerged directly in the fermenting mass. Temporal evolution and spatial distribution of temperature is described in terms of the phase diagram areas which characterizes the cyclic behaviour of temperature and highlights the significant heterogeneity of thermal conditions at different locations in the tank where the average temperature of the fermentation was 21.2 °C, although there were temperature ranges of 4.6°C, and average spatial standard deviation of ±1.21ºC. In the upper part of the tank we found high heterogeneity of temperatures, the higher temperatures and therefore the higher fermentation rates. While at the bottom, it has been computed an area in the phase diagram practically half of the area occupied by the sensors of the upper tank, therefore this location showed higher temperature homogeneity

A comparative analysis of the characterization methods for submicron silicon waveguide propagation loss

An important parameter in integrated optical device is the propagation loss of the waveguide. Its characterization gives the information of the fabrication quality as well as the information of other passive devices on the chip as it is the basic building block of the passive devices. Although, over the last three decades many methods have been developed, there is not a single standard present yet. This paper presents a comparative analysis of the methods existing from the past as well as methods developed very recently in order to provide a complete picture of the pros and cons of different types of methods and from this comparison the best method is suggested according to the authors opinion. To support the claim, apart from the analytical comparison, this paper also presents a comparison performed with the experimental results between the suggested best method which is recently proposed by Massachusetts Institute of Technology (MIT) researchers based on undercoupled all-pass microring structure and the popular cut-back method.

Surface characterization of chemically modified fiber, wood and paper

Inorganic-organic sol-gel hybrid coatings can be used for improving and modifying properties of wood-based materials. By selecting a proper precursor, wood can be made water repellent, decay-, moisture- or UV-resistant. However, to control the barrier properties of sol-gel coatings on wood substrates against moisture uptake and weathering, an understanding of the surface morphology and chemistry of the deposited sol-gel coatings on wood substrates is needed. Mechanical pulp is used in production of wood-containing printing papers. The physical and chemical fiber surface characteristics, as created in the chosen mechanical pulp manufacturing process, play a key role in controlling the properties of the end-use product. A detailed understanding of how process parameters influence fiber surfaces can help improving cost-effectiveness of pulp and paper production. The current work focuses on physico-chemical characterization of modified wood-based materials with surface sensitive analytical tools. The overall objectives were, through advanced microscopy and chemical analysis techniques, (i) to collect versatile information about the surface structures of Norway spruce thermomechanical pulp fiber walls and understand how they are influenced by the selected chemical treatments, and (ii) to clarify the effect of various sol-gel coatings on surface structural and chemical properties of wood-based substrates. A special emphasis was on understanding the effect of sol-gel coatings on the water repellency of modified wood and paper surfaces. In the first part of the work, effects of chemical treatment on micro- and nano-scale surface structure of 1st stage TMP latewood fibers from Norway spruce were investigated. The chemicals applied were buffered sodium oxalate and hydrochloric acid. The outer and the inner fiber wall layers of the untreated and chemically treated fibers were separately analyzed by light microscopy, atomic force microscopy and field-emission scanning electron microscopy. The selected characterization methods enabled the demonstration of the effect of different treatments on the fiber surface structure, both visually and quantitatively. The outer fiber wall areas appeared as intact bands surrounding the fiber and they were clearly rougher than areas of exposed inner fiber wall. The roughness of the outer fiber wall areas increased most in the sodium oxalate treatment. The results indicated formation of more surface pores on the exposed inner fiber wall areas than on the corresponding outer fiber wall areas as a result of the chemical treatments. The hydrochloric acid treatment seemed to increase the surface porosity of the inner wall areas. In the second part of the work, three silane-based sol-gel hybrid coatings were selected in order to improve moisture resistance of wood and paper substrates. The coatings differed from each other in terms of having different alkyl (CH3–, CH3-(CH2)7–) and fluorocarbon (CF3–) chains attached to the trialkoxysilane sol-gel precursor. The sol-gel coatings were deposited by a wet coating method, i.e. spraying or spreading by brush. The effect of solgel coatings on surface structural and chemical properties of wood-based substrates was studied by using advanced surface analyzing tools: atomic force microscopy, X-ray photoelectron spectroscopy and time-of-flight secondary ion spectroscopy. The results show that the applied sol-gel coatings, deposited as thin films or particulate coatings, have different effects on surface characteristics of wood and wood-based materials. The coating which has a long hydrocarbon chain (CH3-(CH2)7–) attached to the silane backbone (octyltriethoxysilane) produced the highest hydrophobicity for wood and wood-based materials.

Synthesis of supported metal nanoparticle catalysts using ligand assisted methods

The synthesis and characterization methods of metal nanoparticles (NPs) have advanced greatly in the last few decades, allowing an increasing understanding of structure-property-performance relationships. However, the role played by the ligands used as stabilizers for metal NPs synthesis or for NPs immobilization on solid supports has been underestimated. Here, we highlight some recent progress in the preparation of supported metal NPs with the assistance of ligands in solution or grafted on solid supports, a modified deposition-reduction method, with special attention to the effects on NPs size, metal-support interactions and, more importantly, catalytic activities. After presenting the general strategies in metal NP synthesis assisted by ligands grafted on solid supports, we highlight some recent progress in the deposition of pre-formed colloidal NPs on functionalized solids. Another important aspect that will be reviewed is related to the separation and recovery of NPs. Finally, we will outline our personal understanding and perspectives on the use of supported metal NPs prepared through ligand-assisted methods.

Sideband characterization and atmospheric observations with various 340 GHz heterodyne receivers

This paper describes sideband response measurements and atmospheric observations with a double sideband and two Single Sideband (SSB) receiver prototypes developed for the multi-beam limb sounder instrument stratosphere-troposphere exchange and climate monitor radiometer. We first show an advanced Fourier-Transform Spectroscopy (FTS) method for sideband response and spurious signal characterization. We then present sideband response measurements of the different prototype receivers and we compare the results of the SSB receivers with sideband measurements by injecting a continuous wave signal into the upper and lower sidebands. The receivers were integrated into a total-power radiometer and atmospheric observations were carried out. The observed spectra were compared to forward model spectra to conclude on the sideband characteristics of the different receivers. The two sideband characterization methods show a high degree of agreement for both SSB receivers with various local oscillator settings. The measured sideband response was used to correct the forward model simulations. This improves the agreement with the atmospheric observations and explains spectral features caused by an unbalanced sideband response. The FTS method also allows to quantify the influence of spurious harmonic responses of the receiver.

Advanced Spectroscopy for the Study of Colourants in Cultural Heritage

Colourants are substances used to change the colour of something, and are classified in three typology of colorants: a) pigments, b) dyes, and c) lakes and hybrid pigments. Their identification is very important when studying cultural heritage; it gives information about the artistic technique, can help in dating, and offers insights on the condition of the object. Besides, the study of the degradation phenomena constitutes a framework for the preventive conservation strategies, provides evidence of the object's original appearance, and contributes to the authentication of works of art. However, the complexity of these systems makes it impossible to achieve a complete understanding using a single technique, making necessary a multi-analytical approach. This work focuses on the set-up and application of advanced spectroscopic methods for the study of colourants in cultural heritage. The first chapter presents the identification of modern synthetic organic pigments using Metal Underlayer-ATR (MU-ATR), and the characterization of synthetic dyes extracted from wool fibres using a combination of Thin Layer Chromatography (TLC) coupled to MU-ATR using AgI@Au plates. The second chapter presents the study of the effect of metallic Ag in the photo-oxidation process of orpiment, and the influence of the different factors, such as light and relative humidity. We used a combination of vibrational and synchrotron radiation-based X-ray microspectroscopy techniques: µ-ATR-FT-IR, µ-Raman, SR-µ-XRF, µ-XANES at S K-, Ag L3- and As K-edges and SR-µ-XRD. The third chapter presents the study of metal carboxylates in paintings, specifically on the formation of Zn and Pb carboxylates in three different binders: stand linseed oil, whole egg, and beeswax. We used micro-ATR-FT-IR, macro FT-IR in total reflection (rMA-FT-IR), portable Near-Infrared spectroscopy (NIR), macro X-ray Powder Diffraction (MA-XRPD), XRPD, and Gas Chromatography Mass-Spectrometry (GC-MS). For the data processing, we explored the data from rMA-FT-IR and NIR with the Principal Component Analysis (PCA).