Minimum error contrast enhancement

Contrast enhancement is an image processing technique where the objective is to preprocess the image so that relevant information can be either seen or further processed more reliably. These techniques are typically applied when the image itself or the device used for image reproduction provides poor visibility and distinguishability of different regions of interest inthe image. In most studies, the emphasis is on the visualization of image data,but this human observer biased goal often results to images which are not optimal for automated processing. The main contribution of this study is to express the contrast enhancement as a mapping from N-channel image data to 1-channel gray-level image, and to devise a projection method which results to an image with minimal error to the correct contrast image. The projection, the minimum-error contrast image, possess the optimal contrast between the regions of interest in the image. The method is based on estimation of the probability density distributions of the region values, and it employs Bayesian inference to establish the minimum error projection.

Statistical Analysis of an SEIR Epidemic Model

This thesis was focussed on statistical analysis methods and proposes the use of Bayesian inference to extract information contained in experimental data by estimating Ebola model parameters. The model is a system of differential equations expressing the behavior and dynamics of Ebola. Two sets of data (onset and death data) were both used to estimate parameters, which has not been done by previous researchers in (Chowell, 2004). To be able to use both data, a new version of the model has been built. Model parameters have been estimated and then used to calculate the basic reproduction number and to study the disease-free equilibrium. Estimates of the parameters were useful to determine how well the model fits the data and how good estimates were, in terms of the information they provided about the possible relationship between variables. The solution showed that Ebola model fits the observed onset data at 98.95% and the observed death data at 93.6%. Since Bayesian inference can not be performed analytically, the Markov chain Monte Carlo approach has been used to generate samples from the posterior distribution over parameters. Samples have been used to check the accuracy of the model and other characteristics of the target posteriors.

MCMC Analysis for Optimization of Stochastic Models

In any decision making under uncertainties, the goal is mostly to minimize the expected cost. The minimization of cost under uncertainties is usually done by optimization. For simple models, the optimization can easily be done using deterministic methods.However, many models practically contain some complex and varying parameters that can not easily be taken into account using usual deterministic methods of optimization. Thus, it is very important to look for other methods that can be used to get insight into such models. MCMC method is one of the practical methods that can be used for optimization of stochastic models under uncertainty. This method is based on simulation that provides a general methodology which can be applied in nonlinear and non-Gaussian state models. MCMC method is very important for practical applications because it is a uni ed estimation procedure which simultaneously estimates both parameters and state variables. MCMC computes the distribution of the state variables and parameters of the given data measurements. MCMC method is faster in terms of computing time when compared to other optimization methods. This thesis discusses the use of Markov chain Monte Carlo (MCMC) methods for optimization of Stochastic models under uncertainties .The thesis begins with a short discussion about Bayesian Inference, MCMC and Stochastic optimization methods. Then an example is given of how MCMC can be applied for maximizing production at a minimum cost in a chemical reaction process. It is observed that this method performs better in optimizing the given cost function with a very high certainty.

Initialization of Continuous Nonlinear Models Using Extended Kalman Filter

The two main objectives of Bayesian inference are to estimate parameters and states. In this thesis, we are interested in how this can be done in the framework of state-space models when there is a complete or partial lack of knowledge of the initial state of a continuous nonlinear dynamical system. In literature, similar problems have been referred to as diffuse initialization problems. This is achieved first by extending the previously developed diffuse initialization Kalman filtering techniques for discrete systems to continuous systems. The second objective is to estimate parameters using MCMC methods with a likelihood function obtained from the diffuse filtering. These methods are tried on the data collected from the 1995 Ebola outbreak in Kikwit, DRC in order to estimate the parameters of the system.

Astringent Food Compounds and Their Interactions with Taste Properties

Astringency is traditionally thought to be induced by plant tannins in foods. Because of this current research concerning the mechanism of astringency is focused on tannin‐protein interactions and thus on precipitation, which may be perceived by mechanoreceptors. However, astringency is elicited by a wide range of different phenolic compounds, as well as, some non‐phenolic compounds in various foods. Many ellagitannins or smaller compounds that contribute to astringent properties do not interact with salivary proteins and may be directly perceived through some receptors. Generally, the higher degree of polymerization of proanthocyanidins can be associated with more intense astringency. However, the astringent properties of smaller phenolic compounds may not be directly predicted from the structure of a compound, although glycosylation has a significant role. The astringency of organic acids may be directly linked to the perception of sourness, and this increases along with decreasing pH. Astringency can be divided into different sub‐qualities, including even other qualities than traditional mouth‐drying, puckering or roughing sensations. Astringency is often accompanied by bitter or sour or both taste properties. The different sub‐qualities can be influenced by different astringent compounds. In general, the glycolysation of the phenolic compound results in more velvety and smooth mouthdrying astringency. Flavonol glycosides and other flavonoid compounds and ellagitannins contribute to this velvety mouthdrying astringency. Additionally, they often lack the bitter properties. Proanthocyanidins and phenolic acids elicit more puckering and roughing astringency with some additional bitter properties. Quercetin 3‐O‐rutinoside, along with other quercetin glycosides, is among the key astringent compounds in black tea and red currants. In foods, there are always various other additional attributes that are perceived at the same with astringency. Astringent compounds themselves may have other sensory characteristics, such as bitter or sour properties, or they may enhance or suppress other sensory properties. Components contributing to these other properties, such as sugars, may also have similar effects on astringent sensations. Food components eliciting sweetness or fattiness or some polymeric polysaccharides can be used to mask astringent subqualities. Astringency can generally be referred to as a negative contributor to the liking of various foods. On the other hand, perceptions of astringent properties can vary among individuals. Many genetic factors that influence perceptions of taste properties, such as variations in perceiving a bitter taste or variations in saliva, may also effect the perception of astringency. Individuals who are more sensitive to different sensations may notice the differences between astringent properties more clearly. This may not have effects on the overall perception of astringency. However, in many cases, the liking of astringent foods may need to be learned by repetitive exposure. Astringency is often among the key sensory properties forming the unique overall flavour of certain foods, and therefore it also influences whether or not a food is liked. In many cases, astringency may be an important sub‐property suppressed by other more abundant sensory properties, but it may still have a significant contribution to the overall flavour and thus consumer preferences. The results of the practical work of this thesis show that the astringent phenolic compounds are mostly located in the skin fractions of black currants, crowberries and bilberries (publications I–III). The skin fractions themselves are rather tasteless. However, the astringent phenolic compounds can be efficiently removed from these skin fractions by consecutive ethanol extractions. Berries contain a wide range of different flavonol glycosides, hydroxycinnamic acid derivatives and anthocyanins and some of them strongly contribute to the different astringent and bitterness properties. Sweetness and sourness are located in the juice fractions along with the majority of sugars and fruit acids. The sweet and sour properties of the juice may be used to mask the astringent and bitterness properties of the extracts. Enzymatic treatments increase the astringent properties and fermented flavour of the black currant juice and decrease sweetness and freshness due to the effects on chemical compositions (IV). Sourness and sweetness are positive contributors to the liking of crowberry and bilberry fractions, whereas bitterness is more negative (V). Some astringent properties in berries are clearly negative factors, whereas some may be more positive. The liking of berries is strongly influenced by various consumer background factors, such as motives and health concerns. The liking of berries and berry fractions may also be affected by genetic factors, such as variations in the gene hTAS2R38, which codes bitter taste receptors (V).

The estimation of tuberculosis transmission parameters using ABC and MCMC methods

The aim of this work is to apply approximate Bayesian computation in combination with Marcov chain Monte Carlo methods in order to estimate the parameters of tuberculosis transmission. The methods are applied to San Francisco data and the results are compared with the outcomes of previous works. Moreover, a methodological idea with the aim to reduce computational time is also described. Despite the fact that this approach is proved to work in an appropriate way, further analysis is needed to understand and test its behaviour in different cases. Some related suggestions to its further enhancement are described in the corresponding chapter.

Effects of alkaline fly ash precipitation on the Sphagnum mosses in Niinsaare bog, NE Estonia

Tiivistelmä: Emäksisen tuhkalaskeuman vaikutus rahkasammaliin Niinsaarensuolla Koillis-Virossa

The effects of alkaline fly ash precipitation on the Sphagnum mosses in Niinsaare bog, NE Estonia

Tiivistelmä: Emäksisen tuhkalaskeuman vaikutus rahkasammaliin Niinsaarensuolla Koillis-Virossa

Impact of precipitation on the water table level of different ombrotrophic raised bog complexes, central Estonia

Sadannan vaikutus vedenpinnan tasoon kohosuolla

On Bayesian predictive modeling : with applications on the analysis of chromatography data in forensic investigations

Med prediktion avses att man skattar det framtida värdet på en observerbar storhet. Kännetecknande för det bayesianska paradigmet är att osäkerhet gällande okända storheter uttrycks i form av sannolikheter. En bayesiansk prediktiv modell är således en sannolikhetsfördelning över de möjliga värden som en observerbar, men ännu inte observerad storhet kan anta. I de artiklar som ingår i avhandlingen utvecklas metoder, vilka bl.a. tillämpas i analys av kromatografiska data i brottsutredningar. Med undantag för den första artikeln, bygger samtliga metoder på bayesiansk prediktiv modellering. I artiklarna betraktas i huvudsak tre olika typer av problem relaterade till kromatografiska data: kvantifiering, parvis matchning och klustring. I den första artikeln utvecklas en icke-parametrisk modell för mätfel av kromatografiska analyser av alkoholhalt i blodet. I den andra artikeln utvecklas en prediktiv inferensmetod för jämförelse av två stickprov. Metoden tillämpas i den tredje artik eln för jämförelse av oljeprover i syfte att kunna identifiera den förorenande källan i samband med oljeutsläpp. I den fjärde artikeln härleds en prediktiv modell för klustring av data av blandad diskret och kontinuerlig typ, vilken bl.a. tillämpas i klassificering av amfetaminprover med avseende på produktionsomgångar.

In-line Monitoring of Precipitation Process using Raman Spectroscopy and ATR-FTIR

Polymorfian jatkuva seuranta saostuksessa on hyödyllistä suunnittelun ja kidetuotteen ominaisuuksien sekä kiteytystä seuraavan jatkoprosessoinnin kannalta. Tässä diplomityössä on tutkittu L-glutamiinihapon kahden (- ja ß) polymorfimuodon liukoisuuden riippuvuutta pH:sta ja lämpötilasta.Tulokseksi saatiin, että kummankin polymorfin liukoisuus kasvoi sekä pH:ta ettälämpötilaa kasvatettaessa. ¿¿muodon liukoisuus oli korkeampi kuin ß-muodon liukoisuus valituilla pH-arvoilla eri lämpötiloissa. Lisäksi seurattiin puolipanostoimisen saostuksen aikana 1-litraisella laboratoriokiteyttimellä muodostuvan kiteisen polymorfiseoksen koostumusta hyödyntäen in-line Raman-spektroskopiaa. Myös liuoksen pH-muutosta seurattiin sekä liuoksen koostumusta ATR FTIR-spektroskopian (Attenuated Total Reflection Fourier Transform Infrared Spectrometer) avulla. Tutkittavina muuttujina olivat mm. sekoitusintensiteetti, sekoitintyyppi, reaktanttien (natriumglutamaatti ja rikkihappo) konsentraatiot sekä syötetyn rikkihapon syöttökohta kiteyttimessä. Työhön sisältyi 36 koetta ja osa kokeista toistettiin tulosten oikeellisuuden tarkistamiseksi. Inline-mittaustulosten verifioimiseksi kidenäytteet analysoitiin myös käyttämällä konfokaali Raman-mikroskooppia. Kidemorfologiaa tutkittiin SEM-kuvien (Scanning Eletronic Microscope) avulla. Työ osoitti, että Raman-spektroskopia on joustava ja luotettava menetelmä saostusprosessin jatkuvaan seurantaan L-glutamiinihapolla. Alhaiset lähtöainepitoisuudet tuottivat pääasiassa ¿¿muotoa, kun taas alhainen sekoitusteho edisti ß-muodon muodostumista. Syöttökohta vaikutti merkittävästi polymorfiaan. Kun rikkihapon syöttökohta oli epäideaalisesti sekoitetulla vyöhykkeellä, nousi ylikylläisyystaso korkeaksi ja päätuote oli tällöin ß-muotoa. 6-lapainen vinolapaturbiini (nousukulma 45o) ja 6-lapainen levyturbiini eivät merkittävästi poikenneet toisistaan muodostuvien polymorfien osalta.

Improvements in Air Intake on Frame 6 Gas Turbines

Työssä tarkastellaan kahta kaasuturbiinin imuilman sisäänottojärjestelmän kehitysmenetelmää, imuilman jäähdytystä ja sähköstaattista suodatusta. Imuilman jäähdytysmenetelmien tarkastelussa käytettiin kahta kaasuturbiinin tehonlisäystekniikoiden laskentatyökalua. Arviointi kohdistettiin Glanford Brigg Generating Station -voimalaitoksen kaasuturbiinityyppiin ja paikallisiin englantilaisiin ilmasto-olosuhteisiin. Tarkastelussa olivat kostutusjäähdytys ja overspray. Tuloksia vertailtiin keskenään ja näiden perusteella arvioitiin menetelmien vaikutuksia tehoon, hyötysuhteeseen sekä veden kulutukseen. Sähköstaattisen suodattimen prototyyppi oli rakennettu Briggin voimalaitokselle. Järjestelmää kehitetään kaupalliseksi tuotteeksi ja tätä varten kerättiin tekninen dokumentaatio kokonaisuudeksi, jota voitiin hyödyntää tuotteistusprosessissa. Imuilman jäähdyttämisellä voidaan saavuttaa merkittävä tehonlisäys ilmasto-olosuhteista riippuen. Menetelmällä voidaan myös tasata lämpötilan vuorokausi-vaihtelusta aiheutuvia tehoeroja. Sähköstaattisen suodattimen prototyyppi saavutti kehitysvaiheelle asetetut tavoitteet. Sähköstaattinen suodatus tarjoaa useita etuja perinteiseen mekaaniseen suodatukseen verrattuna.

Adaptive MCMC methods with applications in environmental and geophysical models

This work presents new, efficient Markov chain Monte Carlo (MCMC) simulation methods for statistical analysis in various modelling applications. When using MCMC methods, the model is simulated repeatedly to explore the probability distribution describing the uncertainties in model parameters and predictions. In adaptive MCMC methods based on the Metropolis-Hastings algorithm, the proposal distribution needed by the algorithm learns from the target distribution as the simulation proceeds. Adaptive MCMC methods have been subject of intensive research lately, as they open a way for essentially easier use of the methodology. The lack of user-friendly computer programs has been a main obstacle for wider acceptance of the methods. This work provides two new adaptive MCMC methods: DRAM and AARJ. The DRAM method has been built especially to work in high dimensional and non-linear problems. The AARJ method is an extension to DRAM for model selection problems, where the mathematical formulation of the model is uncertain and we want simultaneously to fit several different models to the same observations. The methods were developed while keeping in mind the needs of modelling applications typical in environmental sciences. The development work has been pursued while working with several application projects. The applications presented in this work are: a winter time oxygen concentration model for Lake Tuusulanjärvi and adaptive control of the aerator; a nutrition model for Lake Pyhäjärvi and lake management planning; validation of the algorithms of the GOMOS ozone remote sensing instrument on board the Envisat satellite of European Space Agency and the study of the effects of aerosol model selection on the GOMOS algorithm.

Bayesian Estimation of Noise

In mathematical modeling the estimation of the model parameters is one of the most common problems. The goal is to seek parameters that fit to the measurements as well as possible. There is always error in the measurements which implies uncertainty to the model estimates. In Bayesian statistics all the unknown quantities are presented as probability distributions. If there is knowledge about parameters beforehand, it can be formulated as a prior distribution. The Bays’ rule combines the prior and the measurements to posterior distribution. Mathematical models are typically nonlinear, to produce statistics for them requires efficient sampling algorithms. In this thesis both Metropolis-Hastings (MH), Adaptive Metropolis (AM) algorithms and Gibbs sampling are introduced. In the thesis different ways to present prior distributions are introduced. The main issue is in the measurement error estimation and how to obtain prior knowledge for variance or covariance. Variance and covariance sampling is combined with the algorithms above. The examples of the hyperprior models are applied to estimation of model parameters and error in an outlier case.

On performance persistence in emerging markets: Empirical evidence from Asia-Pacific

The purpose of this study is to investigate the performance persistence of international mutual funds, employing a data sample which includes 2,168 European mutual funds investing in Asia-Pacific region; Japan excluded. Also, a number of performance measures is tested and compared, and especially, this study tries to find out whether iterative Bayesian procedure can be used to provide more accurate predictions on future performance. Finally, this study examines whether the cross-section of mutual fund returns can be explained with simple accounting variables and market risk. To exclude the effect of the Asian currency crisis in 1997, the studied time period includes years from 1999 to 2007. The overall results showed significant performance persistence for repeating winners when performance was tested with contingency tables. Also the annualized alpha spreads between the top and bottom portfolios were more than ten percent at their highest. Nevertheless, the results do not confirm the improved prediction accuracy of the Bayesian alphas.