Inventory of sparse forest populations using adaptive cluster sampling

Abstract

Solving large sparse linear systems over the field GF(2)

Nowadays problem of solving sparse linear systems over the field GF(2) remain as a challenge. The popular approach is to improve existing methods such as the block Lanczos method (the Montgomery method) and the Wiedemann-Coppersmith method. Both these methods are considered in the thesis in details: there are their modifications and computational estimation for each process. It demonstrates the most complicated parts of these methods and gives the idea how to improve computations in software point of view. The research provides the implementation of accelerated binary matrix operations computer library which helps to make the progress steps in the Montgomery and in the Wiedemann-Coppersmith methods faster.

Kaupungin ja maaseudun vuorovaikutus alueellisessa kehittämispolitiikassa

In Finland, European Union membership and economic globalisation have changed the position of regions from closed territorial systems to nodes of open international networks. The increasing complexity of cities as globalised knowledge centres and functionally specialised and diversified rural areas, and on the other hand growing disparities between prosperous urban cores and lagging peripheral areas are also essential features in contemporary regional development. These trends have produced new needs to promote mutual dialogue between cities and the countryside in western market economies. Urban-rural interaction is an idea which was developed in the late 1990s within regional policy to pull together these new challenges to regional development and handle cities and the countryside as a whole. The aim of my study is to conceptualise the idea of urban-rural interaction, explain the phenomenon theoretically, clarify past and present urban and rural development and analyse regional policies from the interaction angle. The ultimate purpose is to illustrate the existence and nature of particular interaction policy in a globalising society. The general method is discourse analysis, which I use in three cases: Central Finland, South-Ostrobothnia and South-West Finland. Theoretically I have a two-dimensional approach. On the first hand I use World-System theory to explain how the global economy is moulding urban and rural structures at the regional level. On the other hand I use regime theory to explain local political actions and practises between cities and the countryside under the overlapping pressures deriving from reformulated regional structures and policies.

Adaptation to globalisation in Finland has been carried out by strengthening urban centres. The stress in regional policy has been in urban development. The development of the countryside has mostly been implemented by a separate rural policy. At the end of the 1990s and early 2000s Finnish cities have actually shown themselves to be competitive in global markets. The drawbacks of the new growth centre policy have been the sparse network of prosperous cities and their weak spreading effects, which have hindered comprehensive regional development. Tensions between urban and rural areas have also deepened. In this situation the interaction policy is used as a way of balancing development and moderate conflicts within the regions. From this point of view urban-rural interaction can be seen as a way of tackling the challenges of globalisation.

On the other hand the results emphasise that actors involved in regional development still believe, although the hegemonic discourse is on urban policy, that there are opportunities to stimulate progress in the countryside as well. In the situation where regional authorities control development resources, rural development can be successful only if rural actors manage to establish fruitful relationships with their urban partners. This is also the weakness of the programme-based regional policy. If rural municipalities or other actors are for any reason incapable of building development regimes with cities, the offers of interaction policy will be useless.

The problem of the interaction policy is that the focus and methods of it have so far been rather underdeveloped. In order to improve the efficiency of the interaction policy, further research should concentrate on the social processes which define the position of cities and the countryside as partners of interaction, and practises which promote or prohibit the possibilities of developing the interaction policy. The efforts to define different contents of urban-rural interaction or promote interaction projects should not have such an important role in the future as they have had so far. Instead, the focus of interaction policy should be on questions such as how to manage the political tensions between town and country and how to create a positive atmosphere for regional policy where the needs of urban and rural development are promoted equally.

Immunopathogenesis of Asthma and Atopic Diseases – The specific Role of a selected Panel of Genes in human T helper Cell Differentiation

T helper cell (Th) functions are crucial for proper immune defence against various intra- and extracellular pathogens. According to the specific immune responses, Th cells can be classified into subtypes, Th1 and Th2 cells being the most frequently characterized classes. Th1 and Th2 cells interact with other immune cells by regulating their functions with specific cytokine production. IFN, IL-2 and TNF- are the cytokines predominantly produced by Th1 cells whereas Th2 cells produce Th2-type cytokines, such as IL-4, IL-5 and IL-13. Upon TCR activation and in the presence of polarizing cytokines, Th cells differentiate into effector subtypes from a common precursor cell. IFN and IL-12 are the predominant Th1 polarizing cytokines whereas IL-4 directs Th2 polarization. The cytokines mediate their effects through specific receptor signalling. The differentiation process is complex, involving various signalling molecules and routes, as well as functions of the specific transcription factors. The functions of the Th1/Th2 cells are tightly regulated; however, knowledge on human Th cell differentiation is, as yet, fairly poor. The susceptibility for many immune-mediated disorders often originates from disturbed Th cell responses. Thus, research is needed for defining the molecular mechanisms involved in the differentiation and balanced functions of the Th cells. Importantly, the new information obtained will be crucial for a better understanding of the pathogenesis of immune-mediated disorders, such as asthma or autoimmune diseases. In the first subproject of this thesis, the role of genetic polymorphisms in the human STAT6, GATA3 and STAT4 genes were investigated for asthma or atopy susceptibility in Finnish asthma families by association analysis. These genes code for key transcription factors regulating Th cell differentiation. The study resulted in the identification of a GATA3 haplotype that associated with asthma and related traits (high serum IgE level). In the second subproject, an optimized method for human primary T cell transfection and enrichment was established. The method can be utilized for functional studies for the selected genes of interest. The method was also utilized in the third subproject, which aimed at the identification of novel genes involved in early human Th cell polarization (0-48h) using genome-wide oligonucleotide arrays. As a result, numerous genes and ESTs with known or unknown functions were identified in the study. Using an shRNA knockdown approach, a panel of novel IL-4/STAT6 regulated genes were identified in the functional studies of the genes. Moreover, one of the genes, NDFIP2, with a previously uncharacterized role in the human Th differentiation, was observed to promote IFN production of the differentiated Th1 cells. Taken together, the results obtained have revealed potential new relevant candidate genes serving as a basis for further studies characterizing the detailed networks involved in the human Th cell differentiation as well as in the genetic susceptibility of Th-mediated immune disorders.

Increasing the efficiency of disparity computation by interpolation

Tässä diplomityössä tutkitaan dispariteettikartan laskennan tehostamista interpoloimalla. Kolmiomittausta käyttämällä stereokuvasta muodostetaan ensin harva dispariteettikartta, jonka jälkeen koko kuvan kattava dispariteettikartta muodostetaan interpoloimalla. Kolmiomittausta varten täytyy tietää samaa reaalimaailman pistettä vastaavat kuvapisteet molemmissa kameroissa. Huolimatta siitä, että vastaavien pisteiden hakualue voidaan pienentää kahdesta ulottuvuudesta yhteen ulottuvuuteen käyttämällä esimerkiksi epipolaarista geometriaa, on laskennallisesti tehokkaampaa määrittää osa dispariteetikartasta interpoloimalla, kuin etsiä vastaavia kuvapisteitä stereokuvista. Myöskin johtuen stereonäköjärjestelmän kameroiden välisestä etäisyydestä, kaikki kuvien pisteet eivät löydy toisesta kuvasta. Näin ollen on mahdotonta määrittää koko kuvan kattavaa dispariteettikartaa pelkästään vastaavista pisteistä. Vastaavien pisteiden etsimiseen tässä työssä käytetään dynaamista ohjelmointia sekä korrelaatiomenetelmää. Reaalimaailman pinnat ovat yleisesti ottaen jatkuvia, joten geometrisessä mielessä on perusteltua approksimoida kuvien esittämiä pintoja interpoloimalla. On myöskin olemassa tieteellistä näyttöä, jonkamukaan ihmisen stereonäkö interpoloi objektien pintoja.

Bayesian classification of forest ecological type from satellite data

The main objective of this study was todo a statistical analysis of ecological type from optical satellite data, using Tipping's sparse Bayesian algorithm. This thesis uses "the Relevence Vector Machine" algorithm in ecological classification betweenforestland and wetland. Further this bi-classification technique was used to do classification of many other different species of trees and produces hierarchical classification of entire subclasses given as a target class. Also, we carried out an attempt to use airborne image of same forest area. Combining it with image analysis, using different image processing operation, we tried to extract good features and later used them to perform classification of forestland and wetland.

Vaiheohjatut antenniryhmät GSM-tukiasemissa

Diplomityön aiheena oli selvittää onko Suomessa GSM-tukiasemissa käytössä vaiheohjattuja antenniryhmiäja olisiko tällaisten antennien käyttöön kiinnostusta tai mitään es-teitä. Lähtökohtana tälle työlle oli ajatus GSM-tukiasema-antennista, jota voitaisiin kääntää tarpeen mukaan haluttuun suuntaan. Vaiheohjatut antenniryhmät mahdollistavat juuri tällaisen antennin keilan kääntämisen ja muokkaamisen elektronisesti, ilman kuluvia osia. Keilan muitakin ominaisuuksia voidaan säätää, kuten muotoa ja keilojen määrää. Nämä ominaisuudet mahdollistaisivat esimerkiksi ruuhkaisilla alueilla keilojen lisäämisen, jolloin alueen puhelujen välityskapasiteetti kasvaisi.Tarvittaessa voitaisiin myös keilan muotoa muuttaa. Esimerkiksi hätätilanteessasaadaan haluttu keila suunnattua tarkasti tietylle alueelle tai toiselle tukiasemalle ja näin varmistettua kuuluvuus. Myös huoltotoimenpiteet joissakin tapauksissa helpottuisivat. Etenkin vaikeakulkuisissa paikoissa sijaitsevien tukiasemien ensiapu, esimerkiksi antennin fyysisesti kääntyessä, voitaisiin hoitaa etänä kääntämällä pelkkää keilaa ja tässä tapauksessa kääntää antenni tukiaseman normaalin huollon yhteydessä. Suurimpia ongelmakohtia kyseisen tekniikan käyttöönotossa on ollut hinta, mutta muun muassa uusien valmistustekniikoiden avulla vaiheohjattujen antenniryhmien hintoja ollaan saatu pudotettua.

FPGA-Based Control Design for Power Electronic Applications

Tehoelektoniikkalaitteella tarkoitetaan ohjaus- ja säätöjärjestelmää, jolla sähköä muokataan saatavilla olevasta muodosta haluttuun uuteen muotoon ja samalla hallitaan sähköisen tehon virtausta lähteestä käyttökohteeseen. Tämä siis eroaa signaalielektroniikasta, jossa sähköllä tyypillisesti siirretään tietoa hyödyntäen eri tiloja. Tehoelektroniikkalaitteita vertailtaessa katsotaan yleensä niiden luotettavuutta, kokoa, tehokkuutta, säätötarkkuutta ja tietysti hintaa. Tyypillisiä tehoelektroniikkalaitteita ovat taajuudenmuuttajat, UPS (Uninterruptible Power Supply) -laitteet, hitsauskoneet, induktiokuumentimet sekä erilaiset teholähteet. Perinteisesti näiden laitteiden ohjaus toteutetaan käyttäen mikroprosessoreja, ASIC- (Application Specific Integrated Circuit) tai IC (Intergrated Circuit) -piirejä sekä analogisia säätimiä. Tässä tutkimuksessa on analysoitu FPGA (Field Programmable Gate Array) -piirien soveltuvuutta tehoelektroniikan ohjaukseen. FPGA-piirien rakenne muodostuu erilaisista loogisista elementeistä ja niiden välisistä yhdysjohdoista.Loogiset elementit ovat porttipiirejä ja kiikkuja. Yhdysjohdot ja loogiset elementit ovat piirissä kiinteitä eikä koostumusta tai lukumäärää voi jälkikäteen muuttaa. Ohjelmoitavuus syntyy elementtien välisistä liitännöistä. Piirissä on lukuisia, jopa miljoonia kytkimiä, joiden asento voidaan asettaa. Siten piirin peruselementeistä voidaan muodostaa lukematon määrä erilaisia toiminnallisia kokonaisuuksia. FPGA-piirejä on pitkään käytetty kommunikointialan tuotteissa ja siksi niiden kehitys on viime vuosina ollut nopeaa. Samalla hinnat ovat pudonneet. Tästä johtuen FPGA-piiristä on tullut kiinnostava vaihtoehto myös tehoelektroniikkalaitteiden ohjaukseen. Väitöstyössä FPGA-piirien käytön soveltuvuutta on tutkittu käyttäen kahta vaativaa ja erilaista käytännön tehoelektroniikkalaitetta: taajuudenmuuttajaa ja hitsauskonetta. Molempiin testikohteisiin rakennettiin alan suomalaisten teollisuusyritysten kanssa soveltuvat prototyypit,joiden ohjauselektroniikka muutettiin FPGA-pohjaiseksi. Lisäksi kehitettiin tätä uutta tekniikkaa hyödyntävät uudentyyppiset ohjausmenetelmät. Prototyyppien toimivuutta verrattiin vastaaviin perinteisillä menetelmillä ohjattuihin kaupallisiin tuotteisiin ja havaittiin FPGA-piirien mahdollistaman rinnakkaisen laskennantuomat edut molempien tehoelektroniikkalaitteiden toimivuudessa. Työssä on myösesitetty uusia menetelmiä ja työkaluja FPGA-pohjaisen säätöjärjestelmän kehitykseen ja testaukseen. Esitetyillä menetelmillä tuotteiden kehitys saadaan mahdollisimman nopeaksi ja tehokkaaksi. Lisäksi työssä on kehitetty FPGA:n sisäinen ohjaus- ja kommunikointiväylärakenne, joka palvelee tehoelektroniikkalaitteiden ohjaussovelluksia. Uusi kommunikointirakenne edistää lisäksi jo tehtyjen osajärjestelmien uudelleen käytettävyyttä tulevissa sovelluksissa ja tuotesukupolvissa.

Measuring irregularities and surface defects from printed patterns

Quality inspection and assurance is a veryimportant step when today's products are sold to markets. As products are produced in vast quantities, the interest to automate quality inspection tasks has increased correspondingly. Quality inspection tasks usuallyrequire the detection of deficiencies, defined as irregularities in this thesis. Objects containing regular patterns appear quite frequently on certain industries and science, e.g. half-tone raster patterns in the printing industry, crystal lattice structures in solid state physics and solder joints and components in the electronics industry. In this thesis, the problem of regular patterns and irregularities is described in analytical form and three different detection methods are proposed. All the methods are based on characteristics of Fourier transform to represent regular information compactly. Fourier transform enables the separation of regular and irregular parts of an image but the three methods presented are shown to differ in generality and computational complexity. Need to detect fine and sparse details is common in quality inspection tasks, e.g., locating smallfractures in components in the electronics industry or detecting tearing from paper samples in the printing industry. In this thesis, a general definition of such details is given by defining sufficient statistical properties in the histogram domain. The analytical definition allowsa quantitative comparison of methods designed for detail detection. Based on the definition, the utilisation of existing thresholding methodsis shown to be well motivated. Comparison of thresholding methods shows that minimum error thresholding outperforms other standard methods. The results are successfully applied to a paper printability and runnability inspection setup. Missing dots from a repeating raster pattern are detected from Heliotest strips and small surface defects from IGT picking papers.

Design and implementation of FPGA-based LQ control of active magnetic bearings

The need for high performance, high precision, and energy saving in rotating machinery demands an alternative solution to traditional bearings. Because of the contactless operation principle, the rotating machines employing active magnetic bearings (AMBs) provide many advantages over the traditional ones. The advantages such as contamination-free operation, low maintenance costs, high rotational speeds, low parasitic losses, programmable stiffness and damping, and vibration insulation come at expense of high cost, and complex technical solution. All these properties make the use of AMBs appropriate primarily for specific and highly demanding applications. High performance and high precision control requires model-based control methods and accurate models of the flexible rotor. In turn, complex models lead to high-order controllers and feature considerable computational burden. Fortunately, in the last few years the advancements in signal processing devices provide new perspective on the real-time control of AMBs. The design and the real-time digital implementation of the high-order LQ controllers, which focus on fast execution times, are the subjects of this work. In particular, the control design and implementation in the field programmable gate array (FPGA) circuits are investigated. The optimal design is guided by the physical constraints of the system for selecting the optimal weighting matrices. The plant model is complemented by augmenting appropriate disturbance models. The compensation of the force-field nonlinearities is proposed for decreasing the uncertainty of the actuator. A disturbance-observer-based unbalance compensation for canceling the magnetic force vibrations or vibrations in the measured positions is presented. The theoretical studies are verified by the practical experiments utilizing a custom-built laboratory test rig. The test rig uses a prototyping control platform developed in the scope of this work. To sum up, the work makes a step in the direction of an embedded single-chip FPGA-based controller of AMBs.

Matkapuhelimen puhelinmuistiopalvelimen tietorakenteiden ja lajittelualgoritmien analysointi ja suunnittelu

Puhelinmuistio on yksi matkapuhelimen käytetyimmistä ominaisuuksista. Puhelinmuistion tulee siksi olla kaikissa tilanteissa mahdollisimman nopeasti käytettävissä. Tämä edellyttää puhelinmuistiopalvelimelta tehokkaita tietorakenteita ja lajittelualgoritmeja. Nokian matkapuhelimissa puhelinmuistiopalvelin käyttää hakurakenteena järjestettyjä taulukoita. Työn tavoitteena oli kehittää puhelinmuistiopalvelimen hakutaulukoiden lajittelu mahdollisimman nopeaksi. Useita eri lajittelualgoritmeja vertailtiin ja niiden suoritusaikoja analysoitiin eri tilanteissa. Insertionsort-lajittelualgoritmin todettiin olevan nopein algoritmi lähes järjestyksessä olevien taulukoiden lajitteluun. Analyysin perusteella Quicksort-algoritmi lajittelee nopeimmin satunnaisessa järjestyksessä olevat taulukot. Quicksort-insertionsort –hybridialgoritmin havaittiin olevan paras lajittelualgoritmi puhelinmuistion lajitteluun. Sopivalla parametroinnilla tämä algoritmi on nopea satunnaisessa järjestyksessä olevalle aineistolle. Se kykenee hyödyntämään lajiteltavassa aineistossa valmiina olevaa järjestystä. Algoritmi ei kasvata merkittävästi muistinkulutusta. Uuden algoritmin ansiosta hakutaulukoiden lajittelu nopeutuu parhaimmillaan useita kymmeniä prosentteja.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Inversion and assimilation methods with applications in geophysical remote sensing

Stratospheric ozone can be measured accurately using a limb scatter remote sensing technique at the UV-visible spectral region of solar light. The advantages of this technique includes a good vertical resolution and a good daytime coverage of the measurements. In addition to ozone, UV-visible limb scatter measurements contain information about NO2, NO3, OClO, BrO and aerosols. There are currently several satellite instruments continuously scanning the atmosphere and measuring the UVvisible region of the spectrum, e.g., the Optical Spectrograph and Infrared Imager System (OSIRIS) launched on the Odin satellite in February 2001, and the Scanning Imaging Absorption SpectroMeter for Atmospheric CartograpHY (SCIAMACHY) launched on Envisat in March 2002. Envisat also carries the Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument, which also measures limb-scattered sunlight under bright limb occultation conditions. These conditions occur during daytime occultation measurements. The global coverage of the satellite measurements is far better than any other ozone measurement technique, but still the measurements are sparse in the spatial domain. Measurements are also repeated relatively rarely over a certain area, and the composition of the Earth’s atmosphere changes dynamically. Assimilation methods are therefore needed in order to combine the information of the measurements with the atmospheric model. In recent years, the focus of assimilation algorithm research has turned towards filtering methods. The traditional Extended Kalman filter (EKF) method takes into account not only the uncertainty of the measurements, but also the uncertainty of the evolution model of the system. However, the computational cost of full blown EKF increases rapidly as the number of the model parameters increases. Therefore the EKF method cannot be applied directly to the stratospheric ozone assimilation problem. The work in this thesis is devoted to the development of inversion methods for satellite instruments and the development of assimilation methods used with atmospheric models.

Analysis of patterns in mining time series using Qlucore

Raw measurement data does not always immediately convey useful information, but applying mathematical statistical analysis tools into measurement data can improve the situation. Data analysis can offer benefits like acquiring meaningful insight from the dataset, basing critical decisions on the findings, and ruling out human bias through proper statistical treatment. In this thesis we analyze data from an industrial mineral processing plant with the aim of studying the possibility of forecasting the quality of the final product, given by one variable, with a model based on the other variables. For the study mathematical tools like Qlucore Omics Explorer (QOE) and Sparse Bayesian regression (SB) are used. Later on, linear regression is used to build a model based on a subset of variables that seem to have most significant weights in the SB model. The results obtained from QOE show that the variable representing the desired final product does not correlate with other variables. For SB and linear regression, the results show that both SB and linear regression models built on 1-day averaged data seriously underestimate the variance of true data, whereas the two models built on 1-month averaged data are reliable and able to explain a larger proportion of variability in the available data, making them suitable for prediction purposes. However, it is concluded that no single model can fit well the whole available dataset and therefore, it is proposed for future work to make piecewise non linear regression models if the same available dataset is used, or the plant to provide another dataset that should be collected in a more systematic fashion than the present data for further analysis.

Simulating the performance of paper machine control

The topic of this thesis is the simulation of a combination of several control and data assimilation methods, meant to be used for controlling the quality of paper in a paper machine. Paper making is a very complex process and the information obtained from the web is sparse. A paper web scanner can only measure a zig zag path on the web. An assimilation method is needed to process estimates for Machine Direction (MD) and Cross Direction (CD) profiles of the web. Quality control is based on these measurements. There is an increasing need for intelligent methods to assist in data assimilation. The target of this thesis is to study how such intelligent assimilation methods are affecting paper web quality. This work is based on a paper web simulator, which has been developed in the TEKES funded MASI NoTes project. The simulator is a valuable tool in comparing different assimilation methods. The thesis contains the comparison of four different assimilation methods. These data assimilation methods are a first order Bayesian model estimator, an ARMA model based on a higher order Bayesian estimator, a Fourier transform based Kalman filter estimator and a simple block estimator. The last one can be considered to be close to current operational methods. From these methods Bayesian, ARMA and Kalman all seem to have advantages over the commercial one. The Kalman and ARMA estimators seems to be best in overall performance.