Climatic conditions for crop production in Nordic countries

Selostus: Kasvintuotannon ilmasto-olosuhteet Pohjoismaissa

Monitoring and predicting crop growth and analysing agricultural ecosystems by remote sensing

Selostus: Maatalousekosysteemien analysointi ja sadon ennustaminen kaukokartoituksen avulla

Assessing the risks and uncertainties of regional crop potential under changing climate in Finland

Selostus: Arviointi Suomen kasvintuotantopotentiaalin alueellisista riskeistä ja epävarmuuksista ilmaston muuttuessa

The application of agricultural land rating and crop models to CO2 and climate change issues in Northern regions : the Mackenzie Basin case study

Selostus: Viljelyvyöhykkeiden ja kasvumallien soveltaminen ilmastonmuutoksen tutkimisessa: Mackenzien jokialue, Kanada

Effect of fertilization on flowering and seed crop in Scots pine seed orchards

Summary

Influence of changes in crop cultivation areas on pollen contents of honey

Selostus: Viljelyalojen muutosten vaikutus hunajan siitepölysisältöön

Simulation of spring wheat responses to elevated CO‚́‚ and temperature by using CERES-wheat crop model

Selostus: Kohotettujen CO‚́‚:n ja lämpötilan vaikutukset kevätvehnän fenologiseen kehitykseen ja sadontuottomahdollisuuksiin

Regeneration process from seed crop to saplings : a case study in uneven-aged Norway spruce-dominated stands in southern Finland

Abstract

Fuzzy Principal Components Analysis

Due to the large number of characteristics, there is a need to extract the most relevant characteristicsfrom the input data, so that the amount of information lost in this way is minimal, and the classification realized with the projected data set is relevant with respect to the original data. In order to achieve this feature extraction, different statistical techniques, as well as the principal components analysis (PCA) may be used. This thesis describes an extension of principal components analysis (PCA) allowing the extraction ofa finite number of relevant features from high-dimensional fuzzy data and noisy data. PCA finds linear combinations of the original measurement variables that describe the significant variation in the data. The comparisonof the two proposed methods was produced by using postoperative patient data. Experiment results demonstrate the ability of using the proposed two methods in complex data. Fuzzy PCA was used in the classificationproblem. The classification was applied by using the similarity classifier algorithm where total similarity measures weights are optimized with differential evolution algorithm. This thesis presents the comparison of the classification results based on the obtained data from the fuzzy PCA.

Protection of publicly distributed software components

The nature of client-server architecture implies that some modules are delivered to customers. These publicly distributed commercial software components are under risk, because users (and simultaneously potential malefactors) have physical access to some components of the distributed system. The problem becomes even worse if interpreted programming languages are used for creation of client side modules. The language Java, which was designed to be compiled into platform independent byte-code is not an exception and runs the additional risk. Along with advantages like verifying the code before execution (to ensure that program does not produce some illegal operations)Java has some disadvantages. On a stage of byte-code a java program still contains comments, line numbers and some other instructions, which can be used for reverse-engineering. This Master's thesis focuses on protection of Java code based client-server applications. I present a mixture of methods to protect software from tortious acts. Then I shall realize all the theoretical assumptions in a practice and examine their efficiency in examples of Java code. One of the criteria's to evaluate the system is that my product is used for specialized area of interactive television.