Modelling mean above and below ground litter production based on yield tables

Selective papers of the workshop on "Development of models and forest soil surveys for monitoring of soil carbon", Koli, Finland, April 5-9 2006.

Eye Fundus Image Analysis for Automatic Detection of Diabetic Retinopathy

Diabetes is a rapidly increasing worldwide problem which is characterised by defective metabolism of glucose that causes long-term dysfunction and failure of various organs. The most common complication of diabetes is diabetic retinopathy (DR), which is one of the primary causes of blindness and visual impairment in adults. The rapid increase of diabetes pushes the limits of the current DR screening capabilities for which the digital imaging of the eye fundus (retinal imaging), and automatic or semi-automatic image analysis algorithms provide a potential solution. In this work, the use of colour in the detection of diabetic retinopathy is statistically studied using a supervised algorithm based on one-class classification and Gaussian mixture model estimation. The presented algorithm distinguishes a certain diabetic lesion type from all other possible objects in eye fundus images by only estimating the probability density function of that certain lesion type. For the training and ground truth estimation, the algorithm combines manual annotations of several experts for which the best practices were experimentally selected. By assessing the algorithm’s performance while conducting experiments with the colour space selection, both illuminance and colour correction, and background class information, the use of colour in the detection of diabetic retinopathy was quantitatively evaluated. Another contribution of this work is the benchmarking framework for eye fundus image analysis algorithms needed for the development of the automatic DR detection algorithms. The benchmarking framework provides guidelines on how to construct a benchmarking database that comprises true patient images, ground truth, and an evaluation protocol. The evaluation is based on the standard receiver operating characteristics analysis and it follows the medical practice in the decision making providing protocols for image- and pixel-based evaluations. During the work, two public medical image databases with ground truth were published: DIARETDB0 and DIARETDB1. The framework, DR databases and the final algorithm, are made public in the web to set the baseline results for automatic detection of diabetic retinopathy. Although deviating from the general context of the thesis, a simple and effective optic disc localisation method is presented. The optic disc localisation is discussed, since normal eye fundus structures are fundamental in the characterisation of DR.

Comparison of model-based above ground biomass predictions from LiDAR

In order to reduce greenhouse emissions from forest degradation and deforestation the international programme REDD (Reducing Emissions from Deforestation and forest Degradation) was established in 2005 by the United Nations Framework Convention on Climate Change (UNFCCC). This programme is aimed to financially reward to developing countries for any emissions reductions. Under this programm the project of setting up the payment system in Nepal was established. This project is aimed to engage local communities in forest monitoring. The major objective of this thesis is to compare and verify data obtained from di erect sources - remotely sensed data, namely LiDAR and field sample measurements made by two groups of researchers using two regression models - Sparse Bayesian Regression and Bayesian Regression with Orthogonal Variables.

Biomass Utilization in PFC Co-firing System with the Slagging and Fouling Analysis

Master’s thesis Biomass Utilization in PFC Co-firing System with the Slagging and Fouling Analysis is the study of the modern technologies of different coal-firing systems: PFC system, FB system and GF system. The biomass co-fired with coal is represented by the research of the company Alstom Power Plant. Based on the back ground of the air pollution, greenhouse effect problems and the national fuel security today, the bioenergy utilization is more and more popular. However, the biomass is promoted to burn to decrease the emission amount of carbon dioxide and other air pollutions, new problems form like slagging and fouling, hot corrosion in the firing systems. Thesis represent the brief overview of different coal-firing systems utilized in the world, and focus on the biomass-coal co-firing in the PFC system. The biomass supply and how the PFC system is running are represented in the thesis. Additionally, the new problems of hot corrosion, slagging and fouling are mentioned. The slagging and fouling problem is simulated by using the software HSC Chemistry 6.1, and the emissions comparison between coal-firing and co-firing are simulated as well.

PERFORMANCE ANALYSIS OF A TACTICAL ASSET ALLOCATION – Evidence from the Black–Litterman model based fixed income portfolio

Over time the demand for quantitative portfolio management has increased among financial institutions but there is still a lack of practical tools. In 2008 EDHEC Risk and Asset Management Research Centre conducted a survey of European investment practices. It revealed that the majority of asset or fund management companies, pension funds and institutional investors do not use more sophisticated models to compensate the flaws of the Markowitz mean-variance portfolio optimization. Furthermore, tactical asset allocation managers employ a variety of methods to estimate return and risk of assets, but also need sophisticated portfolio management models to outperform their benchmarks. Recent development in portfolio management suggests that new innovations are slowly gaining ground, but still need to be studied carefully. This thesis tries to provide a practical tactical asset allocation (TAA) application to the Black–Litterman (B–L) approach and unbiased evaluation of B–L models’ qualities. Mean-variance framework, issues related to asset allocation decisions and return forecasting are examined carefully to uncover issues effecting active portfolio management. European fixed income data is employed in an empirical study that tries to reveal whether a B–L model based TAA portfolio is able outperform its strategic benchmark. The tactical asset allocation utilizes Vector Autoregressive (VAR) model to create return forecasts from lagged values of asset classes as well as economic variables. Sample data (31.12.1999–31.12.2012) is divided into two. In-sample data is used for calibrating a strategic portfolio and the out-of-sample period is for testing the tactical portfolio against the strategic benchmark. Results show that B–L model based tactical asset allocation outperforms the benchmark portfolio in terms of risk-adjusted return and mean excess return. The VAR-model is able to pick up the change in investor sentiment and the B–L model adjusts portfolio weights in a controlled manner. TAA portfolio shows promise especially in moderately shifting allocation to more risky assets while market is turning bullish, but without overweighting investments with high beta. Based on findings in thesis, Black–Litterman model offers a good platform for active asset managers to quantify their views on investments and implement their strategies. B–L model shows potential and offers interesting research avenues. However, success of tactical asset allocation is still highly dependent on the quality of input estimates.