Land cover change detection in urban areas provides valuable data on loss of forest and agricultural land to residential and commercial development. Using Landsat 5 Thematic Mapper (1991) and Landsat 7 ETM+ (2000) imagery of Gwinnett County, GA, change images were obtained using image differencing of Normalized Difference Vegetation Index (NDVI), principal components analysis (PCA), and Tasseled Cap-transformed images. Ground truthing and accuracy assessment determined that land cover change detection using the NDVI and Tasseled Cap image transformation methods performed best in the study area, while PCA performed the worst of the three methods assessed. Analyses on vegetative and vegetation changes from 1991- 2000 revealed that these methods perform well for detecting changes in vegetation and/or vegetative characteristics but do not always correspond with changes in land use. Gwinnett County lost an estimated 13,500 hectares of vegetation cover during the study period to urban sprawl, with the majority of the loss coming from forested areas.

The thesis presents an investigation of the potential of measuring plant condition from hyperspectral reflectance data. To do this, some linear methods for embedding the high dimensional hyperspectral data and to perform regression to a plant condition space have been compared. A preprocessing step that aims at normalized illumination intensity in the hyperspectral images has been conducted and some different methods for this purpose have also been compared.A large scale experiment has been conducted where tobacco plants have been grown and treated differently with respect to watering and nutrition. The treatment of the plants has served as ground truth for the plant condition. Four sets of plants have been grown one week apart and the plants have been measured at different ages up to the age of about five weeks. The thesis concludes that there is a relationship between plant treatment and their leaves' spectral reflectance, but the treatment has to be somewhat extreme for enabling a useful treatment approximation from the spectrum. CCA has been the proposed method for calculation of the hyperspectral basis that is used to embed the hyperspectral data to the plant condition (treatment) space. A preprocessing method that uses a weighted normalization of the spectrums for illumination intensity normalization is concluded to be the most powerful of the compared methods.

Functional Magnetic Resonance Imaging (fMRI) is a neuroimaging technique used to study brain functionality to enhance our understanding of the brain. This technique is based on MRI, a painless, noninvasive image acquisition method without harmful radiation. Small local blood oxygenation changes which are reflected as small intensity changes in the MR images are utilized to locate the active brain areas. Radio frequency pulses and a strong static magnetic field are used to measure the correlation between the physical changes in the brain and the mental functioning during the performance of cognitive tasks. This master thesis presents approaches for the analysis of fMRI data. The constrained Canonical Correlation Analysis (CCA) which is able to exploit the spatio-temporal nature of an active area is presented and tested on real human fMRI data. The actual distribution of active brain voxels is not known in the case of real human data. To evaluate the performance of the diagnostic algorithms applied to real human data, a modified Receiver Operating Characteristics (modified ROC) which deals with this lack of knowledge is presented. The tests on real human data reveal the better detection efficiency with the constrained CCA algorithm. A second aim of this thesis was to implement the promising technique of constrained CCA into the software environment SPM. To implement the constrained CCA algorithms into the fMRI part of SPM2, a toolbox containing Matlab functions has been programmed for the further use by neurological scientists. The new SPM functionalities to exploit the spatial extent of the active regions with CCA are presented and tested.

This thesis is a part of a project where a new method, the base descriptor approach, is studied. The purpose of this method is to reduce drift and extract vital information from electronic tongue measurements. Reference solutions, called descriptors, are measured and the measurements are used to find base descriptors. A base descriptor is, in this thesis, a regression vector for prediction of the property that the descriptor represent. The property is in this case the concentration of a chemical substance in the descriptor solution. Measurements from test samples, in this case fruit juices, are projected onto the base descriptors to extract vital and drift-stable information from the test samples. The base descriptors are used to determine the concentrations of the descriptors'chemical substances in the juices and thereby also to classify the different juices. It is assumed that the measurements of samples of juices and descriptors drift the same way. This assumption has to be true in order for the base descriptor approach to work. The base descriptors are calculated by multivariate regression methods like partial least squares regression (PLSR) and principal component regression (PCR). Only two of the descriptors tested in this thesis worked as basis for base descriptors. The base descriptors'predictions of the concentrations of chemical substances in the juices are hard to evaluate since the true concentrations are unknown. Comparing the projections of juice measurements onto the base descriptors with a classification model on the juice measurements performed by principal component analysis (PCA), there is no significant difference in drift of the juice measurements in the results of the two methods. The base descriptors, however, separates the juices for classification somewhat better than the classification of juices performed by PCA.

The purpose of this thesis was to examine the demand for cross directional control of basis weight on a board machine. To analyse the demand, changes made by the control system are studied. The significant changes were expected to be present when a major event occurred on the machine. The events classified as major were changes in basis weight, of grade or of coating blade. Break of board and stoppage of the machine were also included. These events can be seen as large disturbances to the machine. In order to identify the disturbances a methodology had to be developed. The methodology developed is to analyse the output from a model with the actuators of the control system as input and measurement of basis weight as output. The analysis of this output was done using the multivariate method of principal component analysis. The data analysed in this thesis was collected on-line from a board machine operating within the Stora Enso group. Over a period of 3 months, a total of 47 sets of data were collected, each set representing 12-14 hours. The data analysis shows that the variations in the control system are greater than the variation in the measured basis weight. This is a strong indication that the control system is needed and in order to find disturbances in the cross directional profile it is not enough only to analyse the final product, the control signals also have to be analysed. The large disturbances do not necessarily emerge from the major events as assumed. Other causes might havelarger impact to the process then first believed. One of the major obstacles in trying to explain the variations is that the basis weight is controlled by using the centre layer of the board but measured on the final product. This leads to the fact that the errors seen by the measuring system can result from anything on the machine and be compensated by basis weight in the centre layer of the board.

At the Skoghall pappermill, sulphatepaper pulp is produced in a continuous digester originally from 1969. To be able to maintain a high level of production there is a need for a process with few disturbances. Variations in how well the wooden chips are packed in the digester is one form of disturbance. Today there are no available measurements on how well the chips are packed. Instead this is regarded as being constant. The variation in the so called bulk density of the chips is mainly due to variations in the percentage with small dimensions. Chips are classified in relation to their size and one of the smallest classes is referred to as pin chips. These are believed to have a big impact on the bulk density. The amount of pin chips fluctuate more then the other classes, there by causing disturbances. The Skoghall pappermill has invested in a ScanChip. This is an instrument that measures the dimensions of the chips optically. ScanChip presents figures on chip quality, including a measurement of the bulk density. However, it has been shown that this measurement is not valid for the Skoghall pappermill. By using data from ScanChip a model that predicts how well the chips are packed has been devised. This value is the bulk density divided by the basic density. The model has proved to yield good results, despite a relatively small amount of data. A theoretical value of the amount of produced pulp has been computed based on the revolutions of the production screw that feeds chips into the digester. This value takes in consideration how well the chips are packed. The value has shown great similarities with the empirical measurements that are used today. A simulation during one month has shown that differences in the mixture of chips have effected the measurement of produced pulp with up to 7 ton/h. Chips are stored in open pile storages before they are being used in the process of transforming them into pulp. Four screws are used to move chips from the piles to conveyer belts. It has been shown in work done previously, that the movement of the screws contributes to variations in the amount of pin chips measured by ScanChip. During the work with this master’s thesis I have found that there are variations in the piles that make it difficult to predict the amount of pin chips accordingly. However by filtering the measurements of pin chips to remove these variations, the results are improved. A new way of controlling the movements of the screws was operational on the 10 of March and this improved the results. The direction in which the screws are moving influence the speed of the screws, mainly in the pile with the so called sawmill chips. By changing the amount of chips that each screw puts out, the differences in speed have been reduced. The mixtures found in the two piles are not completely homogenous. There are a greater amount of pin chips in the northern parts compared with the southern parts. This could be an effect of the wind direction, and will still cause variations.

The field of autonomous vehicle navigation and localization is a highly active research topic. The aim of this thesis is to evaluate the feasibility to use outdoor visual navigation in a semi-structured environment. The goal is to develop a visual navigation system for an autonomous golf ball collection vehicle operating on driving ranges. The image feature extractors SIFT and PCA-SIFT was evaluated on an image database consisting of images acquired from 19 outdoor locations over a period of several weeks to allow different environmental conditions. The results from these tests show that SIFT-type feature extractors are able to find and match image features with high accuracy. The results also show that this can be improved further by a combination of a lower nearest neighbour threshold and an outlier rejection method to allow more matches and a higher ratio of correct matches. Outliers were found and rejected by fitting the data to a homography model with the RANSAC robust estimator algorithm. A simulator was developed to evaluate the suggested system with respect to pixel noise from illumination changes, weather and feature position accuracy as well as the distance to features, path shapes and the visual servoing target image (milestone) interval. The system was evaluated on a total of 3 paths, 40 test combinations and 137km driven. The results show that with the relatively simple visual servoing navigation system it is possible to use mono-vision as a sole sensor and navigate semi-structured outdoor environments such as driving ranges.

Quasi-Monte Carlo (QMC) methods are playing an increasingly important role in computational finance. This is attributed to the increased complexity of the derivative securities and the sophistication of the financial models. Simple closed-form solutions for the finance applications typically do not exist and hence numerical methods need to be used to approximate their solutions. QMC method has been proposed as an alternative method to Monte Carlo (MC) method to accomplish this objective. Unlike MC methods, the efficiency of QMC-based methods is highly dependent on the dimensionality of the problems. In particular, numerous researches have documented, under the Black-Scholes models, the critical role of the generating matrix for simulating the Brownian paths. Numerical results support the notion that generating matrix that reduces the effective dimension of the underlying problems is able to increase the efficiency of QMC. Consequently, dimension reduction methods such as principal component analysis, Brownian bridge, Linear Transformation and Orthogonal Transformation have been proposed to further enhance QMC. Motivated by these results, we first propose a new measure to quantify the effective dimension. We then propose a new dimension reduction method which we refer as the directional method (DC). The proposed DC method has the advantage that it depends explicitly on the given function of interest. Furthermore, by assigning appropriately the direction of importance of the given function, the proposed method optimally determines the generating matrix used to simulate the Brownian paths. Because of the flexibility of our proposed method, it can be shown that many of the existing dimension reduction methods are special cases of our proposed DC methods. Finally, many numerical examples are provided to support the competitive efficiency of the proposed method.

This high-resolution biogeochemical study investigated spatial and temporal variability in the mixing interface zones within a wetland-aquifer system near a municipal landfill in the city of Norman, Oklahoma. Steep biogeochemical gradients indicating zones of enhanced microbial activity (e.g. iron/sulfate reduction and fermentation) were found at centimeter-scale hydrological and lithological interfaces. The small resolution study was achieved by combining passive diffusion samplers with capillary electrophoresis for chemical analysis. The spatial and temporal variability of biogeochemical processes found at the interfaces was evaluated in a depth profile over a period of three years. Correlations between geochemical parameters were determined using Principal Component Analysis (PCA) and the principal factors obtained were interpreted as a dominant biogeochemical process. Factors scores were mapped by date and depth to determine the spatial-temporal associations of the dominant processes. Fermentation was the process controlling the greatest variability in the dataset followed by iron/sulfate reduction, and methanogenesis. The effect of seasonal and hydrologic changes on biogeochemistry was evaluated from samples collected in a wet/dry period from three locations exhibiting upward, downward, and negligent hydrologic flow between aquifer and wetland. PCA was used to identify the principal biogeochemical processes and to obtain factor scores for evaluating significant seasonal and hydrological differences via analysis of variance. Iron and sulfate reduction were dominated by changes in water table levels and water flow paths, whereas methanogenesis and bacterial barite utilization were dominated by season and associated with a site with negligible flow. A preliminary study on microbial response to changes in geochemical nutrients (e.g. electron acceptors and electron donors) was conducted using in situ microcosms with the purpose of quantifying iron and sulfate reduction rates. Problems encountered in the experiment such as leaks in the microcosms did not allow the determination of respiration rates, therefore the experiments will be repeated in the future. The results suggest that iron and sulfate reduction were stimulated with the addition of sulfate and ferrihydrite (electron acceptors) and acetate and lactate (electron donors). This research demonstrates the importance of assessing biogeochemical processes at interface zones at appropriate scales and reveals the seasonal and hydrological controls on system processes.

Principal components analysis (PCA) has been widely used as a statistical tool for the dimension reduction of multivariate data in various application areas and extensively studied in the long history of statistics. One of the limitations of PCA machinery is that PCA can be applied only to the continuous type variables. Recent advances of information technology in various applied areas have created numerous large diverse data sets with a high dimensional feature space, including high dimensional binary data. In spite of such great demands, only a few methodologies tailored to such binary dataset have been suggested. The methodologies we developed are the model-based approach for generalization to binary data. We developed a statistical model for binary PCA and proposed two stable estimation procedures using MM algorithm and variational method. By considering the regularization technique, the selection of important variables is automatically achieved. We also proposed an efficient algorithm for model selection including the choice of the number of principal components and regularization parameter in this study.