Repolarization inhomogeneity is arrhythmogenic. QT dispersion (QTd) is an easily accessible ECG-variable, related to the repolarization and shown to carry prognostic information. It was originally thought to reflect repolarization inhomogeneity. Lately, arguments have been risen against this hypothesis. Other measures of inhomogeneity are being investigated, such as nondipolar components from principal component analysis (PCA) of the T-wave. In all here described populations, continuous 12-lead ECG was collected during the initial hours of observation and secondary parameters used for description of a large number of ECG-recordings. Paper I studied QTd in 548 patients with chest pain with a median number of 985 ECG-recordings per patient. Paper II explored a spatial aspect of QTd in 276 patients with unstable coronary artery disease. QTd and a derived localized ECG-parameter were compared to angiographical measures. QTd, expressed as the mean value during the observation was a powerful marker of risk. It was however not effective in identifying high-risk patients. Variations in QTd contained no additional prognostic information. In unstable coronary artery disease, QTd was increased by a mechanism unrelated to localization of the disease. Two relevant conditions for observing repolarization inhomogeneity might occur with conduction disturbances and during initial course of ST-elevation myocardial infarction (STEMI). Paper III compared the PCA-parameters of the T-wave in 135 patients with chest pain and conduction disturbance to 665 patients with normal conduction. Nondipolar components were quantified by medians of the nondipolar residue (TWRabsMedian) and ratio of this residue to the total power of the T-wave (TWRrelMedian). Paper IV described the changes in the nondipolar components of the T-wave in 211 patients with thrombolyzed STEMI. TWRabsMedian increased with increasing conduction disturbance and contained a moderate amount of prognostic information. In thrombolyzed STEMI, TWRabsMedian was elevated and has an increased variability. A greater decrease in absolute TWR during initial observation was seen in patients with early ST-resolution. Nondipolar components do however not reflect identical ECG-properties as the ST-elevation and their change does not occur at the same time.

Natural human motion is highly demanded and widely used in a variety of applications such as video games and virtual realities. However, acquisition of full-body motion remains challenging because the system must be capable of accurately capturing a wide variety of human actions and does not require a considerable amount of time and skill to assemble. For instance, commercial optical motion capture systems such as Vicon can capture human motion with high accuracy and resolution while they often require post-processing by experts, which is time-consuming and costly. Microsoft Kinect, despite its high popularity and wide applications, does not provide accurate reconstruction of complex movements when significant occlusions occur. This dissertation explores two different approaches that accurately reconstruct full-body human motion from noisy and ambiguous input data captured by commercial motion capture devices. The first approach automatically generates high-quality human motion from noisy data obtained from commercial optical motion capture systems, eliminating the need for post-processing. The second approach accurately captures a wide variety of human motion even under significant occlusions by using color/depth data captured by a single Kinect camera. The common theme that underlies two approaches is the use of prior knowledge embedded in pre-recorded motion capture database to reduce the reconstruction ambiguity caused by noisy and ambiguous input and constrain the solution to lie in the natural motion space. More specifically, the first approach constructs a series of spatial-temporal filter bases from pre-captured human motion data and employs them along with robust statistics techniques to filter noisy motion data corrupted by noise/outliers. The second approach formulates the problem in a Maximum a Posterior (MAP) framework and generates the most likely pose which explains the observations as well as consistent with the patterns embedded in the pre-recorded motion capture database. We demonstrate the effectiveness of our approaches through extensive numerical evaluations on synthetic data and comparisons against results created by commercial motion capture systems. The first approach can effectively denoise a wide variety of noisy motion data, including walking, running, jumping and swimming while the second approach is shown to be capable of accurately reconstructing a wider range of motions compared with Microsoft Kinect.

The extensive agricultural usage of organohalogenated pesticides has raised many concerns about their potential hazards especially in the soil environment. Environmental metabolomics is an emerging field that investigates the changes in the metabolic profile of native organisms in their environment due to the presence of an environmental stressor. Research presented here explores the potential of Nuclear Magnetic Resonance (NMR)-based metabolomics to examine the sub-lethal exposure of the earthworm, Eisenia fetida to sub-lethal concentrations of organohalogenated pesticides. Various one-dimensional (1-D) and two dimensional (2-D) NMR techniques were compared in a contact filter paper test earthworm metabolomic study using endosulfan, a prevalent pesticide in the environment. The results determined that both the 1H Presaturation Utilizing Gradients and Echos (PURGE) and the 1H-13C Heteronuclear Single Quantum Coherence (HSQC) NMR techniques were most effective in discriminating and identifying significant metabolites in earthworms due to contaminant exposure. These two NMR techniques were further explored in another metabolomic study using various sub-lethal concentrations of endosulfan and an organofluorine pesticide, trifluralin to E. fetida. Principal component analysis (PCA) tests showed increasing separation between the exposed and unexposed earthworms as the concentrations for both contaminants increased. A neurotoxic mode of action (MOA) for endosulfan and a non-polar narcotic MOA for trifluralin were delineated as many significant metabolites, arising from exposure, were identified. The earthworm tissue extract is commonly used as the biological medium for metabolomic studies. However, many overlapping resonances are apparent in an earthworm tissue extract NMR spectrum due to the abundance of metabolites present. To mitigate this spectral overlap, the earthworm’s coelomic fluid (CF) was tested as a complementary biological medium to the tissue extract in an endosulfan exposure metabolomic study to identify additional metabolites of stress. Compared to tests on the tissue extract, a plethora of different metabolites were identified in the earthworm CF using 1-D PURGE and 2-D HSQC NMR techniques. In addition to the neurotoxic MOA identified previously, an apoptotic MOA was also postulated due to endosulfan exposure. This thesis also explored the application of 1-D and 2-D NMR techniques in a soil metabolomic study to understand the exposure of E. fetida to sub-lethal concentrations of endosulfan and its main degradation product, endosulfan sulfate. The earthworm’s CF and tissue extract were both analyzed to maximize the significant metabolites identified due to contaminant exposure. The PCA results identified similar toxicity for both organochlorine contaminants as the same separation, between exposed to the unexposed earthworms, were detected at various concentrations. Both neurotoxic and apopotic MOAs were observed as identical fluctuations of significant metabolites were found. This research demonstrates the potential of NMR-based metabolomics as a powerful environmental monitoring tool to understand sub-lethal organohalogenated pesticide exposure in soil using earthworms as living probes.

Face recognition is a popular research area where there are different approaches studied in the literature. In this thesis, a holistic Principal Component Analysis (PCA) based method, namely Eigenface method is studied in detail and three of the methods based on the Eigenface method are compared. These are the Bayesian PCA where Bayesian classifier is applied after dimension reduction with PCA, the Subspace Linear Discriminant Analysis (LDA) where LDA is applied after PCA and Eigenface where Nearest Mean Classifier applied after PCA. All the three methods are implemented on the Olivetti Research Laboratory (ORL) face database, the Face Recognition Technology (FERET) database and the CNN-TURK Speakers face database. The results are compared with respect to the effects of changes in illumination, pose and aging. Simulation results show that Subspace LDA and Bayesian PCA perform slightly well with respect to PCA under changes in pose / however, even Subspace LDA and Bayesian PCA do not perform well under changes in illumination and aging although they perform better than PCA.

The thesis parts from the view that statistics starts with data, and starts by introducing the data sets studied: marine benthic species counts and chemical measurements made at a set of sites in the Norwegian Ekofisk oil field, with replicates and annually repeated. An introductory chapter details the sampling procedure and shows with reliability calculations that the (transformed) chemical variables have excellent reliability, whereas the biological variables have poor reliability, except for a small subset of abundant species. Transformed chemical variables are shown to be approximately normal. Bootstrap methods are used to assess whether the biological variables follow a Poisson distribution, and lead to the conclusion that the Poisson distribution must be rejected, except for rare species. A separate chapter details more work on the distribution of the species variables: truncated and zero-inflated Poisson distributions as well as Poisson mixtures are used in order to account for sparseness and overdispersion. Species are thought to respond to environmental variables, and regressions of the abundance of a few selected species onto chemical variables are reported. For rare species, logistic regression and Poisson regression are the tools considered, though there are problems of overdispersion. For abundant species, random coefficient models are needed in order to cope with intraclass correlation. The environmental variables, mainly heavy metals, are highly correlated, leading to multicollinearity problems. The next chapters use a multivariate approach, where all species data is now treated simultaneously. The theory of correspondence analysis is reviewed, and some theoretical results on this method are reported (bounds for singular values, centring matrices). An applied chapter discusses the correspondence analysis of the species data in detail, detects outliers, addresses stability issues, and considers different ways of stacking data matrices to obtain an integrated analysis of several years of data, and to decompose variation into a within-sites and between-sites component. More than 40 % of the total inertia is due to variation within stations. Principal components analysis is used to analyse the set of chemical variables. Attempts are made to integrate the analysis of the biological and chemical variables. A detailed theoretical development shows how continuous variables can be mapped in an optimal manner as supplementary vectors into a correspondence analysis biplot. Geometrical properties are worked out in detail, and measures for the quality of the display are given, whereas artificial data and data from the monitoring survey are used to illustrate the theory developed. The theory of display of supplementary variables in biplots is also worked out in detail for principal component analysis, with attention for the different types of scaling, and optimality of displayed correlations. A theoretical chapter follows that gives an in depth theoretical treatment of canonical correspondence analysis, (linearly constrained correspondence analysis, CCA for short) detailing many mathematical properties and aspects of this multivariate method, such as geometrical properties, biplots, use of generalized inverses, relationships with other methods, etc. Some applications of CCA to the survey data are dealt with in a separate chapter, with their interpretation and indication of the quality of the display of the different matrices involved in the analysis. Weighted principal component analysis of weighted averages is proposed as an alternative for CCA. This leads to a better display of the weighted averages of the species, and in the cases so far studied, also leads to biplots with a higher amount of explained variance for the environmental data. The thesis closes with a bibliography and outlines some suggestions for further research, such as a the generalization of canonical correlation analysis for working with singular covariance matrices, the use partial least squares methods to account for the excess of predictors, and data fusion problems to estimate missing biological data.

For several years, FORAN Remote Sensing in Linköping has been using pulseintense laser scannings together with multispectral imaging for developing analysismethods in forestry. One area these laser scannings and images are used for is toclassify the species of single trees in forests. The species have been divided intopine, spruce and deciduous trees, classified by a Maximum Likelihood classifier.This thesis presents the work done on a more spectrally high-resolution imagery,hyperspectral images. These images are divided into more, and finer gradedspectral components, but demand more signal processing. A new classifier, SupportVector Machine, is tested against the previously used Maximum LikelihoodClassifier, to see if it is possible to increase the performance. The classifiers arealso set to divide the deciduous trees into aspen, birch, black alder and gray alder.The thesis shows how the new data set is handled and processed to the differentclassifiers, and shows how a better result can be achieved using a Support VectorMachine.

In recent years, the celebration activities of various types of folk-custom festivals in Taiwan have already been getting more and more attention from civilians. Festivities throughout the whole island are traditionally accompanied by loud and brightly colored firework displays. Among these activities, the firework display during the Chinese Lantern Festival in Kaohsiung City is one of the largest festivals in Taiwan every year. Therefore, it is important to investigate the influences of firework displays on ambient air quality during the Chinese Lantern Festival in Kaohsiung City. Field measurement of ambient gaseous pollutants and particulate matter (PM) was conducted on February 9-12, 2009, the Chinese Lantern Festival, in Kaohsiung City. Moreover, three kinds of firework powders obtained from the same factory producing Kaohsiung Lantern Festival fireworks were burned in a combustion chamber to determine the physicochemical properties of firework aerosols. Metallic elements were analyzed with an inductively coupled plasma-atomic emission spectrometer (ICP-AES). Ionic species and carbonaceous contents in the PM samples were analyzed with an ion chromatography (IC) and an elemental analyzer (EA), respectively. Finally, the source identification and apportionment of PM were analyzed by principal component analysis (PCA), enrichment factor (EF), and receptor modeling (CMB). For inorganic gaseous pollutants, the concentration peaks of NO, NO2, O3, CO were observed during the firework periods, and the concentration peak of NO was approximately 8.8 times higher than those during the non-firework periods. This study further revealed that, even at nighttime, ambient O3 could be reduced dramatically during the firework periods, whenas NO2 concentration increased concurrently, due to titration effects resulting from the prompt reaction of NO with O3 to form NO2 and O2. For organic gaseous pollutants, the concentration peak of toluene during the firework periods was approximately 2.2-4.1 times higher than those during the non-firework periods. Several metallic elements of PM during the firework display periods were obviously higher than those during the non-firework periods. On February 10, the concentrations of Mg, K, Pb, and Sr in PM2.5 were 10 times higher than those during the non-firework periods. Besides, the Cl-/Na+ ratio was slightly smaller than 1 in Kaohsiung Harbor, but it was approximately 3 during the firework display periods since Cl- came form chlorine content in firework aerosols at this time. Moreover, OC/EC ratio increased up to 2.8. In addition to the analysis of gaseous pollutants and PM during the Chinese Lantern Festival in Kaohsiung City, this study burned firework powders in a self-designed combustion chamber to measure the physicochemical properties of firework aerosols. In the results, K, Mg, Cl-, OC were major contents (<10%) in the aerosols produced from the burning firework powders. Moreover, Cl-/Na+ and OC/EC ratio were 15.0~23.4 and 2.9~3.2, respectively. Consequently, Cl-/Na+ and OC/EC ratio can be used as two important indicators of firework displays. Results obtained from PCA and CMB receptor modeling showed that the major sources of aerosols during the firework display periods were firework displays, motor/diesel vehicle exhanst, soil dusts, and marine aerosols. Besides, the firework displays on February 10 contributed approximately 25.2% and 16.6% of PM10 at two sampling sites, respectively.

In recent years, the rapid development of economy and industry in Xiamen Bay causes serious environmental problems, particularly poor air quality and visibility impairment. There are no large-scale industrial emission sources in Kinmen Island, however, its ambient air quality is always the poorest in Taiwan. Moreover, ambient air quality monitoring data showed that PM10 concentrations varied in daytime and at nighttime. Consequently, this study tired to ascertain the potential causes for this phenomenon. This study selected ten particulate matter (PM) sampling sites at Xiamen Bay, including five sites at Kinmen Island and five sites at metro Xiamen. Particulate matter sampling was conducted in daytime (8:00-17:00) and at nighttime (17:00-8:00), which included regular and intensive sampling. Regular sampling was conducted to collect PM10 with high-volume samplers three times a month from April 2009 to April 2010, while intensive sampling was conducted to collect fine (PM2.5) and coarse (PM2.5-10) particles with dichotomous samplers and particle size distribution with a MOUDI at site B2 for consecutive 5 days in the spring and winter of 2009~2010. After sampling, the physicochemical properties of PM, including mass concentrations, particle size distribution, water- soluble ionic species, metallic elements, and carbonaceous contents were further analyzed. The level of atmospheric PM is affected by meteorological condition, thus PM10 concentrations in winter and fall was much higher than those in spring and summer. Results from backward trajectories showed that the concentrations of PM10 blown from the north were generally higher than those from the south. Furthermore, t-test analysis indicated that PM10 concentrations in daytime and at nighttime at site B3 were significantly different (p-value<0.05). During the intensive sampling periods, PM10 concentrations were mainly affected by coarse particles compared to fine particles. The highest concentration for fine and coarse particle modes occurred at the size ranges of 0.32~0.56 £gm and 3.2~5.6 £gm, respectively. The most abundant water-soluble ionic species of PM10 were secondary inorganic aerosols (SO42-, NO3-, and NH4+) which accounted for 85% of total ions. The daytime and nighttime PM10 concentration ratios (D/N) for Mg, K, Ca, Cr, Mn, Fe, Zn, Al, Cu, As, and V were in the same order of magnitude, however, the D/N ratios of Cd, Pb, Ni, and Ti in spring and summer varied higher than an order of magnitude, indicating that the emission sources of PM were different in daytime and at nighttime. Correlation analysis of OC and EC showed that OC and EC at nighttime had a higher correlation than those in daytime, while OC and EC had a higher correlation in Kinmen Island than those in metro Xiamen, indicating carbonaceous sources must be different in summer and winter at Xiamen Bay. Enrichment factor analysis revealed that ceramic industry, stone processing, and cement industry had higher correlation with PM10 concentration than utility power plants. Crustal dusts consisted of road dusts, farmland dusts, and constructive dusts, while biomass burning was not a negligible sources. Results obtained from PCA and CMB receptor modeling showed that major sources of PM in Xiamen Bay were secondary inorganic aerosols, fuel and biomass burning, marine aerosols, vehicular exhansts, and soil dusts. Besides, stone processing, cement industry, ceramic industry, and utility power plants had the highest contribution in winter. Their contributions in daytime and at nighttime were 38% and 45%, respectively.

Multiple Signal Classification (MUSIC) Algorithm based and Wigner Distribution-Principal Component Analysis (WD-PCA) based classification techniques are very recently suggested resonance region approaches for electromagnetic target classification. In this thesis, performances of these two techniques will be compared concerning their robustness for noise and their capacity to handle large number of candidate targets. In this context, classifier design simulations will be demonstrated for target libraries containing conducting and dielectric spheres and for dielectric coated conducting spheres. Small scale aircraft targets modeled by thin conducting wires will also be used in classifier design demonstrations.

This study investigated the concentration distributions of polyclic aromatic hydrocarbons (PAHs) in the sediment cores collected from Fang-Liao submarine canyon. Chemical fingerprinting techniques and statistical analysis were applied to delineate the possible sources of the PAHs in deposited sediment core samples. It is noteworthy that all cores were not dated; therefore the deposition age could not estimate from the depth of deposition directly. The average concentrations of polyclic aromatic hydrocarbons (£U51PAHs) were found ranged from 229 to 638 (ng/g dry wt) in the sediment cores in Fang-Liao submarine canyon. In addition, the low molecular weight PAHs (2-3 ring PAHs) were found dominant in the PAH composition pattern of most samples. Total PAH concentrations were significantly correlated with total organic carbon (TOC) in all the sediment cores. Compared with sediment quality guidelines (SQGs), the PAH concentrations of all sediment samples were lower than those outlined in the criteria, that suggests no evident adverse biological effects caused by PAHs. Results also showed that total PAH concentration of surface sediments (0-2 cm) decreased with the water depth. Identification of PAHs sources suggests that all up-cores were dominated by petrogenic sources, but all down-cores except for S17 and S18 were dominated by pyrogenic sources or mixed sources. In contrast, biogenic sources were found dominant in S17 and S18 as they were characterized by higher ratio of perylene/£Upenta-PAHs(%). Compared with literature, the sediment cores of Fang-Liao submarine canyon were moderately polluted with PAHs. Analysis of diagnostic ratios and hierarchical cluster analysis (HCA) as well as principal component analysis (PCA) all indicate PAHs sources of Fang-Liao submarine canyon were mainly from petroleum and petroleum combustion sources for site of S3,S5,S7,S8 and S17; while pyrogenic or mixed sources for site of S1,S2,S9,S18 and S33.