Age related macular degeneration (AMD) is a public health concern in an aging society. The retinal pigment epithelium (RPE) layer of the eye is a principal site of pathogenesis for AMD. Morphological characteristics of the cells in the RPE layer can be used to discriminate age and disease status of individuals. In this thesis three genotypes of mice of various ages are used to study the predictive abilities of these characteristics. The disease state is represented by two mutant genotypes and the healthy state by the wild-type. Classification analysis is applied to the RPE morphology from the different spatial regions of the RPE layer. Variable reduction is accomplished by principal component analysis (PCA) and classification analysis by the k-nearest neighbor (k-NN) algorithm. In this way the differential ability of the spatial regions to predict age and disease status by cellular variables is explored.

This dissertation explored the way in which bumblebees' visual system helps them discover their first flower. Previous studies found bees have unlearned preferences for parts of a flower, such as its colour and shape. The first study pitted two variables against each other: pattern type: sunburst or bull's eye, versus the location of the pattern: shapes appeared peripherally or centrally. We observed free-flying bees in a flight cage using Radio-Frequency Identification (RFID) tracking. The results show two distinct behavioural preferences: Pattern type predicts landing: bees prefer radial over concentric patterns, regardless of whether the radial pattern is on the perimeter or near the centre of the flower. Pattern location predicts exploration: bees were more likely to explore the inside of artificial flowers if the shapes were displayed near the centre of the flower, regardless of whether the pattern was radial or concentric. As part of the second component, we implemented a mathematical model aimed at explaining how bees come to prefer radial patterns, leafy backgrounds and symmetry. The model was based on unsupervised neural networks used to describe cognitive mechanisms. The results captured with the results of multiple behavioural experiments. The model suggests that bees choose computationally "cheaper" stimuli, those that contain less information. The third study tested the computational load hypothesis generated by the artificial neural networks. Visual properties of symmetry, and spatial frequency were tested. Studying free-flying bees in a flight cage using motion-sensitive video recordings, we found that bees preferred 4-axis symmetrical patterns in both low and high frequency displays.

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.

A face authentication system based on principal component analysis and neural networks is developed in this thesis. The system consists of three stages / preprocessing, principal component analysis, and recognition. In preprocessing stage, normalization illumination, and head orientation were done. Principal component analysis is applied to find the aspects of face which are important for identification. Eigenvectors and eigenfaces are calculated from the initial face image set. New faces are projected onto the space expanded by eigenfaces and represented by weighted sum of the eigenfaces. These weights are used to identify the faces. Neural network is used to create the face database and recognize and authenticate the face by using these weights. In this work, a separate network was build for each person. The input face is projected onto the eigenface space first and new descriptor is obtained. The new descriptor is used as input to each person&amp / #8217 / s network, trained earlier. The one with maximum output is selected and reported as the host if it passes predefined recognition threshold. The algorithms that have been developed are tested on ORL, Yale and Feret Face Databases.

The main task in this thesis is to design a robot vision system with face detection and tracking capability. Hence there are two main works in the thesis: Firstly, the detection of the face on an image that is taken from the camera on the robot must be achieved. Hence this is a serious real time image processing task and time constraints are very important because of this reason. A processing rate of 1 frame/second is tried to be achieved and hence a fast face detection algorithm had to be used. The Eigenface method and the Subspace LDA (Linear Discriminant Analysis) method are implemented, tested and compared for face detection and Eigenface method proposed by Turk and Pentland is decided to be used. The images are first passed through a number of preprocessing algorithms to obtain better performance, like skin detection, histogram equalization etc. After this filtering process the face candidate regions are put through the face detection algorithm to understand whether there is a face or not in the image. Some modifications are applied to the eigenface algorithm to detect the faces better and faster. Secondly, the robot must move towards the face in the image. This task includes robot motion. The robot to be used for this purpose is a Pioneer 2-DX8 Plus, which is a product of ActivMedia Robotics Inc. and only the interfaces to move the robot have been implemented in the thesis software. The robot is to detect the faces at different distances and arrange its position according to the distance of the human to the robot. Hence a scaling mechanism must be used either in the training images, or in the input image taken from the camera. Because of timing constraint and low camera resolution, a limited number of scaling is applied in the face detection process. With this reason faces of people who are very far or very close to the robot will not be detected. A background independent face detection system is tried to be designed. However the resultant algorithm is slightly dependent to the background. There is no any other constraints in the system.

Different approaches to the face recognition are studied in this thesis. These approaches are PCA (Eigenface), Kernel Eigenface and Fisher LDA. Principal component analysis extracts the most important information contained in the face to construct a computational model that best describes the face. In Eigenface approach, variation between the face images are described by using a set of characteristic face images in order to find out the eigenvectors (Eigenfaces) of the covariance matrix of the distribution, spanned by a training set of face images. Then, every face image is represented by a linear combination of these eigenvectors. Recognition is implemented by projecting a new image into the face subspace spanned by the Eigenfaces and then classifying the face by comparing its position in face space with the positions of known individuals. In Kernel Eigenface method, non-linear mapping of input space is implemented before PCA in order to handle non-linearly embedded properties of images (i.e. background differences, illumination changes, and facial expressions etc.). In Fisher LDA, LDA is applied after PCA to increase the discrimination between classes. These methods are implemented on three databases that are: Yale face database, AT&amp / T (formerly Olivetti Research Laboratory) face database, and METU Vision Lab face database. Experiment results are compared with respect to the effects of changes in illumination, pose and expression. Kernel Eigenface and Fisher LDA show slightly better performance with respect to Eigenfaces method under changes in illumination. Expression differences did not affect the performance of Eigenfaces method. From test results, it can be observed that Eigenfaces approach is an adequate method that can be used in face recognition systems due to its simplicity, speed and learning capability. By this way, it can easily be used in real time systems.

This thesis applies an electromagnetic feature extraction technique to design electromagnetic target classifiers for conductors, dielectrics and dielectric coated conductors using their natural resonance related late-time scattered responses. Classifier databases contain scattered data at only a few aspects for each candidate target. The targets are dielectric spheres of varying sizes and refractive indices, perfectly conducting spheres varying sizes and dielectric coated conducting spheres of varying refractive indices and thickness in coating. The applied classifier design technique is suitable for real-time target classification because of the computational efficiency of feature extraction and decision making approaches. The Wigner-Ville Distribution (WD) is employed in this study in addition to the Principal Components Analysis (PCA) technique to extract target features mainly from late-time target responses. WD is applied to the back-scattered responses at different aspects. To decrease aspect dependency, feature vectors are extracted from selected late-time portions of the WD outputs that include natural resonance related information. Principal components analysis is also used to fuse the feature vectors and/or late-time target responses extracted from reference aspects of a given target into a single characteristic feature vector for each target to further reduce aspect dependency.

In the present study, for estimating the Central Asia contribution to the Anatolia, nine Alu insertion polymorphisms (ACE, PV92, FXIIIB, APO, A25, B65, TPA25, D1, HS4.32 ) in 100 individuals from Anatolia were examined. Alu insertion frequency for these loci were calculated as 0,410 / 0,220 / 0,579 / 0,963 / 0,067 / 0,667 / 0,390 / 0,427 / and 0,637 respectively and they were found to be in Hardy-Weinberg equilibrium (p&lt / 0,05). Observed insertion frequencies of each loci were compared with those of the previous observations (Din&ccedil / , 2003 / Comas et al., 2004) and it was found that the present study results were not different than those obtained by Comas et al. (2004). Thus, these two data were pooled (N = 143) and used to examine genetic relationships between populations from Eurasia and Africa. Pairwise Fst statistics indicated that there is higher genetic similarity between Anatolia and all of the Balkans and some of the Caucasian populations. Neighbor Joining (NJ) tree based on Reynold&rsquo / s genetic distances and Principal Component Analysis (PCA) both grouped the Anatolian populations with Balkans and some of the Caucasian populations and show clear differentiation of Asian populations from the Anatolian population. The relative genetic contribution of Central Asian genes to the current Anatolian gene pool was quantified using Admix analysis, considering for comparison populations of Balkans (Greek, Romania, Albania and Hungarian) and Central Asia (Uighur, Uzbeks, Tajicks, Kazaks, Kyrgyzes, Dungans). Estimates suggest roughly 28 % contribution from Asia to Anatolia in concordance with the previous estimation (Benedetto et al., 2001).

In the field of empirical macroeconomics factor-augmented vector autoregressive (FAVAR) models have become a popular tool in explaining how economic variables interact over time. FAVAR is based upon a data-reduction step using factor estimation, which are then employed in a vector autoregressive model. This paper aims to study alternative methods regarding factor estimation. More precisely, we compare the generally used principal component method with the uncomplicated common correlated effect estimation. Results show low divergence between the two factor estimation methods employed, indicating interchangeability between the two estimation approaches.

In order to meet tight product quality specifications for chemical batch processes, it is vital to monitor and control product quality throughout the batch duration. However, the frequent lack of in situ sensors for continuous monitoring of batch product quality complicates the control problem and calls for novel control approaches. This thesis focuses on the study and application of multivariate statistical methods to control product quality in chemical batch processes. These multivariate statistical methods can be used to identify data-driven prediction models that can be integrated within a model predictive control (MPC) framework. The ideal MPC control strategy achieves end-product quality specifications by performing trajectory tracking during the batch operating time. However, due to the lack of in-situ sensors, measurements of product quality are usually obtained by laboratory assays and are, therefore, inherently intermittent. This thesis proposes a new approach to realise trajectory tracking control of batch product quality in those situations where only intermittent measurements are available. The scope of this methodology consists of: 1) the identification of a partial least squares (PLS) model that works as an estimator of product quality, 2) the transformation of the PLS model into a recursive formulation utilising a moving window technique, and 3) the incorporation of the recursive PLS model as a predictor into a standard MPC framework for tracking the desired trajectory of batch product quality. The structure of the recursive PLS model allows a straightforward incorporation of process constraints in the optimisation process. Additionally, a method to incorporate a nonlinear inner relation within the proposed PLS recursive model is introduced. This nonlinear inner relation is a combination of feedforward artificial neural networks (ANNs) and linear regression. Nonlinear models based on this method can predict product quality of highly nonlinear batch processes and can, therefore, be used within an MPC framework to control such processes. The use of linear regression in addition to ANNs within the PLS model reduces the risk of overfitting and also reduces the computational e↵ort of the optimisation carried out by the controller. The benefits of the proposed modelling and control methods are demonstrated using a number of simulated batch processes.