In this thesis, we conduct functional Magnetic Resonance Imaging (fMRI) data analysis with the aim of grouping the brain voxels depending on their responsiveness to a neural task. We mathematically treat the fMRI signals as the convolution of the neural stimulus with the hemodynamic response function (HRF). We first estimate a time series including HRFs for each of the observed fMRI signals from a given set and we cluster them in order to identify the groups of brain voxels. The HRF estimation problem is studied within the Bayesian framework through a blind deconvolution algorithm using MAP approach under completely unsupervised and model-free settings, i.e, stimulus is assumed to be unknown and also no particular shape is assumed for the HRF. Only using a given fMRI signal together with a weak Gaussian prior distribution imposed on HRF favoring &lsquo / smoothness&rsquo / , our method successfully estimates all the components of our framework: the HRF, the stimulus and the noise process. Then, we propose to use a modified version of Hausdorff distance to detect similarities within the space of HRFs, spectrally transform the data using Laplacian Eigenmaps and finally cluster them through EM clustering. According to our simulations, our method proves to be robust to lag, sampling jitter, quadratic drift and AWGN (Additive White Gaussian Noise). In particular, we obtained 100% sensitivity and specificity in terms of detecting active and passive voxels in our real data experiments. To conclude with, we propose a new framework for a mathematical treatment for voxel-based fMRI data analysis and our findings show that even when the HRF is unpredictable due to variability in cognitive processes, one can still obtain very high quality activation detection through the method proposed in this thesis.

Functional Magnetic Resonance Imaging (fMRI) is one of the most popular neuroimaging methods for investigating the activity of the human brain during cognitive tasks. The main objective of the thesis is to identify this underlying brain activation over time, using fMRI signal by detecting active and passive voxels. We performed two sub goals sequentially in order to realize the main objective. First, by using simple, data-driven Fourier Wavelet Regularized Deconvolution (ForWaRD) method, we extracted hemodynamic response function (HRF) which is the information that shows either a voxel is active or passive from fMRI signal. Second, the extracted HRFs of voxels are classified as active and passive using Laplacian Eigenmaps. By this, the active and passive voxels in the brain are identified, and so are the activation areas. The ForWaRD method is directly applied to fMRI signals for the first time. The extraction method is tested on simulated and real block design fMRI signals, contaminated with noise from a time series of real MR images. The output of ForWaRD contains the HRF for each voxel. After HRF extraction, using Laplacian Eigenmaps algorithm, active and passive voxels are classified according to their HRFs. Also with this study, Laplacian Eigenmaps are used for HRF clustering for the first time. With the parameters used in this thesis, the extraction and clustering methods presented here are found to be robust to changes in signal properties. Performance analyses of the underlying methods are explained in terms of sensitivity and specificity metrics. These measurements prove the strength of our presented methods against different kinds of noises and changing signal properties.

Information presented in site investigation reports has a strong influence in design, project costs and safety. For this reason, both the quality and the reliability of site investigation reports are important. However in our country, geotechnical engineering is relegated to second place and site investigation studies, especially parcel-basis ground investigation works / do not receive the attention they deserve. In this study, site investigation reports, that are required for the license of design projects, are examined and the missing/incorrect site investigations, laboratory tests, geotechnical evaluations and geotechnical suggestions that occur in the reports are catalogued. Also, frequency of each mistake is statistically examined / for geotechnical engineers, recommendations and solutions are presented to help them avoid frequent problems.

Aldose reductase, E.C.1.1.1.21, catalyzes the reduction of different types of aldehydes to their corresponding alcohols, and especially reduces various aldo-sugars using NADPH as the coenzyme. Under hyperglycemic conditions aldose reductase is involved in the development of diabetic complications. As a result, interest has been placed over the years on the development of potent aldose reductase inhibitors for possible use in the therapy of these severe diabetic complications. In this study, aldose reductase was isolated from bovine lens by differential centrifugation and ammonium sulfate precipitation. The conditions for the enzyme assay / such as substrate (DL-Glyceraldehyde) and coenzyme (NADPH) concentration, protein amount, effect of sulfate ions, temperature and pH on the enzyme activity were optimized. The inhibitory effects of Punica granatum, Spinacia olaeracea, Allium cepa Allium porrum, Malus flouribunda, Malus domestica extracts were tested on crude bovine lens aldose reductase. Four different types of organic fractions from each crude plant extract were obtained by solvent fractionation. The inhibitory activity of these organic fractions was calculated considering the aldose reductase activity without extracts as 100 %. All six plants were found to inhibit aldose reductase activity to different extent. Among these fractions obtained as / petroleum ether, diethyl ether, ethyl acetate, and n-butanol. Highest inhibitory activity was found for the ethyl acetate fraction. The IC50 values of ethyl acetate fractions of all these plants was calculated as, 25.46 &micro / g/ml, 20.5 &micro / g/ml, 18.5 &micro / g/ml, 12.32 &micro / g/ml, 6.45 &micro / g/ml, 5.4 &micro / g/ml, for Allium porrum, Malus domestica, Spinacia olaeracea, Malus floribunda Allium cepa, Punica granatum respectively.

Turkish Air Quality Control Regulation (AQCR) is in force since 1986. However, AQCR does not contain any standards for odour control. In order to respond to various odour complaints and handle odour problems in Turkey, a regulation for odour control is necessary. Since Turkey is a candidate country for European Union, environmental legislation of Turkey has to be improved to the standards of the other member countries. The purpose of this study is to investigate the &ldquo / odour problem&rdquo / in Turkey, specifically in Ankara, and to establish the odour measurement techniques. The techniques and information acquired throughout this study will form the basis of &ldquo / Odour Regulation&rdquo / in Turkey. For this purpose, odorous gas samples were collected from different industries in Ankara and these samples were analysed with the Olfactometer TO7. The results of the emission measurements have shown that there are numerous industries in Ankara which are discharging high concentrated odorous gases into the environment. Also, field measurements (immission measurements) were performed around a sugar factory in order to determine immission levels. At the end of the immission measurements, a setback distance (buffer zone) of 1.5 km is determined that should be around a sugar factory. The implementation of an odour regulation and odour control technologies in Turkey is expected in near future.

Hot forming is highly utilized in manufacturing of complex shapes. Relatively low flow stresses of materials at elevated temperatures provide ease of manufacturing. On the other side, the current trend is to replace hot forming with cold forming due to the superior mechanical properties and higher dimensional accuracy of the products and less energy consumption. However, cold forming requires high tooling costs and forming loads. In this study, a new process is proposed for production of complex shaped products where the disadvantages of both of the alternatives are tried to be minimized. The basic idea is to control the mode of deformation by heating the specimen locally prior to forming. Electromagnetic induction is used for local heating. Numerical simulations are carried out by finite element method (FEM) for further investigation on the effect of parameters. Thermo-mechanical analysis of heat diffusion and upsetting is supported by electromagnetic analysis of induction heating. The failure modes and operational window of the novel process is established. Conclusions are drawn on the applicability of the process and the effect of process parameters on the efficiency.

As metals have high formability at elevated temperatures, hot forming is preferred and widely used in manufacturing of complicated geometries. The term hot forming is usually used if the whole workpiece is processed at elevated temperatures. However, for certain products high formability is required only locally. Forming by local heating is proposed to provide ease of manufacturing of local forms on the workpiece. Also, tools can be simplified by this method. In this study, local laser heating procedures are applied to obtain local forms on cylindrical bulk metal products in a single step. Locally heated workpieces are formed between two flat dies. Both solid and hollow products have been investigated experimentally and by finite element modeling. The experimental studies and finite element analyses are done simultaneously in order to obtain optimum local deformation characteristics. Three different materials together with different initial geometries and various local laser-heating procedures are applied to search for the process window. The limits of applicability are determined and examples of application are supplied.

This thesis focuses on the feasibility of designing a commercial hybrid electric vehicle (HEV). In this work, relevant system models are developed for the vehicle including powertrain, braking system, electrical machines and battery. Based on these models ten different HEV configurations are assembled for detailed assessment of fuel consumption. This thesis also proposes a smart power management strategy which could be applied to any kind of HEV configuration. The suggested expert system deals with the external information about the driving conditions and modes of the driver as well as the internal states of the internal combustion engine efficiency and the state of charge of the battery, and decides on the power distribution between two different power supplies based on the predefined algorithms. The study illustrates the characteristics of the powertrain components for various HEV configurations. The work also shows the power flow of HEV configurations with the developed smart power management system and therefore, the effectiveness of power management strategies has been evaluated in detail.

In estimating auditory Evoked Potentials (EPs) from ongoing EEG the number of sweeps should be reduced to decrease the experimental time and to increase the reliability of diagnosis. The &macr / rst goal of this study is to demon- strate the use of basic estimation techniques in extracting auditory EPs (AEPs) from small number of sweeps relative to ensemble averaging (EA). For this purpose, three groups of basic estimation techniques are compared to the traditional EA with respect to the signal-to-noise ratio(SNR) improve- ments in extracting the template AEP. Group A includes the combinations of the Subspace Method (SM) with the Wiener Filtering (WF) approaches (the conventional WF and coherence weighted WF (CWWF). Group B con- sists of standard adaptive algorithms (Least Mean Square (LMS), Recursive Least Square (RLS), and one-step Kalman &macr / ltering (KF). The regularization techniques (the Standard Tikhonov Regularization (STR) and the Subspace Regularization (SR) methods) forms Group C. All methods are tested in sim- ulations and pseudo-simulations which are performed with white noise and EEG measurements, respectively. The same methods are also tested with experimental AEPs. Comparisons based on the output signal-to-noise ratio (SNR) show that: 1) the KF and STR methods are the best methods among the algorithms tested in this study,2) the SM can reduce the large amount of the background EEG noise from the raw data, 3) the LMS and WF algo- rithms show poor performance compared to EA. The SM should be used as 1 a pre-&macr / lter to increase their performance. 4) the CWWF works better than the WF when it is combined with the SM, 5) the STR method is better than the SR method. It is observed that, most of the basic estimation techniques show de&macr / nitely better performance compared to EA in extracting the EPs. The KF or the STR e&reg / ectively reduce the experimental time (to one-fourth of that required by EA). The SM is a useful pre-&macr / lter to signi&macr / cantly reduce the noise on the raw data. The KF and STR are shown to be computationally inexpensive tools to extract the template AEPs and should be used instead of EA. They provide a clear template AEP for various analysis methods. To reduce the noise level on single sweeps, the SM can be used as a pre-&macr / lter before various single sweep analysis methods. The second goal of this study is to to present a new approach to extract single sweep AEPs without using a template signal. The SM and a modi- &macr / ed scale-space &macr / lter (MSSF) are applied consecutively. The SM is applied to raw data to increase the SNR. The less-noisy sweeps are then individu- ally &macr / ltered with the MSSF. This new approach is assessed in both pseudo- simulations and experimental studies. The MSSF is also applied to actual auditory brainstem response (ABR) data to obtain a clear ABR from a rel- atively small number of sweeps. The wavelet transform coe&plusmn / cients (WTCs) corresponding to the signal and noise become distinguishable after the SM. The MSSF is an e&reg / ective &macr / lter in selecting the WTCs of the noise. The esti- mated single sweep EPs highly resemble the grand average EP although less number of sweeps are evaluated. Small amplitude variations are observed among the estimations. The MSSF applied to EA of 50 sweeps yields an ABR that best &macr / ts to the grand average of 250 sweeps. We concluded that the combination of SM and MSSF is an e&plusmn / cient tool to obtain clear single sweep AEPs. The MSSF reduces the recording time to one-&macr / fth of that re- quired by EA in template ABR estimation. The proposed approach does not use a template signal (which is generally obtained using the average of small number of sweeps). It provides unprecedented results that support the basic assumptions in the additive signal model.

Reinforced concrete frames display nonlinear behavior both due to its composite nature and the material properties of concrete itself. The yielding of the reinforcement, the non-uniform distribution of aggregates and the development of cracks under loading are the main reasons of nonlinearity. The stiffness of a frame element depends on the combination of the modulus of elasticity and the geometric properties of its section - area and the moment of inertia. In practice, the elastic modulus is assumed to be constant throughout the element and the sectional properties are assumed to remain constant under loading. In this study, it is assumed that the material elasticity depends on the reinforcement ratio and its distribution over the section. Also, the cracks developing in the frame element reduces the sectional properties. In case of linear analysis, the material and sectional parameters are assumed to be constant. In practice, the modulus of elasticity E is a predefined value based on previous experiments and the moment of inertia I is assumed to be constant throughout the analysis. However, in this study, E and I are assumed to be combined. In other words, they cannot be separated from each other throughout the analysis. These two parameters are handled as a single parameter as EI . This parameter is controlled by the reinforcement ratio and its configuration, sectional properties and deformation of the member. Two types of analysis, namely a sectional and a finite element analyses, are used in this study. From the sectional analysis, the parameter EI is calculated based on the sectional geometry, material properties and the axial load applied on the section. The parameter EI is then used in the finite element analysis to calculate the sectional forces and the nodal displacements. For the nonlinear analysis, the Newton-Raphson iterative approach is followed until convergence is obtained.