AR-PCA-HMM approach for sensorimotor task classification in EEG-based brain-computer interfaces

We propose an approach based on Hidden Markov models (HMMs) combined with principal component analysis (PCA) for classification of four-class single trial motor imagery EEG data for brain computer interfacing (BCI) purposes. We extract autoregressive (AR) parameters from EEG data and use PCA to decrease the number of features for better training of HMMs. We present experimental results demonstrating the improvements provided by our approach over an existing HMM-based EEG single trial classification approach as well as over state-of-the-art classification methods

Isolation and molecular characterization of lactic acid bacteria from raw milk

Thesis (Master)--Izmir Institute of Technology, Biotechnology, Izmir, 2002Includes bibliographical references (leaves: 84-91)Text in English; Abstract: Turkish and Englishviii, 91 leavesLactic acid bacteria are industrially important because they are used as starter cultures in food production, they produce antimicrobial compounds and they are used in the formulation of probiotic products. Several dairy products such as raw milk, traditionally fermented cheese (produced without the use of commercial starter cultures), and kefir which are produced in country are good sources of novel lactic acid bacterial strains. These lactic acid bacterial strains may have potential for the production of new fermented dairy products with characteristic aroma and flavour. Therefore, the isolation of lactic acid bacteria from natural products and their identification are important. For many years, several phenotypic methods have been used to identify lactic acid bacteria, but they are not often capable of effectively differentiating subspecies and strains within a genus. New methods based on the genotypic properties have been developed and used for the proper classification of bacteria The aim of this research was the isolation of lactic acid bacteria from raw milk and the identification of the lactic acid bacterial isolates by biochemical tests, polymerase chain reaction (PCR)-based methods and pulsed field gel electrophoresis (PFGE). Lactic acid bacteria were isolated from cow.s raw milk and identified by biochemical reactions. Two PCR based methods, ITS-PCR (Internal Transcribed Spacer-PCR) and PCR-RFLP (PCR- Restriction Fragment Length Polymorphism) were then used for the differentiation of reference strains of lactic acid bacteria. PCR-RFLP method, based on the amplification and restriction digestion of 16S rRNA gene, was found to be useful for the identification. Thirteen raw milk isolates were identified as Lactococcus lactis, 24 as Enterococcus spp., and 2 as Lactococcus lactis subsp. cremoris by PCR-RFLP method. Pulsed field gel electrophoresis was also optimized for the identification of reference strains. Restriction profiles obtained by digesting the genomic DNA with Sma I enabled differentiation of the reference strains of Lactococcus, Enterococcus, and Streptococus thermophilus

An innovative capital markets instrument: real estate certificates

Think about an instrument that directly links capital markets and real estate projects, an instrument which allows investors to buy and sell their claims or their shares on certain real estate projects in organized exchanges. These certificates will further allow investors to become the owner of a residential or commercial property using the certificates they own. Real estate certificates or real estate shares exactly serve this purpose. This instrument is intended to bring liquidity of organized exchanges to real estate market while benefiting the dynamism of real estate projects to attract a new investor base to capital markets

Sparse signal representation for complex-valued imaging

We propose a sparse signal representation-based method for complex-valued imaging. Many coherent imaging systems such as synthetic aperture radar (SAR) have an inherent random phase, complex-valued nature. On the other hand sparse signal representation, which has mostly been exploited in real-valued problems, has many capabilities such as superresolution and feature enhancement for various reconstruction and recognition tasks. For complex-valued problems, the key challenge is how to choose the dictionary and the representation scheme for effective sparse representation. We propose a mathematical framework and an associated optimization algorithm for a sparse signal representation-based imaging method that can deal with these issues. Simulation results show that this method offers improved results compared to existing powerful imaging techniques

Sparse representation-based SAR imaging

There is increasing interest in using synthetic aperture radar (SAR) images in automated target recognition and decision-making tasks. The success of such tasks depends on how well the reconstructed SAR images exhibit certain features of the underlying scene. Based on the observation that typical underlying scenes usually exhibit sparsity in terms of such features, we develop an image formation method which formulates the SAR imaging problem as a sparse signal representation problem. Sparse signal representation, which has mostly been exploited in real-valued problems, has many capabilities such as superresolution and feature enhancement for various reconstruction and recognition tasks. However, for problems of complex-valued nature, such as SAR, a key challenge is how to choose the dictionary and the representation scheme for effective sparse representation. Since we are usually interested in features of the magnitude of the SAR reflectivity field, our new approach is designed to sparsely represent the magnitude of the complex-valued scattered field. This turns the image reconstruction problem into a joint optimization problem over the representation of magnitude and phase of the underlying field reflectivities. We develop the mathematical framework for this method and propose an iterative solution for the corresponding joint optimization problem. Our experimental results demonstrate the superiority of this method over previous approaches in terms of both producing high quality SAR images as well as exhibiting robustness to uncertain or limited data

Multiple feature-enhanced SAR imaging using sparsity in combined dictionaries

Nonquadratic regularization-based image formation is a recently proposed framework for feature-enhanced radar imaging. Specific image formation techniques in this framework have so far focused on enhancing one type of feature, such as strong point scatterers, or smooth regions. However, many scenes contain a number of such feature types. We develop an image formation technique that simultaneously enhances multiple types of features by posing the problem as one of sparse representation based on combined dictionaries. This method is developed based on the sparse representation of the magnitude of the scattered complex-valued field, composed of appropriate dictionaries associated with different types of features. The multiple feature-enhanced reconstructed image is then obtained through a joint optimization problem over the combined representation of the magnitude and the phase of the underlying field reflectivities

Multiple feature-enhanced synthetic aperture radar imaging

Non-quadratic regularization based image formation is a recently proposed framework for feature-enhanced radar imaging. Specific image formation techniques in this framework have so far focused on enhancing one type of feature, such as strong point scatterers, or smooth regions. However, many scenes contain a number of such features. We develop an image formation technique that simultaneously enhances multiple types of features by posing the problem as one of sparse signal representation based on overcomplete dictionaries. Due to the complex-valued nature of the reflectivities in SAR, our new approach is designed to sparsely represent the magnitude of the complex-valued scattered field in terms of multiple features, which turns the image reconstruction problem into a joint optimization problem over the representation of the magnitude and the phase of the underlying field reflectivities. We formulate the mathematical framework needed for this method and propose an iterative solution for the corresponding joint optimization problem. We demonstrate the effectiveness of this approach on various SAR images

Sparse representation-based synthetic aperture radar imaging

There is increasing interest in using synthetic aperture radar (SAR) images in automated target recognition and decision-making tasks. The success of such tasks depends on how well the reconstructed SAR images exhibit certain features of the underlying scene. Based on the observation that typical underlying scenes usually exhibit sparsity in terms of such features, we develop an image formation method which formulates the SAR imaging problem as a sparse signal representation problem. Sparse signal representation, which has mostly been exploited in real-valued problems, has many capabilities such as superresolution and feature enhancement for various reconstruction and recognition tasks. However, for problems of complex-valued nature, such as SAR, a key challenge is how to choose the dictionary and the representation scheme for effective sparse representation. Since we are usually interested in features of the magnitude of the SAR reflectivity field, our new approach is designed to sparsely represent the magnitude of the complex-valued scattered field. This turns the image reconstruction problem into a joint optimization problem over the representation of magnitude and phase of the underlying field reflectivities. We develop the mathematical framework for this method and propose an iterative solution for the corresponding joint optimization problem. Our experimental results demonstrate the superiority of this method over previous approaches in terms of both producing high quality SAR images as well as exhibiting robustness to uncertain or limited data

THE EFFECTS OF INTERACTIVE BOARD APPLICATIONS SUPPORTED BY COMPUTER SIMULATIONS ON PRE-SERVICE SCIENCES TEACHERS’ SELF-REGULATED LEARNING

The purpose of this study is to investigate the effects of interactive boards applications supported by computer simulations on pre-service science teachers’ self-regulated learning. Quasi-experimental research with pre test/ post test control group design was used in the study. Pre-service science teachers in Siirt University, education faculty, science education department, constructed study group. Sections was randomly assigned experimental and control groups. In experimental group 32 pre-service science teachers (11 male; 21 female), in control group 33 pre-service science teachers (13 male; 20 female) were assigned to the groups. In total, 65 pre-service science teachers participated to the study. "Motivated strategies for learning questionnaire” was applied as the data collection tool. The questionnaire was translated into Turkish by Büyüköztürk and et.al. (2004). During the analysis in this respect, arithmetic averages, standard deviation, independent sampling t-test and analysis of covariance (ANCOVA) were used. The study compared the pre-test and post test scores of the science teacher candidates in the experiment and control group with SPSS 16.0 statistical package software.  Article visualizations

Searches for supersymmetry with the ATLAS detector using final states with two leptons and missing transverse momentum in s=7TeV proton-proton collisions

Çetin, Serkant Ali (Dogus Author)Results of three searches are presented for the production of supersymmetric particles decaying into final states with missing transverse momentum and exactly two isolated leptons, e or μ. The analysis uses a data sample collected during the first half of 2011 that corresponds to a total integrated luminosity of 1fb-1 of s=7TeV proton-proton collisions recorded with the ATLAS detector at the Large Hadron Collider. Opposite-sign and same-sign dilepton events are separately studied, with no deviations from the Standard Model expectation observed. Additionally, in opposite-sign events, a search is made for an excess of same-flavour over different-flavour lepton pairs. Effective production cross sections in excess of 9.9 fb for opposite-sign events containing supersymmetric particles with missing transverse momentum greater than 250 GeV are excluded at 95% CL. For same-sign events containing supersymmetric particles with missing transverse momentum greater than 100 GeV, effective production cross sections in excess of 14.8 fb are excluded at 95% CL. The latter limit is interpreted in a simplified electroweak gaugino production model excluding chargino masses up to 200 GeV, under the assumption that slepton decay is dominant