Ripple-free deadbeat control of sampled-data systems

Ankara : Department of Electrical and Electronics Engineering and Institute of Engineering and Sciences, Bilkent Univ., 1990.Thesis (Master's) -- Bilkent University, 1990.Includes bibliographical references leaves 41-42In this thesis, we consider the ripple-free deadbeat control problem for linear, multivariable sampled-data systems represented by state-space models. Existing results concerning the deadbeat/ripple-free deadbeat regulation and tracking problems are based on controller configurations of either constant state-feedback or discrete dynamic output feedback. In the thesis, the problem is analyzed for two new sampled-data controllers, namely, generalized sampled-data hold functions and multirate-output controllers. Some necessary and sufficient solvability conditions for the problem are stated by theorems in time-domain and frequency domain in terms of the open-loop system parameters. Several special cases are also considered as corollaries.Mumcuoğlu, Erkan ÜnalM.S

Image Analysis for Cystic Fibrosis: Computer-Assisted Airway Wall and Vessel Measurements from Low-Dose, Limited Scan Lung CT Images

Cystic fibrosis (CF) is a life-limiting genetic disease that affects approximately 30,000 Americans. When compared to those of normal children, airways of infants and young children with CF have thicker walls and are more dilated in high-resolution computed tomographic (CT) imaging. In this study, we develop computer-assisted methods for assessment of airway and vessel dimensions from axial, limited scan CT lung images acquired at low pediatric radiation doses. Two methods (threshold- and model-based) were developed to automatically measure airway and vessel sizes for pairs identified by a user. These methods were evaluated on chest CT images from 16 pediatric patients (eight infants and eight children) with different stages of mild CF related lung disease. Results of threshold-based, corrected with regression analysis, and model-based approaches correlated well with both electronic caliper measurements made by experienced observers and spirometric measurements of lung function. While the model-based approach results correlated slightly better with the human measurements than those of the threshold method, a hybrid method, combining these two methods, resulted in the best results

Image analysis for cystic fibrosis: Automatic lung airway wall and vessel measurement on CT images

Cystic Fibrosis (CF) is the most common lethal genetic disorder in the Caucasian population, affecting about 30,000 people in the United States. It results in inflammation, hence thickening of airway (AW) walls. It has been demonstrated that AW inflammation begins early in life producing structural AW damage. Because this damage can be present in patients who are relatively asymptomatic, lung disease can progress insidiously. High-resolution computed tomographic imaging has also shown that the AWs of infants and young children with CF have thicker walls and are more dilated than those of normal children. The purpose of this study was to develop computerized methods which allow rapid, efficient and accurate assessment of computed tomographic AW and vessel (V) dimensions from axial CT lung images. For this purpose, a full-width-half-max based automatic AW and V size measurement method was developed. The only user input required is approximate center marking of AW and V by an expert. The method was evaluated on a patient population of 4 infants and 4 children with different stages of mild CF related lung disease. This new automated method for assessing early AW disease in infants and children with CF represents a potentially useful outcome measure for future intervention trials

Subcellular localization prediction with new protein encoding schemes

Subcellular localization is one of the key properties in functional annotation of proteins. Support vector machines (SVMs) have been widely used for automated prediction of subcellular localizations. Existing methods differ in the protein encoding schemes used. In this study, we present two methods for protein encoding to be used for SVM-based subcellular localization prediction: n-peptide compositions with reduced amino acid alphabets for larger values of n and pairwise sequence similarity scores based on whole sequence and N-terminal sequence. We tested the methods on a common benchmarking data set that consists of 2,427 eukaryotic proteins with four localization sites. As a result of 5-fold cross-validation tests, the encoding with n-peptide compositions provided the accuracies of 84.5, 88.9, 66.3, and 94.3 percent for cytoplasmic, extracellular, mitochondrial, and nuclear proteins, where the overall accuracy was 87.1 percent. The second method provided 83.6, 87.7, 87.9, and 90.5 percent accuracies for individual locations and 87.8 percent overall accuracy. A hybrid system, which we called PredLOC, makes a final decision based on the results of the two presented methods which achieved an overall accuracy of 91.3 percent, which is better than the achievements of many of the existing methods. The new system also outperformed the recent methods in the experiments conducted on a new-unique SWISSPROT test set

SVM-based detection of distant protein structural relationships using pairwise probabilistic suffix trees

A new method based on probabilistic suffix trees (PSTs) is defined for pairwise comparison of distantly related protein sequences. The new definition is adopted in a discriminative framework for protein classification using pairwise sequence similarity scores in feature encoding. The framework uses support vector machines (SVMs) to separate structurally similar and dissimilar examples. The new discriminative system, which we call as SVM-PST, has been tested for SCOP family classification task, and compared with existing discriminative methods SVM-BLAST and SVM-Pairwise, which use BLAST similarity scores and dynamic-programming-based alignment scores, respectively. Results have shown that SVM-PST is more accurate than SVM-BLAST and competitive with SVM-Pairwise. In terms of computational efficiency, PST-based comparison is much better than dynamic-programming-based alignment. We also compared our results with the original family-based PST approach from which we were inspired. The present method provides a significantly better solution for protein classification in comparison with the family-based PST model

Protein solvent accessibility prediction using support vector machines and sequence conservations

A two-stage method is developed for the single sequence prediction of protein solvent accessibility from solely its amino acid sequence. The first stage classifies each residue in a protein sequence as exposed or buried using support vector machine (SVM). The features used in the SVM are physicochemical properties of the amino acid to be predicted as well as the information coming from its neighboring residues. The SVM-based predictions are refined using pairwise conservative patterns, called maximal unique matches (MUMs). The MUMs are identified by an efficient data structure called suffix tree. The baseline predictions, SVM-based predictions and MUM-based refinements are tested on a nonredundant protein data set and similar to 73% prediction accuracy is achieved for a solvent accessibility threshold that provides an evenly distribution between buried and exposed classes. The results demonstrate that the new method achieves slightly better accuracy than recent methods using single sequence prediction

A discriminative method for remote homology detection based on n-peptide compositions with reduced amino acid alphabets

In this study, n-peptide compositions are utilized for protein vectorization over a discriminative remote homology detection framework based on support vector machines (SVMs). The size of amino acid alphabet is gradually reduced for increasing values of n to make the method to conform with the memory resources in conventional workstations. A hash structure is implemented for accelerated search of n-peptides. The method is tested to see its ability to classify proteins into families on a subset of SCOP family database and compared against many of the existing homology detection methods including the most popular generative methods; SAM-98 and PSI-BLAST and the recent SVM methods; SVM-Fisher, SVM-BLAST and SVM-Pairwise. The results have demonstrated that the new method significantly outperforms SVM-Fisher, SVM-BLAST, SAM-98 and PSI-BLAST, while achieving a comparable accuracy with SVM-Pairwise. In terms of efficiency, it performs much better than SVM-Pairwise. It is shown that the information of n-peptide compositions with reduced amino acid alphabets provides an accurate and efficient means of protein vectorization for SVM-based sequence classification. (c) 2006 Elsevier Ireland Ltd. All rights reserved

Elektron Mikroskop Tomografisi Görüntülerinde Mitokondri Tespiti ve Bölütlemesi

Mitokondrinin Geçirimli Elektron Mikroskobu (Tranmission Electron Microscope - TEM) görüntüleri kullanarak otomatik tespiti ve bölütlemesi üzerine yapılan çalışmaların kökeninde mitokondrinin fiziksel özellikleri ile hastalıklar arasındaki ilişkisini araştıran klinik çalışmalar yatmaktadır. Günümüzde mitokondrinin yapısında oluşan değişiklikleri sayısal olarak araştıran çalışmalarda görüntüler elle bölütlenmektedir. Bu konudaki az sayıda otomatik algoritma geliştirme çalışması ise, daha kolay sayılabilecek tipteki görüntüler üzerinde ve elle seçilmiş kısmi görümtü alanlarında (sadece bir mitokondri içeren) çalışmak üzere tasarlanmıştır. Tüm görüntü üzerinde (onlarca ve hatta daha fazla mitokondri içeren) çalışabilen ve mitokondriyi diğer hücre içi yapılardan ayıredebilen yöntemler henüz mevcut değildir. Bu projenin amacı, mitokondrilerin TEM görüntüleri üzerinde otomatik tespiti ve bölütlemesini başaran yazılım ve algoritmalar geliştirmektir. Bu konuda önceki çalışmamızın (Mumcuoglu, 2012) eksikliklerini geliştirmek hedeflenmektedir. Önceki çalışmalar sadece 2 boyutlu (2B) görüntüler üzerinde geliştirilmiş ve denenmişti. Bu çalışmadaysa, yine 2B görüntüler üzerinde çalışan daha gürbüz algoritmalar ve aynı zamanda 3B’a (çoklu kesit) uyarlanmış yöntemlerin geliştirilmesi hedeflenmektedir

Discriminative remote homology detection using maximal unique sequence matches

We define a new pairwise sequence comparison scheme, for distantly related proteins and report its performance on remote homology detection task. The new scheme compares two protein sequences by using the maximal unique matches (MUM) between them. Once identified, the length of all nonoverlapping MUMs is used to define the simflarity between two sequences. To detect the homology of a protein to a protein family, we utilize the feature vectors containing all pairwise similarity scores between the test protein and the proteins in the training set. Support vector machines are employed for the binary classification in the same way that the recent works have done. The new method is shown to be more accurate than the recent methods including SVM-Fisher and SVM-BLAST, and competitive with SVM-Pairwise. In terms of computational efficiency, the new method performs much better than SVM-Pairwise