As the complexity of a spatial phenomenon increases, traditional modeling becomes impractical. Alternatively, data-driven modeling, which is based on the analysis of data characterizing the phenomena, can be used. In this thesis, the generation of understandable and reliable spatial models using observational data is addressed. An interpretability oriented data-driven fuzzy modeling approach is proposed. The methodology is based on construction of fuzzy models from data, tuning and fuzzy model simplification. Mamdani type fuzzy models with triangular membership functions are considered. Fuzzy models are constructed using fuzzy clustering algorithms and simulated annealing metaheuristic is adapted for the tuning step. To obtain compact and interpretable fuzzy models a simplification methodology is proposed. Simplification methodology reduced the number of fuzzy sets for each variable and simplified the rule base. Prototype software is developed and mean annual precipitation data of Turkey is examined as case study to assess the results of the approach in terms of both precision and interpretability. In the first step of the approach, in which fuzzy models are constructed from data, &quot / Fuzzy Clustering and Data Analysis Toolbox&quot / , which is developed for use with MATLAB, is used. For the other steps, the optimization of obtained fuzzy models from data using adapted simulated annealing algorithm step and the generation of compact and interpretable fuzzy models by simplification algorithm step, developed prototype software is used. If the accuracy is the primary objective then the proposed approach can produce more accurate solutions for training data than geographically weighted regression method. The minimum training error value produced by the proposed approach is 74.82 mm while the error obtained by geographically weighted regression method is 106.78 mm. The minimum error value on test data is 202.93 mm. An understandable fuzzy model for annual precipitation is generated only with 12 membership functions and 8 fuzzy rules. Furthermore, more interpretable fuzzy models are obtained when Gath-Geva fuzzy clustering algorithms are used during fuzzy model construction.

Für drehzahl- und lagegeregelte elektrische Antriebssysteme mit mechanisch-elastischen Übertragungselementen exitiert eine Fülle von Regelstrategien. Jede von ihnen weist spezifische Vor- aber auch Nachteile auf, welche letztlich auf die eigene prinzipbedingte Güteschranke zurückzuführen sind. Um eine solche Güteschranke zu durchbrechen muß man grundsätzlich die Strukturbeschränkung des Reglers angreifen. Hierfür werden zwei Möglichkeiten aufgezeigt, ein parametervariables und ein strukturvariables Regelkonzept. Mit der Parameter-und der Strukturvariabilität lassen sich konventionelle Regler qualitativ aufwerten, und unterschiedliche Regelprinzipien kombinieren. Insbesondere können Nutz-Nichtlinearitäten in systematischer Weise in lineare Regler integriert werden. Das parametervariable Regelkonzept verfolgt dem Ziel, das dynamische Regelverhalten konventioneller Regler durch Online-Tuning der Reglerparameter zu verbessern. Hierbei können unterschiedliche Parametersätze vorteilhaft kombiniert werden, so dass die Güteschranke des konventionellen Reglers überwunden wird. Der systematische Entwurf vereint die klassischen linearen Methoden mit der heuristischen fuzzy-basierten Vorgehensweise. Beim strukturvariablen Konzept handelt es sich um ein auf das Sliding-Mode-Prinzip basierte Regelverfahren, dessen Hauptgegenstand die Güterobustheit der Regelung gegenüber Parameterunsicherheiten ist. Um die Nachteile des Sliding-Mode-Prinzips zu vermeiden und damit die Realisierbarkeit für elektrische Antriebssysteme zu erhöhen, werden Methoden für die Rekonstruktion der Zustandsgrößen und für die Chattering-Reduktion vorgeschlagen. Auch das strukturvariables Konzept beinhaltet einen starken Bezug zu linearen Prinzipien. Möglichkeiten zur Kombination beider vorgeschlagenen Konzepte werden ansatzmässig aufgezeigt. Die beiden Konzepte werden in Simulation und an einer Versuchsanlage experimentell erprobt

Natural sound, such as conspecific vocalizations and human speech, represents an important part of the sensory signals animals and humans encounter in their daily lives. This dissertation investigates the neural mechanisms involved in creating response selectivity for complex features of natural acoustic signals and demonstrates that selectivity for spectral motion cues provides a neural mechanism to encode communication signals in the auditory midbrain. Spectral motion is defined as the movement of sound energy upward or downward in frequency at a certain velocity, and is believed to provide the auditory system with an important perceptual cue in the processing of human speech. Using the Mexican free-tailed bat, tadarida brasiliensis, as a model system, this research examined the role of selectivity for spectral motion cues, such as direction and velocity, in creating response selectivity for specific features of the social communication signals emitted by these animals. We show that auditory neurons in the midbrain nucleus of the inferior colliculus (IC) are specifically tuned for the frequency-modulated (FM) direction and velocities found in their conspecific vocalizations. This close agreement between neural tuning and features of natural conspecific signals shows that auditory neurons have evolved to specifically encode features of signals that are vital for the survival of the animal. Furthermore, we find that the neural computations resulting in selectivity for spectral motion are analogous to mechanisms observed in selectivity for visual motion, suggesting the evolution of similar neural mechanisms across sensory modalities. / text

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Esant dideliems saugomų duomenų kiekiams, yra svarbus našus jų apdorojimas, taigi, vartotojams reikia vis didesnio duomenų bazių našumo. Šiame darbe sprendžiama problema, kaip paskatinti duomenų bazes veikti greičiau, kai duomenų bazių lentelės turi labai daug įrašų. Todėl skiriamas dėmesys duomenų bazių spartos derinimui, ar duomenų bazių spartos optimizavimui. Išnagrinėjus duomenų bazių esamus spartinimo metodus ir priežastis, kurios mažina našumą, yra siūlomas metodas, kuris leidžia sparčiau apdoroti ir filtruoti duomenis bei greičiau pateikti vartotojui užklausos rezultatą. Darbui atlikti pasirinkta MS SQL Server duomenų bazių valdymo sistema. Eksperimento metu atliktas užklausų greičio tyrimas, palyginant sudarytą metodą su virtualių lentelių metodu. / When the amount of stored data is growing, it is very important to get them fast and users are expecting to see how database performance is rising. Using database performance tuning, or database performance optimization, it is possible to make a database system run faster. In this paper after analysis of database performance optimization and performance tuning methods was suggested a method which enables to process data from database more quick and to user to get query result faster. To perform the research the MS SQL Server Database Management System was chosen. The experiment was performed in order to evaluate how method works. The experiment results show that compared with views, this method has better query performance.

Duomenų kubo konstravimas yra laikui ir kompiuteriniams resursams imlus procesas. Nepaisant to, šis darbas turi būti atliktas norint pasinaudoti greitų užklausų iš ypatingai didelių OLAP kubų teikiamais privalumais . Telekomunikacijų bendrovės surenka didelius duomenų kiekius apie įvykius telekomunikaciniuose tinkluose. Kiekviena duomenų porcija aprašo daug informacijos (pavyzdžiui: paslaugos tipą, iniciatorių, gavėją, pradžios laiką, trukmę, perduotų duomenų kiekį, skambučio kryptį, kainą, tinklo sąsajos adresą ir t.t.). Mobiliojo ryšio rinkoje yra įprasta apdovanoti kiekvieną abonentą tam tikru prizu (pinigais, nuolaidomis ar nauju mobiliuoju telefonu) mainais į 24 mėnesių sutartį naudotis konkretaus operatoriaus paslaugomis. Taigi kas 24 mėnesius abonentas turi galimybę pakeisti paslaugos teikėją. Tam, kad ryšio operatorius išlaikytų savo klientus, už sutarties pratęsimą taip pat turi pasiūlyti dovaną. Kad būtų galima tai atlikti nepatiriant finansinių nuostolių – mobiliojo ryšio operatorius privalo žinoti kiekvieno abonento naudojimosi paslaugomis statistiką. Šiame dokumente aprašoma pora būtų kaip pakeisti duomenų pirminį vaizdą (struktūrą ir sudėtį) siekiant pagreitinti duomenų kubų konstravimo procesą. Vienas šių metodų – duomenų agregavimas iki didžiausio, vis dar tinkamo analizei, lygio. Kitas metodas – tai lėtai kintančių kubo dimensijų sintezavimas taip sumažinant kubo dydį ir pagreitinant jo kūrimą. / Data cube pre computing is time and computer resources consuming task. In spite of this it needs to be done in order to construct an OLAP cube to take advantage of fast querying in data sets enormous in its sizes. Telecommunication industries collect huge amount of data about events in its networks. Every data portion holds a lot of information (i.e. service type, originator, receiver, time for start, duration, data volume, calling direction, cost, network interface address, etc.). In mobile telecommunication industries it is common to award each customer / subscriber by some prize (money, cell phone, discount to services and so on) in return of 24 month obligation to use one’s services. So, every 24 months subscriber gains ability to choose another telecommunication network. In order to maintain stable amount of subscribers’ service provider must offer something in return. In order to do that correctly, without financial loses, one must know exact usage statistics of each subscriber. This paper covers couple tips to arrange data in data warehouses (data marts) in order to achieve greater data cube pre processing speed. One of these methods covers partial data aggregation to highest degree, still sufficient to answer specific queries. Another method shows the ability to synthesize data cube dimensions in order to lower data volumes, that data cube pre calculation could take less time.

Clustering, classification, and factor analysis are three popular data mining techniques. In this dissertation, we investigate these methods in high dimensional data analysis. Since there are much more features than the sample sizes and most of the features are non-informative in high dimensional data, dimension reduction is necessary before clustering or classification can be made. In the first part of this dissertation, we reinvestigate an existing clustering procedure, optimal discriminant clustering (ODC; Zhang and Dai, 2009), and propose to use cross-validation to select the tuning parameter. Then we develop a variation of ODC, sparse optimal discriminant clustering (SODC) for high dimensional data, by adding a group-lasso type of penalty to ODC. We also demonstrate that both ODC and SDOC can be used as a dimension reduction tool for data visualization in cluster analysis. In the second part, three existing sparse principal component analysis (SPCA) methods, Lasso-PCA (L-PCA), Alternative Lasso PCA (AL-PCA), and sparse principal component analysis by choice of norm (SPCABP) are applied to a real data set the International HapMap Project for AIM selection to genome-wide SNP data, the classification accuracy is compared for them and it is demonstrated that SPCABP outperforms the other two SPCA methods. Third, we propose a novel method called sparse factor analysis by projection (SFABP) based on SPCABP, and propose to use cross-validation method for the selection of the tuning parameter and the number of factors. Our simulation studies show that SFABP has better performance than the unpenalyzed factor analysis when they are applied to classification problems.

With more and more integration of analog and RF circuits in scaled CMOS technologies, process variation is playing a critical role which makes it difficult to achieve all the performance specifications across all the process corners. Moreover, at scaled technology nodes, due to lower voltage and current handling capabilities of the devices, they suffer from reliability issues that reduce the overall lifetime of the system. Finally, traditional static style of designing analog and RF circuits does not result in optimal performance of the system. A new design paradigm is emerging toward digitally assisted analog and RF circuits and systems aiming to leverage digital correction and calibration techniques to detect and compensate for the manufacturing imperfections and improve the analog and RF performance offering a high level of integration. The objective of the proposed research is to design digital friendly and performance tunable adaptive analog/RF circuits and systems with digital enhancement techniques for higher performance, better process variation tolerance, and more reliable operation and developing strategy for testing the proposed adaptive systems. An adaptation framework is developed for process variation tolerant RF systems which has two parts – optimized test stimulus driven diagnosis of individual modules and power optimal system level tuning. Another direct tuning approach is developed and demonstrated on a carbon nanotube based analog circuit. An adaptive switched mode power amplifier is designed which is more digital-intensive in nature and has higher efficiency, improved reliability and better process resiliency. Finally, a testing strategy for adaptive RF systems is shown which reduces test time and test cost compared to traditional testing.

This work focuses on modeling and real-time control of a motion simulator for dynamic testing of a two-axis gyro-stabilized head mirror used in modern tanks. For this purpose, a six-degree-of freedom Stewart Platform which can simulate disturbances on the stabilized head mirror during operation of the tank is employed. Mathematical models of the Stewart Platform are constructed using MATLAB and ADAMS. Control system infrastructure is constructed and real-time control system elements are employed. Controller tuning is achieved by using the developed mathematical models in MATLAB. These parameters are applied in the real-time control system and fine tuning is achieved. Accuracy of the motion simulator is tested by mounting an Inertial Measurement Unit on the Stewart Platform. Further control system strategies are discussed by means of simulation.

Organic and inorganic nanostructured materials, nano- and mesoscale objects and devices, and their integration into existing microelectronic technologies have been at the center of recent fundamental and applied research in nanotechnology. One of the critical needs is to develop an enhanced predictive capability of structure-property correlations and enable robust high performance systems by design. My thesis work was concerned with the theoretical and experimental studies of electronic and mechanical properties of chemically functionalized nanowires. I will describe a theoretical approach for investigating structure-property correlations in atomic-sized metallic wires based on the Density Functional Theory (DFT) for structure calculations and the Non-equilibrium Green's Function (NEGF) technique for electronic transport properties simulations. This synergistic approach is shown to yield the atomic structure of the smallest niobium nanowires. Furthermore, the method was applied to simulate electronic properties of chemically functionalized graphene nanoribbons. Further, I will demonstrate an experimental technique for simultaneous measurements of force and conductance in atomic-size objects based on quartz tuning fork piezoelectric sensors. A peculiar scaling effect, relevant for a broad range of test and measurement applications, namely the squeeze film effect, was observed during the development of the sensors. Using theoretical analysis based on finite element simulations of the hydrodynamic behavior of the sensors in a broad range of ambient conditions, I explain the observed phenomenon.