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531	Elektrodinaminių lėtinimo įtaisų tyrimas taikant lygiagrečiąsias kompiuterines sistemas / Investigation of the electrodynamic retard devices using parallel computer systems Pomarnacki, Raimondas 06 January 2012 (has links) Disertacijoje nagrinėjamos mikrobangų įtaisų analizės ir sintezės proble-mos, taikant lygiagrečiąsias kompiuterines sistemas. Pagrindiniai tyrimo objektai yra daugialaidės mikrojuostelinės linijos ir meandrinės mikrojuostelinės vėlinimo linijos. Šie objektai leidžia perduoti, sinchronizuoti bei vėlinti siunčiamus signalus ir yra neatsiejama dalis daugelio mikrobangų prietaisų. Jų operatyvi ir tiksli analizė bei sintezė sąlygoja įtaisų kūrimo spartinimą. Pagrindinis disertacijos tikslas – sukurti lygiagrečiąsias metodikas ir algoritmus, skirtus sparčiai ir tiksliai atlikti minėtų linijų analizę ir sintezę. Sukurtų algoritmų ir metodikų taikymo sritis – mikrobangų įtaisų modeliavimo ir automatizuoto projektavimo progra-minė įranga. / An analysis using numerical methods can calculate electrical and construction characteristics parameters of microwave devices quite accurately. However, numerical methods require a lot of computation resources and time for calculations to be made. Rapid perfection of the computer technologies and software with implementation of the numerical methods has laid down the conditions to the rapid design of the microwave devices using computers. Electronics and Electrical Engineering Elektrodinaminės lėtinimo sistemos Daugialaidės mikrojuostelinės linijos Skaitiniai metodai Lygiagretūs skaičiavimai Electrodynamic retard lines Multiconductor microstrip lines Meander microstrip delay lines Numerical methods Parallel calculations
532	Melizmų sintezė dirbtinių neuronų tinklais / Melisma Synthesis Using Artificial Neural Networks Leonavičius, Romas 12 January 2007 (has links) Modern methods of speech synthesis are not suitable for restoration of song signals due to lack of vitality and intonation in the resulted sounds. The aim of presented work is to synthesize melismas met in Lithuanian folk songs, by applying Artificial Neural Networks. An analytical survey of rather a widespread literature is presented. First classification and comprehensive discussion of melismas are given. The theory of dynamic systems which will make the basis for studying melismas is presented and finally the relationship for modeling a melisma with nonlinear and dynamic systems is outlined. Investigation of the most widely used Linear Prediction Coding method and possibilities of its improvement. The modification of original Linear Prediction method based on dynamic LPC frame positioning is proposed. On its basis, the new melisma synthesis technique is presented. Developed flexible generalized melisma model, based on two Artificial Neural Networks – a Multilayer Perceptron and Adaline – as well as on two network training algorithms – Levenberg- Marquardt and the Least Squares error minimization – is presented. Moreover, original mathematical models of Fortis, Gruppett, Mordent and Trill are created, fit for synthesizing melismas, and their minimal sizes are proposed. The last chapter concerns experimental investigation, using over 500 melisma records, and corroborates application of the new mathematical models to melisma synthesis of one performer. Electronics and Electrical Engineering Mordentas Fortis Mordent Gruppett Dirbtinių neuronų tinklai Grupetas TPK Trelė Adaline Daina Song Sintezė Synthesis VNR Artificial neural network Melizma LPC Multilayer perceptron DNT Tiesinės prognozės koeficientai ANT Levenberg-Marquardt Mean opinion score Linear prediction coding Foršlagas Adalina Daugiasluoksnis perceptronas Melisma MOS Vidutinės nuomonės rezultatas Trill
533	Melizmų sintezė dirbtinių neuronų tinklais / Melisma Synthesis Using Artificial Neural Networks Leonavičius, Romas 12 January 2007 (has links) Modern methods of speech synthesis are not suitable for restoration of song signals due to lack of vitality and intonation in the resulted sounds. The aim of presented work is to synthesize melismas met in Lithuanian folk songs, by applying Artificial Neural Networks. An analytical survey of rather a widespread literature is presented. First classification and comprehensive discussion of melismas are given. The theory of dynamic systems which will make the basis for studying melismas is presented and finally the relationship for modeling a melisma with nonlinear and dynamic systems is outlined. Investigation of the most widely used Linear Prediction Coding method and possibilities of its improvement. The modification of original Linear Prediction method based on dynamic LPC frame positioning is proposed. On its basis, the new melisma synthesis technique is presented. Developed flexible generalized melisma model, based on two Artificial Neural Networks – a Multilayer Perceptron and Adaline – as well as on two network training algorithms – Levenberg- Marquardt and the Least Squares error minimization – is presented. Moreover, original mathematical models of Fortis, Gruppett, Mordent and Trill are created, fit for synthesizing melismas, and their minimal sizes are proposed. The last chapter concerns experimental investigation, using over 500 melisma records, and corroborates application of the new mathematical models to melisma synthesis of one performer. Electronics and Electrical Engineering Melisma Grupetas Mean opinion score Foršlagas Trill Mordentas Dirbtinių neuronų tinklai VNR TPK Song Synthesis Melizma Adalina Vidutinės nuomonės rezultatas Artificial neural network Gruppett Linear prediction coding LPC DNT Daugiasluoksnis perceptronas Daina ANT MOS Levenberg-Marquardt Multilayer perceptron Mordent Tiesinės prognozės koeficientai Fortis Adaline Sintezė Trelė

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