The dissertation examines the thermal flow detection and prediction prob-lems during an autonomous aircraft flight. The main research object is the thermal flows and artificial neural networks. Thermal flows are a very im-portant source for improving autonomous aircraft flight parameters, such as flight time and duration. The primary aim of the dissertation is to create methodologies and algorithms to detect, identify and to successfully predict the parameters the thermal flows. The application are of the methods and algorithms developed is autonomous aircraft control system synthesis, research on mesoscale meteorological phenomena and synthesis of computing systems using biological models. The following objectives are carried out: thermal flow sensing using aircraft navigational parameters measurement data, thermal flow simulation modeling and data input necessary for modeling. The dissertation consists of an introduction, four chapters, conclusions, bibliography, and list of author publications on the topic as well as three annexes. The introductory chapter discusses the research problem and the relevance of the research described in the thesis, formulates the goal and objectives, describes the research methodology, scientific novelty, the practical significance of the results, hypotheses. In the end of the introduction a list of author's publications on the topic and the structure of the dissertation are presented. The first section provides a review of previous... [to full text] / Disertacijoje nagrinėjamos terminių srautų paieškos ir prognozavimo autonominio orlaivio skrydžio metu problemos. Pagrindinis tyrimų objektas yra terminių srautų aparatinis aptikimas ir prognozavimas. Terminiai srautai yra labai svarbus autonominio orlaivio skrydžio charakteristikų, kaip antai skrydžio laikas ir trukmė, gerinimo šaltinis. Pagrindinis disertacijos tikslas – sukurti metodikas ir algoritmus, leidžiančius aptikti terminį srautą, nustatyti bei sėkmingai prognozuoti jo parametrus. Sukurtų metodų ir algoritmų taikymo sritis – autonominių orlaivių valdymo sistemų sintezė, meteorologiniai mezomastelinių meteorologinių reiškinių tyrimai, biologinius skaičiavimo modelius naudojančių sistemų sintezė. Darbe sprendžiami keli uždaviniai: terminio srauto aptikimas naudojant orlaivio navigacinių parametrų matavimo duomenis, terminio srauto modeliavimas bei modeliui reikalingų duomenų pateikimas. Disertaciją sudaro įvadas, keturi skyriai, rezultatų apibendrinimas, naudotos literatūros ir autoriaus publikacijų disertacijos tema sąrašai ir tris priedai. Įvadiniame skyriuje aptariama tiriamoji problema, darbo aktualumas, aprašomas tyrimų objektas, formuluojamas darbo tikslas bei uždaviniai, aprašoma tyrimų metodika, darbo mokslinis naujumas, darbo rezultatų praktinė reikšmė, ginamieji teiginiai. Įvado pabaigoje pristatomos disertacijos tema autoriaus paskelbtos publikacijos ir konferencijų pranešimai bei disertacijos struktūra. Pirmajame skyriuje pateikiama su disertacijos... [toliau žr. visą tekstą]

Disertacijoje nagrinėjamos terminių srautų paieškos ir prognozavimo autonominio orlaivio skrydžio metu problemos. Pagrindinis tyrimų objektas yra terminių srautų aparatinis aptikimas ir prognozavimas. Terminiai srautai yra labai svarbus autonominio orlaivio skrydžio charakteristikų, kaip antai skrydžio laikas ir trukmė, gerinimo šaltinis. Pagrindinis disertacijos tikslas – sukurti metodikas ir algoritmus, leidžiančius aptikti terminį srautą, nustatyti bei sėkmingai prognozuoti jo parametrus. Sukurtų metodų ir algoritmų taikymo sritis – autonominių orlaivių valdymo sistemų sintezė, meteorologiniai mezomastelinių meteorologinių reiškinių tyrimai, biologinius skaičiavimo modelius naudojančių sistemų sintezė. Darbe sprendžiami keli uždaviniai: terminio srauto aptikimas naudojant orlaivio navigacinių parametrų matavimo duomenis, terminio srauto modeliavimas bei modeliui reikalingų duomenų pateikimas. Disertaciją sudaro įvadas, keturi skyriai, rezultatų apibendrinimas, naudotos literatūros ir autoriaus publikacijų disertacijos tema sąrašai ir tris priedai. Įvadiniame skyriuje aptariama tiriamoji problema, darbo aktualumas, aprašomas tyrimų objektas, formuluojamas darbo tikslas bei uždaviniai, aprašoma tyrimų metodika, darbo mokslinis naujumas, darbo rezultatų praktinė reikšmė, ginamieji teiginiai. Įvado pabaigoje pristatomos disertacijos tema autoriaus paskelbtos publikacijos ir konferencijų pranešimai bei disertacijos struktūra. Pirmajame skyriuje pateikiama su disertacijos... [toliau žr. visą tekstą] / The dissertation examines the thermal flow detection and prediction prob-lems during an autonomous aircraft flight. The main research object is the thermal flows and artificial neural networks. Thermal flows are a very im-portant source for improving autonomous aircraft flight parameters, such as flight time and duration. The primary aim of the dissertation is to create methodologies and algorithms to detect, identify and to successfully predict the parameters the thermal flows. The application are of the methods and algorithms developed is autonomous aircraft control system synthesis, research on mesoscale meteorological phenomena and synthesis of computing systems using biological models. The following objectives are carried out: thermal flow sensing using aircraft navigational parameters measurement data, thermal flow simulation modeling and data input necessary for modeling. The dissertation consists of an introduction, four chapters, conclusions, bibliography, and list of author publications on the topic as well as three annexes. The introductory chapter discusses the research problem and the relevance of the research described in the thesis, formulates the goal and objectives, describes the research methodology, scientific novelty, the practical significance of the results, hypotheses. In the end of the introduction a list of author's publications on the topic and the structure of the dissertation are presented. The first section provides a review of previous... [to full text]

In the presented master paper: Analysis of car number plate recognition, optical character recognition (OCR), OCR software, OCR devices and systems are reviewed. Image processing operators and artificial neural networks are presented. Analysis and application of image processing operators for detection of number plate is done. Experimental results of estimation of Kohonen and multilayer feedforward artificial neural network learning parameters are presented. Number plate recognition is performed by the use of multilayer feedforward artificial neural network. Model of number plate recognition system is created. Number plate recognition software works in Microsoft© Windows™ operating system. Software is written with C++ language. Experimental results of system model operation are presented.

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.

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.