Darbe atlikta eksponentinių-sinusinių modelių įvertinimo specialiu mažiausių kvadratų metodu analizė. Apžvelgti pagrindiniai signalo parametrai. Aprašyti signalo modeliai bei jų formos. Išnagrinėtas visuminių mažiausių kvadratų metodas bei jam alternatyvūs metodai: kovariacinis, Tufts-Kumaresan ir Pisarenko. Pateikti šių metodų matematiniai aprašymai. Signalų modelių parametrų analizei sukurtos MATLAB programos bei pateikti jų programiniai kodai. Skaitiniais eksperimentais ištirta, kaip kiekvienas iš metodų veikia, esant skirtingam signalo-triukšmo santykiui. Gauti rezultatai iliustruoti grafiškai. Remiantis sumodeliuotais rezultatais, suformuluotos išvados apie nagrinėjamų metodų galimybes. / The aim of this study is to explore exponential-sinusoidal signal model estimation by a special least squares method. The main signal parameters are considered. Signal models and their forms are described. The total least squares method as well as its alternatives – the covariance, Tufts-Kumaresan and Pisarenko methods – are analysed. The mathematical description of these methods is given. MATLAB–based programs to analyse signal model parameters are developed and their codes are presented. We investigated the performance of each of these methods for different signal noise ratio values. The results obtained are illustrated graphically. Conclusions about the method properties are drawn on the basis of simulation experiments.

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 [ ...].

Magistro baigiamojo darbo tikslas – įgyvendinti realiuoju laiku veikiančius objektų sekimo vaizde algoritmus lauku programuojamų loginių matricų įrenginyje (LPLM) ir ištirti šių algoritmų veikimą. Iškelti uždaviniai pasiekti 3 etapais. Atlikta analitinė objektų sekimo vaizde literatūros apžvalga, išanalizuoti objektų sekimo vaizde algoritmai bei jų įgyvendinimo galimybės LPLM įrenginiuose. Sukurti algoritmai ir programos įgyvendintos viename ir keliuose LPLM įrenginiuose (sinchroniškai) taikant VHDL programavimo kalbą ir veikia realiu laiku. Atlikti sukurtų algoritmų tyrimai ir gautų rezultatų analizė. Ištirtas objektų sekimo stabilumas keičiant apšviestumo lygį, fono sudėtingumą, objekto spalvą, judesio greitį, atstumą iki kameros ir posūkio kampą. Darbo apimtis – 69 psl. teksto be priedų, 72 iliustr., 70 bibliografinių šaltinių, 3 priedai. / The aim of master’s thesis is to investigate the object tracking methods and implement the object tracking algorithms in field programmable gate array (FPGA) devices for real-time execution. The aim is achieved by performing 3 tasks. The analytical review of object tracking methods is performed, reviewing the abilities of algorithms implementation on FPGAs. The object tracking algorithms are implemented in VHDL and distributed on one and few FPGA chips in parallel and works in real-time. The implemented algorithms are investigated and results are analyzed. The stability of different object tracking is investigated by changing the illumination, background complexity, object color, moving velocity, distance to camera and rotation angle. Thesis consists of: 69 p. text without appendixes, 72 figures, 70 bibliographical entries, 3 appendixes included.