Darius Plonis

Dynamic adaptation of the jitter buffer for video streaming applications

Paper presents a new algorithm proposed to dynamically adapt jitter buffer, used in video transcoding applications. When the video content provider is moving continuously, the wireless network connectivity conditions changes in time. The resulting jitter should be compensated in video transcoder by selecting jitter buffer high enough to compensate all possible jitter variations. The algorithm, proposed in this paper updates the jitter buffer according to current jitter measurements and jitter prediction results in order to reduce the total latency of the video transcoding in live transmissions.

A Robust Identification of the Protein Standard’s Bands in Two-Dimensional Electrophoresis Gel Images

Abstract The aim of the investigation presented in this paper was to develop a software-based assistant for the protein analysis workflow. The prior characterization of the unknown protein in two-dimensional electrophoresis gel images is performed according to the molecular weight and isoelectric point of each protein spot estimated from the gel image before further sequence analysis by mass spectrometry. The paper presents a method for automatic and robust identification of the protein standard band in a two-dimensional gel image. In addition, the method introduces the identification of the positions of the markers, prepared by using pre-selected proteins with known molecular mass. The robustness of the method was achieved by using special validation rules in the proposed original algorithms. In addition, a self-organizing map-based decision support algorithm is proposed, which takes Gabor coefficients as image features and searches for the differences in preselected vertical image bars. The experimental investigation proved the good performance of the new algorithms included into the proposed method. The detection of the protein standard markers works without modification of algorithm parameters on two-dimensional gel images obtained by using different staining and destaining procedures, which results in different average levels of intensity in the images.

Influence Coefficients of Constructive Parameters of Meander Slow-Wave System with Additional Shields

Abstract Constructions of meander slow-wave systems with additional shields grounded at different positions are presented. The construction of meander slow-wave systems with additional shields grounded at both edges is investigated in detail. The influence of the main constructive parameters on the electrical characteristics of meander slow-wave systems with additional shields grounded at both edges is evaluated. The main constructive parameters of the investigated system are: the length of the conductor, the width of meander conductor, the width of additional shield, and the width of the gap between adjacent meander conductors. Sonnet® software package, which is based on the method of moments, has been used during the investigation. The results of the influence of constructive parameters to the phase delay time, input impedance, and the width of pass-band of the system are discussed.

The prediction of cut-off frequencies of models of gyroelectric waveguides using artificial neural networks

Gyroelectric n and p types waveguides were usually investigated using differential Maxwells equations, coupled mode and partial area methods, coherent approaching, least square methods. Computation time of one particular model of this type of waveguide might take quite a long time and even to a couple of days using one of these analytical methods. The whole investigation may require a lot of time in the first stage of research until the right model of waveguide will be found. The artificial neural networks were adjusted for the investigation of gyroelectric n GaAs waveguides. Multilayer perceptron network was selected during investigation. Advantages of artificial neural networks comparing with analytical methods are presented in this paper. The investigation showed that difference between results, obtained using analytical methods, and results, obtained by using artificial neural networks, do not differ by more than 12%. On the other hand prediction using artificial neural networks is performed about 2000 times faster than using traditional methods.