-From the perspective of average viewer, high definition video streams such as HD (High Definition) and UHD (Ultra HD) are increasing their internet presence year over year. This is not surprising, having in mind expansion of HD streaming services, such as YouTube, Netflix etc. Therefore, high definition video streams are starting to challenge network resource allocation with their bandwidth requirements and statistical characteristics. Need for analysis and modeling of this demanding video traffic has essential importance for better quality of service and experience support. In this paper we use an easy-to-apply statistical model for prediction of 4K video traffic. Namely, seasonal autoregressive modeling is applied in prediction of 4K video traffic, encoded with HEVC (High Efficiency Video Coding). Analysis and modeling were performed within R programming environment using over 17.000 high definition video frames. It is shown that the proposed methodology provides good accuracy in high definition video traffic modeling.

-Visual inspection of radiographic images by radiologists is a regular practice in making a diagnosis. Thus the enhancement of details in radiographs can improve inspection and diagnosis certainty. Through this paper we perform the analysis of the fractional gradient for visual improvement of chest radiographs. Two implementations of the fractional derivative operator, based on central fractional differences, are evaluated. Also we tested two norms for calculation of the magnitude of the fractional gradient, Euclidean and infimum norm, and the conducted tests for both norms are consistent.