The paper discusses the problem of video transmission in an IP network. The authors consider the ability of using the most popular video codecs that use both the MPEG2 Transport Stream and Dynamic Adaptive Streaming over Hypertext Transfer Protocol (DASH). The main emphasis was given to ensuring the quality of service and quality assessment methods, taking into account not only the service- or network provider’s point of view but also the end user’s perspective. Two quality assessment approaches were presented, i.e. objective and subjective methods. The authors presented the results of the quality evaluation for H.264/MPEG-4, H.265/HEVC and VP9 codecs. The objective measurements, proved by statistical analysis of user opinion scores, confirmed the ability of using H.265 and VP9 codecs in both real time and streaming transmissions, while the quality of video streaming over HTTP with the H.264 codec proved inadequate. The authors also presented a connection between the dynamics of network bandwidth changing and MPEG-DASH mechanism operation and their influence on the quality experienced by users.

The paper deals with analysis of recognition of surface quality with reflective structures. Such surfaces are common in metallic materials cut using a saw or polished. There are no easy methods to identify such elements after machining. This issue is crucial in the industry for quality control as recognition of the elements, for instance after failure, allows for a detailed study of their manufacturing process. Firstly, six cuboid steel elements were obtained from a larger beam with a circular saw. Then, the bidirectional reflection distribution function (BRDF) was obtained for each element 3 times. The BRDF profiles were used in custom recognition software based on the K-nearest neighbors algorithm. In total, 140 variants of the classifier were tested and analyzed. Additionally, each variant was solved 200 times with different splits of the dataset. The results showed a high multiclass accuracy in all considered variants of the algorithm, with multiple variants achieving 100% accuracy. This level of performance was attained with only 1 to 2 training samples per class. Its low numerical complexity, easy experimental procedure, and “one-shot” nature allow for fast recognition, which is crucial in industrial applications.

Wind turbines are nowadays one of the most promising energy sources. Every year, the amount of energy produced from the wind grows steadily. Investors demand turbine manufacturers to produce bigger, more efficient and robust units. These requirements resulted in fast development of condition-monitoring methods. However, significant sizes and varying operational conditions can make diagnostics of the wind turbines very challenging.

The paper shows the case study of a wind turbine that had suffered a serious rolling element bearing (REB) fault. The authors compare several methods for early detection of symptoms of the failure. The paper compares standard methods based on spectral analysis and a number of novel methods based on narrowband envelope analysis, kurtosis and cyclostationarity approach.

The very important problem of proper configuration of the methods is addressed as well. It is well known that every method requires setting of several parameters. In the industrial practice, configuration should be as standard and simple as possible. The paper discusses configuration parameters of investigated methods and their sensitivity to configuration uncertainties