Specific emitter identification (SEI) is the process of identifying individual emitters by analyzing the radio frequency emissions, based on the fact that each device contains unique hardware imperfections. While the majority of previous research focuses on obtaining features that are discriminative, the reliability of the features is rarely considered. For example, since device characteristics of the same emitter vary when it is operating at different carrier frequencies, the performance of SEI approaches may degrade when the training data and the test data are collected from the same emitters with different frequencies. To improve performance of SEI under varying frequency, we propose an approach based on continuous wavelet transform (CWT) and domain adversarial neural network (DANN). The proposed approach exploits unlabeled test data in addition to labeled training data, in order to learn representations that are discriminative for individual emitters and invariant for varying frequencies. Experiments are conducted on received signals of five emitters under three carrier frequencies. The results demonstrate the superior performance of the proposed approach when the carrier frequencies of the training data and the test data differ.

This study describes the methodology for modelling a worm and worm wheel of a double enveloping worm gear with the use of a CAD system. An algorithm for generating a globoid helix is described. In addition, the methodology for modelling an hourglass worm thread with a straight axial tooth profile is presented. The shape of the hourglass worm tooth end with and without a trace modification is proposed. Moreover, a method for achieving a geometric modification of the tooth trace was developed. Next, the method for modelling the worm wheel teeth is described. A solid model of using a machining worm as a hob is applied. Owing to the limitations of a CAD system, which prevents the use of a direct machining simulation, an indirect modelling method is introduced. In the present study, different CAD techniques, both solid and surface, are applied. Knowledge of the correct modelling of the hourglass worm and worm wheel facilitates their generation and conducting various analyses, including a tooth contact analysis. CAD models are utilised to analyse the geometrical contact pattern in a CAD environment, to carry out FEM analysis, to manufacture real parts or to prototype models using the technique of rapid prototyping. They can be also used as master models for measurement, e.g. in optical technics.

Plasma sprayed ceramic coatings serve as protective layers and are frequently exposed to aggressive wear, corrosion, or high-temperature environment. Currently, alumina and alumina-titania are some of the most popular protective ceramic composite coatings used in the industry. The present work deals with the investigation of the influence of TiO₂ content in the feedstock powder on the resulting microstructure and properties of Al₂O₃, Al₂O₃ + 3 wt% TiO₂, Al₂O₃ + 13 wt% TiO₂ and Al₂O₃ + 40 wt% TiO₂ coatings developed via atmospheric plasma spraying (APS). Specifically, the phase composition, morphology, and microstructure, as well as the mechanical and tribological performance of the coatings were examined. Results revealed that higher content of TiO₂ induced the transformation of phases, leading to the formation of intermediary Al₂TiO₅ and Al₂- xTi₁- xO₅ phases. Also, the dominant α–Al₂O₃ to γ–Al₂O₃ transformation confirmed the formulation of well-melted lamellas within the coating structure. It was also shown that the increase in TiO₂ content decreased the micro-hardness of the coatings due to the formation of the intermediary phases as mentioned above and thus, affected their tribological performance. The lowest volumetric wear, equal to 7.2×10⁻⁵ mm³/(N ∙ m), was reported for Al₂O₃ + 13 wt% TiO₂ coating.