Abstract:Microwave devices are widely used in the industry and in the specialized laboratory analyses. Development of such devices requires the possibility of modeling of microwave energy distribution in the specific resonant chambers. Until now, such modeling was possible only with the use of commercial software or was limited to specific cases. The paper presents an open-source module for ELMER software for solving timeharmonic Maxwell's equations, allowing modeling of microwave waveguide lines. Three test cases of different resonant chambers are investigated at 2.45 GHz frequency. Modeling results obtained from the open-source ELMER Vectorial Helmholtz module show that the application of this software can be effective in R&D works, enabling high-tech small and medium enterprises involvement in advanced microwave technology.

Railways deliver a safe and sustainable form of transport and are typically pointed as one the safest form of transportation. Nevertheless, train accidents still happen, and when they happen, the consequences concern serious fatalities and injuries. Since every case is unique, the most frequent causes of train accidents are mechanical derailments, failures, as well as human errors and ignorance. In order to mitigate the risks posed by both physical and human related factors, various technological advancements have been designed and implemented. Among many existing Train Control and Monitoring Systems (TCMS), one can observe that recently developed artificial intelligence (AI) methods are also considered to be integrated part of the modern TCMS solutions. Following recent AI improvements and trends, in this paper we aim to present and discuss our newly developed TCMS system. In particular, both the system architecture and features are described along with the expected benefits of its implementation.

Research, development and innovation (RDI) projects are undertaken in order to improve existing, or develop new, more efficient products and services. Moreover, the goal of innovation is to produce new knowledge through research, and disseminating it through education and training. In this line of thinking, this paper reports and discusses the lessons learned from the undertaken project, regarding three areas: machine learning (artificial intelligence), computational intelligence, and database management systems (DBMS). As nowadays, a numerous of the RDI projects are oriented towards the development of dataintensive solutions, the authors are confident that these lessons will be valuable not only for data engineers, but also for those researchers and practitioners who are dealing with the issues related to building and validating machine learning models, applications of moving averages to high-frequency data streams, and the implementation and deployment of DBMS.

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