The optimum corn harvest time differs between individual harvest scenarios, depending on the intended use of the crop and on the technical equipment of the actual farm. It is therefore economically significant to specify the period as precisely as possible. The harvest maturity of silage corn is currently determined from the targeted sampling of plants cultivated over large areas. In this context, the paper presents an alternative, more detail-oriented approach for estimating the correct harvest time; the method focuses on the relationship between the ripeness data obtained via photogrammetry and the parameters produced by the chemical analysis of corn. The relevant imaging methodology utilizing a spectral camera-equipped unmanned aerial vehicle (UAV) allows the user to acquire the spectral reflectance values and to compute the vegetation indices. Furthermore, the authors discuss the statistical data analysis centered on both the nutritional values found in the laboratory corn samples and on the information obtained from the multispectral images. This discussion is associated with a detailed insight into the computation of correlation coefficients. Statistically significant linear relationships between the vegetation indices, the normalized difference red edge index (NDRE) and the normalized difference vegetation index (NDVI) in particular, and nutritional values such as dry matter, starch, and crude protein are evaluated to indicate different aspects of and paths toward predicting the optimum harvest time. The results are discussed in terms of the actual limitations of the method, the benefits for agricultural practice, and planned research.

The paper reports on the solution, principles, and application results related to a system for diagnosing main transformers in power plants via the radiofrequency method. The subject of the diagnostics is the occurrence of partial discharge activity in transformers. The technical solution of the system is characterized in the introductory section of the article. There then follows a description of the operating principle and the implemented novel advanced methods for signal detection and source localization. The results obtained from practical application of the system within the diagnostics of high-power transformers are presented as well. Because ambient electromagnetic disturbance was recognized as a major issue during the system development, novel detection methods were proposed, implemented, and verified. The principal approach utilizes an external radiofrequency sensor to detect outer impulse disturbance and to eliminate disturbance-triggered acquisitions, and it also ensures direct real-time visualization of the desired impulse signals. The ability of weak signal detection was verified via artificial impulse signal injection into the transformer. The developed detection methods were completed with localization techniques for signal source estimation. The desired impulse signal was detected and localized during full operation of the main transformer, despite the presence of strong electromagnetic interference.

The authors present an analysis of conditions on the boundary between layers having varied electromagnetic properties. The research is performed using consistent theoretical derivation of analytical formulas, and the underlying problem is considered also in view of multiple boundaries including the effect of the propagation of electromagnetic waves with different instantaneous speeds. The paper comprises a theoretical analysis and references to the generated algorithms. The algorithms were assembled to enable simple evaluation of all components of the electromagnetic field in relation to the wave propagation speed in a heterogeneous environment. The proposed algorithms are compared by means of different numerical methods for the modelling of electromagnetic waves on the boundary between materials; moreover, the electromagnetic field components in common points of the model were also subject to comparison. When in conjunction with tools facilitating the analysis of material response to the source of a continuous signal, the algorithms constitute a supplementary instrument for the design of a layered material. Such design allows us to realize, for example, a recoilless plane, recoilless transition between different types of environment, and filters for both optical and radio frequencies.

There are some suitable methods for the measurement of ultrashort solitary electromagnetic pulses (EMP) that are generated by high-power microwave generators. The characteristics of EMPs are high-power level (P_max = 250 MW) and very short-time duration (t_p euro (1, 60) ns). Special requirements for measurement methods have to be considered because of the specific EMPs properties. In this paper, two suitable methods for this application are presented. The first one, the calorimetric method, utilizes the thermal impacts of microwave absorption. The second method presented-the magnetooptic (MO) method-uses the Faraday's MO effect as a sensor principle. A combined calorimetric sensor was realized and there were some experimental EMP measurements made with good results. The sensor utilizing the MO method is still in development.

The paper discusses mathematical tools to evaluate novel noise spectroscopy based analysis and describes, via physical similarity, the mathematical models expressing the quantitative character of the modeled task. Using the Stefan–Boltzmann law, the authors indicate finding the spectral density of the radiated power of a hemisphere, and, for the selected frequency interval and temperature, they compare the simplified models with the expression of noise spectral density according to the Johnson–Nyquist formula or Nyquist’s expression of the function of spectral density based on a derivation of Planck’s law. The related measurements and evaluations, together with analyses of the noise spectroscopy of periodic resonant structures, are also outlined in the given context.