Meander structures are highly relevant in the Internet-of-Things (IoT) communication systems, their miniaturization remains as one of the key design issues. Meander structures allow to decrease the size of the IoT device, while maintaining the same operating parameters of the IoT device. Meander structures can also work as the delay systems, which can be used for the delay and synchronization of signals in IoT devices. The design procedure of the meander delay systems is time-consuming and cumbersome because of the complexity of the numerical and analytical methods employed during the design process. New methods, which will accelerate the synthesis procedure of the meander delay systems, should be investigated. This is especially relevant when the procedure of synthesis must be repeated many times until the appropriate configuration of the IoT device is found. We present the procedure of synthesis of the meander delay system using the Pareto-optimal multilayer perceptron network and multiple linear regression model with the M5 descriptor. The prediction results are compared with results, which were obtained using the commercial Sonnet© software package and with the results of physical experiment. The difference between the experimentally achieved and predicted results did not exceed 1.53 %. Moreover, the prediction of parameters of the meander delay system allowed to speed up the procedure of synthesis multiple times from hours to only 2.3 s.

Artificial intelligence used in unmanned aerial vehicle (UAV) flight control systems tends to leave UAV control systems without any radio communication emissions, whose signatures in an electromagnetic spectrum (ES) are widely used to detect UAVs. There will be problems in the near future in detecting any dangerous threats associated with UAV swarms, kamikaze unmanned aerial vehicles (UAVs), or any other UAVs with electrically powered thrust engines because of the UAV’s flight capabilities in full radio silence mode. This article presents a different approach to the detection of electrically powered multi-rotor UAVs. The main idea is to register the electromagnetic spectrum of the electric thrust engines of the UAV, which varies because of the changing flight conditions. An experiment on a UAV’s electric thrust engine-produced electromagnetic spectrum is carried out, presenting the results of the flight-dependent characteristics, which were observed in the electromagnetic spectrum. The electromagnetic signature of the UAV’s electric thrust engines is analyzed, discussed, and compared with the most similar behaving electric engine, which was used on the ground as a domestic electric appliance. A precision tunable magnetic antenna is designed, manufactured, and tested in this article. The physical experiments have shown that the ES of the electric thrust engines of multi-rotor UAVs can be detected and recorded for recognition. The unique signatures of the ES of the multi rotor UAV electric engine are recorded and presented as a result of the carried-out experiments. A precision tunable magnetic antenna is evaluated for the reception of the UAV’s signature. Moreover, results were obtained during the performed experiments and discussions about the development of the future techniques for the identification of the ES fingerprints of the UAV’s electric thrust engine are carried out.

Photogrammetry depends critically on the quality of the images used to reconstruct accurate and detailed 3D models. Selection of high-quality images not only improves the accuracy and resolution of the resulting 3D models, but also contributes to the efficiency of the photogrammetric process by reducing data redundancy and computational demands. This study presents a novel approach to image quality evaluation tailored for photogrammetric applications that uses the key point descriptors typically encountered in image matching. Using a LightGBM ranker model, this research evaluates the effectiveness of key point descriptors such as SIFT, SURF, BRISK, ORB, KAZE, FREAK, and SuperPoint in predicting image quality. These descriptors are evaluated for their ability to indicate image quality based on the image patterns they capture. Experiments conducted on various publicly available image datasets show that descriptor-based methods outperform traditional no-reference image quality metrics such as BRISQUE, NIQE, PIQE, and BIQAA and a simple sharpness-based image quality evaluation method. The experimental results highlight the potential of using key-point-descriptor-based image quality evaluation methods to improve the photogrammetric workflow by selecting high-quality images for 3D modeling.

Typically, analyses of meander structures (MSs) for transfer characteristics are conducted using specialized commercial software based on numerical methods. However, these methods can be time-consuming, particularly when a researcher is seeking to perform a preliminary study of the designed structures. This study aims to explore the application of neural networks in the design and analysis of meander structures. Three different feedforward neural network (FFNN), time delay neural network (TDNN), and convolutional neural network (CNN) techniques were investigated for the analysis and design of the meander structures in this article. The geometric dimensions or top-view images of 369 different meander structures were used for training an FFNN, TDNN, and CNN. The investigated networks were designed to predict such electrodynamic parameters as the delay time ($t_{\mathrm{d}}$), reflection coefficient ($S_{11}$), and transmission coefficient ($S_{21}$) in the 0–10 GHz frequency band. A sufficiently low mean absolute error (MAE) was achieved with all three methods for the analysis of MSs. Using an FFNN, the characteristic $t_{\mathrm{d}}$ was predicted with a $3.3$ ps average MAE. The characteristic $S_{21}$ was predicted with a $0.64$ dB average MAE, and $S_{11}$ was predicted with a $2.47$ dB average MAE. The TDNN allowed the average MAEs to be reduced to $0.9$ ps, $0.11$ dB, and $1.63$ dB, respectively. Using a CNN, the average MAEs were $27.5$ ps, $0.44$ dB, and $1.36$ dB, respectively. The use of neural networks has allowed accelerating the analysis procedure from approximately 120 min on average to less than 5 min.

Multilayer meander structures for wireless communications are presented in this paper. The miniaturization of meander structures is solved by positioning the meander conductor in multiple layers. The influence of the increasing number of layers and connecting vias on the operational parameters of the meander structures is investigated. Three-dimensional models of the meander structures are designed and analyzed in the CST Microwave Studio^© software package. The general mathematical model of the meander structure is presented. The computer-based simulation is verified by a physical experiment and analytical calculations. The investigation shows that it is possible to miniaturize the meander structure by placing it into different layers and connecting the meander conductors with vias. The overall length of the meander structure is decreased by 48% from 16.24 mm to 8.4 mm, while the delay time ${\mathit{t}}_{\mathrm{d}}$ is changed only by less than 3.2% and increased till 1.145 ns, which is 35 ps. The overall dimensions of the miniaturized meander structure are 8.4 × 17.35 × 0.76 mm. The designed structure is suitable for operation at a 2.4–2.5 GHz ISM frequency band.