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.INDEX TERMS Antenna arrays, antenna measurements, artificial neural networks, Internet of Things.

Influence of transverse asymmetry of meander systems on their frequency characteristics and rejection properties is considered. The model of the systems based on the multiconductor line method is used. Examples of calculated characteristics are presented. Properties of non-homogeneous meander structures, asymmetrical with respect to the longitudinal plane perpendicular to the central part of the system, are revealed. In the meander system containing electrodes with wide central parts of meander conductors and narrowed peripheral parts, the stop-band appears at q = p where q is the phase angle between voltages or current on neighbor conductors. At transverse asymmetry, the period of inhomogeneities along the meander conductor becomes two times greater with respect to the period of inhomogeneities at absence of transverse asymmetry, wherefore input impedance rapidly changes and the stop-band appears when phase angle q approaches to p/2. Increase of variation of characteristic impedances Z(0) or Z(0) or Z(p) followed by increase of the width of the stop-band. Ill. 5, bibl. 14, tabl. 3 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.108.2.136

The process of designing microwave devices is difficult and time-consuming because the analytical and numerical methods used in the design process are complex. Therefore, it is necessary to search for new methods that will allow for an acceleration of synthesis and analytic procedures. This is especially important in cases where the procedures of synthesis and analysis have to be repeated many times, until the correct device configuration is found. Artificial neural networks are one of the possible alternatives for the acceleration of the design process. In this paper we present a procedure for analyzing a hybrid meander system (HMS) using the feed-forward backpropagation network (FFBN). We compared the prediction results of the transmission factor and the reflection factor , obtained using the FFBN, with results obtained using traditional analytical and numerical methods, as well as with experimental results. The comparisons show that prediction results significantly depend on the FFBN structure. In terms of the lowest difference between the characteristics calculated using the method of moments (MoM) and characteristics predicted using the FFBN, the best prediction was achieved using the FFBN with three hidden layers, which included 18 neurons in the first hidden layer, 14 neurons in the second hidden layer, and 2 neurons in the third hidden layer. Differences between the predicted and calculated results did not exceed 7% for the parameter and 5% for the parameter. The prediction of parameters using the FFBN allowed the analysis procedure to be sped up from hours to minutes. The experimental results correlated with the predicted characteristics.

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The usage of techniques of the artificial neural networks (ANNs) in the field of microwave devices has recently increased. The advantages of ANNs in comparison with traditional full-wave methods are that the prediction speed when the traditional time-consuming iterative calculations are not required and also the complex mathematical model of the microwave device is no longer needed. Therefore, the design of microwave device could be repeated many times in real time. However, methods of artificial neural networks still lag behind traditional full-wave methods in terms of accuracy. The prediction accuracy depends on the structure of the selected neural network and also on the obtained dataset for the training of the network. Therefore, the paper presents a systematic review of the implementation of ANNs in the field of the design and analysis of microwave devices. The guidelines for the systematic literature review and the systematic mapping research procedure, as well as the Preferred Report Items for Systematic Reviews and Meta-Analysis statements (PRISMA) are used to conduct literature search and report the results. The goal of the paper is to summarize the application areas of usage of ANNs in the field of microwave devices, the type and structure of the used artificial neural networks, the type and size of the dataset, the interpolation and the augmentation of the training dataset, the training algorithm and training errors and also to discuss the future perspectives of the usage of ANNs in the field of microwave devices.