This article proposed to build a system founded on metamaterial sensor antennas, which can be used to evaluate impurities in aqueous substances according to the quality of transmission between the sensor antennas. In order to do this, a dedicated setup with tests in several frequencies was deployed so as to monitor the behavior of transmission variation between sensors. These sensors are microstrip antennas with a ground plane of resonant cleaved metallic rings; the substrate functions as a metamaterial for the irradiating element. In this study, an analysis was made of transmission between the sensors, looking for variation in angles of incidence of signal and of distance between the antennas. The sensor was tested at various operating frequencies, as such 1.8 GHz, 2.4 GHz, 3.4 GHz and 4.1 GHz, resulting in different values of sensitivity. The prototypes were constructed and tested so as to analyze the dielectric effects of the impurities on NaCl and C₂H₄O₂ substances. The research aims to use these control systems of impurities in industrial premises.

Recently research show that some parameters such as the shapes of antenna patch and the ground plane when geometrically altered produces changes in the current density distribution of the planar structure and consequently in the resonant modes. This paper presents a new microstrip fractal antenna using the technique of inserting slots of shape fractal in ground plane in order to increase the bandwidth and insertion discontinuities in the feed line to reach specific behaviors in three resonant modes. The FR-4 substrate with dimensions 85.0 x 85.0 x 1.57 mm³ is used. Also, it used different techniques of impedance matching in feed line of antenna with changes of the width of the transmission line in order to obtain a variation in the current distribution and consequently of the impedance bandwidth for S11 ≤ -10dB for C-band (3.625 GHz – 4.2 GHz) and S-band (2.0 GHz – 4.0 GHz). Good agreement between measured and simulated results is achieved. Proposed fractal microstrip antenna can be easily designed, built and applied in wireless communication.

This study analyzes two distinct approaches for predicting path loss at frequencies of 800 MHz, 1800 MHz, and 2600 MHz in suburban areas. These frequencies are commonly utilized in broadcasting, 4G LTE, and 5G networks. Two models of Artificial Neural Networks (ANN) were implemented: an Error-Based Neural Network (EBNN), which incorporates error correction by combining empirical propagation models with an ANN, and a Terrain Parameters Based Neural Network (TBNN), which uses input parameters commonly applied in related studies, such as the distance from the transmitter to the receiver, receiver altitude, average terrain level, and azimuth angle between the transmitter and receiver. The performance of these models was evaluated using root mean square error (RMSE) and the Wilcoxon rank-sum test, comparing them with empirical propagation models such as SUI, ECC-33, Ericsson, and TR 25.942. The results were then compared with data obtained from a measurement campaign conducted along three routes in the city of Natal, Brazil. The findings from both simulations and actual measurements showed good metric alignment, particularly highlighting the performance of the error-based model. The primary contribution of this study is demonstrating that these techniques enable more accurate prediction of signal information, thereby reducing errors in the planning and implementation of wireless networks.