This article describes the manufacturing of a horn antenna using a 3D commercial printer. The horn antenna was chosen for its simplicity and practical versatility. The standardised horn antenna is one of the most widely used antennas in microwave technology. A standardised horn antenna can be connected to standardised waveguides. The horn antenna has been selected so that this antenna can be fabricated by 3D printing and thus obtain the equivalent of a standardised horn antenna. This 3D horn antenna can then be excited by a standardised waveguide. The 3Dprinted horn antenna with metallic layers has very good impedance characteristics, standing wave ratio and radiation patterns that are close to those of a standardised horn antenna. The 3D-based horn antenna is suitable for applications where low antenna weight is required, such as aerospace and satellite technologies. The article also describes a manufacturing procedure for a horn antenna (E-sector horn antenna) that is plated with galvanic layers of silver and gold. The design of the plated horn antenna in the Matlab application using the Antenna Toolbox extension is also described, including 3D printing procedures, post-processing procedures (plating) and practical testing of its functionality. The measured results are compared to simulations of the standardised horn antenna and then analysed.

No abstract

Background Google Trends proves to be a novel tool to ascertain the level of public interest in pathology and treatments. From anticipating nascent epidemics with data-driven prevention campaigns to identifying interest in cosmetic or bariatric surgery, Google Trends provides physicians real-time insight into the latest consumer trends. Methods We used Google Trends to identify temporal trends and variation in the search volume index of four groups of keywords that assessed practitioner-nomenclature inquiries, in addition to podiatric-specific searches for pain, traumatic injury, and common podiatric pathology over a 10-year period. The Mann-Kendall trend test was used to determine a trend in the series, and the Wilcoxon signed-rank test was used to determine whether there was a significant difference between summer and winter season inquiries. Significance was set at P ≤ .05. Results The terms “podiatrist” and “foot doctor” experienced increasing Search Volume Index (SVI) and seasonal variation, whereas the terms “foot surgeon” and “podiatric surgeon” experienced no such increase. “Foot pain,” “heel pain,” “toe pain,” and “ankle pain” experienced a significant increase in SVI, with “foot pain” maintaining the highest SVI at all times. Similar results were seen with the terms “foot fractures,” “bunion,” “ingrown toenail,” and “heel spur.” These terms all experienced statistically significant increasing trends; moreover, the SVI was significantly higher in the summer than in the winter for each of these terms. Conclusions The results of this study show the utility in illustrating seasonal variation in Internet interest of pathologies today's podiatrist commonly encounters. By identifying the popularity and seasonal variation of practitioner- and pathology-specific search inquiries, resources can be allocated to effectively address current public inquiries. With this knowledge, providers can learn what podiatric-specific interests are trending in their local communities and market their practice accordingly throughout the year.

In this paper, we propose a new approach to passively locate the 3D position of a signal source. This novel technique, called the power gain difference (PGD), is based only on measuring the received signal strength (RSS) with multiple sensors deployed in the area of interest, while the target transmit power or the equivalent isotropic radiated power (EIRP) is assumed to be unknown. Next, the signal source position is estimated using the knowledge of the ratios of RSS measured on different sensors. First, this article presents the geometric representation and the analytical solution of the model of the PGD technique. Second, the PGD dilution of precision was analyzed in order to gauge the accuracy of measuring the RSS. Finally, a numerical simulation of the performance of the proposed method was carried out and the results are discussed. It seems that the PGD technique has the potential to be a simple and effective solution of the 3D localization problem.

In this communication, artificial neural networks are used to estimate the initial structure of a multiband planar antenna. The neural networks are trained on a set of selected normalized multiband antennas characterized by time-efficient modal analysis with limited accuracy. Using the Deep Learning Toolbox in Matlab, several types of neural networks have been created and trained on the sample planar multiband antennas. In the neural network learning process, suitable network types were selected for the design of these antennas. The trained networks, depending on the desired operating bands, will select the appropriate antenna geometry. This is further optimized using Newton’s method in HFSS. The use of the neural pre-design concept speeds up and simplifies the design of multiband planar antennas. The findings presented in this paper will be used to refine and accelerate the design of planar multiband antennas.