Coronary Artery Disease (CAD) occurs when the coronary vessels become hardened and narrowed, limiting blood flow to the heart muscles. It is the most common type of heart disease and has the highest mortality rate. Early diagnosis of CAD can prevent the disease from progressing and can make treatment easier. Optimal treatment, in addition to the early detection of CAD, can improve the prognosis for these patients. This study proposes a new method for non-invasive diagnosis of CAD using iris images. In this study, iridology, a method of analyzing the iris to diagnose health conditions, was combined with image processing techniques to detect the disease in a total of 198 volunteers, 94 with CAD and 104 without. The iris was transformed into a rectangular format using the integral differential operator and the rubber sheet methods, and the heart region was cropped according to the iris map. Features were extracted using wavelet transform, first-order statistical analysis, a Gray-Level Co-Occurrence Matrix (GLCM), and a Gray Level Run Length Matrix (GLRLM). The model’s performance was evaluated based on accuracy, sensitivity, specificity, precision, score, mean, and Area Under the Curve (AUC) metrics. The proposed model has a 93% accuracy rate for predicting CAD using the Support Vector Machine (SVM) classifier. With the proposed method, coronary artery disease can be preliminarily diagnosed by iris analysis without needing electrocardiography, echocardiography, and effort tests. Additionally, the proposed method can be easily used to support telediagnosis applications for coronary artery disease in integrated telemedicine systems.

In this study, in order to evaluate the exposed radiofrequency electromagnetic field (RF-EMF) levels, and to control their compliance with the limits determined by International Commission on Non-Ionizing Radiation Protection (ICNIRP), extensive short-term/band-selective and long-term RF-EMF measurements were conducted at 92 primary and secondary schools in the Ilkadım district of Samsun/Turkey. The measurements were performed once each in May, June, October and December in 2016, using the PMM-8053 EMF meter. It was seen from the measurement results that the maximum average electric field strength (E_avg) was recorded, 2.34 V/m, in October, when students were at school. It was concluded from the measurement results that the measured E_avg levels recorded at 92 schools were below the limits determined by ICNIRP. According to the band-selective measurement results performed using a Narda SRM-3006 EMF meter, the five main electric field strength (E) sources that had the most contribution in total E were LTE800, GSM900, GSM1800, UMTS2100 and WLAN services. With the use of these main E sources, an empirical model was then proposed that helps to determine the total E with 99.6% accuracy. It was also concluded from the long-term broadband measurement result that the number of active users affected the total E in the medium directly, and that the measured E levels were significantly higher in daytime than those of recorded in night-time. In the final stage of the study, all measurement results were transferred on scaled color maps. The use of these maps helped to determine and maintain control on the levels of RF-EMF exposure at schools using, or intending to install, such systems, and also to take measures for future precautions.

As a result of the dense deployment of wireless devices and base stations, measuring and evaluating the electromagnetic (EM) exposure levels they emit have become important to human health especially if they exceed the limits defined in the standards. Base stations, Wi-Fi equipment and other electronic devices are used heavily, especially in densely crowded places like shopping centers. In this study, electric field strength (E) measurements were conducted at one of the largest shopping malls in Turkey. Broadband E measurements were performed using PMM 8053 EM field strength meter for 24 h a day for the duration of one week while frequency selective measurements were carried out with SRM–3006 EM field strength meter. It is concluded from the measurements that the mean measured total E in the band between 100 kHz and 3 GHz is 0.59 V/m while the maximum E is 7.88 V/m, which are both below the limit determined by International Commission on Non-Ionizing Radiation Protection. Evolutions show that E can increase by up to 55% during the daytime. Analyses demonstrate that 71.3% of total E is caused by UMTS2100, 16.3% is produced by GSM900, 6.2% by LTE, 3.5% by Wi-Fi, and 2.7% is generated by devices that use the remaining frequency bands. Based on the detailed statistical analysis of long-term E measurement results, it can be concluded that the measured E levels are not in normal distribution and that they are statistically different with respect to days. Furthermore, distribution of E can be best modeled with the non-parametric approach.

Radio-frequency electromagnetic field (RF-EMF) forecasting plays an important role in the evaluation of regulatory compliance, network planning and system optimization. The knowledge of RFEMF levels is essential to ensure compliance with standards and avoid public health concerns, especially with the arrival of new frequencies and scenarios in fifth-generation (5G) and sixth generation (6G) wireless networks. This work provides a comprehensive study on time series forecasting for RF-EMF measured in frequency from 100 kHz -3 GHz. The state-of-the-art deep learning model architectures consist of deep neural network (DNN), convolutional neural network (CNN), long-short term memory (LSTM), and transformer are applied for time series forecasting. The prediction performance is evaluated under three different scenarios -namely single-step input single-step output (SISO), multi-step input single-step output (MISO), and multi-step input multi-step output (MIMO). The findings from the simulation demonstrate that SISO forecasting is inadequate in predicting long-term radio-frequency electromagnetic fields (RF-EMF) data as it lacks accuracy while MISO and MIMO forecasting scenarios offer more precise predictions. Specifically, in these two scenarios where the input width and label width are both set to 20 steps, the LSTM and CNN models exhibit superior performance compared to other models. Nonetheless, as the input width and label width in a MIMO scenario increase, the accuracy of both CNN and LSTM models decline considerably, whereas the transformer model consistently maintains good performance. Additionally, the transformer model continues to deliver accurate predictions as the label width and shift length increase, which is not the case for DNN, CNN, and LSTM models.

This study presents a novel dual-band microstrip antenna operating at 28/38 GHz, which is designed for fifth generation (5G) and next-generation communications. The objective was to create a high-gain, single-element solution that addresses millimeter-wave (mmWave) challenges, like attenuation and signal loss, offering a more efficient alternative to complex array antennas. The antenna was designed using Rogers RT/duroid 5880 as a substrate, and CST simulations were used to optimize the return loss, gain, and efficiency. Analytical methods and parametric analyses were used to further optimize the design. Additionally, an SMP connector was integrated into the simulated model using Antenna Magus software, followed by further refinement through additional parametric studies. The final compact antenna (33 × 27 × 1.6 mm³) demonstrates excellent performance with simplified fabrication. The antenna achieved bandwidths of 1.12 GHz at 28 GHz and 1.27 GHz at 38 GHz, with remarkably low return loss values of −53.04 dB and −83.65 dB, respectively. The gain values reached 7.82 dBi at 28 GHz and 8.98 dBi at 38 GHz—prototype measurements closely aligned with simulations, confirming reliability. This study introduces a high-performance, single-element antenna that is both simple and complex. The meticulous optimization process, including SMP connector variations, minimized the fabrication sensitivity and improved the overall performance, thereby marking a significant advancement in antenna design.