Keratoconus (KCN) is an eye condition that affects the cornea. The main objective of this study is to evaluate the accuracy of keratoconus detection from corneal parameters including elevation, topography and pachymetry using machine learning algorithms. We developed several machine learning models to detect keratoconus from corneal elevation, topography and pachymetry parameters that were obtained from 5881 eyes of 2800 patients in Brazil using a Pentacam Scheimpflug instrument. Elevation parameters provided the highest area under the curve (AUC) parameter of 0.99 in detecting normal from keratoconus cases and an AUC of 0.88 in detecting different severity levels when using only three most promising corneal parameters including minimum curvature radius, eccentricity of the cornea and asphericity of the cornea. The developed algorithm can distinguish early KCN eyes from healthy eyes with a high accuracy obtaining an AUC of 0.97. From a clinical point of view the detection of early KCN is very important because KCN patients are usually misdiagnosed due to early symptoms. Results suggest that elevation parameters may retain more useful information for detecting keratoconus than historically believed. INDEX TERMS artificial intelligence; elevation, topography and pachymetry raw data; machine learning; keratoconus; support vector machine.
The Internet of Things (IoT) has become a part of modern life where it is used for data acquisition and long-range wireless communications. Regardless of the IoT application profile, every wireless communication transmission is enabled by highly efficient antennas. The role of the antenna is thus very important and must not be neglected. Considering the high demand of IoT applications, there is a constant need to improve antenna technologies, including new antenna designs, in order to increase the performance level of WSNs (Wireless Sensor Networks) and enhance their efficiency by enabling a long range and a low error-rate communication link. This paper proposes a new antenna design that is able to increase the performance level of IoT applications by means of an original design. The antenna was designed, simulated, tested, and evaluated in a real operating scenario. From the obtained results, it ensured a high level of performance and can be used in IoT applications specific to the 868 MHz frequency band.By inserting two notches along x axis, we find an optimal structure of the microstrip patch antenna with a reflection coefficient of −34.3 dB and a bandwidth of 20 MHz. After testing the designed novel antenna in real IoT operating conditions, we concluded that the proposed antenna can increase the performance level of IoT wireless communications.
In this paper, we propose an approach of optimization of meander line antennas by using genetic algorithm. Such antennas are used in RFID applications. As opposed to other approaches for meander antennas, we propose the use of only two optimization objectives, i.e. gain and size. As an example, we have optimized a single meander dipole antenna, resonating at 869 MHz.
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