B. Zakeri, H. Bernety,
Volume 10, Issue 4 (December 2014)
Abstract
Band-notch characteristic has been of great interest recently to overcome the electromagnetic interference of Ultra-wideband systems (UWB) with other existing ones. In this paper, we present a novel microstrip-fed antenna with band rejection property appropriate for UWB applications. Band-notch characteristic has been achieved by adding a rectangular resonant element to the ground section. A prototype was fabricated and measured based upon optimal parameters. Experimental results show consistency with simulation results. Measurement results confirm that the antenna covers the UWB band and satisfies a band rejection in the frequency span of 5 GHz to 5.7 GHz to restrain it from interference with Wireless Local Area Network (WLAN). Then, to achieve better isolation, a rectangular strip is appended to the band-notch creating part of the ground section to enhance obtained VSWR up to 30 through simulation. In addition, by applying a similar technique, a dual band-notched characteristic with an average simulated VSWR of around 13.75 has been achieved for WLAN and the downlink of X band satellite communication systems with each more than 10. Features such as small size, omnidirectional pattern and perfect isolation make the antenna suitable for any UWB applications.
Eisa Zarepour, Mohammad Reza Mohammadi, Morteza Zakeri-Nasrabadi, Sara Aein, Razieh Sangsari, Leila Taheri, Mojtaba Akbari, Ali Zabihallahpour,
Volume 20, Issue 3 (September 2024)
Abstract
Using mobile phones for medical applications are proliferating due to high-quality embedded sensors. Jaundice, a yellow discoloration of the skin caused by excess bilirubin, is a prevalent physiological problem in newborns. While moderate amounts of bilirubin are safe in healthy newborns, extreme levels are fatal and cause devastating and irreversible brain damage. Accurate tests to measure jaundice require a blood draw or dedicated clinical devices facing difficulty where clinical technology is unavailable. This paper presents a smartphone-based screening tool to detect neonatal hyperbilirubinemia caused by the high bilirubin production rate. A machine learning regression model is trained on a pretty large dataset of images, including 446 samples, taken from newborns' sternum skin in four medical centers in Iran. The learned model is then used to estimate the level of bilirubin. Experimental results show a mean absolute error of 1.807 mg/dl and a correlation of 0.701 between predicted bilirubin by the proposed method and the TSB values as ground truth.
