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Cellular phone용 단일 전원 MMIC single-ended 주파수 혼합기 개발
강현일,이상은,오재응,오승건,곽명현,마동성 대한전자공학회 1997 電子工學會論文誌, D Vol.d34 No.10
An MMIC downconverting mixer for cellular phone application has been successfully developed using an MMIC process including $1 \mu\textrm{m}$ ion implanted gaAs MESFET and passive lumped elements consisting of spiral inductor, $Si_3N_4$ MIM capacitor and NiCr resistor. The configuration of the mixer presented in this paper is single-ended dual-gate FET mixer with common-source self-bias circuits for single power supply operation. The dimension of the fabricated circuit is $1.4 mm \times 1.03 mm $ including all input matching circuits and a mixing circuit. The conversion gian and noise figure of the mixer at LO powr of 0 dBm are 5.5dB and 19dB, respectively. The two-tone IM3 characteristics are also measured, showing -60dBc at RF power of -30dBm. Allisolations between each port show better than 20dB.
Evaluation of the Usefulness of Detection of Abdominal CT Kidney and Vertebrae using Deep Learning
이현종(Hyun-Jong Lee),곽명현(Myeong-Hyeun kwak),윤혜원(Hye-Won Yoon),류은진(Eun-Jin Ryu),송현경(Hyeon-Gyeong Song),홍주완(Joo-Wan Hong) 한국방사선학회 2021 한국방사선학회 논문지 Vol.15 No.1
전산화단층촬영은 질병 진단 등 의료분야에 중요한 역할을 담당하고 있지만, 검사 건수 및 검사 별 영상 증가가 지속되고 있다. 최근 의료분야에 딥러닝 이용이 활발히 이루어지고 있으며, 의료영상을 이용한 딥러닝 중 객체 검출을 통해 보조적 질병 진단에 활용되고 있다. 본 연구는 객체 검출 딥러닝 중 YOLOv3 모델을 이용하여 복부 CT 중 콩팥과 척추를 검출하여 정확도를 평가하고자 한다. 연구 결과 콩팥과 척추의 검출 정확도는 83.00%와 82.45% 였으며, 이를 통해 딥러닝을 이용한 의료영상 객체 검출에 대한 기초자료로 활용될 수 있을 것이라 사료된다. CT is important role in the medical field, such as disease diagnosis, but the number of examination and CT images are increasing. Recently, deep learning has been actively used in the medical field, and it has been used to diagnose auxiliary disease through object detection during deep learning using medical images. The purpose of study to evaluate accuracy by detecting kidney and vertebrae during abdominal CT using object detection deep learning in YOLOv3. As a results of the study, the detection accuracy of the kidney and vertebrae was 83.00%, 82.45%, and can be used as basic data for the object detection of medical images using deep learning.