scholarly journals GaAs wideband low noise amplifier design for breast cancer detection system

Author(s):  
Lei Yan ◽  
Viktor Krozer ◽  
Sebastien Delcourt ◽  
Vitaliy Zhurbenko ◽  
Tom Keinicke Johansen ◽  
...  
2017 ◽  
Vol 7 (1.3) ◽  
pp. 69
Author(s):  
M. Ramana Reddy ◽  
N.S Murthy Sharma ◽  
P. Chandra Sekhar

The proposed work shows an innovative designing in TSMC 130nm CMOS technology. A 2.4 GHz common gate topology low noise amplifier (LNA) using an active inductor to attain the low power consumption and to get the small chip size in layout design. By using this Common gate topology achieves the noise figure of 4dB, Forward gain (S21) parameter of 14.7dB, and the small chip size of 0.26 mm, while 0.8mW power consuming from a 1.1V in 130nm CMOS gives the better noise figure and improved the overall performance.


2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
K. Yousef ◽  
H. Jia ◽  
R. Pokharel ◽  
A. Allam ◽  
M. Ragab ◽  
...  

This paper presents the design of ultra-wideband low noise amplifier (UWB LNA). The proposed UWB LNA whose bandwidth extends from 2.5 GHz to 16 GHz is designed using a symmetric 3D RF integrated inductor. This UWB LNA has a gain of 11 ± 1.0 dB and a NF less than 3.3 dB. Good input and output impedance matching and good isolation are achieved over the operating frequency band. The proposed UWB LNA is driven from a 1.8 V supply. The UWB LNA is designed and simulated in standard TSMC 0.18 µm CMOS technology process.


PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262349
Author(s):  
Esraa A. Mohamed ◽  
Essam A. Rashed ◽  
Tarek Gaber ◽  
Omar Karam

Breast cancer is one of the most common diseases among women worldwide. It is considered one of the leading causes of death among women. Therefore, early detection is necessary to save lives. Thermography imaging is an effective diagnostic technique which is used for breast cancer detection with the help of infrared technology. In this paper, we propose a fully automatic breast cancer detection system. First, U-Net network is used to automatically extract and isolate the breast area from the rest of the body which behaves as noise during the breast cancer detection model. Second, we propose a two-class deep learning model, which is trained from scratch for the classification of normal and abnormal breast tissues from thermal images. Also, it is used to extract more characteristics from the dataset that is helpful in training the network and improve the efficiency of the classification process. The proposed system is evaluated using real data (A benchmark, database (DMR-IR)) and achieved accuracy = 99.33%, sensitivity = 100% and specificity = 98.67%. The proposed system is expected to be a helpful tool for physicians in clinical use.


2008 ◽  
Vol 51 (2) ◽  
pp. 494-496
Author(s):  
Hee-Sauk Jhon ◽  
Ickhyun Song ◽  
Jongwook Jeon ◽  
MinSuk Koo ◽  
Byung-Gook Park ◽  
...  

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