High-performance AlInAs/GaInAs/InP DHBT X-band power cell with InP emitter ballast resistor

Author(s):  
M. Chen ◽  
C. Nguyen ◽  
T. Liu ◽  
D. Rensch
1997 ◽  
Vol 7 (10) ◽  
pp. 323-325 ◽  
Author(s):  
R.S. Virk ◽  
M.Y. Chen ◽  
Chanh Nguyen ◽  
Takyiu Liu ◽  
M. Matloubian ◽  
...  

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Rongliang Yang ◽  
Xuchun Gui ◽  
Li Yao ◽  
Qingmei Hu ◽  
Leilei Yang ◽  
...  

AbstractLightweight, flexibility, and low thickness are urgent requirements for next-generation high-performance electromagnetic interference (EMI) shielding materials for catering to the demand for smart and wearable electronic devices. Although several efforts have focused on constructing porous and flexible conductive films or aerogels, few studies have achieved a balance in terms of density, thickness, flexibility, and EMI shielding effectiveness (SE). Herein, an ultrathin, lightweight, and flexible carbon nanotube (CNT) buckypaper enhanced using MXenes (Ti3C2Tx) for high-performance EMI shielding is synthesized through a facile electrophoretic deposition process. The obtained Ti3C2Tx@CNT hybrid buckypaper exhibits an outstanding EMI SE of 60.5 dB in the X-band at 100 μm. The hybrid buckypaper with an MXene content of 49.4 wt% exhibits an EMI SE of 50.4 dB in the X-band with a thickness of only 15 μm, which is 105% higher than that of pristine CNT buckypaper. Furthermore, an average specific SE value of 5.7 × 104 dB cm2 g−1 is exhibited in the 5-μm hybrid buckypaper. Thus, this assembly process proves promising for the construction of ultrathin, flexible, and high-performance EMI shielding films for application in electronic devices and wireless communications.


2011 ◽  
Vol 47 (6) ◽  
pp. 416
Author(s):  
M.H. Wong ◽  
D.F. Brown ◽  
M.L. Schuette ◽  
H. Kim ◽  
V. Balasubramanian ◽  
...  
Keyword(s):  

Author(s):  
Zenon R. Szczepaniak ◽  
Andrzej Arvaniti ◽  
Jaroslaw Popkowski ◽  
Emanuela Orzel-Tatarczuk

2021 ◽  
Vol 20 ◽  
pp. 199-206
Author(s):  
Seda Postalcioglu

This study focused on the classification of EEG signal. The study aims to make a classification with fast response and high-performance rate. Thus, it could be possible for real-time control applications as Brain-Computer Interface (BCI) systems. The feature vector is created by Wavelet transform and statistical calculations. It is trained and tested with a neural network. The db4 wavelet is used in the study. Pwelch, skewness, kurtosis, band power, median, standard deviation, min, max, energy, entropy are used to make the wavelet coefficients meaningful. The performance is achieved as 99.414% with the running time of 0.0209 seconds


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