Spatial gain profiles of a continuous wave radio-frequency pumped atomic xenon laser

1997 ◽  
Vol 70 (15) ◽  
pp. 1921-1922 ◽  
Author(s):  
S. N. Tskhai ◽  
F. J. Blok ◽  
Yu. B. Udalov ◽  
P. J. M. Peters ◽  
W. J. Witteman ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Continuous Wave ◽  
Wave Radio

scholarly journals Design and multiphysics analysis of a 176 MHz continuous-wave radio-frequency quadrupole

2014 ◽  
Vol 17 (7) ◽  
Author(s):  
S. V. Kutsaev ◽  
B. Mustapha ◽  
P. N. Ostroumov ◽  
A. Barcikowski ◽  
D. Schrage ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Continuous Wave ◽  
Radio Frequency Quadrupole ◽  
Wave Radio

scholarly journals Development and beam test of a continuous wave radio frequency quadrupole accelerator

2012 ◽  
Vol 15 (11) ◽  
Author(s):  
P. N. Ostroumov ◽  
B. Mustapha ◽  
A. Barcikowski ◽  
C. Dickerson ◽  
A. A. Kolomiets ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Continuous Wave ◽  
Beam Test ◽  
Radio Frequency Quadrupole ◽  
Wave Radio

Proton injector operational results on a high-power continuous-wave radio-frequency quadrupole accelerator

2000 ◽  
Vol 71 (2) ◽  
pp. 767-770 ◽  
Author(s):  
Joseph D. Sherman ◽  
Gerald O. Bolme ◽  
Lash D. Hansborough ◽  
Thomas W. Hardek ◽  
Debora M. Kerstiens ◽  
...  
Keyword(s):  
Radio Frequency ◽  
High Power ◽  
Continuous Wave ◽  
Radio Frequency Quadrupole ◽  
Wave Radio ◽  
Proton Injector

scholarly journals Phase noise coherence of two continuous wave radio frequency signals of different frequency

2017 ◽  
Vol 11 (1) ◽  
pp. 77-85 ◽  
Author(s):  
Daniel Rönnow ◽  
Shoaib Amin ◽  
Mahmoud Alizadeh ◽  
Efrain Zenteno
Keyword(s):  
Radio Frequency ◽  
Phase Noise ◽  
Continuous Wave ◽  
Wave Radio ◽  
Radio Frequency Signals

scholarly journals An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning

2020 ◽  
Vol 10 (19) ◽  
pp. 6885
Author(s):  
Sahar Ujan ◽  
Neda Navidi ◽  
Rene Jr Landry
Keyword(s):  
Feature Extraction ◽  
Radio Frequency ◽  
Transfer Learning ◽  
Communication Networks ◽  
Continuous Wave ◽  
Critical Role ◽  
Video Stream ◽  
Radio Frequency Interference ◽  
Wave Interference ◽  
Continuous Wave Interference

Radio Frequency Interference (RFI) detection and characterization play a critical role in ensuring the security of all wireless communication networks. Advances in Machine Learning (ML) have led to the deployment of many robust techniques dealing with various types of RFI. To sidestep an unavoidable complicated feature extraction step in ML, we propose an efficient Deep Learning (DL)-based methodology using transfer learning to determine both the type of received signals and their modulation type. To this end, the scalogram of the received signals is used as the input of the pretrained convolutional neural networks (CNN), followed by a fully-connected classifier. This study considers a digital video stream as the signal of interest (SoI), transmitted in a real-time satellite-to-ground communication using DVB-S2 standards. To create the RFI dataset, the SoI is combined with three well-known jammers namely, continuous-wave interference (CWI), multi- continuous-wave interference (MCWI), and chirp interference (CI). This study investigated four well-known pretrained CNN architectures, namely, AlexNet, VGG-16, GoogleNet, and ResNet-18, for the feature extraction to recognize the visual RFI patterns directly from pixel images with minimal preprocessing. Moreover, the robustness of the proposed classifiers is evaluated by the data generated at different signal to noise ratios (SNR).

Sign in / Sign up

Export Citation Format

Share Document