Radio Frequency Interference messages during breath testing of suspected impaired drivers using the Intoxilyzer® 5000C: A 13-year retrospective analysis*

2016 ◽  
Vol 49 (3) ◽  
pp. 127-137
Author(s):  
Jean-Paul F.P. Palmentier ◽  
Karen L. Woodall ◽  
Carlo Savoia
Keyword(s):  
Radio Frequency ◽  
Retrospective Analysis ◽  
Radio Frequency Interference ◽  
Breath Testing

Radio Frequency Interference (RFI): Effects on Evidentiary Breath Testing Using the Breathalyzer® Models 900 and 900A

1985 ◽  
Vol 18 (4) ◽  
pp. 211-221 ◽  
Author(s):  
B.P. Samija ◽  
N.K. Shajani ◽  
B.K. Wong
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Interference ◽  
Breath Testing

ANALYSIS OF POTENTIAL RADIO FREQUENCY INTERFERENCE ON ANTENNA POSITIONER (3M) IN RANCABUNGUR GROUND STATION

2019 ◽  
Author(s):  
Rumadi Rumadi ◽  
◽  
Dicka Ariptian Rahayu ◽  
Nur Salma Yusuf Hasanah ◽  
Zhauhar Rainaldy Ardhana ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Interference ◽  
Ground Station

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).

scholarly journals Onboard Radio Frequency Interference as the Origin of Inter-Satellite Biases for Microwave Humidity Sounders

2019 ◽  
Vol 11 (7) ◽  
pp. 866 ◽  
Author(s):  
Imke Hans ◽  
Martin Burgdorf ◽  
Stefan A. Buehler
Keyword(s):  
Time Series ◽  
Radio Frequency ◽  
Time Dependent ◽  
Radio Frequency Interference ◽  
Climate Variables ◽  
Compelling Evidence ◽  
Climate Data ◽  
The Earth ◽  
Correction Scheme

Understanding the causes of inter-satellite biases in climate data records from observations of the Earth is crucial for constructing a consistent time series of the essential climate variables. In this article, we analyse the strong scan- and time-dependent biases observed for the microwave humidity sounders on board the NOAA-16 and NOAA-19 satellites. We find compelling evidence that radio frequency interference (RFI) is the cause of the biases. We also devise a correction scheme for the raw count signals for the instruments to mitigate the effect of RFI. Our results show that the RFI-corrected, recalibrated data exhibit distinctly reduced biases and provide consistent time series.

Use of Frequency-Selective Surface for Suppressing Radio-Frequency Interference from Wireless Charging Pads

2014 ◽  
Vol 61 (8) ◽  
pp. 3969-3977 ◽  
Author(s):  
Peng Wu ◽  
Fan Bai ◽  
Quan Xue ◽  
Xun Liu ◽  
S. Y. Ron Hui
Keyword(s):  
Radio Frequency ◽  
Frequency Selective Surface ◽  
Radio Frequency Interference ◽  
Wireless Charging ◽  
Frequency Selective
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