Advanced Antenna Pattern Prediction Software

2006 ◽  
Author(s):  
Thomas Jaroszewicz ◽  
Elizabeth Bleszynski ◽  
Marek Bleszynski ◽  
Vladimir Rokhlin
Keyword(s):  
Antenna Pattern ◽  
Pattern Prediction

Increasing Accuracy of Stock Price Pattern Prediction through Data Augmentation for Deep Learning

2019 ◽  
Vol 4 (2) ◽  
pp. 1-12
Author(s):  
김영준 ◽  
이인선 ◽  
Hong Joo Lee ◽  
김여정
Keyword(s):  
Deep Learning ◽  
Stock Price ◽  
Data Augmentation ◽  
Pattern Prediction

An Investigation of Features for Fundamental Frequency Pattern Prediction in Electrolaryngeal Speech Enhancement

2019 ◽  
Author(s):  
Mohammad Eshghi ◽  
Kou Tanaka ◽  
Kazuhiro Kobayashi ◽  
Hirokazu Kameoka ◽  
Tomoki Toda
Keyword(s):  
Fundamental Frequency ◽  
Speech Enhancement ◽  
Frequency Pattern ◽  
Pattern Prediction

scholarly journals Measurement of the Sea Surface Height with Airborne GNSS Reflectometry and Delay Bias Calibration

2021 ◽  
Vol 13 (15) ◽  
pp. 3014
Author(s):  
Feng Wang ◽  
Dongkai Yang ◽  
Guodong Zhang ◽  
Jin Xing ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Arrival Time ◽  
Elevation Angle ◽  
Satellite System ◽  
Sea Surface Height ◽  
Antenna Pattern ◽  
Sea Surface ◽  
Observation Method ◽  
Global Navigation Satellite ◽  
The Impact

Sea surface height can be measured with the delay between reflected and direct global navigation satellite system (GNSS) signals. The arrival time of a feature point, such as the waveform peak, the peak of the derivative waveform, and the fraction of the peak waveform is not the true arrival time of the specular signal; there is a bias between them. This paper aims to analyze and calibrate the bias to improve the accuracy of sea surface height measured by using the reflected signals of GPS CA, Galileo E1b and BeiDou B1I. First, the influencing factors of the delay bias, including the elevation angle, receiver height, wind speed, pseudorandom noise (PRN) code of GPS CA, Galileo E1b and BeiDou B1I, and the down-looking antenna pattern are explored based on the Z-V model. The results show that (1) with increasing elevation angle, receiver height, and wind speed, the delay bias tends to decrease; (2) the impact of the PRN code is uncoupled from the elevation angle, receiver height, and wind speed, so the delay biases of Galileo E1b and BeiDou B1I can be derived from that of GPS CA by multiplication by the constants 0.32 and 0.54, respectively; and (3) the influence of the down-looking antenna pattern on the delay bias is lower than 1 m, which is less than that of other factors; hence, the effect of the down-looking antenna pattern is ignored in this paper. Second, an analytical model and a neural network are proposed based on the assumption that the influence of all factors on the delay bias are uncoupled and coupled, respectively, to calibrate the delay bias. The results of the simulation and experiment show that compared to the meter-level bias before the calibration, the calibrated bias decreases the decimeter level. Based on the fact that the specular points of several satellites are visible to the down-looking antenna, the multi-observation method is proposed to calibrate the bias for the case of unknown wind speed, and the same calibration results can be obtained when the proper combination of satellites is selected.

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