Study of seismic Propagation Paths and Regional Traveltimes in the California-Nevada Region

1961 ◽  
Author(s):  
L. C. Pakiser ◽  
R. E. Warrick
2021 ◽  
Vol 13 (5) ◽  
pp. 890
Author(s):  
Aleksandra Nina ◽  
Milan Radovanović ◽  
Luka Č. Popović

Atmospheric properties have a significant influence on electromagnetic (EM) waves, including the propagation of EM signals used for remote sensing. For this reason, changes in the received amplitudes and phases of these signals can be used for the detection of the atmospheric disturbances and, consequently, for their investigation. Some of the most important sources of the temporal and space variations in the atmospheric parameters come from the outer space. Although the solar radiation dominates in these processes, radiation coming out of the solar system also can induces enough intensive disturbance in the atmosphere to provide deflections in the EM signal propagation paths. The aim of this issue is to present the latest research linking events and processes in outer space with changes in the propagation of the satellite and ground-based signals used in remote sensing.


2021 ◽  
pp. 1-10
Author(s):  
Chien-Cheng Leea ◽  
Zhongjian Gao ◽  
Xiu-Chi Huanga

This paper proposes a Wi-Fi-based indoor human detection system using a deep convolutional neural network. The system detects different human states in various situations, including different environments and propagation paths. The main improvements proposed by the system is that there is no cameras overhead and no sensors are mounted. This system captures useful amplitude information from the channel state information and converts this information into an image-like two-dimensional matrix. Next, the two-dimensional matrix is used as an input to a deep convolutional neural network (CNN) to distinguish human states. In this work, a deep residual network (ResNet) architecture is used to perform human state classification with hierarchical topological feature extraction. Several combinations of datasets for different environments and propagation paths are used in this study. ResNet’s powerful inference simplifies feature extraction and improves the accuracy of human state classification. The experimental results show that the fine-tuned ResNet-18 model has good performance in indoor human detection, including people not present, people still, and people moving. Compared with traditional machine learning using handcrafted features, this method is simple and effective.


Radio Science ◽  
1977 ◽  
Vol 12 (3) ◽  
pp. 435-440 ◽  
Author(s):  
D. C. Cox ◽  
H. W. Arnold ◽  
A. J. Rustako

Author(s):  
X. Yin ◽  
G. Steinbock ◽  
G. E. Kirkelund ◽  
T. Pedersen ◽  
P. Blattnig ◽  
...  

2020 ◽  
Author(s):  
Victor U. J. Nwankwo ◽  
Jean-Pierre Raulin ◽  
Dra. Emilia Correia ◽  
William F. Denig ◽  
Olanike Akinola ◽  
...  

2008 ◽  
Vol 26 (4) ◽  
pp. 843-852 ◽  
Author(s):  
T. K. Yeoman ◽  
G. Chisham ◽  
L. J. Baddeley ◽  
R. S. Dhillon ◽  
T. J. T. Karhunen ◽  
...  

Abstract. The Super Dual Auroral Radar Network (SuperDARN) network of HF coherent backscatter radars form a unique global diagnostic of large-scale ionospheric and magnetospheric dynamics in the Northern and Southern Hemispheres. Currently the ground projections of the HF radar returns are routinely determined by a simple rangefinding algorithm, which takes no account of the prevailing, or indeed the average, HF propagation conditions. This is in spite of the fact that both direct E- and F-region backscatter and 1½-hop E- and F-region backscatter are commonly used in geophysical interpretation of the data. In a companion paper, Chisham et al. (2008) have suggested a new virtual height model for SuperDARN, based on average measured propagation paths. Over shorter propagation paths the existing rangefinding algorithm is adequate, but mapping errors become significant for longer paths where the roundness of the Earth becomes important, and a correct assumption of virtual height becomes more difficult. The SuperDARN radar at Hankasalmi has a propagation path to high power HF ionospheric modification facilities at both Tromsø on a ½-hop path and SPEAR on a 1½-hop path. The SuperDARN radar at Þykkvibǽr has propagation paths to both facilities over 1½-hop paths. These paths provide an opportunity to quantitatively test the available SuperDARN virtual height models. It is also possible to use HF radar backscatter which has been artificially induced by the ionospheric heaters as an accurate calibration point for the Hankasalmi elevation angle of arrival data, providing a range correction algorithm for the SuperDARN radars which directly uses elevation angle. These developments enable the accurate mappings of the SuperDARN electric field measurements which are required for the growing number of multi-instrument studies of the Earth's ionosphere and magnetosphere.


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