Search Results for author:
Found 6 papers, 3 papers with code
An ADMM-Based Geometric Configuration Optimization in RSSD-Based Source Localization By UAVs with Spread Angle Constraint
Deploying multiple unmanned aerial vehicles (UAVs) to locate a signal-emitting source covers a wide range of military and civilian applications like rescue and target tracking.
Machine Learning Methods for Inferring the Number of UAV Emitters via Massive MIMO Receive Array
The simulation results show that the machine learning-based methods can achieve good results in signal classification, especially neural networks, which can always maintain the classification accuracy above 70\% with massive MIMO receive array.
Communication-efficient Coordinated RSS-based Distributed Passive Localization via Drone Cluster
Simulation results show that the proposed DMM performs better than the existing distributed Gauss-Newton method (DGN) in terms of root of mean square error (RMSE) under a limited low communication overhead constraint.
Seismic Signal Denoising and Decomposition Using Deep Neural Networks
We demonstrate the effect of our method on improving earthquake detection.
Geophysics Signal Processing
CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection
It learns the time-frequency characteristics of the dominant phases in an earthquake signal from three component data recorded on a single station.
PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method
As the number of seismic sensors grows, it is becoming increasingly difficult for analysts to pick seismic phases manually and comprehensively, yet such efforts are fundamental to earthquake monitoring.
Geophysics Applications
