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Signal Processing Commons

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Full-Text Articles in Signal Processing

System And Method For Radio Tomographic Image Formation, Richard K. Martin Aug 2019

System And Method For Radio Tomographic Image Formation, Richard K. Martin

AFIT Patents

A system and method for generating radio tomographic images is provided. A plurality of transceivers positioned around a region to be imaged is divided into a plurality of pixels. A control apparatus is configured to cause each of the plurality of transceivers in turn to send a signal to each of the other transceivers. The control apparatus is further configured to determine an attenuation in the received signals, generate weighing, derivative, and attenuation matrices from the signals, group the pixels into a plurality of provinces, select each province in turn and solve for a change in attenuation in each of …


Improving Regional And Teleseismic Detection For Single-Trace Waveforms Using A Deep Temporal Convolutional Neural Network Trained With An Array-Beam Catalog, Joshua T. Dickey, Brett J. Borghetti, William Junek Jan 2019

Improving Regional And Teleseismic Detection For Single-Trace Waveforms Using A Deep Temporal Convolutional Neural Network Trained With An Array-Beam Catalog, Joshua T. Dickey, Brett J. Borghetti, William Junek

Faculty Publications

The detection of seismic events at regional and teleseismic distances is critical to Nuclear Treaty Monitoring. Traditionally, detecting regional and teleseismic events has required the use of an expensive multi-instrument seismic array; however in this work, we present DeepPick, a novel seismic detection algorithm capable of array-like detection performance from a single-trace. We achieve this performance through three novel steps: First, a high-fidelity dataset is constructed by pairing array-beam catalog arrival-times with single-trace waveforms from the reference instrument of the array. Second, an idealized characteristic function is created, with exponential peaks aligned to the cataloged arrival times. Third, a deep …