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Full-Text Articles in Electrical and Computer Engineering
An Eigenvector-Based Test For Local Stationarity Applied To Array Processing, Jorge Quijano, Lisa M. Zurk
An Eigenvector-Based Test For Local Stationarity Applied To Array Processing, Jorge Quijano, Lisa M. Zurk
Electrical and Computer Engineering Faculty Publications and Presentations
In sonar array processing, a challenging problem is the estimation of the data covariance matrix in the presence of moving targets in the water column, since the time interval of data local stationarity is limited. This work describes an eigenvector-based method for proper data segmentation into intervals that exhibit local stationarity, providing data-driven higher bounds for the number of snapshots available for computation of time-varying sample covariance matrices. Application of the test is illustrated with simulated data in a horizontal array for the detection of a quiet source in the presence of a loud interferer.
A Comparative Study Of Reservoir Computing For Temporal Signal Processing, Alireza Goudarzi, Peter Banda, Matthew R. Lakin, Christof Teuscher, Darko Stefanovic
A Comparative Study Of Reservoir Computing For Temporal Signal Processing, Alireza Goudarzi, Peter Banda, Matthew R. Lakin, Christof Teuscher, Darko Stefanovic
Computer Science Faculty Publications and Presentations
Reservoir computing (RC) is a novel approach to time series prediction using recurrent neural networks. In RC, an input signal perturbs the intrinsic dynamics of a medium called a reservoir. A readout layer is then trained to reconstruct a target output from the reservoir's state. The multitude of RC architectures and evaluation metrics poses a challenge to both practitioners and theorists who study the task-solving performance and computational power of RC. In addition, in contrast to traditional computation models, the reservoir is a dynamical system in which computation and memory are inseparable, and therefore hard to analyze. Here, we compare …