V
Victor DeBrunner
Researcher at Florida State University
Publications - 191
Citations - 1822
Victor DeBrunner is an academic researcher from Florida State University. The author has contributed to research in topics: Adaptive filter & Filter (signal processing). The author has an hindex of 18, co-authored 187 publications receiving 1735 citations. Previous affiliations of Victor DeBrunner include Florida A&M University – Florida State University College of Engineering & Virginia Tech.
Papers
More filters
Journal ArticleDOI
Novel Adaptive Nonlinear Predistorters Based on the Direct Learning Algorithm
Dayong Zhou,Victor DeBrunner +1 more
TL;DR: This paper proposes several novel adaptive nonlinear predistorters based on direct learning algorithms - the nonlinear filtered-x RLS (NFXRLS) algorithm, the non linear adjoint LMS (NALMS) algorithm), and thenonlinear adjoint RLS [NARLS] algorithm and develops a "instantaneous equivalent linear" (IEL) filter.
Journal ArticleDOI
Efficient Adaptive Nonlinear Filters for Nonlinear Active Noise Control
Dayong Zhou,Victor DeBrunner +1 more
TL;DR: It is found that the computational complexity of NANC/NSP can be reduced even more using block-oriented nonlinear models, such as the Wiener, Hammerstein, or linear-nonlinear-linear (LNL) models for the NSP.
Journal ArticleDOI
A New Active Noise Control Algorithm That Requires No Secondary Path Identification Based on the SPR Property
Dayong Zhou,Victor DeBrunner +1 more
TL;DR: This paper introduces a new ANC algorithm suitable for single-tone noises as well as some specific narrowband noises that does not require the identification of the secondary path, though its convergence can be very slow in some special cases.
Journal ArticleDOI
Multiscale storm identification and forecast
TL;DR: A recently developed hierarchical K-Means clustering method for weather images that can be employed to identify storms at different scales and choose different scales to forecast based on the time scale of interest is described.
Journal ArticleDOI
Resolution in time-frequency
TL;DR: A new measure H/sub p/ is introduced that is related to the Heisenberg uncertainty principle that predicts the compactness of discrete-time signal descriptions in the sample-frequency phase plane and conjecture a lower limit on the compaction in the phase plane.