The nature of statistical learning theory
The nature of statistical learning theory
State space model and noise filtering design in transmultiplexer systems
Signal Processing
Time delay estimation and signal reconstruction using multi-rate measurements
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Multirate Statistical Signal Processing (Signals and Communication Technology)
Multirate Statistical Signal Processing (Signals and Communication Technology)
Introduction to Machine Learning
Introduction to Machine Learning
Theory and design of multirate sensor arrays
IEEE Transactions on Signal Processing
Spectrum estimation using multirate observations
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
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The problem of reconstructing a known high-resolution signal from a set of its low-resolution parts exposed to additive white Gaussian noise is addressed in this paper from the perspective of statistical multirate signal processing. To enhance the performance of the existing high-resolution signal reconstruction procedure that is based on using a set of linear periodically time-varying (LPTV) Wiener filter structures, we propose two empirical methods combining empirical mode decomposition- and least squares support vector machine regression-based noise reduction schemes with these filter structures. The methods originate from the idea of reducing the effects of white Gaussian noise present in the low-resolution observations before applying them directly to the LPTV Wiener filters. Performances of the proposed methods are evaluated over one-dimensional simulated signals and two-dimensional images. Simulation results show that, under certain conditions, considerable improvements have been achieved by the proposed methods when compared with the previous study that only uses a set of LPTV Wiener filter structures for the signal reconstruction process.