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- W2000428544 abstract "Given an astrophysical observation with an arbitrary carrier frequency and an unknown scale under an additive white noise, s'(t)ensuremath{equiv}s(ensuremath{alpha}t)+n(t), its wavelet transform is W'(a,b)ensuremath{equiv}(s'(t),${mathit{h}}_{mathit{a}mathit{b}}$(t)), as computed by the inner product with a daughter wavelet ${mathit{h}}_{mathit{a}mathit{b}}$(t)ensuremath{equiv}h((t-b)/a)/a. W'(a,b) equals the original transform W(a,b)ensuremath{equiv}(s(t),${mathit{h}}_{mathit{a}mathit{b}}$(t)) displaced along the radial direction W'(a,b)=W(ensuremath{alpha}a,ensuremath{alpha}b) plus noise in the time-scale joint-representation plane. A bank of wedge-shaped detectors collects those displaced transforms W'(a,b) to create a set of invariant features. These features are fed into a two-layer feed-forward artificial neural network, to interpolate discrete sampling, as demonstrated successfully for real-time-signal automatic classification. Useful wavelet applications in turbulence onset, spectrum analyses, fractal aggregates, and bubble-chamber particle-track pattern-recognition problems are indicated but are modeled, in the interest of simplicity, in a one-dimensional example." @default.
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- W2000428544 title "Neural network and wavelet transform for scale-invariant data classification" @default.
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- W2000428544 doi "https://doi.org/10.1103/physreve.48.1497" @default.
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