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A dynamic model reproducing helicopter GR instability is presented and transformed to a time-invariant nonlinear system using a multi-blade coordinate transformation based on Fourier transform mapping the dynamic state variables into a non-rotating reference frame.

So, Fourier transform maps convolution in the time domain to multiplication in the frequency domain.

The Poisson transform maps hyperfunctions on the Shilov boundary S="K/L to the eigenspaces.

The CLSA is based on a state-level transform mapping learned using minimum Kullback Leibler divergence between pairs of HMM states in the input and output languages.

Our unitary transform maps the space of images onto a reproducing kernel space defined on SIM 2), allowing us to robustly relate Euclidean (and scaling) invariant operators on images to left-invariant operators on the corresponding continuous wavelet transform.

Unlike traditional wavelet-based image derivative estimators that use only the horizontal and vertical wavelet coefficients, the proposed transform maps the diagonal as well as the horizontal and vertical wavelet coefficients to the horizontal and vertical derivatives of the image.

Bilinear transform maps every point in the -plane to the -plane without aliasing.

The bilinear transform maps the analog space to the discrete sample space.

The bilinear transform maps the analog domain to the discrete domain one-to-one.

due to the fact that the Téodorescu transform maps the space of -integrable functions with compact support to if (see, e.g., [5]).

In this paper, we propose to model the fractional transform mapping the real world space onto the camera plane and to integrate the projection in the particle filter through the importance density.

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