On robust estimation of discrete Hilbert transform of noisy data

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Why is it important?

Hilbert transform can be defined in terms of Cauchy principal value integral which ranges between negative infinity to positive infinity. Sinc interpolation and conformal mapping are used to define the integrand within a bounded domain which ensures rapid decay of approximation error. A new regularization technique is used to address the noise issue in the data. An optimal regularization parameter is selected using an intuitive graphical method.

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