This article presents a new subspace-based technique for reducing the noise of signals in time-series. In the proposed approach, the signal is initially represented as a data matrix. Then using Singular Value Decomposition (SVD), noisy data matrix is divided into signal subspace and noise subspace. In this subspace division, each derivative of the singular values with respect to rank order is u...

The likelihood ratio for a continuous gravitational wave signal is viewed geometrically as function of the orientation two vectors; one representing optimal signal-to-noise ratio, and other maximised or F-statistic. Analytic marginalisation over angle between vectors yields marginalised which Further analytic explored using different choices prior. Monte-Carlo simulations show that ratios had i...

This paper presents an adaptive digital resolution improvement method for extrapolating and recursive analog-to-digital converters (ADCs). The presented adaptively enhanced ADC (AE-ADC) digitally estimates the digital equivalent of the input signal by utilizing an adaptive digital filter (ADF). The least mean squares (LMS) algorithm also determines the coefficients of the ADF block. In this sch...

A new simple and efficient dual tree analytic wavelet transform based on Discrete Cosine Harmonic Wavelet Transform DCHWT (ADCHWT) has been proposed and is applied for signal and image denoising. The analytic DCHWT has been realized by applying DCHWT to the original signal and its Hilbert transform. The shift invariance and the envelope extraction properties of the ADCHWT have been found to be ...

The paper investigates the properties of ambiguity functions of 2-D analytic, quaternionic and monogenic signals. In the introduction the notions of the above signals and their Wigner distributions and ambiguity functions are recalled. The properties of the ambiguity functions are investigated using two kinds of test signals: A band-pass test signal in the form of a sum of two harmonic signals ...

this article presents a new subspace-based technique for reducing the noise ofsignals in time-series. in the proposed approach, the signal is initially representedas a data matrix. then using singular value decomposition (svd), noisy datamatrix is divided into signal subspace and noise subspace. in this subspace division,each derivative of the singular values with respect to rank order is used ...