We consider the line spectral estimation problem which aims to recover a mixture of complex sinusoids from a small number of randomly observed time domain samples. Compressed sensing methods formulates line spectral estimation as a sparse signal recovery problem by discretizing the continuous frequency parameter space into a finite set of grid points. Discretization, however, inevitably incurs ...

conclusion the study data highlighted good value of iodine-based material decomposition images of spectral ct in assessment of both lipiodol deposition and residual cancer for follow-up of hcc patients previously treated with tace. background it is critical to follow up hepatocellular carcinoma (hcc) after transcatheter arterial chemoembolization (tace) in clinical practice. computed tomography...

The classical method for estimating the spectral density of a multivariate time series is to first calculate the periodogram, and then smooth it to obtain a consistent estimator. Typically, to ensure the estimate is positive definite, all the elements of the periodogram are smoothed the same way. There are, however, many situations for which different components of the spectral matrix have diff...

Singular spectrum analysis (SSA) is a method of time-series analysis based on the singular value decomposition of an associated Hankel matrix. We present an approach to SSA using an effective and numerically stable high-degree polynomial approximation of a spectral projector, which also provides a means of time-series forecasting. Several numerical examples illustrating the algorithm are given.

Extensions of singular spectrum analysis (SSA) for processing of non-rectangular images and time series with gaps are considered. A circular version is suggested, which allows application of the method to the data given on a circle or on a cylinder, e.g. cylindrical projection of a 3D ellipsoid. The constructed Shaped SSA method with planar or circular topology is able to produce low-rank appro...

We extend the Bloch-decomposition based time-splitting spectral method introduced in an earlier paper [13] to the case of (non-)linear Klein-Gordon equations. This provides us with an unconditionally stable numerical method which achieves spectral convergence in space, even in the case where the periodic coefficients are highly oscillatory and/or discontinuous. A comparison to a traditional pse...

Classical nonparametric spectral analysis uses sliding windows to capture the dynamic nature of most real-world time series. This universally accepted approach fails to exploit the temporal continuity in the data and is not well-suited for signals with highly structured time-frequency representations. For a time series whose time-varying mean is the superposition of a small number of oscillator...

The potential energy hyper surfaces (FES) of the unimolecular rearrangements of a) Nitromethane itei totrans acknitromethane b) nitrometharie (/) to methyl nitrite (3) and c) naromethane decomposition tomethyl and nitrogen dioxide were searched using the ab !nth° MP2 method. Split valence 6-310(d.p) basisset was used for geometry optimizations, frequency and 1RC computations along each reaction...

The spectral estimation of unevenly sampled data has been widely investigated in astronomical and medical areas. However the investigations are usually carried out in the context of periodicity detection and deterministic signal. Here we consider estimating the spectral density of stationary time series with missing data. An asymptotically unbiased estimation approach is proposed. The simulatio...

A speech enhancement method employing sparse reconstruction of the power spectral density is proposed. The overcomplete dictionary of the power spectral density is learned by approximation K-singular value decomposition algorithm with non negative constraint. The power spectral density of clean speech signal is reconstructed by least angle regression method with a norm termination rule, and the...