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The experimental estimation of frequency response functions characterizing SISO linear systems is a well established topic.
Thus, the estimation of frequency of SNPs for vSAGs and viral contigs was number of SNPs per kb of effective mapped viral genome per 1 Mb of mapped virome reads.
We consider the estimation of frequency for a sinusoidal signal contaminated by an additive polynomial background signal and additive white Gaussian noise.
Recent developments in optimization and parameter estimation of frequency dependent passive damping of sandwich structures with viscoelastic core are presented in this paper.
In practice, the estimation of frequency response functions (FRFs) is often complicated by the influence of noise on the measured data as well as by spectral leakage in the case of arbitrary (non-periodic) excitation.
The method is based on the histogram test driven by stochastic signal with defined probability density function (p.d.f .. Further enhancement that allows an estimation of frequency dependency of effective number of bits (ENOB) is also presented.
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The paper presents a new method for the classification and parameter estimation of frequency-shift-keying (FSK) signals, which exploits compressive sampling, and thus, allows to reduce the sampling rate beyond the limit of the Shannon theorem.
It operates as a post-processing step and, thus, decouples the estimation of frequencies and amplitudes from the time-domain solver which offers a considerable flexibility.
The results show that the proposed method gives a good estimation of frequencies in comparison with the full finite element method results.
In "Estimation of frequency-dependent site amplification factors" section, we assumed that radiation pattern of earthquake k was common to stations i and j if we picked up the interstation distance was less than 30 km and epicentral distance was longer than 100 km; however, the assumption might not be valid in some cases.
The advantage of this model is that (1) the estimation of frequencies does not suffer from co-linearity between Xis and (2) it is robust to coverage bias between regions (see Methods for detailed arguments).
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