Power-spectrum function

Finding the values of G allows the determination of the frequency-domain spectrum. The power-spectrum function, which may be closely approximated by a constant times the square of G f), is used to determine the amount of power in each frequency spectrum component. The function that results is a positive real quantity and has units of volts squared. From the power spectra, broadband noise may be attenuated so that primary spectral components may be identified. This attenuation is done by a digital process of ensemble averaging, which is a point-by-point average of a squared-spectra set. 

If a homodyne method is used, the measured autocorrelation function (g,x) can be interpreted by using the Siegert relation. Equation 5.454. The translational and rotational diffusion coefficients for several specific shapes of the particles are given in Table 5.9. The respective power spectrum functions can be calculated by using the Fourier transform. Equation 5.449b. 

A Fourier transform of the autocorrelation function of a stochastic process gives the power spectrum function which shows the strength or energy of the process as a function of frequency [17]. Frequency analysis of a stochastic process is based on the assumption that it contains features changing at different frequencies, and thus it can be described using sine and cosine functions having the same frequencies [16]. The power spectrum is defined in terms of the covariance function of the process, Vk = Cov(e,. et k). as... 

Here, a bar on the top of an expression implies the expected value. The autocorrelation is the inverse Fourier transform of the power spectrum function, i.e.. 

The detectability of critical defects with CT depends on the final image quality and the skill of the operator, see figure 2. The basic concepts of image quality are resolution, contrast, and noise. Image quality are generally described by the signal-to-noise ratio SNR), the modulation transfer function (MTF) and the noise power spectrum (NFS). SNR is the quotient of a signal and its variance, MTF describes the contrast as a function of spatial frequency and NFS in turn describes the noise power at various spatial frequencies [1, 3]. 

The energy spectral density function (or power spectrum) P f) is given by the absolute square of P f) ... 

It is a well known fact, called the Wiener-Khintchine Theorem [gardi85], that the correlation function and power spectrum are Fourier Transforms of one another ... 

For Kolmogorov statistics, it turns out that the power spectrum is infinite at the origin, which means that the variance is infinite. The structure function can be used instead of the co-variance to overcome this problem. It is defined as... 

Fig. 6 shows the FFT spectrum for calculated bed pressure drop fluctuations at various centrifugal accelerations. The excess gas velocity, defined by (Uo-U ,, was set at 0.5 m/s. Here, 1 G means numerical result of particle fluidization behavior in a conventional fluidized bed. In Fig. 6, the power spectrum density function has typical peak in each centrifugal acceleration. However, as centrifugal acceleration increased, typical peak shifted to high frequency region. Therefore, it is considered that periods of bubble generation and eruption are shorter, and bubble velocity is faster at hi er centrifugal acceleration. 

The phase spectrum 0(n) is defined as 0(n) = arctan(A(n)/B(n)). One can prove that for a symmetrical peak the ratio of the real and imaginary coefficients is constant, which means that all cosine and sine functions are in phase. It is important to note that the Fourier coefficients A(n) and B(n) can be regenerated from the power spectrum P(n) using the phase information. Phase information can be applied to distinguish frequencies corresponding to the signal and noise, because the phases of the noise frequencies randomly oscillate. 

Autocorrelation function Power spectrum (Spectral power density)... 

An adaptation of Fourier analysis to 2D separations can be established by calculating the autocovariance function (Marchetti et al., 2004). The theoretical background of that approach is that the power spectrum and the autocovariance function of a signal constitute a Fourier pair, that is, the power spectmm is obtained as the Fourier transform of the autocovariance function. 

Fig. 8. The 195Pt-NMR spectra of a DMF solution of [Pt2(en)3(PRI)2(N02) (N03)](N03)2 0.5 H20 (11) at 5°C, acquired on a Bruker WM-250 spectrometer operating at 53.6 MHz. (a) Power spectrum of the Fourier transform of a 1 K FID accumulated with a 5-jjls pulse width, 100-kHz spectral width, and 2000 K transients, (b and c) Normal Fourier transforms of 1 K FIDs accumulated with 10-fis pulsewidths, 42-kHz spectral width, and 64 K transients per spectrum. All FIDs were treated with 400-Hz line broadening functions to suppress noise (58).

A spectrum is the distribution of physical characteristics in a system. In this sense, the Power Spectrum Density (PSD) provides information about fundamental frequencies (and their harmonics) in dynamical systems with oscillatory behavior. PSD can be used to study periodic-quasiperiodic-chaotic routes [27]. The filtered temperature measurements y t) were obtained as discrete-time functions, then PSD s were computed from Fast Fourier Transform (FFT) in order to compute the fundamental frequencies. 

Wiener-Khintchine theorem). The right-hand side of this equation is often called the power spectrum. It is given by the autocorrelation function, Eq. 2.55. The Fourier transform of the autocorrelation function is related to the spectral moments,... 

There are many experiments which determine only specific frequency components of the power spectra. For example, a measurement of the diffusion coefficient yields the zero frequency component of the power spectrum of the velocity autocorrelation function. Likewise, all other static coefficients are related to autocorrelation functions through the zero frequency component of the corresponding power spectra. On the other hand, measurements or relaxation times of molecular internal degrees of freedom provide information about finite frequency components of power spectra. For example, vibrational and nuclear spin relaxation times yield finite frequency components of power spectra which in the former case is the vibrational resonance frequency,28,29 and in the latter case is the Larmour precessional frequency.8 Experiments which probe a range of frequencies contribute much more to our understanding of the dynamics and structure of the liquid state than those which probe single frequency components. 

The power dissipation is linearly related to <7BB(k, co) which is called, for obvious reasons, the power spectrum of the random process Bk. It should be noted that the energy dissipated by a system when it is exposed to an external field is related to a time-correlation function CBB(k, t) which describes the detailed way in which spontaneous fluctuations regress in the equilibrium state. This result, embodied in Eq. (51), is called the fluctuation... 

From these relations we see that the width and shift of the power spectrum and consequently the spectroscopic lines are related through the Kronig-Kramers dispersion relations. Exactly the same arguments apply to the Laplace transform of the time-correlation function, H(/co). The real and imaginary parts, C H(co) and C"//(/(0), are related by Kramers-Kronig dispersion relation. 

The interesting thing to note is that G(co) is none other than the power spectrum of the time-correlation function (see (Eq. 144)). Bochner s theorem gives us reason to regard the power spectrum as a probability distribution function. The same conclusion applies to the memory functions corresponding to C(t). 

The power spectrum normalized time-correlation function, C(t), like any distribution function, can be decomposed into a continuous and a discrete part, C7c(co) and Gd(co), respectively ... 

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