The fast Fourier transform

As explained before, the FT can be calculated by fitting the signal with all allowed sine and cosine functions. This is a laborious operation as this requires the calculation of two parameters (the amplitude of the sine and cosine function) for each considered frequency. For a discrete signal of 1024 data points, this requires the calculation of 1024 parameters by linear regression and the calculation of the inverse of a 1024 by 1024 matrix. 

Because the FFT algorithm requires the number of data points to be a power of 2, it follows that the signal in the time domain has to be extrapolated (e.g. by zero filling) or cut off to meet that requirement. This has consequences for the resolution in the frequency domain as this virtually expands or shortens the measurement time. 

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Fast Fourier Transformation is widely used in many fields of science, among them chemoractrics. The Fast Fourier Transformation (FFT) algorithm transforms the data from the "wavelength" domain into the "frequency" domain. The method is almost compulsorily used in spectral analysis, e, g., when near-infrared spectroscopy data arc employed as independent variables. Next, the spectral model is built between the responses and the Fourier coefficients of the transformation, which substitute the original Y-matrix. 

Other methods can be used in space, such as the finite element method, the orthogonal collocation method, or the method of orthogonal collocation on finite elements (see Ref. 106). Spectral methods employ Chebyshev polynomials and the Fast Fourier Transform and are quite useful for nyperbohc or parabohc problems on rec tangular domains (Ref. 125). 

The fast Fourier transform (FFT) is used to calculate the Fourier transform as well as the inverse Fourier transform. A discrete Fourier transform of length N can be written as the sum of two discrete Fourier transforms, each of length N/2. 

This process is continued until there is only one component. For this reason, the number N is taken as a power of 2. The vector [yj] is filled with zeroes, if need be, to make N = 2 for some p. For the computer program, see Ref. 26. The standard Fourier transform takes N operations to calculation, whereas the fast Fourier transform takes only N log2 N. For large N, the difference is significant at N = 100 it is a factor of 15, but for N = 1000 it is a factor of 100. 

The Fourier analyzer is a digital deviee based on the eonversion of time-domain data to a frequeney domain by the use of the fast Fourier transform. The fast Fourier transform (FFT) analyzers employ a minieomputer to solve a set of simultaneous equations by matrix methods. 

From the time function F t) and the calculation of [IT], the values of G may be found. One way to calculate the G matrix is by a fast Fourier technique called the Cooley-Tukey method. It is based on an expression of the matrix as a product of q square matrices, where q is again related to N by = 2 . For large N, the number of matrix operations is greatly reduced by this procedure. In recent years, more advanced high-speed processors have been developed to carry out the fast Fourier transform. The calculation method is basically the same for both the discrete Fourier transform and the fast Fourier transform. The difference in the two methods lies in the use of certain relationships to minimize calculation time prior to performing a discrete Fourier transform. 

If further resolution is necessary one-third octave filters can be used but the number of required measurements is most unwieldy. It may be necessary to record the noise onto tape loops for the repeated re-analysis that is necessary. One-third octave filters are commonly used for building acoustics, and narrow-band real-time analysis can be employed. This is the fastest of the methods and is the most suitable for transient noises. Narrow-band analysis uses a VDU to show the graphical results of the fast Fourier transform and can also display octave or one-third octave bar graphs. 

The framework we adopted for measuring the scaling behavior from AFM images is the following. The 2-D power spectral density (PSD) of the Fast Fourier Transform of the topography h(x, y) is estimated [541, then averaged over the azimuthal angle

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Noticing the fact that the formula for determining surface deformation takes the form of convolution, the fast Fourier transform (FFT) technique has been applied in recent years to the calculations of deformation [35,36]. The FFT-based approach would give exact results if the convolution functions, i.e., pressure and surface topography take periodic form. For the concentrated contact problems, however. 

Omitting more details on this point, we refer the readers to the well-developed algorithm of the fast Fourier transform, in the framework of which Q arithmetic operations, Q fa 2N log. N, N = 2 , are necessary in connection with computations of these sums (instead of 0 N ) in the case of the usual summation), thus causing 0(nilog,- 2) arithmetic operations performed in the numerical solution of the Dirichlet problem (2) in a rectangle. 

The fast Fourier transform showed the splitting of the particles (Figure 7, inset). 

Figure 7. High resolution TEM image of a single Au nanoparticle observed inside a stem of alfalfa seedlings grown in gold emiched medium. The inset corresponds to the fast Fourier transform of the crystalline particle. (Reprinted from Ref. [28], 2002, with permission from American Chemical Society)...

These four steps are illustrated in Fig. 40.17 where two triangles (array of 32 data points) are convoluted via the Fourier domain. Because one should multiply Fourier coefficients at corresponding frequencies, the signal and the point-spread function should be digitized with the same time interval. Special precautions are needed to avoid numerical errors, of which the discussion is beyond the scope of this text. However, one should know that when J(t) and h(t) are digitized into sampled arrays of the size A and B respectively, both J(t) and h(t) should be extended with zeros to a size of at least A + 5. If (A -i- B) is not a power of two, more zeros should be appended in order to use the fast Fourier transform. 

E.O. Brigham, The Fast Fourier Transform. Prentice-Hall, Englewood Cliffs NJ, 1974. 

However, in order to be able to apply the inverse Fourier transformation, we need to know the dependence of the signal not only for a particular value of k (one gradient pulse), but as a continuous function. In practice, it is the Fast Fourier Transform (FFT) that is performed rather than the full, analytical Fourier Transform, so that the sampling of k-space at discrete, equidistant steps (typically 32, 64, 128) is being performed. 

This is not a fundamental mathematics bookt nor is it intended to serve a textbook for a specific course, but rather as a reference for students in chemistry and physics at all university levels. Although it is not computer-based, I have made many references to current applications - in particular to try to convince students that they should know more about what goes on behind the screen when they do one of their computer experiments. As an example, most students in the sciences now use a program for the fast Fourier transform. How many of them have any knowledge of the basic mathematics involved ... 

The fast Fourier transform can be carried out by rearranging the various terms in the summations involved in the discrete Fourier transform. It is, in effect, a special book-keeping scheme that results in a very important simplification of the numerical evaluation of a Fburier transform. It was introduced into the scientific community in the mid-sixties and has resulted in what is probably one of the few significant advances in numerical methods of analysis since the invention of the digital computer. 

The spectral method is used for direct numerical simulation (DNS) of turbulence. The Fourier transform is taken of the differential equation, and the resulting equation is solved. Then the inverse transformation gives the solution. When there are nonlinear terms, they are calculated at each node in physical space, and the Fourier transform is taken of the result. This technique is especially suited to time-dependent problems, and the major computational effort is in the fast Fourier transform. 

Periodic data, using the Fast Fourier Transform. 

Brigham, E. O. (1974). The Fast Fourier Transform. Englewood Cliffs Prentice Hall. 

An alternative approach involves integrating out the elastic degrees of freedom located above the top layer in the simulation.76 The elimination of the degrees of freedom can be done within the context of Kubo theory, or more precisely the Zwanzig formalism, which leads to effective (potentially time-dependent) interactions between the atoms in the top layer.77-80 These effective interactions include those mediated by the degrees of freedom that have been integrated out. For periodic solids, a description in reciprocal space decouples different wave vectors q at least as far as the static properties are concerned. This description in turn implies that the computational effort also remains in the order of L2 InL, provided that use is made of the fast Fourier transform for the transformation between real and reciprocal space. The description is exact for purely harmonic solids, so that one can mimic the static contact mechanics between a purely elastic lattice and a substrate with one single layer only.81... 

The Yates algorithm is a formal procedure for estimating the P s for full two-level factorial designs [Yates (1936)]. The Yates algorithm is related to the fast Fourier transform. We describe the Yates algorithm here, and illustrate it s use for the 1 full factorial design discussed in Section 14.2. 

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