Discrete-time Fourier transform
In mathematics, the discrete-time Fourier transform (DTFT) is a form of Fourier analysis that is applicable to a sequence of values. The DTFT is often used to analyze samples of a continuous function. The term "discrete-time" refers to the fact that the transform operates on discrete data, often samples whose interval has units of time. From uniformly spaced samples it produces a function of frequency that is a periodic summation of the continuous Fourier transform of the original continuous function. Under certain theoretical conditions, described by the sampling theorem, the original continuous function can  be recovered perfectly from the DTFT and thus from the original discrete samples. The DTFT itself is a continuous function of frequency, but discrete samples of it can be readily calculated via the discrete Fourier transform (DFT) (see ), which is by far the most common method of modern Fourier analysis. Both transforms are invertible. The inverse DTFT is the original sampled data sequence. The inverse DFT is a periodic summation of the original sequence. The fast Fourier transform (FFT) is an algorithm for computing one cycle of the DFT, and its inverse produces one cycle of the inverse DFT. Definition . The discrete-time Fourier transform of a discrete sequence of real or complex numbers , for all integers , is a Fourier series, which produces a periodic function of a frequency variable. When the frequency variable, ω, has normalized units of "radians/sample", the periodicity is , and the Fourier series is: The utility of this frequency domain function is rooted in the Poisson summation formula. Let be the Fourier transform of any function, , whose samples at some interval ("seconds") are equal (or proportional) to the sequence, i.e. .  Then the periodic function represented by the Fourier series is a periodic summation of in  terms of frequency in hertz ("cycles/sec"): The integer has units of "cycles/sample", and is the sample-rate, ("samples/sec"). So comprises exact copies of that are shifted by multiples of hertz and combined by addition. For sufficiently large the term can be observed in the region with little or no distortion (aliasing) from the other terms. In Fig.1, the extremities of the distribution in the upper left corner are masked by aliasing in the periodic summation (lower left). We also note that is the Fourier transform of . Therefore, an alternative definition of DTFT is: The modulated Dirac comb  function is a mathematical abstraction sometimes referred to as "impulse sampling". Inverse transform. An operation that recovers the discrete data sequence from the DTFT function is called an "inverse DTFT". For instance, the inverse continuous Fourier transform of both sides of produces the sequence in the form of a modulated Dirac comb function: However, noting that is periodic, all the necessary information is contained within any interval of length . In both and , the summations over n are a Fourier series, with coefficients . The standard formulas for the Fourier coefficients are also the inverse transforms: Periodic data. When the input data  sequence is -periodic, can be computationally reduced to a discrete Fourier transform (DFT), because: The DFT coefficients are given by: Substituting this expression into the inverse transform formula confirms: as expected. The inverse DFT in the line above is sometimes referred to as a Discrete Fourier series (DFS). Sampling the DTFT. When the DTFT is continuous, a common practice is to compute an arbitrary number of samples () of one cycle of the periodic function :  where formula_6 is a periodic summation: The formula_6 sequence is the inverse DFT. Thus, our sampling of the DTFT causes the inverse transform to become periodic . The array of values is known as a "periodogram", and the parameter is called NFFT in the Matlab function of the same name. In order to evaluate one cycle of formula_6 numerically, we require a finite-length sequence. For instance, a long sequence might be truncated by a window function of length resulting in three cases worthy of special mention. For notational simplicity, consider the values below to represent the values modified by the window function. Case: Frequency decimation. , for some integer (typically 6 or 8) A cycle of formula_6 reduces to a summation of segments of length .  The DFT  then goes by various names, such as: Recall that decimation of sampled data in one domain (time or frequency) produces overlap (sometimes known as aliasing) in the other, and vice versa. Compared to an -length DFT, the formula_6 summation/overlap causes decimation in frequency, leaving only DTFT samples least affected by spectral leakage. That is usually a priority when implementing an FFT filter-bank (channelizer). With a conventional window function of length , scalloping loss would be unacceptable. So multi-block windows are created using FIR filter design tools.  Their frequency profile is flat at the highest point and falls off  quickly at the midpoint between the remaining DTFT samples. The larger the value of parameter , the better the potential performance. Case: . When a symmetric, -length window function (formula_12) is truncated by 1 coefficient it is called "periodic" or "DFT-even". The truncation affects the DTFT.  A DFT of the truncated sequence samples the DTFT at frequency intervals of . To sample formula_12 at the same frequencies, for comparison, the DFT is computed for one cycle of the periodic summation, formula_14 Case: Frequency interpolation. In this case, the DFT simplifies to a more familiar form: In order to take advantage of a  fast Fourier transform algorithm for computing the DFT, the summation is usually performed over all terms, even though of them are zeros. Therefore, the case is often referred to as zero-padding. Spectral leakage, which increases as decreases, is detrimental to certain important performance metrics, such as resolution of multiple frequency components and the amount of noise measured by each DTFT sample. But those things don't always matter, for instance when the sequence is a noiseless sinusoid (or a constant), shaped by a window function. Then it is a common practice to use "zero-padding" to graphically display and  compare the detailed leakage patterns of window functions. To illustrate that for a rectangular window, consider the sequence: Figures 2 and 3 are plots of the magnitude of two different sized DFTs, as indicated in their labels. In both cases, the dominant component is at the signal frequency: . Also visible in Fig 2 is the spectral leakage pattern of the rectangular window. The illusion in Fig 3 is a result of sampling the DTFT at just its zero-crossings. Rather than the DTFT of a finite-length sequence, it gives the impression of an infinitely long sinusoidal sequence. Contributing factors  to the illusion are the use of a rectangular window, and the choice of a frequency (1/8 = 8/64) with exactly 8 (an integer) cycles per 64 samples. A Hann window would produce a similar result, except the peak would be widened to 3 samples (see DFT-even Hann window). Convolution. The convolution theorem for sequences is: An important special case is the circular convolution of sequences and defined by formula_19 where formula_6 is a periodic summation. The discrete-frequency nature of formula_21 means that the product with the continuous function formula_22 is also discrete, which results in considerable simplification  of the inverse transform: For and sequences whose non-zero duration is less than or equal to , a final simplification is: The significance of this result is explained at Circular convolution and Fast convolution algorithms. Symmetry properties. When the real and imaginary parts of a complex function are decomposed into their even and odd parts, there are four components, denoted below by the subscripts RE, RO, IE, and IO. And there is a one-to-one mapping between the four components of a complex time function and the four components of its complex frequency transform: formula_25 From this, various relationships  are apparent, for example: Relationship to the Z-transform. formula_26 is a Fourier series that can also be expressed in terms of the bilateral Z-transform.  I.e.: where the formula_28 notation distinguishes the Z-transform from the Fourier transform. Therefore, we can also express a portion of the Z-transform in terms of the Fourier transform: Note that when parameter changes, the terms of formula_26 remain a constant separation formula_31 apart, and their width scales up or down. The terms of remain a constant width and their separation scales up or down. Table of discrete-time Fourier transforms. Some common  transform pairs are shown in the table below. The following notation applies: Properties. This table shows some mathematical operations in the time domain and the corresponding effects in the frequency domain.