In mathematics, the historical unnormalized sinc function is defined for x ≠ 0 by
In either case, the value at x = 0 is defined to be the limiting value
- for all real a ≠ 0.
The normalization causes the definite integral of the function over the real numbers to equal 1 (whereas the same integral of the unnormalized sinc function has a value of π). As a further useful property, the zeros of the normalized sinc function are the nonzero integer values of x.
The normalized sinc function is the Fourier transform of the rectangular function with no scaling. It is used in the concept of reconstructing a continuous bandlimited signal from uniformly spaced samples of that signal.
The only difference between the two definitions is in the scaling of the independent variable (the x-axis) by a factor of π. In both cases, the value of the function at the removable singularity at zero is understood to be the limit value 1. The sinc function is then analytic everywhere and hence an entire function.
The term sinc // was introduced by Philip M. Woodward in his 1952 paper "Information theory and inverse probability in telecommunication", in which he said the function "occurs so often in Fourier analysis and its applications that it does seem to merit some notation of its own", and his 1953 book Probability and Information Theory, with Applications to Radar.
The zero crossings of the unnormalized sinc are at non-zero integer multiples of π, while zero crossings of the normalized sinc occur at non-zero integers.
The local maxima and minima of the unnormalized sinc correspond to its intersections with the cosine function. That is, sin(ξ)/ = cos(ξ) for all points ξ where the derivative of sin(x)/ is zero and thus a local extremum is reached. This follows from the derivative of the sinc function,
The first few terms of the infinite series for the x-coordinate of the nth extremum with positive x-coordinate are
and where odd n lead to a local minimum and even n to a local maximum. Because of symmetry around the y-axis, there exist extrema with x-coordinates −xn. In addition, there is an absolute maximum at ξ0 = (0,1).
The normalized sinc function has a simple representation as the infinite product
Euler discovered that
the Euler's product can be recast as a sum
where the rectangular function is 1 for argument between −1/ and 1/, and zero otherwise. This corresponds to the fact that the sinc filter is the ideal (brick-wall, meaning rectangular frequency response) low-pass filter.
This Fourier integral, including the special case
- It is an interpolating function, i.e., sinc(0) = 1, and sinc(k) = 0 for nonzero integer k.
- The functions xk(t) = sinc(t − k) (k integer) form an orthonormal basis for bandlimited functions in the function space L2(R), with highest angular frequency ωH = π (that is, highest cycle frequency fH = 1/).
Other properties of the two sinc functions include:
- The unnormalized sinc is the zeroth-order spherical Bessel function of the first kind, j0(x). The normalized sinc is j0(πx).
- where Si(x) is the sine integral.
- λ sinc(λx) (not normalized) is one of two linearly independent solutions to the linear ordinary differential equation
- The other is cos(λx)/, which is not bounded at x = 0, unlike its sinc function counterpart.
- where the normalized sinc is meant.
- The following improper integral involves the (not normalized) sinc function:
Relationship to the Dirac delta distribution
This is not an ordinary limit, since the left side does not converge. Rather, it means that
for every Schwartz function, as can be seen from the Fourier inversion theorem. In the above expression, as a → 0, the number of oscillations per unit length of the sinc function approaches infinity. Nevertheless, the expression always oscillates inside an envelope of ±1/, regardless of the value of a.
This complicates the informal picture of δ(x) as being zero for all x except at the point x = 0, and illustrates the problem of thinking of the delta function as a function rather than as a distribution. A similar situation is found in the Gibbs phenomenon.
All sums in this section refer to the unnormalized sinc function.
The sum of sinc(n) over integer n from 1 to ∞ equals π − 1/.
When the signs of the addends alternate and begin with +, the sum equals 1/.
The product of 1-D sinc functions readily provides a multivariate sinc function for the square, Cartesian, grid (lattice): sincC(x, y) = sinc(x)sinc(y) whose Fourier transform is the indicator function of a square in the frequency space (i.e., the brick wall defined in 2-D space). The sinc function for a non-Cartesian lattice (e.g., hexagonal lattice) is a function whose Fourier transform is the indicator function of the Brillouin zone of that lattice. For example, the sinc function for the hexagonal lattice is a function whose Fourier transform is the indicator function of the unit hexagon in the frequency space. For a non-Cartesian lattice this function can not be obtained by a simple tensor-product. However, the explicit formula for the sinc function for the hexagonal, body centered cubic, face centered cubic and other higher-dimensional lattices can be explicitly derived using the geometric properties of Brillouin zones and their connection to zonotopes.
This construction can be used to design Lanczos window for general multidimensional lattices.
- Olver, Frank W. J.; Lozier, Daniel M.; Boisvert, Ronald F.; Clark, Charles W., eds. (2010), "Numerical methods", NIST Handbook of Mathematical Functions, Cambridge University Press, ISBN 978-0-521-19225-5, MR 2723248
- Woodward, P. M.; Davies, I. L. (March 1952). "Information theory and inverse probability in telecommunication" (PDF). Proceedings of the IEE - Part III: Radio and Communication Engineering. 99 (58): 37–44. doi:10.1049/pi-3.1952.0011.
- Poynton, Charles A. (2003). Digital video and HDTV. Morgan Kaufmann Publishers. p. 147. ISBN 978-1-55860-792-7.
- Woodward, Phillip M. (1953). Probability and information theory, with applications to radar. London: Pergamon Press. p. 29. ISBN 978-0-89006-103-9. OCLC 488749777.
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