Signal Processing

Download Algebraic Codes on Lines, Planes, and Curves: An Engineering by Richard E. Blahut PDF

By Richard E. Blahut

Algebraic geometry is frequently hired to encode and decode indications transmitted in verbal exchange structures. This booklet describes the elemental rules of algebraic coding thought from the point of view of an engineer, discussing a couple of purposes in communications and sign processing. The valuable proposal is that of utilizing algebraic curves over finite fields to build error-correcting codes. the newest advancements are awarded together with the speculation of codes on curves, with no using specific arithmetic, substituting the serious concept of algebraic geometry with Fourier remodel the place attainable. the writer describes the codes and corresponding interpreting algorithms in a way that enables the reader to judge those codes opposed to useful purposes, or to aid with the layout of encoders and decoders. This ebook is proper to practising conversation engineers and people focused on the layout of recent verbal exchange platforms, in addition to graduate scholars and researchers in electric engineering.

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Extra resources for Algebraic Codes on Lines, Planes, and Curves: An Engineering Approach

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L, j=1 and L Vr−k = − j=1 with all terms from the given sequence V0 , V1 , . . , Vr−1 . Finally, we have L − L L k Vr−k = k=1 j Vr−k−j k k=1 j=1 L L = k Vr−k−j j j=1 k=1 L =− j Vr−j . j=1 This completes the proof. 2 (Massey’s theorem) If ( (x), L) is a linear recursion that produces the sequence V0 , V1 , . . , Vr−1 , but ( (x), L) does not produce the sequence V = (V0 , V1 , . . , Vr−1 , Vr ), then L(V ) ≥ r + 1 − L. Proof: Suppose that the recursion ( (x), L ) is any linear recursion that produces the longer sequence V .

N − 1, k=0 where 0 = 1. The left side of this equation can be interpreted as the set of coefficients of a polynomial product modulo xn − 1. Translated into the language of polynomials, the equation becomes (x)V (x) = 0 (mod xn − 1), with n−1 V (x) = Vj x j . j=0 In the inverse Fourier transform domain, the cyclic convolution becomes λi vi = 0, where λi and vi are the ith components of the inverse Fourier transform. Thus λi must be zero whenever vi is nonzero. In this way, the connection polynomial (x) that achieves the cyclic complexity locates, by its zeros, the nonzeros of the polynomial V (x).

When initialized with (A, B, 2, 3), where A and B are any two integers. This is true even if A and B both equal 1, but, for that sequence, the recursion is not of minimum length, so then the recursion (−x2 − x + 1, 4) does not determine the linear complexity. The linear complexity of the Fibonacci sequence – or any nontrivial segment of it – is 2 because the Fibonacci sequence cannot be produced by a linear recursion of shorter length. If, however, the first n symbols of the Fibonacci sequence are periodically repeated, then the periodic sequence of the form (for n = 8) 1, 1, 2, 3, 5, 8, 13, 21, 1, 1, 2, 3, 5, 8, 13, 21, 1, 1, .

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