Fiber Optics Handbook: Fiber, Devices, and Systems for by Optical Society of America

By Optical Society of America

Fiber optics is the most well liked subject in communications and this publication from the world's prime specialists essentially lays out all of the info of optical communications engineering
* crucial technical consultant and suggestions package for the super-fast, super-broad fiber structures and units powering the fastest-growing communications infrastructure
* tools for producing above height functionality
* transparent reasons and solutions to tricky demanding situations for WDM, DWDM, amplifiers, solitons, and different key applied sciences

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8 You are U; given an image: g = . Compute the eigenvectors of ggT and v; of gTg. Then SST = 001111 The eigenvalues of ggT will be computed from the characteristic equation: l-X 2 0 2 6-X 1 0 1 l-X + (1- X)[(6 - X)(1 - X) - l] - 2[2(1- = 0 + (1- X)[(6 - X)(1 - X) - 1- 41 = 0 X)] = 0 32 Image Processing: Fundamentals The One eigenvalue is X = 1. 146 be the eigenvector which corresponds to eigenvalue 21 Xi. 23). So wehavereallyonlytwoindependent equations for the three unknowns. W e choose the value of x1 to be 1.

E. 319 v3 = What is the singular value decomposition of an image? 27) i=l since the only non-zero terms are those with i = j . How can we approximate an image using SVD? 9 1 A 256 X 256 grey image with 256 grey levels is to be transmitted. How many terms can be kept in its SVD before the transmission of the transformed image becomes too inefficient in comparison withthe transmission of the original image? ) 1 Assume that X7 is incorporated into one of the vectors. When term of the SVD expansion of the image we must transmit: we transmit one 35 Image 2 X 32 X 256 bits.

That is whywe chose subscripts T and c to denote these matrices ( r = rows, c = columns). 41) m=l n=l H is a ( N 2X N 2 ) matrix. We may think of it as partitioned inN X N submatrices stacked together. Then it can beshown that NKHN, is the H,, such submatrix. 43) m=l n=l The product V n V z is the product between an ( N X 1) matrix with the only non-zero element at position n , with a (l X N ) matrix, with the only non-zero element at position m. So, it is an N X N square matrix with the only non-zero element at position ( n ,m ) .

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