By Optical Society of America
Fiber optics is the most popular subject in communications and this publication from the world's prime specialists basically lays out the entire info of optical communications engineering * crucial technical advisor and suggestions package for the super-fast, super-broad fiber platforms and units powering the fastest-growing communications infrastructure * tools for producing above height functionality * transparent reasons and solutions to difficult demanding situations for WDM, DWDM, amplifiers, solitons, and different key applied sciences
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Additional info for Fiber Optics Handbook: Fiber, Devices, and Systems for Optical Communications
Scaling options are available. The first example is a microscopic image. A satellite image is given as a second example. For the first example, the principal component analysis on Z is given in detail. For the second example, only the raw images and resulting score images are given. They clearly show some of the basic properties of principal component analysis. Â < previous page < previous page page_131 page_132 next page > next page > Page 132 Demonstration of a Score Calculation This is a very simple demonstration of score calculation for the microscopic image.
They did so because their precision was not that high and because short integers require less storage and computer memory and they are quicker for calculations. g. 04095 and even larger, was made necessary, because instrumental precision required it. No matter how the pixels are stored and used as integers, one should realize that multivariate calculations give results that have decimals and therefore should be represented as floating-point numbers. This fact increases the demands for computer and storage memory and also slows down the calculations.
The second score image is in contrast with the first one. This is a result of the orthogonality demand of scores. Â < previous page < previous page page_130 page_131 next page > next page > Page 131 Orthogonality can be explained as 'looking optimally different' in the visual sense. Score images 3 and 4 are very noise and very few meaningful structural elements can be seen in them. 1 % of the total sum of squares. Both examples show the following properties of principal component analysis: 36 37 The components come in descending order.