Best Selling Books by R. Pearson

Fiber installation has five goals: Low optical power lossLow installation cost Low installation timeLow reflectance andHigh reliability Achieving these goals requires understanding of network components and their functions. This book presents that essential understanding. That understanding includes the: FunctionsConcepts of functioningComponent types, advantages, and performance characteristics, with common numbers, units of measure and typical values, Installation limitations and concernsIn short, this book presents the language, which I call ''fiber-optese'', of fiber network installation.This book presents this understanding with a ''divide and conquer'' strategy, which follows. Ch. 1 presents the structure and evolution of fiber networks. This chapter details the structure of networks and identification of components, ranging from simple point-to-point links for data communication to increasingly complex links, such as those used in multi-wavelength (DWDM, CWDM) and passive optical networks (PONs). Ch. 2 presents the concepts and language of light and its behavior. With the information in this chapter, the reader understands the behavior of light in the components. Ch. 3-9 present the details of the components in the network: fiber, cables, connectors, splices, passive devices, optoelectronics, and hardware. From these chapters, the reader recognizes the consequences of and reasons for use of different types of components.Ch.10 presents tables of the numbers of fiber optics and a glossary of common terms. With the understanding presented in this book, the reader can approach network installation with an understanding of the reasons for installation rules and the consequences of violating such rule

This is a book of the 1589 PowerPoint slides for the text Professional Fiber Optic Installation, V9. The slides are for Chapters 1-20. The slides contain the key concepts and the graphics of the text. These slides are available for training for a fee.

A study placed the numbers of workers who have attained National Vocational Qualifications (NVQs) at level 4 or above in the United Kingdom (UK) in an international context by comparing this attainment with the numbers of workers reaching similar attainment in a sample of other countries: Australia, France, Germany, Korea, Japan, Taiwan, and the United States. The study, based on available literature and data, found that 23.4 percent of workers in the UK had attained NVQ level 4 or above in Spring 1995, whereas the other countries'' workers who had reached that skill level ranged from 18.1 percent in Australia to 30 percent in the United States. The study also showed that the UK has shown recent improvement in its attainment rate and is likely to reach 30 percent in the early years of the 21st century (a few years past its goal of reaching that level by the year 2000). In addition, the UK and France have the highest graduation rates at NVQ level 4 and above, just ahead of the United States. Significant growth in professional, technical, administrative and managerial occupations, all of which require higher skill levels, is expected in the five countries for which forecasts are available, along with a significant decrease in the number of 20- to 24-year-olds. For the UK to meet its target, there will have to be further improvements in the profile of the existing work force and in the supply of those qualifying in the education and training system and entering employment. (An appendix provides further information about modeling future attainment rates at NVQ level 4 and above of those in employment. Contains 170 references.) (KC)

11 Cardstock Biplanes plus 3 Flying Postcards. Paper Airplanes.

Linear threshold element is the generic term for a device which forms the sum a sub 0 + a sub 1 x sub 1 + a sub 2 x sub 2 + ... + a sub d x sub d from an input vector (x sub 1, x sub 2 ..., x sub d) and yields one of two outputs depending on whether or not the sum is positive. A pattern classification machine may utilize a linear threshold element along with a controller which receives the one of the two values corresponding to correct classification of the input vector. The purpose of the controller is to modify the gain vector (a sub 0, a sub 1 ..., a sub d) so that the next input vector has a greater likelihood of being correctly classified by the threshold element. This likelihood depends on the value of the gain vector and an adaptive algorithm of the steepest descent variety can be used to attempt to adjust the gain vector to its optimal value as the machine is exposed to a stationary sequence of statistically independent input vectors. The components of these vectors are commonly two valued, and it has been shown that convergence of the expected value of the gain vector is dependent on the value of the adjustment parameter, the values of the components, and the distribution of the input vectors. It is shown that a bound on the adjustment parameter, simply related to the values of the input components, is sufficient to insure this convergence. The variance of the gain vector is derived under the assumptions of a uniform input sequence and oppositely signed components of equal magnitude and it is shown that a similar bound on the adjustment parameter implies convergence of the variance. The variance is graphed under representative conditions. (Author).