Alexander D. Poularikas is chairman of the electrical and computer engineering department at the University of Alabama in Huntsville, USA. He previously held positions at University of Rhode Island, Kingston, USA and the University of Denver, Colorado, USA. He has published, coauthored, and edited 14 books and served as an editor-in-chief of numerous book series. A Fulbright scholar, lifelong senior member of the IEEE, and member of Tau Beta Pi, Sigma Nu, and Sigma Pi, he received the IEEE Outstanding Educators Award, Huntsville Section in 1990 and 1996. Dr. Poularikas holds a Ph.D from the University of Arkansas, Fayetteville, USA.
Adaptive filters are used in many diverse applications, appearing in everything from military instruments to cellphones and home appliances. Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB covers the core concepts of this important field, focusing on a vital part of the statistical signal processing area the least mean square (LMS) adaptive filter.
This largely self-contained text:
Discusses random variables, stochastic processes, vectors, matrices, determinants, discrete random signals, and probability distributions
Explains how to find the eigenvalues and eigenvectors of a matrix and the properties of the error surfaces
Explores the Wiener filter and its practical uses, details the steepest descent method, and develops the Newton`s algorithm
Addresses the basics of the LMS adaptive filter algorithm, considers LMS adaptive filter variants, and provides numerous examples
Delivers a concise introduction to MATLAB, supplying problems, computer experiments, and more than 110 functions andillegalscript files
Featuring robust appendices complete with mathematical tables and formulas, Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB clearly describes the key principles of adaptive filtering and effectively demonstrates how to apply them to solve real-world problems.
Vectors. Matrices. Processing of Discrete Deterministic Signals: Discrete Systems. Discrete-Time Random Processes. The Wiener Filter. Eigenvalues of Rx: Properties of the Error Surface. Newton´s and Steepest Descent Methods. The Least Mean-Square Algorithm. Variants of Least Mean-Square Algorithm. Appendices.
Managing Risk: Technology and Communications is a practical guide to the effective management of technology and communications risks. Frequent high profile scares, like the Sasser worm and WiFi vulnerabilities, make a proactive approach essential and this book shows you how to put in place expedient checks, balances and countermeasures.Business networks are threatened by a host of factors, from employee abuse to non-compliance with data protection and libel laws, from hacker attacks to viruses and from extortion and terrorism to natural disaster.The costs of failing to manage systems risks can be immense and go beyond simple loss of productivity or even fraudulent losses to brand damage, theft of business secrets, expensive litigation, diminished customer confidence and adverse impacts on personnel and share value. This practical handbook includes examples, checklists and case studies to help you manage such hazards.The book covers: accessibility of information; acceptable use of information; directors legal duties; general legal compliance; protecting networks from external and internal threats; encouraging security awareness at management and employee level; reputational risk management; and national and international risk and security standards.Managing Risk: Technology and Communications is the indispensable work of reference for IT and technology managers, HR managers, IT legal advisors, company secretaries and anyone seeking practical guidance on technology risks and their management.
