Найти книгу: "Subband Adaptive Filtering"

Subband adaptive filtering is rapidly becoming one of the most effective techniques for reducing computational complexity and improving the convergence rate of algorithms in adaptive signal processing applications. This book provides an introductory, yet extensive guide on the theory of various subband adaptive filtering techniques. For beginners, the authors discuss the basic principles that underlie the design and implementation of subband adaptive filters. For advanced readers, a comprehensive coverage of recent developments, such as multiband tap–weight adaptation, delayless architectures, and filter–bank design methods for reducing band–edge effects are included. Several analysis techniques and complexity evaluation are also introduced in this book to provide better understanding of subband adaptive filtering. This book bridges the gaps between the mixed–domain natures of subband adaptive filtering techniques and provides enough depth to the material augmented by many MATLAB® functions and examples. Key Features: Acts as a timely introduction for researchers, graduate students and engineers who want to design and deploy subband adaptive filters in their research and applications. Bridges the gaps between two distinct domains: adaptive filter theory and multirate signal processing. Uses a practical approach through MATLAB®-based source programs on the accompanying CD. Includes more than 100 M-files, allowing readers to modify the code for different algorithms and applications and to gain more insight into the theory and concepts of subband adaptive filters. Subband Adaptive Filtering is aimed primarily at practicing engineers, as well as senior undergraduate and graduate students. It will also be of interest to researchers, technical managers, and computer scientists.

Build an agile, responsive portfolio with a new approach to global asset allocation Adaptive Asset Allocation is a no-nonsense how-to guide for dynamic portfolio management. Written by the team behind Gestaltu.com, this book walks you through a uniquely objective and unbiased investment philosophy and provides clear guidelines for execution. From foundational concepts and timing to forecasting and portfolio optimization, this book shares insightful perspective on portfolio adaptation that can improve any investment strategy. Accessible explanations of both classical and contemporary research support the methodologies presented, bolstered by the authors' own capstone case study showing the direct impact of this approach on the individual investor. Financial advisors are competing in an increasingly commoditized environment, with the added burden of two substantial bear markets in the last 15 years. This book presents a framework that addresses the major challenges both advisors and investors face, emphasizing the importance of an agile, globally-diversified portfolio. Drill down to the most important concepts in wealth management Optimize portfolio performance with careful timing of savings and withdrawals Forecast returns 80% more accurately than assuming long-term averages Adopt an investment framework for stability, growth, and maximum income An optimized portfolio must be structured in a way that allows quick response to changes in asset class risks and relationships, and the flexibility to continually adapt to market changes. To execute such an ambitious strategy, it is essential to have a strong grasp of foundational wealth management concepts, a reliable system of forecasting, and a clear understanding of the merits of individual investment methods. Adaptive Asset Allocation provides critical background information alongside a streamlined framework for improving portfolio performance.

A new, more accurate take on the classical approach to volatility evaluation Inside Volatility Filtering presents a new approach to volatility estimation, using financial econometrics based on a more accurate estimation of the hidden state. Based on the idea of «filtering», this book lays out a two-step framework involving a Chapman-Kolmogorov prior distribution followed by Bayesian posterior distribution to develop a robust estimation based on all available information. This new second edition includes guidance toward basing estimations on historic option prices instead of stocks, as well as Wiener Chaos Expansions and other spectral approaches. The author's statistical trading strategy has been expanded with more in-depth discussion, and the companion website offers new topical insight, additional models, and extra charts that delve into the profitability of applied model calibration. You'll find a more precise approach to the classical time series and financial econometrics evaluation, with expert advice on turning data into profit. Financial markets do not always behave according to a normal bell curve. Skewness creates uncertainty and surprises, and tarnishes trading performance, but it's not going away. This book shows traders how to work with skewness: how to predict it, estimate its impact, and determine whether the data is presenting a warning to stay away or an opportunity for profit. Base volatility estimations on more accurate data Integrate past observation with Bayesian probability Exploit posterior distribution of the hidden state for optimal estimation Boost trade profitability by utilizing «skewness» opportunities Wall Street is constantly searching for volatility assessment methods that will make their models more accurate, but precise handling of skewness is the key to true accuracy. Inside Volatility Filtering shows you a better way to approach non-normal distributions for more accurate volatility estimation.

Drawing on their work on performance management within the ‘beyond budgeting’ movement over the past ten years, including many interviews and case studies, Jeremy Hope, Peter Bunce and Franz Roosli set out in this book an executive guide to building a new management model based on eight key change management issues: 1. Governance: From rules and budgets to purpose and values 2. Success: From fixed targets to relative improvement 3. Organization: From centralized functions to customer-oriented teams 4. Accountability: From narrow targets to holistic success criteria 5. Trust: From central control to local autonomy 6. Transparency: From closed information to open book management 7. Rewards: From individual incentives to team-based reward 8. Risk: From complying with rules to understanding pressure points This book is about rethinking how we manage organizations in a post-industrial, post credit crunch world where innovative management models represent the only remaining source of sustainable competitive advantage.[i] The changes suggested by the authors will enable and encourage a cultural climate change that will help organizations to attract and keep the best people as well as drive continuous innovation and growth. Above all, The CEO's Dilemma is about learning how to change business – based on best practice and innovation drawn from leaders world-wide who have built and managed successful organizations.

The survival of the fastest Information technology is now essential to business evolution. Companies that invest in IT as a future resource will live to see the future. The rest won't. The hard reality of the new «Information Darwinism,» a term coined by author Eric Marks, is brilliantly and provocatively described in e-Darwinism, a look at the make-or-break impact of IT on accelerating the global struggle for market share. The book offers a compelling look at just how the Internet has transformed business strategy and business strategy creation, IT strategy, and manufacturing strategy for manufacturing and service firms, and made significant inroads in driving revenue enhancement and cost savings as well as reducing a firm's time to market. The book also describes how much the ability to leverage IT has become a requirement for measuring-and selecting-future leaders.

This book is intended primarily as a handbook for engineers who must design practical systems. Its primary goal is to discuss model development in sufficient detail so that the reader may design an estimator that meets all application requirements and is robust to modeling assumptions. Since it is sometimes difficult to a priori determine the best model structure, use of exploratory data analysis to define model structure is discussed. Methods for deciding on the “best” model are also presented. A second goal is to present little known extensions of least squares estimation or Kalman filtering that provide guidance on model structure and parameters, or make the estimator more robust to changes in real-world behavior. A third goal is discussion of implementation issues that make the estimator more accurate or efficient, or that make it flexible so that model alternatives can be easily compared. The fourth goal is to provide the designer/analyst with guidance in evaluating estimator performance and in determining/correcting problems. The final goal is to provide a subroutine library that simplifies implementation, and flexible general purpose high-level drivers that allow both easy analysis of alternative models and access to extensions of the basic filtering. Supplemental materials and up-to-date errata are downloadable at http://booksupport.wiley.com.