Ali N. Akansu – författare

Multimedia technologies are becoming more sophisticated, enabling the Internet to accommodate a rapidly growing audience with a full range of services and efficient delivery methods. Although the Internet now puts communication, education, commerce and socialization at our finger tips, its rapid growth has raised some weighty security concerns with respect to multimedia content. The owners of this content face enormous challenges in safeguarding their intellectual property, while still exploiting the Internet as an important resource for commerce.Data Hiding Fundamentals and Applications focuses on the theory and state-of-the-art applications of content security and data hiding in digital multimedia. One of the pillars of content security solutions is the imperceptible insertion of information into multimedia data for security purposes; the idea is that this inserted information will allow detection of unauthorized usage.- Provides a theoretical framework for data hiding, in a signal processing context- Realistic applications in secure, multimedia delivery- Compression robust data hiding- Data hiding for proof of ownership--WATERMARKING- Data hiding algorithms for image and video watermarking

The uniqueness of this book is that it covers such important aspects of modern signal processing as block transforms from subband filter banks and wavelet transforms from a common unifying standpoint, thus demonstrating the commonality among these decomposition techniques. In addition, it covers such "hot" areas as signal compression and coding, including particular decomposition techniques and tables listing coefficients of subband and wavelet filters and other important properties.The field of this book (Electrical Engineering/Computer Science) is currently booming, which is, of course, evident from the sales of the previous edition. Since the first edition came out there has been much development, especially as far as the applications. Thus, the second edition addresses new developments in applications-related chapters, especially in chapter 4 "Filterbrook Families: Design and Performance," which is greatly expanded.- Unified and coherent treatment of orthogonal transforms, subbands, and wavelets- Coverage of emerging applications of orthogonal transforms in digital communications and multimedia- Duality between analysis and synthesis filter banks for spectral decomposition and synthesis and analysis transmultiplexer structures- Time-frequency focus on orthogonal decomposition techniques with applications to FDMA, TDMA, and CDMA

Multimedia technologies are becoming more sophisticated, enabling the Internet to accommodate a rapidly growing audience with a full range of services and efficient delivery methods. Although the Internet now puts communication, education, commerce and socialization at our finger tips, its rapid growth has raised some weighty security concerns with respect to multimedia content. The owners of this content face enormous challenges in safeguarding their intellectual property, while still exploiting the Internet as an important resource for commerce.

Data Hiding Fundamentals and Applications focuses on the theory and state-of-the-art applications of content security and data hiding in digital multimedia. One of the pillars of content security solutions is the imperceptible insertion of information into multimedia data for security purposes; the idea is that this inserted information will allow detection of unauthorized usage.

Provides a theoretical framework for data hiding, in a signal processing context Realistic applications in secure, multimedia delivery Compression robust data hiding Data hiding for proof of ownership--WATERMARKING Data hiding algorithms for image and video watermarking

This book bridges the fields of finance, mathematical finance and engineering, and is suitable for engineers and computer scientists who are looking to apply engineering principles to financial markets. The book builds from the fundamentals, with the help of simple examples, clearly explaining the concepts to the level needed by an engineer, while showing their practical significance. Topics covered include an in depth examination of market microstructure and trading, a detailed explanation of High Frequency Trading and the 2010 Flash Crash, risk analysis and management, popular trading strategies and their characteristics, and High Performance DSP and� Financial Computing. The book has many examples to explain financial concepts, and the presentation is enhanced with the visual representation of relevant market data. It provides relevant MATLAB codes for readers to further their study. Please visit the companion website on http://booksite.elsevier.com/9780128015612/ Provides engineering perspective to financial problems In depth coverage of market microstructure Detailed explanation of High Frequency Trading and 2010 Flash Crash Explores risk analysis and management Covers high performance DSP & financial computing

This book bridges the fields of finance, mathematical finance and engineering, and is suitable for engineers and computer scientists who are looking to apply engineering principles to financial markets. The book builds from the fundamentals, with the help of simple examples, clearly explaining the concepts to the level needed by an engineer, while showing their practical significance. Topics covered include an in depth examination of market microstructure and trading, a detailed explanation of High Frequency Trading and the 2010 Flash Crash, risk analysis and management, popular trading strategies and their characteristics, and High Performance DSP and  Financial Computing. The book has many examples to explain financial concepts, and the presentation is enhanced with the visual representation of relevant market data. It provides relevant MATLAB codes for readers to further their study. Please visit the companion website on http://booksite.elsevier.com/9780128015612/- Provides engineering perspective to financial problems- In depth coverage of market microstructure- Detailed explanation of High Frequency Trading and 2010 Flash Crash- Explores risk analysis and management- Covers high performance DSP & financial computing

Wavelet and subband transforms have been of great interest in the fields of - gineering and applied mathematics. The theories of these powerful signal p- cessing tools have matured and many applications utilizing them are emerging in different disciplines. This book, comprised of eleven chapter contributions from prominent researchers in the field, focuses on communications and mul- media applications of wavelet and subband transforms. The first six chapters of this book deal with a variety of communications applications that significantly benefit from wavelet and subband theories. S- ilarly, the remaining five chapters present recent advances in multimedia - plications of wavelet and subband transforms. These chapters interconnect the requirements of applications with the underlying theory and their engineering solutions. Hence, the reader can easily trace the entire path from fundamentals to the purpose and merit of application in hand. A combined list of references for the entire volume is given at the end of the text that should be helpful to the interested reader for a further study. This book is anticipated to be of particular interest to engineers and sci- tists who want to learn about state-of-the-art subband and wavelet transform applications as well as their theoretical underpinnings. It can also serve as a supplementary book for graduate level engineering and applied mathematics courses on wavelet and subband transforms.

This book provides an in-depth, integrated, and up-to-date exposition of the topic of signal decomposition techniques. Application areas of these techniques include speech and image processing, machine vision, information engineering, High-Definition Television, and telecommunications. The book will serve as the major reference for those entering the field, instructors teaching some or all of the topics in an advanced graduate course and researchers needing to consult an authoritative source.n The first book to give a unified and coherent exposition of multiresolutional signal decomposition techniquesn Classroom tested textbook clearly describes the commonalities among three key methods-transform coding, and wavelet transformsn Gives comparative performance evaluations of many proposed techniques

Wavelet and subband transforms have been of great interest in the fields of engineering and applied mathematics. The theories of these powerful signal-processing tools have matured and many applications utilizing them are emerging in different disciplines. This book, comprising eleven chapter contributions from prominent researchers in the field, focuses on communications and multimedia applications of wavelet and subband transforms. The first six chapters of this book deal with a variety of communications applications that significantly benefit from wavelet and subband theories. Similarly, the remaining five chapters present the most recent advances in multimedia applications of wavelet and subband transforms. These chapters interconnect the requirements of applications with the underlying theory and their engineering solutions. Hence, the reader can easily trace the entire path from fundamentals to the purpose and merit of the application in hand. A combined list of references for the entire volume is given at the end of the text that should be helpful to the interested reader for further study.The book should be of particular interest to engineers and scientists who want to learn about state-of-the-art subband and wavelet transform applications as well as their theoretical underpinnings. It can also serve as a supplementary book for graduate-level engineering and applied mathematics courses on wavelet and subband transforms.

The scientists and engineers of today are relentless in their continuing study and analysis of the world about us from the microcosm to the macrocosm. A central purpose of this study is to gain sufficient scientific information and insight to enable the development of both representative and useful models of the superabundance of physical processes that surround us. The engineers need these models and the associated insight in order to build the information processing systems and control systems that comprise these new and emerging technologies. Much of the early modeling work that has been done on these systems has been based on the linear time-invariant system theory and its extensive use of Fourier transform theory for both continuous and discrete systems and signals. However many of the signals arising in nature and real systems are neither stationary nor linear but tend to be concentrated in both time and frequency. Hence a new methodology is needed to take these factors properly into account.

The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches.

Key features:

Highlights signal processing and machine learning as key approaches to quantitative finance. Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.

The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches.

Key features:

Highlights signal processing and machine learning as key approaches to quantitative finance. Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.

The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance.Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems.Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques.Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.

Wavelet and subband transforms have been of great interest in the fields of - gineering and applied mathematics. The theories of these powerful signal p- cessing tools have matured and many applications utilizing them are emerging in different disciplines. This book, comprised of eleven chapter contributions from prominent researchers in the field, focuses on communications and mul- media applications of wavelet and subband transforms. The first six chapters of this book deal with a variety of communications applications that significantly benefit from wavelet and subband theories. S- ilarly, the remaining five chapters present recent advances in multimedia - plications of wavelet and subband transforms. These chapters interconnect the requirements of applications with the underlying theory and their engineering solutions. Hence, the reader can easily trace the entire path from fundamentals to the purpose and merit of application in hand. A combined list of references for the entire volume is given at the end of the text that should be helpful to the interested reader for a further study. This book is anticipated to be of particular interest to engineers and sci- tists who want to learn about state-of-the-art subband and wavelet transform applications as well as their theoretical underpinnings. It can also serve as a supplementary book for graduate level engineering and applied mathematics courses on wavelet and subband transforms.

The scientists and engineers of today are relentless in their continuing study and analysis of the world about us from the microcosm to the macrocosm. A central purpose of this study is to gain sufficient scientific information and insight to enable the development of both representative and useful models of the superabundance of physical processes that surround us. The engineers need these models and the associated insight in order to build the information processing systems and control systems that comprise these new and emerging technologies. Much of the early modeling work that has been done on these systems has been based on the linear time-invariant system theory and its extensive use of Fourier transform theory for both continuous and discrete systems and signals. However many of the signals arising in nature and real systems are neither stationary nor linear but tend to be concentrated in both time and frequency. Hence a new methodology is needed to take these factors properly into account.

The scientists and engineers of today are relentless in their continuing study and analysis of the world about us from the microcosm to the macrocosm. A central purpose of this study is to gain sufficient scientific information and insight to enable the development of both representative and useful models of the superabundance of physical processes that surround us. The engineers need these models and the associated insight in order to build the information processing systems and control systems that comprise these new and emerging technologies. Much of the early modeling work that has been done on these systems has been based on the linear time-invariant system theory and its extensive use of Fourier transform theory for both continuous and discrete systems and signals. However many of the signals arising in nature and real systems are neither stationary nor linear but tend to be concentrated in both time and frequency. Hence a new methodology is needed to take these factors properly into account.