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Группа авторов Financial Signal Processing and Machine Learning


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.

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Saeed Vaseghi V. Multimedia Signal Processing


Multimedia Signal Processing is a comprehensive and accessible text to the theory and applications of digital signal processing (DSP). The applications of DSP are pervasive and include multimedia systems, cellular communication, adaptive network management, radar, pattern recognition, medical signal processing, financial data forecasting, artificial intelligence, decision making, control systems and search engines. This book is organised in to three major parts making it a coherent and structured presentation of the theory and applications of digital signal processing. A range of important topics are covered in basic signal processing, model-based statistical signal processing and their applications. Part 1: Basic Digital Signal Processing gives an introduction to the topic, discussing sampling and quantization, Fourier analysis and synthesis, Z-transform, and digital filters. Part 2: Model-based Signal Processing covers probability and information models, Bayesian inference, Wiener filter, adaptive filters, linear prediction hidden Markov models and independent component analysis. Part 3: Applications of Signal Processing in Speech, Music and Telecommunications explains the topics of speech and music processing, echo cancellation, deconvolution and channel equalization, and mobile communication signal processing. Covers music signal processing, explains the anatomy and psychoacoustics of hearing and the design of MP3 music coder Examines speech processing technology including speech models, speech coding for mobile phones and speech recognition Covers single-input and multiple-inputs denoising methods, bandwidth extension and the recovery of lost speech packets in applications such as voice over IP (VoIP) Illustrated throughout, including numerous solved problems, Matlab experiments and demonstrations Companion website features Matlab and C++ programs with electronic copies of all figures. This book is ideal for researchers, postgraduates and senior undergraduates in the fields of digital signal processing, telecommunications and statistical data analysis. It will also be a valuable text to professional engineers in telecommunications and audio and signal processing industries.

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Manel Martinez-Ramon Digital Signal Processing with Kernel Methods


A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors. Presents the necessary basic ideas from both digital signal processing and machine learning concepts Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition.

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Группа авторов Advanced Digital Signal Processing and Noise Reduction


Signal processing plays an increasingly central role in the development of modern telecommunication and information processing systems, with a wide range of applications in areas such as multimedia technology, audio-visual signal processing, cellular mobile communication, radar systems and financial data forecasting. The theory and application of signal processing deals with the identification, modelling and utilisation of patterns and structures in a signal process. The observation signals are often distorted, incomplete and noisy and hence, noise reduction and the removal of channel distortion is an important part of a signal processing system. Advanced Digital Signal Processing and Noise Reduction, Third Edition, provides a fully updated and structured presentation of the theory and applications of statistical signal processing and noise reduction methods. Noise is the eternal bane of communications engineers, who are always striving to find new ways to improve the signal-to-noise ratio in communications systems and this resource will help them with this task. * Features two new chapters on Noise, Distortion and Diversity in Mobile Environments and Noise Reduction Methods for Speech Enhancement over Noisy Mobile Devices. * Topics discussed include: probability theory, Bayesian estimation and classification, hidden Markov models, adaptive filters, multi-band linear prediction, spectral estimation, and impulsive and transient noise removal. * Explores practical solutions to interpolation of missing signals, echo cancellation, impulsive and transient noise removal, channel equalisation, HMM-based signal and noise decomposition. This is an invaluable text for senior undergraduates, postgraduates and researchers in the fields of digital signal processing, telecommunications and statistical data analysis. It will also appeal to engineers in telecommunications and audio and signal processing industries.

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Jacob Benesty Fundamentals of Signal Enhancement and Array Signal Processing


A comprehensive guide to the theory and practice of signal enhancement and array signal processing, including matlab codes, exercises and instructor and solution manuals Systematically introduces the fundamental principles, theory and applications of signal enhancement and array signal processing in an accessible manner Offers an updated and relevant treatment of array signal processing with rigor and concision Features a companion website that includes presentation files with lecture notes, homework exercises, course projects, solution manuals, instructor manuals, and Matlab codes for the examples in the book

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Saeed Vaseghi V. Advanced Digital Signal Processing and Noise Reduction


Digital signal processing plays a central role in the development of modern communication and information processing systems. The theory and application of signal processing is concerned with the identification, modelling and utilisation of patterns and structures in a signal process. The observation signals are often distorted, incomplete and noisy and therefore noise reduction, the removal of channel distortion, and replacement of lost samples are important parts of a signal processing system. The fourth edition of Advanced Digital Signal Processing and Noise Reduction updates and extends the chapters in the previous edition and includes two new chapters on MIMO systems, Correlation and Eigen analysis and independent component analysis. The wide range of topics covered in this book include Wiener filters, echo cancellation, channel equalisation, spectral estimation, detection and removal of impulsive and transient noise, interpolation of missing data segments, speech enhancement and noise/interference in mobile communication environments. This book provides a coherent and structured presentation of the theory and applications of statistical signal processing and noise reduction methods. Two new chapters on MIMO systems, correlation and Eigen analysis and independent component analysis Comprehensive coverage of advanced digital signal processing and noise reduction methods for communication and information processing systems Examples and applications in signal and information extraction from noisy data Comprehensive but accessible coverage of signal processing theory including probability models, Bayesian inference, hidden Markov models, adaptive filters and Linear prediction models Advanced Digital Signal Processing and Noise Reduction is an invaluable text for postgraduates, senior undergraduates and researchers in the fields of digital signal processing, telecommunications and statistical data analysis. It will also be of interest to professional engineers in telecommunications and audio and signal processing industries and network planners and implementers in mobile and wireless communication communities.

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François Auger Signal Processing with Free Software. Practical Experiments


An ideal resource for students, industrial engineers, and researchers, Signal Processing with Free Software Practical Experiments presents practical experiments in signal processing using free software. The text introduces elementary signals through elementary waveform, signal storage files and elementary operations on signals and then presents the first tools to signal analysis such as temporal and frequency characteristics leading to Time-frequency analysis. Non-parametric spectral analysis is also discussed as well as signal processing through sampling, resampling, quantification, and analog and digital filtering. Table of Contents: 1. Generation of Elementary Signals. Generation of Elementary Waveform. – Elementary Operations on the Signals. – Format of Signal Storage Files. 2. First tools of Signal Analysis. Measurement of Temporal and Frequency Characteristics of a Signal. Time-Frequency Analysis of a Signal. 3. Non-parametric Spectral Analysis. 4. Signal Processing. Sampling. – Resampling. – Quantification. – “Analog” Filtering. Digital Filtering

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Mohammad Nakhostin Signal Processing for Radiation Detectors


Presents the fundamental concepts of signal processing for all application areas of ionizing radiation This book provides a clear understanding of the principles of signal processing of radiation detectors. It puts great emphasis on the characteristics of pulses from various types of detectors and offers a full overview on the basic concepts required to understand detector signal processing systems and pulse processing techniques. Signal Processing for Radiation Detectors covers all of the important aspects of signal processing, including energy spectroscopy, timing measurements, position-sensing, pulse-shape discrimination, and radiation intensity measurement. The book encompasses a wide range of applications so that readers from different disciplines can benefit from all of the information. In addition, this resource: Describes both analog and digital techniques of signal processing Presents a complete compilation of digital pulse processing algorithms Extrapolates content from more than 700 references covering classic papers as well as those of today Demonstrates concepts with more than 340 original illustrations Signal Processing for Radiation Detectors provides researchers, engineers, and graduate students working in disciplines such as nuclear physics and engineering, environmental and biomedical engineering, and medical physics and radiological science, the knowledge to design their own systems, optimize available systems or to set up new experiments.

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Hussein Baher Signal Processing and Integrated Circuits


This book provides a balanced account of analog, digital and mixed-mode signal processing with applications in telecommunications. Part I Perspective gives an overview of the areas of Systems on a Chip (Soc) and mobile communication which are used to demonstrate the complementary relationship between analog and digital systems. Part II Analog (continuous-time) and Digital Signal Processing contains both fundamental and advanced analysis, and design techniques, of analog and digital systems. This includes analog and digital filter design; fast Fourier transform (FFT) algorithms; stochastic signals; linear estimation and adaptive filters. Part III Analog MOS Integrated Circuits for Signal Processing covers basic MOS transistor operation and fabrication through to the design of complex integrated circuits such as high performance Op Amps, Operational Transconductance Amplifiers (OTA's) and Gm-C circuits. Part IV Switched-capacitor and Mixed-mode Signal Processing outlines the design of switched-capacitor filters, and concludes with sigma-delta data converters as an extensive application of analog and digital signal processing Contains the fundamentals and advanced techniques of continuous-time and discrete-time signal processing. Presents in detail the design of analog MOS integrated circuits for signal processing, with application to the design of switched-capacitor filters. Uses the comprehensive design of integrated sigma-delta data converters to illustrate and unify the techniques of signal processing. Includes solved examples, end of chapter problems and MATLAB® throughout the book, to help readers understand the mathematical complexities of signal processing. The treatment of the topic is at the senior undergraduate to graduate and professional levels, with sufficient introductory material for the book to be used as a self-contained reference.

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Ivars Bilinskis Digital Alias-free Signal Processing


As demand for applications working in extended frequency ranges increases, classical Digital signal processing (DSP) techniques, not protected against aliasing, are becoming less effective. Digital alias-free signal processing (DASP) is a technique for overcoming the problems of aliasing at extended frequency ranges. Based on non-uniform or randomised sampling techniques and the development of novel algorithms, it creates the capacity to suppress potential aliasing crucial for high frequency applications and to reduce the complexity of designs. This book provides practical and comprehensive coverage of the theory and techniques behind alias-free digital signal processing. Key features: Analyses issues of sampling, randomised and pseudo-randomised quantisation and direct and indirectly randomised sampling. Examines periodic and hybrid sampling, including information on processing algorithms and potential limitations imposed by signal dynamics. Sets out leading methods and techniques for complexity reduced designs, in particular designs of large aperture sensor arrays, massive data acquisition and compression from a number of signal sources and complexity-reduced processing of non-uniform data. Presents examples of engineering applications using these techniques including spectrum analysis, waveform reconstruction and the estimation of various parameters, emphasising the importance of the technique for developing new technologies. Links DASP and traditional technologies by mapping them into embedded systems with standard inputs and outputs. Digital Alias-free Signal Processing is ideal for practising engineers and researchers working on the development of digital signal processing applications at extended frequencies. It is also a valuable reference for electrical and computer engineering graduates taking courses in signal processing or digital signal processing.

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Ali Akansu - Wikipedia

Ali Naci Akansu (born May 6, 1958) is a Turkish-American electrical engineer and scientist. He is best known for his seminal contributions to the theory and applications of linear subspace methods including sub-band and wavelet transforms, particularly the binomial QMF and the statistically optimized filter bank, which he developed in 1990 and 1991, respectively.

Akansu, A: Financial Signal Processing and Machine ...

Akansu, A: Financial Signal Processing and Machine Learning (Wiley - IEEE) | Akansu, Ali N., Kulkarni, Sanjeev R., Malioutov, Dmitry | ISBN: 9781118745670 ...

A Primer for Financial Engineering - 1st Edition

Ali N. Akansu received the BS degree from the Technical University of Istanbul, Turkey, in 1980, the MS and Ph.D degrees from the Polytechnic University, Brooklyn, New York in 1983 and 1987, respectively, all in Electrical Engineering.

Ali N. Akansu

Ali N. Akansu received the B.S. degree from the Technical University of Istanbul, Turkey, in 1980, the M.S. and Ph.D degrees from the Polytechnic University, Brooklyn, New York, in 1983 and 1987, respectively, all

Ali Akansu | Semantic Scholar

Ali N. Akansu is a Turkish American scientist best known for his contributions to the theory and applications of sub-band and wavelet transforms. Wikipedia Create Alert. Related topics. Related topics 12 relations. Binomial QMF Daubechies wavelet Discrete Fourier transform Financial signal processing. Expand. Papers overview. Semantic Scholar uses AI to extract papers important to this topic ...

Ali N. Akansu - Risk.net

Akansu has published numerous articles and several books on his research work. His two recent books are entitled A Primer for Financial Engineering: Financial Signal Processing & Electronic Trading, Elsevier, 2015, and Financial Signal Processing and Machine Learning, Wiley-IEEE Press, 2016.

Prof. Dr Ali N. Akansu: “Algorithmic Trading” | DnT

Ali N. Akansu received the B.S. degree from the Technical University of Istanbul, Turkey, in 1980, the M.S. and Ph.D degrees from the Polytechnic University, Brooklyn, New York, in 1983 and 1987, respectively, all in Electrical Engineering. Since 1987, he has been with the New Jersey Institute of Technology, where he is a Professor of Electrical and Computer Engineering. He was a Founding ...

Ali AKANSU | PhD | New Jersey Institute of Technology, New ...

Ali Akansu In this paper, we present a novel switch design of a large scale multicast packet switch which is featured by a modular switch architecture and a distributed resource allocation algorithm.

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View Ali Akansu’s profile on LinkedIn, the world's largest professional community. Ali has 10 jobs listed on their profile. See the complete profile on LinkedIn and discover Ali’s connections ...

Ali Akansu — New Jersey Research Community

Ali Akansu. Professor, Electrical and Computer Engineering; New Jersey Institute of Technology. 2134 Citations; 25 h-Index; 1988 … 2019. Research output per year. Overview; Fingerprint; Network; Research Output (186) Press / Media (5) Similar Profiles (18) If you made any changes in Pure these will be visible here soon. Fingerprint Dive into the research topics where Ali Akansu is active ...

‪Ali N. Akansu‬ - ‪Google Scholar‬

‪Professor of Electrical & Computer Engineering, NJIT, Newark NJ, USA‬ - ‪Cited by 6,222‬ - ‪Signals & Transforms‬ - ‪Data Engineering‬ - ‪High Performance DSP‬ - ‪Financial Signal Processing‬ - ‪Financial Engineering‬

A Primer for Financial Engineering: Financial Signal ...

Ali N. Akansu received the BS degree from the Technical University of Istanbul, Turkey, in 1980, the MS and Ph.D degrees from the Polytechnic University, Brooklyn, New York in 1983 and 1987, respectively, all in Electrical Engineering. He has been with the Electrical & Computer Engineering Department of the New Jersey Institute of Technology since 1987. He was an academic visitor at David ...

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Für Ali Akansu Praxis für Krankengymnastik in Lauingen, Donau sind noch keine Bewertungen abgegeben worden. Wenn Sie Erfahrungen mit diesem Unternehmen gesammelt haben, teilen Sie diese hier mit anderen Seitenbesuchern. Geben Sie jetzt die erste Bewertung ab! Jetzt bewerten. Bewertung schreiben . Geben Sie jetzt Ihre Bewertung für Ali Akansu Praxis für Krankengymnastik in Lauingen, Donau ...

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Ali N. Akansu, Electrical and Computer Engineering Department, New Jersey Institute of Technology (NJIT), USA Dr. Akansu is a Professor of Electrical and Computer Engineering at NJIT, USA. Prof. Akansu was VP R&D at IDT Corporation and the founding President and CEO of PixWave, Inc. He has sat on the board of an investment fund and has been an academic visitor at David Sarnoff Research Center ...

A Primer for Financial Engineering: Financial Signal ...

A Primer for Financial Engineering: Financial Signal Processing and Electronic Trading von Akansu, Ali N.; Torun, Mustafa U. und Verleger Academic Press. Sparen Sie bis zu 80% durch die Auswahl der eTextbook-Option für ISBN: 9780128015612, 9780128017500, 0128017503. Die Druckversion dieses Lehrbuchs hat ISBN: 9780128015612, 0128015616.

Akansu, Ali N. 1958- [WorldCat Identities]

Multiresolution signal decomposition : transforms, subbands, and wavelets by Ali N Akansu ( ) 40 editions published between 1992 and 2001 in English and held by 2,432 WorldCat member libraries worldwide

Ali Naci Akansu - Vikipedi

Ali Naci Akansu, Türk bilim insanı ve akademisyen.. Kariyeri. Kabataş Erkek Lisesi'nden 1975 yılında mezun olmuş ve İstanbul Teknik Üniversitesi'nin (İTÜ) Elektronik ve Haberleşme Mühendisliği Bölümünde yüksek ögrenimine başlamıştır. İTÜ'den mezun olduktan sonra lisansüstü öğrenimine devam etmek için Amerika Birleşik Devletleri'ne gitmiştir.

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Financial Signal Processing and Machine Learning Wiley ...

Financial Signal Processing and Machine Learning Wiley - IEEE: Amazon.in: Akansu, Ali N., Kulkarni, Sanjeev R., Malioutov, Dmitry M.: Books

Financial Signal Processing and Machine Learning / Edition ...

Ali N. Akansu, Sanjeev R. Kulkarni, and Dmitry Malioutov 1.1 Introduction 1. 1.2 A Bird's-Eye View of Finance 2. 1.2.1 Trading and Exchanges 4. 1.2.2 Technical Themes in the Book 5. 1.3 Overview of the Chapters 6. 1.3.1 Chapter 2: "Sparse Markowitz Portfolios" by Christine De Mol 6

Financial Signal Processing and Machine Learning | Wiley ...

Ali N. Akansu, Electrical and Computer Engineering Department, New Jersey Institute of Technology (NJIT), USA Dr. Akansu is a Professor of Electrical and Computer Engineering at NJIT, USA. Prof. Akansu was VP R&D at IDT Corporation and the founding President and CEO of PixWave, Inc. He has sat on the board of an investment fund and has been an ...

Akansu, Ali N. - Webcat Plus

Financial signal processing and machine learning: edited by Ali N. Akansu, Sanjeev R. Kulkarni, Dmitry Malioutov: IEEE Press Wiley: 2016: A primer for financial engineering : financial signal processing and electronic trading: Ali N. Akansu and Mustafa U. Torun: Academic Press: c2015: Data hiding fundamentals and applications : content security in digital media : Husrev T. Sencar, Mahalingam ...

Financial Signal Processing and Machine Learning by Ali N ...

List of Contributors xiii Preface xv 1 Overview 1 Ali N. Akansu, Sanjeev R. Kulkarni, and Dmitry Malioutov 1.1 Introduction 1 1.2 A Bird's-Eye View of Finance 2 1.2.1 Trading and Exchanges 4 1.2.2 Technical Themes in the Book 5 1.3 Overview of the Chapters 6 1.3.1 Chapter 2: Sparse Markowitz Portfolios by Christine De Mol 6 1.3.2 Chapter 3: Mean-Reverting Portfolios: Tradeoffs between Sparsity ...

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Financial Signal Processing and Machine Learning: Akansu, Ali N., Kulkarni, Sanjeev R., Malioutov, Dmitry M.: 9781118745670: Books - Amazon.ca

A Primer for Financial Engineering: Financial Signal ...

Ali N. Akansu received the BS degree from the Technical University of Istanbul, Turkey, in 1980, the MS and Ph.D degrees from the Polytechnic University, Brooklyn, New York in 1983 and 1987, respectively, all in Electrical Engineering.

Ali N. Akansu Inventions, Patents and Patent Applications ...

Ali N. Akansu has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO). Transportable Sensor Devices. Publication number: 20090316945 Abstract: A transportable sensor or set of sensors maybe carried conveniently by a user in a ...

PORTFOLIO RISK IN MULTIPLE FREQUENCIES Mustafa U. Torun ...

Mustafa U. Torun, Ali N. Akansu, and Marco Avellaneda Portfolio risk, introduced by Markowitz in 1952, and de-fined as the standard deviation of the portfolio return, is an important metric in the Modern Portfolio Theory (MPT). A popular method for portfolio selection is to manage the risk and return of a portfolio according to the cross-correlations of returns for various financial assets ...

Ali Akansu at New Jersey Institute of Technology ...

Ali Akansu. Professor in the Engineering department at New Jersey Institute of Technology. 100%. Would take again. 3.3. Level of Difficulty. Rate Professor Akansu. I'm Professor AkansuSubmit a Correction. Professor Akansu's Top Tags. Skip class? You won't pass. Inspirational Respected 😎 Most helpful rating. Mar 24th, 2017 "Learn it here and now" was his motto. Do the homework problems, they ...

Multiresolution Signal Decomposition - Ali N Akansu - Bok ...

Primer for Financial Engineering Ali N Akansu, Mustafa U Torun 629. Ladda ned. Laddas ned direkt Övrig information Ali N. Akansu received the BS degree from the Technical University of Istanbul, Turkey, in 1980, the MS and Ph.D degrees from the Polytechnic University, Brooklyn, New York in 1983 and 1987, respectively, all in Electrical Engineering. He has been with the Electrical & Computer ...

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Financial signal processing and machine learning / edited ...

List of Contributors xiii Preface xv 1 Overview 1 /Ali N. Akansu, Sanjeev R. Kulkarni, and Dmitry Malioutov 1.1 Introduction 1 1.2 A Bird's-Eye View of Finance 2 1.2.1 Trading and Exchanges 4 1.2.2 Technical Themes in the Book 5 1.3 Overview of the Chapters 6 1.3.1 Chapter 2: "Sparse Markowitz Portfolios" by Christine De Mol 6 1.3.2 Chapter 3: "Mean-Reverting Portfolios: Tradeoffs between ...

Multiresolution Signal Decomposition - 2nd Edition

Ali N. Akansu received the BS degree from the Technical University of Istanbul, Turkey, in 1980, the MS and Ph.D degrees from the Polytechnic University, Brooklyn, New York in 1983 and 1987, respectively, all in Electrical Engineering. He has been with the Electrical & Computer Engineering Department of the New Jersey Institute of Technology since 1987. He was an academic visitor at David ...

Financial Signal Processing and Machine Learning by John ...

Ali N. Akansu, Electrical and Computer Engineering Department, New Jersey Institute of Technology (NJIT), USADr. Akansu is a Professor of Electrical and Computer Engineering at NJIT, USA. Prof. Akansu was VP R&D at IDT Corporation and the founding President and CEO of PixWave, Inc. He has sat on the board of an investment fund and has been an ...

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Ali Akansu. Professor of Electrical and Computer Engineering at New Jersey Institute of Technology. Access Ali's Contact Information . Export. Share . Ali Akansu Contact Information. Last Update. 5/7/2020 8:09 AM. Email. a***@njit.edu. Direct Phone (908) ***-**** Get Email Address Get Phone Number. HQ Phone (973) 596-3000 . Company New Jersey Institute of Technology. Location. University ...

WikiZero - Ali Naci Akansu

Ali N. Akansu teknoloji şirket kurucusu olmuştur ve şirketlerin Yönetim Kurulu Üyeliği görevlerinde bulunmustur. Teknoloji girişimcisi olarak da profesyonel çalışmalarına devam eden Prof.Dr. Akansu özel bir yatırım fonunda yönetici direktör olarak görev yapmistir. Ali N. Akansu 50 civarında doktora ve master ogrencisinin tez hocalıklarını yapmıştır. Kendisinin ...

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[PDF] Financial Signal Processing and Machine Learning ...

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.

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Read "A Primer for Financial Engineering Financial Signal Processing and Electronic Trading" by Ali N. Akansu available from Rakuten Kobo. This book bridges the fields of finance, mathematical finance and engineering, and is suitable for engineers and compute...

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NJIT professor Ali Akansu, Ph.D., wants to prevent another flash crash on Wall Street. An electrical and computing engineer who is an expert in the relatively new field of adapting signal ...

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Sudharman K. Jayaweera Signal Processing for Cognitive Radios


This book examines signal processing techniques for cognitive radios. The book is divided into three parts: Part I, is an introduction to cognitive radios and presents a history of the cognitive radio (CR), and introduce their architecture, functionalities, ideal aspects, hardware platforms, and state-of-the-art developments. Dr. Jayaweera also introduces the specific type of CR that has gained the most research attention in recent years: the CR for Dynamic Spectrum Access (DSA). Part II of the book, Theoretical Foundations, guides the reader from classical to modern theories on statistical signal processing and inference. The author addresses detection and estimation theory, power spectrum estimation, classification, adaptive algorithms (machine learning), and inference and decision processes. Applications to the signal processing, inference and learning problems encountered in cognitive radios are interspersed throughout with concrete and accessible examples. Part III of the book, Signal Processing in Radios, identifies the key signal processing, inference, and learning tasks to be performed by wideband autonomous cognitive radios. The author provides signal processing solutions to each task by relating the tasks to materials covered in Part II. Specialized chapters then discuss specific signal processing algorithms required for DSA and DSS cognitive radios.

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Thomas Kaiser Digital Signal Processing for RFID


This book discusses the fundamentals of RFID and the state-of-the-art research results in signal processing for RFID, including MIMO, blind source separation, anti-collision, localization, covert RFID and chipless RFID. Aimed at graduate students as well as academic and professional researchers/engineers in RFID technology, it enables readers to become conversant with the latest theory and applications of signal processing for RFID. Key Features: Provides a systematic and comprehensive insight into the application of modern signal processing techniques for RFID systems Discusses the operating principles, channel models of RFID, RFID protocols and analog/digital filter design for RFID Explores RFID-oriented modulation schemes and their performance Highlights research fields such as MIMO for RFID, blind signal processing for RFID, anti-collision of multiple RFID tags, localization with RFID, covert RFID and chipless RFID Contains tables, illustrations and design examples

9992.75 RUR

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Ranjan Mallik K. Digital Signal Processing


Digital signal processing is essential for improving the accuracy and reliability of a range of engineering systems, including communications, networking, and audio and video applications. Using a combination of programming and mathematical techniques, it clarifies, or standardizes the levels or states of a signal, in order to meet the demands of designing high performance digital hardware. Written by authors with a wealth of practical experience working with digital signal processing, this text is an excellent step-by-step guide for practitioners and researchers needing to understand and quickly implement the technology. Split into six, self-contained chapters, Digital Signal Processing: A Practitioner’s Approach covers: basic principles of signal processing such as linearity, stability, convolution, time and frequency domains, and noise; descriptions of digital filters and their realization, including fixed point implementation, pipelining, and field programmable gate array (FGPA) implementation; Fourier transforms, especially discrete (DFT), and fast Fourier transforms (FFT); case studies demonstrating difference equations, direction of arrival (DoA), and electronic rotating elements, and MATLAB programs to accompany each chapter. A valuable reference for engineers developing digital signal processing applications, this book is also a useful resource for electrical and computer engineering graduates taking courses in signal processing.

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Johannes Stahl Single Channel Phase-Aware Signal Processing in Speech Communication


An overview on the challenging new topic of phase-aware signal processing Speech communication technology is a key factor in human-machine interaction, digital hearing aids, mobile telephony, and automatic speech/speaker recognition. With the proliferation of these applications, there is a growing requirement for advanced methodologies that can push the limits of the conventional solutions relying on processing the signal magnitude spectrum. Single-Channel Phase-Aware Signal Processing in Speech Communication provides a comprehensive guide to phase signal processing and reviews the history of phase importance in the literature, basic problems in phase processing, fundamentals of phase estimation together with several applications to demonstrate the usefulness of phase processing. Key features: Analysis of recent advances demonstrating the positive impact of phase-based processing in pushing the limits of conventional methods. Offers unique coverage of the historical context, fundamentals of phase processing and provides several examples in speech communication. Provides a detailed review of many references and discusses the existing signal processing techniques required to deal with phase information in different applications involved with speech. The book supplies various examples and MATLAB® implementations delivered within the PhaseLab toolbox. Single-Channel Phase-Aware Signal Processing in Speech Communication is a valuable single-source for students, non-expert DSP engineers, academics and graduate students.

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John Leis W. Digital Signal Processing Using MATLAB for Students and Researchers


Quickly Engages in Applying Algorithmic Techniques to Solve Practical Signal Processing Problems With its active, hands-on learning approach, this text enables readers to master the underlying principles of digital signal processing and its many applications in industries such as digital television, mobile and broadband communications, and medical/scientific devices. Carefully developed MATLAB® examples throughout the text illustrate the mathematical concepts and use of digital signal processing algorithms. Readers will develop a deeper understanding of how to apply the algorithms by manipulating the codes in the examples to see their effect. Moreover, plenty of exercises help to put knowledge into practice solving real-world signal processing challenges. Following an introductory chapter, the text explores: Sampled signals and digital processing Random signals Representing signals and systems Temporal and spatial signal processing Frequency analysis of signals Discrete-time filters and recursive filters Each chapter begins with chapter objectives and an introduction. A summary at the end of each chapter ensures that one has mastered all the key concepts and techniques before progressing in the text. Lastly, appendices listing selected web resources, research papers, and related textbooks enable the investigation of individual topics in greater depth. Upon completion of this text, readers will understand how to apply key algorithmic techniques to address practical signal processing problems as well as develop their own signal processing algorithms. Moreover, the text provides a solid foundation for evaluating and applying new digital processing signal techniques as they are developed.

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A self-contained approach to DSP techniques and applications in radar imaging The processing of radar images, in general, consists of three major fields: Digital Signal Processing (DSP); antenna and radar operation; and algorithms used to process the radar images. This book brings together material from these different areas to allow readers to gain a thorough understanding of how radar images are processed. The book is divided into three main parts and covers: * DSP principles and signal characteristics in both analog and digital domains, advanced signal sampling, and interpolation techniques * Antenna theory (Maxwell equation, radiation field from dipole, and linear phased array), radar fundamentals, radar modulation, and target-detection techniques (continuous wave, pulsed Linear Frequency Modulation, and stepped Frequency Modulation) * Properties of radar images, algorithms used for radar image processing, simulation examples, and results of satellite image files processed by Range-Doppler and Stolt interpolation algorithms The book fully utilizes the computing and graphical capability of MATLAB? to display the signals at various processing stages in 3D and/or cross-sectional views. Additionally, the text is complemented with flowcharts and system block diagrams to aid in readers' comprehension. Digital Signal Processing Techniques and Applications in Radar Image Processing serves as an ideal textbook for graduate students and practicing engineers who wish to gain firsthand experience in applying DSP principles and technologies to radar imaging.

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Sean C. Keenan Financial Institution Advantage and the Optimization of Information Processing


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Fournier Regis Signal and Image Processing for Biometrics


The aim of this book is to deal with biometrics in terms of signal and image processing methods and algorithms. This will help engineers and students working in digital signal and image processing deal with the implementation of such specific algorithms. It discusses numerous signal and image processing techniques that are very often used in biometric applications. In particular, algorithms related to hand feature extraction, speech recognition, 2D/3D face biometrics, video surveillance and other interesting approaches are presented. Moreover, in some chapters, Matlab codes are provided so that readers can easily reproduce some basic simulation results. This book is suitable for final-year undergraduate students, postgraduate students, engineers and researchers in the field of computer engineering and applied digital signal and image processing. 1. Introduction to Biometrics, Bernadette Dorizzi. 2. Introduction to 2D Face Recognition, Amine Nait-Ali and Dalila Cherifi. 3. Facial Soft Biometrics for Person Recognition, Antitza Dantcheva, Christelle Yemdji, Petros Elia and Jean-Luc Dugelay. 4. Modeling, Reconstruction and Tracking for Face Recognition, Catherine Herold, Vincent Despiegel, Stéphane Gentric,
Séverine Dubuisson and Isabelle Bloch. 5. 3D Face Recognition, Mohsen Ardabilian, Przemyslaw Szeptycki, Di Huang and Liming Chen. 6. Introduction to Iris Biometrics, Kamel Aloui, Amine Nait-Ali, Régis Fournier and Saber Naceur. 7. Voice Biometrics: Speaker Verification and Identification, Foezur Chowdhury, Sid-Ahmed Selouani
and Douglas O’Shaughnessy. 8. Introduction to Hand Biometrics, Régis Fournier and Amine Nait-Ali. 9. Multibiometrics, Romain Giot, Baptiste Hemery, Estelle Cherrier and
Christophe Rosenberger. 10. Hidden Biometrics, Amine Nait-Ali, Régis Fournier, Kamel Aloui and
Noureddine Belgacem. 11. Performance Evaluation of Biometric Systems, Mohamad El-Abed, Romain Giot, Baptiste Hemery, Julien Mahier
and Christophe Rosenberger. 12. Classification Techniques for Biometrics, Amel Bouchemha, Chérif Nait-Hamoud, Amine Nait-Ali and
Régis Fournier. 13. Data Cryptography, Islam Naveed and William Puech. 14. Visual Data Protection, Islam Naveed and William Puech. 15. Biometrics in Forensics, Guillaume Galou and Christophe Lambert.

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John Meriwether McAllister FPGA-based Implementation of Signal Processing Systems


An important working resource for engineers and researchers involved in the design, development, and implementation of signal processing systems The last decade has seen a rapid expansion of the use of field programmable gate arrays (FPGAs) for a wide range of applications beyond traditional digital signal processing (DSP) systems. Written by a team of experts working at the leading edge of FPGA research and development, this second edition of FPGA-based Implementation of Signal Processing Systems has been extensively updated and revised to reflect the latest iterations of FPGA theory, applications, and technology. Written from a system-level perspective, it features expert discussions of contemporary methods and tools used in the design, optimization and implementation of DSP systems using programmable FPGA hardware. And it provides a wealth of practical insights—along with illustrative case studies and timely real-world examples—of critical concern to engineers working in the design and development of DSP systems for radio, telecommunications, audio-visual, and security applications, as well as bioinformatics, Big Data applications, and more. Inside you will find up-to-date coverage of: FPGA solutions for Big Data Applications, especially as they apply to huge data sets The use of ARM processors in FPGAs and the transfer of FPGAs towards heterogeneous computing platforms The evolution of High Level Synthesis tools—including new sections on Xilinx's HLS Vivado tool flow and Altera's OpenCL approach Developments in Graphical Processing Units (GPUs), which are rapidly replacing more traditional DSP systems FPGA-based Implementation of Signal Processing Systems, 2nd Edition is an indispensable guide for engineers and researchers involved in the design and development of both traditional and cutting-edge data and signal processing systems. Senior-level electrical and computer engineering graduates studying signal processing or digital signal processing also will find this volume of great interest.

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James Candy V. Model-Based Signal Processing


A unique treatment of signal processing using a model-based perspective Signal processing is primarily aimed at extracting useful information, while rejecting the extraneous from noisy data. If signal levels are high, then basic techniques can be applied. However, low signal levels require using the underlying physics to correct the problem causing these low levels and extracting the desired information. Model-based signal processing incorporates the physical phenomena, measurements, and noise in the form of mathematical models to solve this problem. Not only does the approach enable signal processors to work directly in terms of the problem's physics, instrumentation, and uncertainties, but it provides far superior performance over the standard techniques. Model-based signal processing is both a modeler's as well as a signal processor's tool. Model-Based Signal Processing develops the model-based approach in a unified manner and follows it through the text in the algorithms, examples, applications, and case studies. The approach, coupled with the hierarchy of physics-based models that the author develops, including linear as well as nonlinear representations, makes it a unique contribution to the field of signal processing. The text includes parametric (e.g., autoregressive or all-pole), sinusoidal, wave-based, and state-space models as some of the model sets with its focus on how they may be used to solve signal processing problems. Special features are provided that assist readers in understanding the material and learning how to apply their new knowledge to solving real-life problems. * Unified treatment of well-known signal processing models including physics-based model sets * Simple applications demonstrate how the model-based approach works, while detailed case studies demonstrate problem solutions in their entirety from concept to model development, through simulation, application to real data, and detailed performance analysis * Summaries provided with each chapter ensure that readers understand the key points needed to move forward in the text as well as MATLAB(r) Notes that describe the key commands and toolboxes readily available to perform the algorithms discussed * References lead to more in-depth coverage of specialized topics * Problem sets test readers' knowledge and help them put their new skills into practice The author demonstrates how the basic idea of model-based signal processing is a highly effective and natural way to solve both basic as well as complex processing problems. Designed as a graduate-level text, this book is also essential reading for practicing signal-processing professionals and scientists, who will find the variety of case studies to be invaluable. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department

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