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Signal Processing Magazine, IEEE

Issue 5 • Date Sept. 2011

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Displaying Results 1 - 25 of 29
  • [Front cover]

    Publication Year: 2011 , Page(s): C1
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  • Table of contents

    Publication Year: 2011 , Page(s): 1
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  • New Honor, New Initiatives, and New Impact to Come [From the Editor]

    Publication Year: 2011 , Page(s): 2 - 4
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  • Editorial Board

    Publication Year: 2011 , Page(s): 2
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  • SPS Publications Excel [President's Message]

    Publication Year: 2011 , Page(s): 6
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  • Signal Processing: The Driving Force Behind Smarter, Safer, and More Connected Vehicles [Special Reports]

    Publication Year: 2011 , Page(s): 8 - 13
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (867 KB) |  | HTML iconHTML  

    Academic and corporate research projects being conducted worldwide are all reaching toward a common goal: creating vehicles that are safer, easier to drive, and more convenient to use. With the help of signal processing and related technologies and methodologies, the various projects are developing systems that can detect and/or prevent dangerous situations, automate various types of driving functions, and enhance the driving experience. Here's a look at a few of the most interesting initiatives. Researchers at North Carolina state university have developed computer vision software that allows a car to travel in its lane without any direct human control. View full abstract»

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  • Improving the Visibility of Financial Applications Among Signal Processing Researchers[From the Guest Editors]

    Publication Year: 2011 , Page(s): 14 - 15
    Cited by:  Papers (1)
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  • Statistical Modeling of High-Frequency Financial Data

    Publication Year: 2011 , Page(s): 16 - 25
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2272 KB) |  | HTML iconHTML  

    The availability of high-frequency data on transactions, quotes, and order flow in electronic order-driven markets has revolutionized data processing and statistical modeling techniques in finance and brought up new theoretical and computational challenges. Market dynamics at the transaction level cannot be characterized solely in terms the dynamics of a single price, and one must also take into account the interaction between buy and sell orders of different types by modeling the order flow at the bid price, ask price, and possibly other levels of the limit order book. View full abstract»

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  • The Science Behind Risk Management

    Publication Year: 2011 , Page(s): 26 - 36
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (894 KB) |  | HTML iconHTML  

    Risk management (RM) has a long and storied history in both engineering and finance. As far back as 1800 BC, inscribed in the Code of Hammurabi in ancient Babylon, there is evidence that insurance premiums were paid by farmers to cover the risk of a crop failure. This was essentially an insurance policy, or a way to manage risk, which became a growth industry in Europe during the 1600s with the advent of global trade and the need to mitigate shipping risks. View full abstract»

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  • Multifactor Models

    Publication Year: 2011 , Page(s): 37 - 48
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (837 KB) |  | HTML iconHTML  

    This article surveys the existing literature on the most widely used factor models employed in the realm of a financial asset pricing field. Through the concrete application of evaluating risks in the hedge fund industry, this article demonstrates that signal processing techniques are an interesting alternative to the selection of factors and can provide more efficient estimation procedures than classical techniques. View full abstract»

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  • A Subspace Approach to Portfolio Analysis

    Publication Year: 2011 , Page(s): 49 - 60
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2023 KB) |  | HTML iconHTML  

    In this article, we highlight the subspace approach to portfolio analysis. We focus on equities and show that the subspace approach leads to the decomposition of a portfolio in terms of the range space and the orthogonal subspace of systematic risk factors. The subspace decomposi tion helps us to decompose the performance of a portfolio in terms of a modified information coefficient, the portfolio risk, and market volatilities (one temporal and one cross-sectional). The subspace approach gives a road map on how to model returns and risk. Some applications of the subspace approach are also presented. View full abstract»

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  • Portfolio Risk in Multiple Frequencies

    Publication Year: 2011 , Page(s): 61 - 71
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1048 KB) |  | HTML iconHTML  

    Portfolio risk, introduced by Markowitz in 1952 and defined 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. In a real-world scenario, estimated empirical financial correlation matrix contains significant level of intrinsic noise that needs to be filtered prior to risk calculations. View full abstract»

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  • Time-Series Models of Dynamic Volatility and Correlation

    Publication Year: 2011 , Page(s): 72 - 82
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1669 KB) |  | HTML iconHTML  

    Economic and financial time series typically exhibit time-varying conditional (given the past) standard deviations and correlations. The conditional standard deviation is also called the volatility. Higher volatilities increase the risk of assets and higher conditional correlations cause an increased risk in portfolios. Therefore, models of time-varying volatilities and correlations are essential for risk management. View full abstract»

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  • Business Analytics Based on Financial Time Series

    Publication Year: 2011 , Page(s): 83 - 93
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1585 KB) |  | HTML iconHTML  

    Baniya merchants of the Mughal Empire, burgher merchants of the Swedish Empire, and chonin merchants of the Tokugawa Shogunate had the same questions on their mind as business people do today. To which townspeople should I sell my wares? Of folks that buy from me, are there any that might stop buying from me? Which groups buy which goods? Which saris should I show Ranna Devi to make as much money as I can? How much timber will people want in the coming weeks and months? The world has changed over the centuries with globalization, rapid transportation, instantaneous communication, expansive enterprises, and an explosion of data and signals along with ample computation to process them. In this new age, many continue to answer the aforementioned and other critical business questions in the old-fashioned way, i.e., based on intuition, gut instinct, and personal experience. In our globalized world, however, this is not sufficient anymore and it is essential to replace the business person's gut instinct with science. That science is business analytics. Business analytics is a broad umbrella entailing many problems and solutions, such as demand forecasting and conditioning, resource capacity planning, workforce planning, salesforce modeling and optimization, revenue forecasting, customer/product analytics, and enterprise recommender systems. In our department, we are in creasingly directing our focus on developing models and techniques to address such business problems. The goal of this article is to provide the reader with an overview of this interesting new area of research and then hone in on applications that might require the use of sophisticated signal processing methodologies and utilize financial signals as input. View full abstract»

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  • Aesthetics and Emotions in Images

    Publication Year: 2011 , Page(s): 94 - 115
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3585 KB) |  | HTML iconHTML  

    In this tutorial, we define and discuss key aspects of the problem of computational inference of aesthetics and emotion from images. We begin with a background discussion on philosophy, photography, paintings, visual arts, and psychology. This is followed by introduction of a set of key computational problems that the research community has been striving to solve and the computational framework required for solving them. We also describe data sets available for performing assessment and outline several real-world applications where research in this domain can be employed. A significant number of papers that have attempted to solve problems in aesthetics and emotion inference are surveyed in this tutorial. We also discuss future directions that researchers can pursue and make a strong case for seriously attempting to solve problems in this research domain. View full abstract»

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  • Financial Applications of Nonextensive Entropy [Applications Corner]

    Publication Year: 2011 , Page(s): 116 - 141
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (265 KB) |  | HTML iconHTML  

    Many traditional signal processing techniques in finance have limited ability to explain trading processes and distributional properties of the actual market prices. This is typically manifested in model misspecification and pricing and forecasting inaccuracy. For instance, the assumption that log stock returns are normally distributed is widely used in modern mathematical finance. View full abstract»

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  • Incorporating Financial Applications in Signal Processing Curricula

    Publication Year: 2011 , Page(s): 122 - 125
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (68 KB) |  | HTML iconHTML  

    Modern signal processing heavily relies on probability and statistics. Naturally, these subjects are emphasized in the signal processing curricula at both the undergraduate and graduate levels. Historically, however, both probability and statistics were developed to solve various applied problems arising in economics and finance. View full abstract»

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  • Speech Recognition, Machine Translation, and Speech Translation—A Unified Discriminative Learning Paradigm [Lecture Notes]

    Publication Year: 2011 , Page(s): 126 - 133
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (936 KB) |  | HTML iconHTML  

    In the past two decades, significant progress has been made in automatic speech recognition (ASR) [2], [9] and statistical machine translation (MT) [12]. Despite some conspicuous differences, many problems in ASR and MT are closely related and techniques in the two fields can be successfully cross-pollinated. In this lecture note, we elaborate on the fundamental connections between ASR and MT, and show that the unified ASR discriminative training paradigm recently developed and presented in [7] can be extended to train MT models in the same spirit. View full abstract»

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  • A Simple Algorithm for Fitting a Gaussian Function [DSP Tips and Tricks]

    Publication Year: 2011 , Page(s): 134 - 137
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (189 KB) |  | HTML iconHTML  

    Gaussian functions are suitable for describing many processes in mathematics, science, and engineering, making them very useful in the fields of signal and image processing. For example, the random noise in a signal, induced by complicated physical factors, can be simply modeled with the Gaussian distribution according to the central limit theorem from the probability theory. View full abstract»

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  • TechWare: Financial Data and Analytic Resources [Best of the Web]

    Publication Year: 2011 , Page(s): 138 - 141
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (171 KB) |  | HTML iconHTML  

    In this issue, “Best of the Web” focuses on data resources and analytic tools for quantitative analysis of financial markets. An abundance of financial data is reshaping trading, investment research, and risk management. The broad availability of this information creates opportunities to introduce analysis techniques that are new to the financial industry. The financial industry is currently dominated by a handful of workhorse models, such as the capital asset pricing model and the Black-Scholes options pricing model. View full abstract»

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  • Steganography in Digital Media: Principles, Algorithms, and Applications (Fridrich, J. 2010) [Book Reviews]

    Publication Year: 2011 , Page(s): 142 - 144
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  • Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity (Starck, J.-L., et al; 2010) [Book Reviews]

    Publication Year: 2011 , Page(s): 144 - 146
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  • Statistics and Data Analysis for Financial Engineering (Ruppert, D.; 2011) [Book Reviews]

    Publication Year: 2011 , Page(s): 146 - 147
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  • [Dates Ahead]

    Publication Year: 2011 , Page(s): 148
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  • “Trends” Expert Overview Sessions Revived at ICASSP 2011 [In the Spotlight]

    Publication Year: 2011 , Page(s): 160
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Aims & Scope

IEEE Signal Processing Magazine publishes tutorial-style articles on signal processing research and applications, as well as columns and forums on issues of interest.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Min Wu
University of Maryland, College Park
United States 

http://www/ece.umd.edu/~minwu/