Statistical digital

The main thrust is to provide students with a solid understanding of a number of important and related advanced topics in digital signal processing such as Wiener filters, power spectrum estimation, signal modeling and adaptive filtering. Scores of worked examples illustrate fine points, compare techniques and algorithms and facilitate comprehension of fundamental concepts. Also features an abundance of interesting and challenging problems at the end of every chapter.
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MONSON H. HAYES is a Professor of Electrical and Computer Engineering at the Georgia Institute of Technology in Atlanta, Georgia. He received his B.A. degree in Physics from the University of California, Berkeley, and his M.S.E.E. and Sc.D. degrees in Electrical Engineering and Computer Science from M.I.T. His research interests are in digital signal processing with applications in image and video processing.
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Nowadays, digital terrain models (DTM) are an important source of spatial data for various applications in many scientific disciplines. Therefore, special attention is given to their main characteristic ‐ accuracy. At it is well known, the source data for DTM creation contributes a large amount of errors, including gross errors, to the final product.
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As part of its new Digital Government program, the National Science Foundation (NSF) requested that the Computer Science and Telecommunications Board (CSTB) undertake an indepth study of how information technology research and development could more effectively support advances in the use of information technology in government.
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Theoretical study of earthquakes as well as practical application is actually important to determine the earthquake hazard assessment and risk in a specific region. This is probably the most important contribution of seismology to society. recent earthquake showed the inadequacy of a large part of the building construction in and around the epicentral area, so the study of earthquakes has become and more important.
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Receiver Structure for PAM Signals In this chapter we first discuss the main building blocks of a digital receiver (Section 4.1). The discussion is intended as an introduction to get a qualitative understanding of the design issues involved. In Section 4.2 we are concerned with the question under what conditions the samples of the received signal contain the entire information on the continuoustime signal. The samples obtained under such conditions provide socalled sufficient statistics for the digital receiver to be discussed in Section 4.3.
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This bestselling, easy to read book offers the most complete discussion on the theories and principles behind today's most advanced communications systems. Throughout, Haykin emphasizes the statistical underpinnings of communication theory in a complete and detailed manner. Readers are guided though topics ranging from pulse modulation and passband digital transmission to random processes and errorcontrol coding. The fifth edition has also been revised to include an extensive treatment of digital communications....
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.Corporate Finance DeMYSTiFieD® .DeMYSTiFieD® Series Accounting Demystified Advanced Statistics Demystified Algebra Demystified Alternative Energy Demystified ASP.NET 2.
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PART B Basic Material Digital Communication Receivers: Synchronization, Channel Estimation, and Signal Processing Heinrich Meyr, Marc Moeneclaey, Stefan A. Fechtel Copyright 1998 John Wiley & Sons, Inc. Print ISBN 0471502758 Online ISBN 0471200573 Chapter I 1.I Stationary Basic Material and Cyclostationary Processes In telecommunications, both the informationbearing transmitted signal and the disturbances introduced by the channel are modeled as random processes.
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Characterizing the Performance of Adaptive Filters 19.3 Analytical Models, Assumptions, and Deﬁnitions System Identiﬁcation Model for the Desired Response Signal • Statistical Models for the Input Signal • The Independence Assumptions • Useful Deﬁnitions 19.4 Analysis of the LMS Adaptive Filter Mean Analysis • MeanSquare Analysis 19.5 Performance Issues Basic Criteria for Performance • Identifying Stationary Systems • Tracking TimeVarying Systems Normalized Step Sizes • Adaptive and Matrix Step Sizes • Other TimeVarying Step Size Methods 19.
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Detection and classification arise in signal processing problems whenever a decision is to be made among a finite number of hypotheses concerning an observed waveform. Signal detection algorithms decide whether the waveform consists of “noise alone” or “signal masked by noise.” Signal classification algorithms decide whether a detected signal belongs to one or another of prespecified classes of signals. The objective of signal detection and classification theory is to specify systematic strategies for designing algorithms which minimize the average number of decision errors.
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As your choice of today will directly influence your picture of tomorrow, it is important to correctly choose your projector depending on the field of application you would like to use it in. A digital cinema projector remains after all a cinema projector! To choose the projector model it is essential that it is correctly adapted to the size of the screen that it has to illuminate ...
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The focus of this chapter is on inverse problems—what they are, where they manifest themselves in the realmof digital signal processing (DSP), and how they might be “solved1.”
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The past 20 years witnessed an expansion of power spectrum estimation techniques, which have proved essential in many applications, such as communications, sonar, radar, speech/image processing, geophysics, and biomedical signal processing [13, 11, 7]. In power spectrum estimation the process under consideration is treated as a superposition of statistically uncorrelated harmonic components. The distribution of power among these frequency components is the power spectrum. As such, phase relations between frequency components are suppressed....
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Much of modern digital signal processing is concernedwith the extraction of information fromsignals whichare noisy, orwhichbehave randomlywhile still revealingsomeattributeor parameterof a system or environment under observation. The term in popular use now for this kind of computation is statistical signal processing, and much of this Handbook is devoted to this very subject.
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Processes encountered in statistical signal processing, communications, and time series analysis applications are often assumed stationary. The plethora of available
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Recursive LeastSquares Adaptive Filters Array Algorithms Elementary Circular Rotations • Elementary Hyperbolic Rotations • SquareRootFree and Householder Transformations • A Numerical Example Geometric Interpretation • Statistical Interpretation Geometric Interpretation • Statistical Interpretation Reducing to the Regularized Form • Time Updates Estimation Errors and the Conversion Factor • Update of the Minimum Cost Motivation • A Very Useful Lemma • The Inverse QR Algorithm • The QR Algorithm The Prewindowed Case • LowRank Property • A Fast Array Algorithm • ...
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Much has changed in the 3 years since the first edition of this book. The physics of heat, light, sound and energy is still the same, so there is little change in the first three parts. Apart from the correction of a few errors, a few new developments are mentioned, some new methods are included and statistics updated. Part 4 has many new elements that reflect societal changes, especially changes in public attitudes. Three years ago there were many who denied global warming or who regarded renewable energy technologies as ‘ kids ’ stuff ’. Today only a few of these survive.
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The field of digital image segmentation is continually evolving. Most recently, the advanced segmentation methods such as Template Matching, Spatial and Temporal ARMA Processes, Mean Shift Iterative Algorithm, Constrained Compound Markov Random Field (CCMRF) model and Statistical Pattern Recognition (SPR) methods form the core of a modernization effort that resulted in the current text. This new edition of "Advanced Image Segmentation" is but a reflection of the significant progress that has been made in the field of image segmentation in just the past few years.
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This raw curve can be converted into the complete Gaussian by adding an adjustable mean, µ, and standard deviation, F. In addition, the equation must be normalized so that the total area under the curve is equal to one, a requirement of all probability distribution functions. This results in the general form of the normal distribution, one of the most important relations in statistics and probability: EQUATION 28 Equation for the normal distribution, also called the Gauss distribution, or simply a Gaussian.
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