Autocorrelation
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Back to TopConferences related to Autocorrelation
Back to Top2012 IEEE 12th International Conference on Data Mining (ICDM)
ICDM has established itself as the world's premier research conference in data mining covering all aspects of data mining in a wide range related areas such as statistics, machine learning, pattern recognition, databases and data warehousing, data visualization, knowledgebased systems, and high performance computing.
2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)
ISSPIT 2011 is a premiere technical forum for researchers in the fields of signal processing and information technology.
2009 IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement (AMUEM 2009)
The workshop is focused on measurement uncertainty definition and estimation. It s aimed at:  promoting the exchange of ideas between researchers from universities, metrological institutes, and companies about measurement uncertainty issues;  promoting the discussion about the most recent approaches to uncertainty expression and estimation;  identifying problems that arise when dealing with the most advanced measuring systems and effective solutions to these problems;  providing information about
2005 1st International Conference on Neural Interface and Control (CNIC)
Periodicals related to Autocorrelation
Back to TopGeoscience and Remote Sensing, IEEE Transactions on
Theory, concepts, and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.
Information Theory, IEEE Transactions on
The fundamental nature of the communication process; storage, transmission and utilization of information; coding and decoding of digital and analog communication transmissions; study of random interference and informationbearing signals; and the development of informationtheoretic techniques in diverse areas, including data communication and recording systems, communication networks, cryptography, detection systems, pattern recognition, learning, and automata.
Instrumentation and Measurement, IEEE Transactions on
Measurements and instrumentation utilizing electrical and electronic techniques.
Selected Topics in Signal Processing, IEEE Journal of
The Journal of Selected Topics in Signal Processing (JSTSP) solicits special issues on topics that cover the entire scope of the IEEE Signal Processing Society, as outlined in the SPS Constitution, Article II. JSTSP only publishes papers that are submitted in response to a specific CallforPapers. These calls are listed on the JSTSP website, and instructions for submitting papers to ...
Xplore Articles related to Autocorrelation
Back to TopA Mathematical Comparison of Point Detectors
M. Zuliani; C. Kenney; B. S. Manjunath 2004 Conference on Computer Vision and Pattern Recognition Workshop, 2004
Selecting salient points from two or more images for computing correspondences is a fundamental problem in image analysis. Three methods originally proposed by Harris et al. in [A combined corner and edge detector], by Noble et al. in [Descriptions of image surfaces] and by Shi et al. in [Good features to track] proved to be quite effective and robust and ...
ADMA  A New Multiple Access System
Rueywen Liu; Rendong Ying 2008 4th IEEE International Conference on Circuits and Systems for Communications, 2008
The autocorrelation division multiple access system (ADMA) is a new multiple access system that is for MIMO wireless communication under "strong" co channel interferences, such as the case in the unlicensed spectrum. The key element of ADMA system is the ADMA filter. In theory, the ADMA filter when perfectly computed can eliminate all cochannel interferences and multiple access interferences regardless ...
Generation Of Fractal Images And Comparison Of Their PSDs With Several Models
P. K. Williams; D. G. Lubnau; D. F. Elliott TwentySecond Asilomar Conference on Signals, Systems and Computers, 1988
First Page of the Article ![](/xploreAssets/images/absImages/00754043.png)
MultipleSymbol Differential Detection for Unitary SpaceTimeFrequency Coding
Ziyan Jia; Shiro Handa; Fumihito Sasamori; Shinjiro Oshita 2008 4th IEEE International Conference on Circuits and Systems for Communications, 2008
In this paper, multiplesymbol differential detection (MSDD) is applied for differential unitary spacetime frequency coding (DUSTFC) scheme over frequency selective fading multipleinput multipleoutput (MIMO) channels to compensate for the performance loss of conventional differential detection comparing with coherent detection. The decision metrics of time and frequency domain MSDD for DUSTFC are derived by considering the time and frequency domain fading ...
Systolic array for 2D circular convolution using the chinese remainder theorem
1993 IEEE International Symposium on Circuits and Systems, 1993
First Page of the Article ![](/xploreAssets/images/absImages/00693006.png)
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Educational Resources on Autocorrelation
Back to TopeLearning
A Mathematical Comparison of Point Detectors
M. Zuliani; C. Kenney; B. S. Manjunath 2004 Conference on Computer Vision and Pattern Recognition Workshop, 2004
Selecting salient points from two or more images for computing correspondences is a fundamental problem in image analysis. Three methods originally proposed by Harris et al. in [A combined corner and edge detector], by Noble et al. in [Descriptions of image surfaces] and by Shi et al. in [Good features to track] proved to be quite effective and robust and ...
ADMA  A New Multiple Access System
Rueywen Liu; Rendong Ying 2008 4th IEEE International Conference on Circuits and Systems for Communications, 2008
The autocorrelation division multiple access system (ADMA) is a new multiple access system that is for MIMO wireless communication under "strong" co channel interferences, such as the case in the unlicensed spectrum. The key element of ADMA system is the ADMA filter. In theory, the ADMA filter when perfectly computed can eliminate all cochannel interferences and multiple access interferences regardless ...
Generation Of Fractal Images And Comparison Of Their PSDs With Several Models
P. K. Williams; D. G. Lubnau; D. F. Elliott TwentySecond Asilomar Conference on Signals, Systems and Computers, 1988
First Page of the Article ![](/xploreAssets/images/absImages/00754043.png)
MultipleSymbol Differential Detection for Unitary SpaceTimeFrequency Coding
Ziyan Jia; Shiro Handa; Fumihito Sasamori; Shinjiro Oshita 2008 4th IEEE International Conference on Circuits and Systems for Communications, 2008
In this paper, multiplesymbol differential detection (MSDD) is applied for differential unitary spacetime frequency coding (DUSTFC) scheme over frequency selective fading multipleinput multipleoutput (MIMO) channels to compensate for the performance loss of conventional differential detection comparing with coherent detection. The decision metrics of time and frequency domain MSDD for DUSTFC are derived by considering the time and frequency domain fading ...
Systolic array for 2D circular convolution using the chinese remainder theorem
1993 IEEE International Symposium on Circuits and Systems, 1993
First Page of the Article ![](/xploreAssets/images/absImages/00693006.png)
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IEEEUSA EBooks

The central results of the WienerKoimogoroff smoothing and prediction theory for stationary time series are developed by a new method. The approach is motivated by physical considerations based on electric circuit theory and does not involve integral equations or the autocorrelation function. The cases treated are the Â¿inftnite lagÂ¿ smoothinl problem, the case of pure prediction (without noise), and the general smoothing prediction problem. Finally, the basic assumptions of the theory are discussed in order to clarify the question of when the theory will be appropriate, and to avoid possible misapplication.

Continuous wave (CW) radar is one of the earliest forms of radar. It is found today mostly in short range radar applications such as proximity fuzes, radar altimeters, atmospheric probing, ground penetrating radar and automotive applications. The CW signal is also useful in velocity measuring radars such as airborne Doppler navigation radars, artillery muzzle velocity and police radars. In military applications CW waveforms are sometimes referred to as low probability of intercept (LPI) waveforms, because of their low peak power. This chapter discusses many modulation waveforms of the CW signal. Modulation increases bandwidth which is inversely related to the range resolution. The periodic ambiguity function (PAF) is a natural tool to describe the delay Doppler response of CW periodic signal, and it is revisited in this chapter. An important family of modulation waveforms are periodic phase codes with ideal periodic autocorrelation (PACF). They all yield PACF with zero delay sidelobes. Examples reconsidered here are P4, Frank and Golomb biphase. Frequency modulation is also found in CW radars. These waveforms do not yield zero delay sidelobes, but with proper weighting (on receive) the sidelobes can be reduced. Among the frequency modulations discussed are: sawtooth, sinusoidal and triangular waveforms. Methods to control the delay peak response by utilizing harmonics of the periodic modulation are described. Simple implementation of CW radar receiver is described. It is based on mixing the received delayed return with the original transmitted signal. This kind of processing belongs to the family of stretch processors discussed in an earlier chapter.

Sensitivities of Mean Square Estimation Error with Respect to Quantizer Parameters
This chapter contains sections titled: Change in MSEE due to Changes in Output Autocorrelation, Partial Derivatives of b(m) with Respect to {dn}, Partial Derivatives of b(m) with respect to {yn}

Two additional frequency modulated signals are described that are different from the linearFM signal discussed in the previous chapter. The first is the Costas signal, in which the frequency evolution is randomlike, in contrast with the linear evolution in LFM. The resulting ambiguity function (AF) has a thumbtack shape, in contrast to the ridge found in LFM. The Costas signal is explained in detail, and the Welch construction algorithms are described. Appendix 5.1 includes a MATLAB code that implements those algorithms. Because LFM exhibited relatively high autocorrelation sidelobes, some form of amplitude weighting was necessary in order to reshape the spectrum and thus reduce the autocorrelation sidelobes. In nonlinearFM, the second signal discussed in this chapter, the spectrum is shaped not by amplitude weighting, but by spending more time in the frequencies that need to be emphasized. Several different nonlinear frequency evolution laws and their performances are described.

This chapter contains sections titled: Identification of Nonlinear Distortion in Digital Wireless Systems Orthogonalization of the Behavioral Model Autocorrelation Function and Spectral Analysis of the Orthogonalized Model Relationship Between System Performance and Uncorrelated Distortion Examples Measurement of Uncorrelated Distortion Summary

Generalized AlmostCyclostationary Processes
In Chapter 2, the class of the generalized almostcyclostationary (GACS) processes is presented and characterized. GACS processes have multivariate statistical functions that are almostperiodic function of time. The (generalized) Fourier series of these functions have both coefficients and frequencies, named lagdependent cycle frequencies, that depend on the lag shifts of the processes. Almostcyclostationary processes are obtained as special case when the frequencies do not depend on the lag parameters. The problems of linear filtering and sampling of GACS processes are addressed. The cyclic correlogram is shown to be, under mild conditions, a meansquare consistent and asymptotically Normal estimator of the cyclic autocorrelation function. Such a function allows a complete secondorder characterization in the widesense of GACS processes. Numerical examples of communications through Doppler channels due to relative motion between transmitter and receiver with constant relative radial acceleration are considered. Simulation results on statistical function estimation are carried out to illustrate the theoretical results. Proofs of the results in Chapter 2 are reported in Chapter 3.

This chapter provides an overview of integration of the Gaussian probability density function and the Qfunction. It explains the weighted sum of random variables and properties of Gaussian variables. The chapter presents the central limit theorem and ensembles average, autocorrelation functions of random processes. It also explores statistical properties of additive white Gaussian noise (AWGN). The chapter provides stepbystep code exercises and instructions to implement execution sequences. The MATLAB command randn(1,b) generates a 1??b vector whose elements are realizations of independent and identically distributed Gaussian random variables with zero mean and unit variance. The chapter summarizes the analytical relationship among the input, the output, and the impulse response of a linear system in the time domain. It is designed to help teach and understand communication systems using a classroomtested, active learning approach.

Optimal Linear Estimators for Quantized Stationary Processes
This chapter contains sections titled: Introduction, Autocorrelation of the Quantizer Output, A New Interpretation of the Describing function, Optimal Linear Filters for Quantized Measurements, Joint Optimization of the Quantizer and Filter, Summary

The ambiguity function (AF) represents the time response of a filter matched to a given finite energy signal, when the signal is received with a delay and a Doppler shift relative to the nominal values (zeros) expected by the filter. The ambiguity function is defined by the complex envelope of the signal. The ambiguity function is a major tool for studying and analyzing radar signals. It will be used extensively in the following chapters, where different signals will be described. The chapter presents four important properties of the ambiguity function and proves them. The properties are: Maximum at the origin. Constant volume. Symmetry with respect to the origin. Shearing due to Linear FM LFM induced shearing is explained. The cuts of the AF along the delay and Doppler axes are described and related to the autocorrelation and spectrum. The chapter ends with a definition of the periodic ambiguity function (PAF). The PAF is an important tool for analyzing the delayDoppler response of long periodic signals (including CW), when processed by a correlation receiver with a finite reference signal extended over an integer number of periods. The main properties of the PAF are discussed. Appendix 3.1 contains a MATLAB code for generating numerical 3D plots of the AF, of most user defined signals.

This book describes several modules of the Code Excited Linear Prediction (CELP) algorithm. The authors use the Federal Standard1016 CELP MATLAB® software to describe in detail several functions and parameter computations associated with analysisbysynthesis linear prediction. The book begins with a description of the basics of linear prediction followed by an overview of the FS1016 CELP algorithm. Subsequent chapters describe the various modules of the CELP algorithm in detail. In each chapter, an overall functional description of CELP modules is provided along with detailed illustrations of their MATLAB® implementation. Several code examples and plots are provided to highlight some of the key CELP concepts. Link to MATLAB® code found within the book Table of Contents: Introduction to Linear Predictive Coding / Autocorrelation Analysis and Linear Prediction / Line Spectral Frequency Computation / Spectral Distortion / The Codebook Search / The FS1016 Decoder