Autocorrelation
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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 TopOn the condition number of Gaussian samplecovariance matrices
A. B. Kostinski; A. C. Koivunen IEEE Transactions on Geoscience and Remote Sensing, 2000
The authors examine the reasons behind the fact that the Gaussian autocorrelationfunction model, widely used in remote sensing, yields a particularly illconditioned samplecovariance matrix in the case of many strongly correlated samples. The authors explore the question numerically and relate the magnitude of the matrixcondition number to the nonnegativity requirement satisfied by all correlation functions. They show that the condition ...
G. S. Mollova Proceedings of Third International Conference on Electronics, Circuits, and Systems, 1996
This paper proposes a new approachfixedlevels least squares (FLLS) method for linearphase FIR filter design. It is mainly directed towards rejection of Gibbs phenomenon through introduction of a set of equally spaced fixed levels in transition band and subsequent redefinition of the approximated and weight functions. Detailed mathematical solution of the problem as well as a numerical example are given. ...
Comparison of different order cumulants in a speech enhancement system by adaptive Wiener filtering
J. M. Salavedra; E. Masgrau; A. Moreno; X. Jove [1993 Proceedings] IEEE Signal Processing Workshop on HigherOrder Statistics, 1993
The authors study some speech enhancement algorithms based on the iterative Wiener filtering method due to Lim and Oppenheim (1978), where the AR spectral estimation of the speech is carried out using a secondorder analysis. But in their algorithms the authors consider an AR estimation by means of a cumulant (third and fourthorder) analysis. The authors provide a behavior comparison ...
Robust Spectral Estimation: Autocorrelation Based Minimum Free Energy Method
S. D. Silverstein; J. M. Pimbley TwentySecond Asilomar Conference on Signals, Systems and Computers, 1988
First Page of the Article ![](/xploreAssets/images/absImages/00753984.png)
FarField Multicast Beamforming for Uniform Linear Antenna Arrays
Eleftherios Karipidis; Nicholas D. Sidiropoulos; ZhiQuan Luo IEEE Transactions on Signal Processing, 2007
The problem of transmit beamforming to multiple cochannel multicast groups is considered for the important special case when the channel vectors are Vandermonde. This arises when a uniform linear antenna antenna (ULA) array is used at the transmitter under farfield lineofsight propagation conditions, as provisioned in 802.16e and related wireless backhaul scenarios. Two design approaches are pursued: (i) minimizing the ...
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Educational Resources on Autocorrelation
Back to TopeLearning
On the condition number of Gaussian samplecovariance matrices
A. B. Kostinski; A. C. Koivunen IEEE Transactions on Geoscience and Remote Sensing, 2000
The authors examine the reasons behind the fact that the Gaussian autocorrelationfunction model, widely used in remote sensing, yields a particularly illconditioned samplecovariance matrix in the case of many strongly correlated samples. The authors explore the question numerically and relate the magnitude of the matrixcondition number to the nonnegativity requirement satisfied by all correlation functions. They show that the condition ...
G. S. Mollova Proceedings of Third International Conference on Electronics, Circuits, and Systems, 1996
This paper proposes a new approachfixedlevels least squares (FLLS) method for linearphase FIR filter design. It is mainly directed towards rejection of Gibbs phenomenon through introduction of a set of equally spaced fixed levels in transition band and subsequent redefinition of the approximated and weight functions. Detailed mathematical solution of the problem as well as a numerical example are given. ...
Comparison of different order cumulants in a speech enhancement system by adaptive Wiener filtering
J. M. Salavedra; E. Masgrau; A. Moreno; X. Jove [1993 Proceedings] IEEE Signal Processing Workshop on HigherOrder Statistics, 1993
The authors study some speech enhancement algorithms based on the iterative Wiener filtering method due to Lim and Oppenheim (1978), where the AR spectral estimation of the speech is carried out using a secondorder analysis. But in their algorithms the authors consider an AR estimation by means of a cumulant (third and fourthorder) analysis. The authors provide a behavior comparison ...
Robust Spectral Estimation: Autocorrelation Based Minimum Free Energy Method
S. D. Silverstein; J. M. Pimbley TwentySecond Asilomar Conference on Signals, Systems and Computers, 1988
First Page of the Article ![](/xploreAssets/images/absImages/00753984.png)
FarField Multicast Beamforming for Uniform Linear Antenna Arrays
Eleftherios Karipidis; Nicholas D. Sidiropoulos; ZhiQuan Luo IEEE Transactions on Signal Processing, 2007
The problem of transmit beamforming to multiple cochannel multicast groups is considered for the important special case when the channel vectors are Vandermonde. This arises when a uniform linear antenna antenna (ULA) array is used at the transmitter under farfield lineofsight propagation conditions, as provisioned in 802.16e and related wireless backhaul scenarios. Two design approaches are pursued: (i) minimizing the ...
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IEEEUSA EBooks

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

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

Coherent Train of Diverse Pulses
The chapter describes methods for pulse to pulse diversity. The methods described are used for reduction in the height of the recurrent (range) lobes of the autocorrelation function (ACF), reduction of the near range sidelobes (namely around the mainlobe) and for increasing the overall bandwidth of the signal, while maintaining relatively narrow instantaneous bandwidth. The signals described include: Phase coded pulse train  used to lower range recurrent lobes. Steppedfrequency signal Â Â used for increasing bandwidth. The chapter also describes a simple processor that is often used with it  the stretch processor. The specific case of a stepped frequency LFM pulse train is described in details together with means of nullifying ACF grating lobes and Costas ordering the pulses. Complementary phase coded pulse trains yield zero ACF sidelobes around the mainlobe area. Methods for generating binary and polyphase complementary codes for different lengths and set size are described. Signals based on complementary sets based on the PONS construction and orthogonal matrices are described with more details. Subcomplementary phase coded pulse trains. Orthogonal coded pulse trains, with focus on orthogonal coded LFM pulse trains, LFMLFM pulse trains and LFMNLFM pulse trains.

Spectra, Covariance, and Correlation Functions
This chapter contains sections titled: The Autocorrelation Function The Intensity Spectrum Autovariance, Autocorrelation, and Spectra of Linearly Filtered Waves Crosscorrelation Functions, Spectra, and Generalizations This chapter contains sections titled: References

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}

Appendix IV: Stationarity, Ergodicity, and Autocorrelation Functions of Random Processes
A practical approach to obtaining nonlinear dynamic models from stimulus response data Nonlinear modeling of physiological systems from stimulusresponse data is a longstanding problem that has substantial implications for many scientific fields and associated technologies. These disciplines include biomedical engineering, signal processing, neural networks, medical imaging, and robotics and automation. Addressing the needs of a broad spectrum of scientific and engineering researchers, this book presents practicable, yet mathematically rigorous methodologies for constructing dynamic models of physiological systems. Nonlinear Dynamic Modeling of Physiological Systems provides the most comprehensive treatment of the subject to date. Starting with the mathematical background upon which these methodologies are built, the book presents the methodologies that have been developed and used over the past thirty years. The text discusses implementation and computational issues and gives il ustrative examples using both synthetic and experimental data. The author discusses the various modeling approachesnonparametric, including the Volterra and Wiener models; parametric; modular; and connectionistand clearly identifies their comparative advantages and disadvantages along with the key criteria that must guide successful practical application. Selected applications covered include neural and sensory systems, cardiovascular and renal systems, and endocrine and metabolic systems. This lucid and comprehensive text is a valuable reference and guide for the community of scientists and engineers who wish to develop and apply the skills of nonlinear modeling to physiological systems.

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.

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.

Chapter 1 provides a brief overview of the book which presents, from a thorough signal theory basis, a comprehensive and straight forward account of the power spectral density and its applications. A direct Fourier approach, rather than the more generally used autocorrelation function approach, is used.

Fractal Analysis of Heart Rate Variability
This chapter contains sections titled: Introduction The fBm Model The Autocorrelation Function for DFGN The Probability Density Function for DFGN A Maximum Likelihood Estimator for DFGN PSD Estimators for fBm and DFGN A Wavelet Estimator for DFGN The Heart Rate Variability Signal This chapter contains sections titled: References