Higher order statistics
3,468 resources related to Higher order statistics
IEEE Organizations related to Higher order statistics
Back to TopConferences related to Higher order statistics
Back to TopIECON 2014  40th Annual Conference of the IEEE Industrial Electronics Society
Applications of power electronics, artificial intelligence, robotics, and nanotechnology in electrification of automotive, military, biomedical, and utility industries.
2012 Australian Communications Theory Workshop (AusCTW)
 Coded modulation  Coding theory and practice  Communication systems  Channel characteristics and modeling  Detection and estimation  OFDM and DMT processing techniques  Utrawide band communications  DSP for communications  Information theory and statistics  Iterative decoding algorithms  Multiuser detection  Crosslayer PHYMACNET arrangements  Fourth generation cellular systems  Blind signal separation techniques
Periodicals related to Higher order statistics
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.
Signal Processing Letters, IEEE
Rapid dissemination of new results in signal processing worldwide.
Signal Processing, IEEE Transactions on
The technology of transmission, recording, reproduction, processing, and measurement of speech; other audiofrequency waves and other signals by digital, electronic, electrical, acoustic, mechanical, and optical means; the components and systems to accomplish these and related aims; and the environmental, psychological, and physiological factors of thesetechnologies.
Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on
Applications, review, and tutorial papers within the scope of the Systems, Man and Cybernetics Society. Currently, this covers: (1) Integration of the theories of communication, control cybernetics, stochastics, optimization and system structure towards the formulation of a general theory of systems; (2) Development of systems engineering technology including problem definition methods, modeling, and stimulation, methods of systems experimentation, human factors ...
Xplore Articles related to Higher order statistics
Back to TopDetection of Glottal Activity Using Different Attributes of Source Information
Nagaraj Adiga; S. R. M. Prasanna IEEE Signal Processing Letters, 2015
The major activity during speech production is glottal activity and is earlier detected using strength of excitation (SoE). This work uses the normalized autocorrelation peak strength (NAPS) and higher order statistics (HOS) as additional features for detecting glottal activity. The three features, namely, SoE, NAPS, and HOS, are, respectively indicators of different attributes of glottal activity, namely, energy, periodicity, and ...
Analysis of the SAR imaging process of the ocean surface using Volterra models
J. M. Le Caillec; R. Garello; B. Chapron IEEE Journal of Oceanic Engineering, 2002
The synthetic aperture radar (SAR) process of the ocean surface mapping is studied using a decomposition based on a Volterra model. By a mathematical expansion of the complex exponential of the complete SAR transform, these models decompose the nonlinear distortion mechanisms of the SAR spectrum over different spectra of polynomial interactions. Thus, they offer an alternative modeling (to the exact ...
Sea ice classification using SAR backscatter statistics
J. A. Nystuen; F. W. Garcia IEEE Transactions on Geoscience and Remote Sensing, 1992
Sea ice classification accuracy using standard statistics and higher order texture statistics generated from greylevel cooccurrence (GLC) matrices were compared for synthetic aperture radar (SAR) data collected during the Marginal Ice Zone Experiment (MIZEX) in April 1987. Standard stepwise discriminate analysis was used to identify the statistics modes useful for discrimination. Range was the most effective statistic, correctly classifying the ...
E. Uchino; M. Ohta; K. Hatakeyama Industrial Electronics, Control and Instrumentation, 1991. Proceedings. IECON '91., 1991 International Conference on, 1991
The authors describe various state estimation methods of a quantized sound environmental system. The elimination of not only the usual background noise but also the quantization noise owing to digital observation is discussed. The methods include the traditional Kalman filter as a special case when Gaussian distribution is employed and where there is no level quantization. The level quantization mechanism ...
X. Yu; J. W. Modestino; X. Tian Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies., 2005
The Gilbert model (1st order Markov chain model) and the singlemultiplexer model are two frequently used models in the study of packetloss processes in communication networks. In this paper we investigate the accuracy of the Gilbert model, and higherorder Markov chain extended Gilbert models, in characterizing the packetloss process associated with a transport network modeled in terms of a singlemultiplexer. ...
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Educational Resources on Higher order statistics
Back to TopeLearning
Detection of Glottal Activity Using Different Attributes of Source Information
Nagaraj Adiga; S. R. M. Prasanna IEEE Signal Processing Letters, 2015
The major activity during speech production is glottal activity and is earlier detected using strength of excitation (SoE). This work uses the normalized autocorrelation peak strength (NAPS) and higher order statistics (HOS) as additional features for detecting glottal activity. The three features, namely, SoE, NAPS, and HOS, are, respectively indicators of different attributes of glottal activity, namely, energy, periodicity, and ...
Analysis of the SAR imaging process of the ocean surface using Volterra models
J. M. Le Caillec; R. Garello; B. Chapron IEEE Journal of Oceanic Engineering, 2002
The synthetic aperture radar (SAR) process of the ocean surface mapping is studied using a decomposition based on a Volterra model. By a mathematical expansion of the complex exponential of the complete SAR transform, these models decompose the nonlinear distortion mechanisms of the SAR spectrum over different spectra of polynomial interactions. Thus, they offer an alternative modeling (to the exact ...
Sea ice classification using SAR backscatter statistics
J. A. Nystuen; F. W. Garcia IEEE Transactions on Geoscience and Remote Sensing, 1992
Sea ice classification accuracy using standard statistics and higher order texture statistics generated from greylevel cooccurrence (GLC) matrices were compared for synthetic aperture radar (SAR) data collected during the Marginal Ice Zone Experiment (MIZEX) in April 1987. Standard stepwise discriminate analysis was used to identify the statistics modes useful for discrimination. Range was the most effective statistic, correctly classifying the ...
E. Uchino; M. Ohta; K. Hatakeyama Industrial Electronics, Control and Instrumentation, 1991. Proceedings. IECON '91., 1991 International Conference on, 1991
The authors describe various state estimation methods of a quantized sound environmental system. The elimination of not only the usual background noise but also the quantization noise owing to digital observation is discussed. The methods include the traditional Kalman filter as a special case when Gaussian distribution is employed and where there is no level quantization. The level quantization mechanism ...
X. Yu; J. W. Modestino; X. Tian Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies., 2005
The Gilbert model (1st order Markov chain model) and the singlemultiplexer model are two frequently used models in the study of packetloss processes in communication networks. In this paper we investigate the accuracy of the Gilbert model, and higherorder Markov chain extended Gilbert models, in characterizing the packetloss process associated with a transport network modeled in terms of a singlemultiplexer. ...
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This chapter contains sections titled: Introduction Higher Order Statistics: Definition and Main Properties Bispectrum and Bicoherence: Definitions, Properties, and Estimation Methods Analysis of Nonlinear Signals: Quadratic Phase Coupling Identification of Linear Systems Interaction Among Cardiorespiratory Signals Clinical Applications of HOS: Bispectral Index for Assessment of Anaesthesia Depth

This chapter discusses equalizers for singlecarrier transmission in wireless systems. We first set up a timediscrete model for the channel, filters, and equalizer. A noisewhitening filter or precursor equalizer ensures white noise at the equalizer input. Transmit filter, channel, matched filter, and noise whitening filter can be modeled together by an equivalent timediscrete channel. We then turn to the various types of equalizers. Linear equalizers consist of linear filters, usually tapped delay lines (though IIR filters and lattice filters are also possible), whose coefficients are optimized according to certain criteria. Zeroforcing equalizers eliminate intersymbol interference, but lead to noise enhancement, while minimum mean square error (MMSE) equalizers trade off these error sources. Adaptation algorithms for the coefficients trade off complexity, convergence rate, and misadjustment. Example algorithms include the least mean square (LMS) algorithm (stochastic gradient method), the recursive least squares algorithm (RLS), or direct computation of the Wiener filter. Decision feedback equalizer (DFE) consist of a feedforward filters and a feedback filter that eliminates the postcursor impact. DFEs generally perform well, but must avoid error propagation. The best performance is obtained by maximumlikelihood sequence estimators (MLSE) or Viterbi equalizers. They act similar to Viterbi decoders for convolutional codes, since the channel can be interpreted as a rate1 convolutional encoder. Finally, we discuss blind equalizers, which do not require a training sequence for detection. Certain signal properties, e.g., constant envelope, finite symbol alphabet, cyclostationarity, or spectral correlation, can be used to separate the effect of the channel on the received signal from the signal modulation, and perform implicit equalization. The bestknown algorithms are the constantmodulus algorithm (CMA), blind MLSE, and algorithms using higher order statistics.
Standards related to Higher order statistics
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