Covariance matrix

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In probability theory and statistics, a covariance matrix (also known as dispersion matrix) is a matrix whose element in the i, j position is the covariance between the i and j elements of a random vector. (Wikipedia.org)






Conferences related to Covariance matrix

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2018 11th Global Symposium on Millimeter Waves (GSMM)

The main theme of the GSMM2018 is Millimeter-wave Propagation: Hardware, Measurements and Systems. It covers millimeter-wave and THz devices, circuits, systems, and applications, with a special focus on mmWave propagation. The conference will include keynote talks, technical sessions, panels, and exhibitions on the listed topics.

  • 2017 10th Global Symposium on Millimeter-Waves (GSMM)

    The main theme of the symposium is Millimeter-Wave and Terahertz Sensing and Communications. It covers millimeter- wave and THz antennas, circuits, devices, systems and applications.

  • 2016 Global Symposium on Millimeter Waves (GSMM) & ESA Workshop on Millimetre-Wave Technology and Applications

    The main theme of the conference is millimeter-wave and terahertz sensing and communications and the conference covers different topics related to millimeter-wave and terahertz technologies, such as: antennas and propagation, passive and active devices, radio astronomy, earth observation and remote sensing, communications, wireless power transfer, integration and packaging, photonic systems, and emerging technologies.

  • 2015 Global Symposium On Millimeter Waves (GSMM)

    The main theme of the GSMM 2015 is “Future Millimeter-wave and Terahertz Wireless and Wireline”. It will cover all emerging and future millimeter wave and terahertz software and hardware aspects ranging from communicating devices, circuits, systems and applications to passive and active sensing and imaging technologies and applications. The GSMM 2015 will feature world-class keynote speeches, technical sessions, panel discussions and industrial exhibitions in the following (but not limited to) topics listed below.In addition to the regular program, the GSMM 2015 will organize a unique industrial forum for presenting and discussing future wireless technologies and trends including 5G and Terahertz Wireless Systems.

  • 2012 5th Global Symposium on Millimeter Waves (GSMM 2012)

    The aim of the conferences is to bring together people involved in research and development of millimeter-wave components, equipment and systems, and to explore common areas.

  • 2009 Global Symposium On Millimeter Waves (GSMM 2009)

    The GSMM2009 will be held in Sendai, Japan from April 20 to April 22, 2009. The GSMM2009 is the second international conference in its name after the three conferences of TSMMW, MINT-MIS, and MilliLab Workshop on Millimeter-wave Technology and Applications were integrated into GSMM (Global Symposium on Millimeter Waves) in 2007. The main theme of the GSMM2009 is "Millimeter Wave Communications at Hand" and it will focus on millimeter wave devices and systems to realize Giga-bit wireless applications. The

  • 2008 Global Symposium On Millimeter Waves (GSMM 2008)

    Frequency Management and Utilization, Millimeter-Wave Communication Systems, Devices and Circuit Technologies, Wireless Access Systems, Mobile Access Systems, Satellite Communications, LANs and PANs, Home Link Systems, Photonics, Antennas and Propagation, Phased Array Antennas, Signal Processing, Wearable Devices and Systems, Automotive Radars and Remote Sensing, Supporting and Related Technologies


2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

AVSS 2018 addresses underlying theory, methods, systems, and applications of video and signal based surveillance.


2018 23rd International Conference on Methods & Models in Automation & Robotics (MMAR)

New theoretical and technological developments in: predictive control, robust and adaptive control, networked control systems, fuzzy logic, neural networks and intelligent control, modelling and identification, discrete events and hybrid systems, fault detection, diagnosis, fault tolerant control, computer aided control systems design, mathematical foundations of robotics, motion planning and algorithms, human-robot interaction.


2018 24th International Conference on Pattern Recognition (ICPR)

ICPR will be an international forum for discussions on recent advances in the fields of Pattern Recognition, Machine Learning and Computer Vision, and on applications of these technologies in various fields

  • 2016 23rd International Conference on Pattern Recognition (ICPR)

    ICPR'2016 will be an international forum for discussions on recent advances in the fields of Pattern Recognition, Machine Learning and Computer Vision, and on applications of these technologies in various fields.

  • 2014 22nd International Conference on Pattern Recognition (ICPR)

    ICPR 2014 will be an international forum for discussions on recent advances in the fields of Pattern Recognition; Machine Learning and Computer Vision; and on applications of these technologies in various fields.

  • 2012 21st International Conference on Pattern Recognition (ICPR)

    ICPR is the largest international conference which covers pattern recognition, computer vision, signal processing, and machine learning and their applications. This has been organized every two years by main sponsorship of IAPR, and has recently been with the technical sponsorship of IEEE-CS. The related research fields are also covered by many societies of IEEE including IEEE-CS, therefore the technical sponsorship of IEEE-CS will provide huge benefit to a lot of members of IEEE. Archiving into IEEE Xplore will also provide significant benefit to the all members of IEEE.

  • 2010 20th International Conference on Pattern Recognition (ICPR)

    ICPR 2010 will be an international forum for discussions on recent advances in the fields of Computer Vision; Pattern Recognition and Machine Learning; Signal, Speech, Image and Video Processing; Biometrics and Human Computer Interaction; Multimedia and Document Analysis, Processing and Retrieval; Medical Imaging and Visualization.

  • 2008 19th International Conferences on Pattern Recognition (ICPR)

    The ICPR 2008 will be an international forum for discussions on recent advances in the fields of Computer vision, Pattern recognition (theory, methods and algorithms), Image, speech and signal analysis, Multimedia and video analysis, Biometrics, Document analysis, and Bioinformatics and biomedical applications.

  • 2002 16th International Conference on Pattern Recognition


2018 26th European Signal Processing Conference (EUSIPCO)

Audio and acoustic signal processingSpeech and language processingImage and video processingMultimedia signal processingSignal processing theory and methodsSensor array and multichannel signal processingSignal processing for communicationsRadar and sonar signal processingSignal processing over graphs and networksNonlinear signal processingStatistical signal processingCompressed sensing and sparse modelingOptimization methodsMachine learningBio-medical image and signal processingSignal processing for computer vision and roboticsComputational imaging/ Spectral imagingInformation forensics and securitySignal processing for power systemsSignal processing for educationBioinformatics and genomicsSignal processing for big dataSignal processing for the internet of thingsDesign/implementation of signal processing systemsOther signal processing areas

  • 2017 25th European Signal Processing Conference (EUSIPCO)

    Audio and acoustic signal processingSpeech and language processingImage and video processingMultimedia signal processingSignal processing theory and methodsSensor array and multichannel signal processingSignal processing for communicationsRadar and sonar signal processingSignal processing over graphs and networksNonlinear signal processingStatistical signal processingCompressed sensing and sparse modelingOptimization methodsMachine learningBio-medical image and signal processingSignal processing for computer vision and roboticsInformation forensics and securitySignal processing for power systemsSignal processing for educationBioinformatics and genomicsSignal processing for big dataSignal processing for the internet of thingsDesign and implementation of signal processing systemsOther signal processing areas

  • 2016 24th European Signal Processing Conference (EUSIPCO)

    EUSIPCO is the flagship conference of the European Association for Signal Processing (EURASIP). The 24th edition will be held in Budapest, Hungary, from 29th August - 2nd September 2016. EUSIPCO 2016 will feature world-class speakers, oral and poster sessions, keynotes, exhibitions, demonstrations and tutorials and is expected to attract in the order of 600 leading researchers and industry figures from all over the world.

  • 2015 23rd European Signal Processing Conference (EUSIPCO)

    EUSIPCO is the flagship conference of the European Association for Signal Processing (EURASIP). The 23rd edition will be held in Nice, on the French Riviera, from 31st August - 4th September 2015. EUSIPCO 2015 will feature world-class speakers, oral and poster sessions, keynotes, exhibitions, demonstrations and tutorials and is expected to attract in the order of 600 leading researchers and industry figures from all over the world.

  • 2014 22nd European Signal Processing Conference (EUSIPCO)

    EUSIPCO is one of the largest international conferences in the field of signal processing and addresses all the latest developments in research and technology. The conference will bring together individuals from academia, industry, regulation bodies, and government, to discuss and exchange ideas in all the areas and applications of signal processing. The conference will feature world-class keynote speakers, special sessions, plenary talks, tutorials, and technical sessions.

  • 2013 21st European Signal Processing Conference (EUSIPCO)

    The EUSIPCO is organized by the European Association for Signal, Speech, and Image Processing (EURASIP). The focus will be on signal processing theory, algorithms, and applications.

  • 2012 20th European Signal Processing Conference

    The focus: signal processing theory, algorithms and applications. Papers will be accepted based on quality, relevance, and novelty and will be indexed in the main databases. Organizers: University POLITEHNICA of Bucharest and Telecom ParisTech.


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Periodicals related to Covariance matrix

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Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Communications Letters, IEEE

Covers topics in the scope of IEEE Transactions on Communications but in the form of very brief publication (maximum of 6column lengths, including all diagrams and tables.)


Communications, IEEE Transactions on

Telephone, telegraphy, facsimile, and point-to-point television, by electromagnetic propagation, including radio; wire; aerial, underground, coaxial, and submarine cables; waveguides, communication satellites, and lasers; in marine, aeronautical, space and fixed station services; repeaters, radio relaying, signal storage, and regeneration; telecommunication error detection and correction; multiplexing and carrier techniques; communication switching systems; data communications; and communication theory. In addition to the above, ...


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Most published Xplore authors for Covariance matrix

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Xplore Articles related to Covariance matrix

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A no-reference sharpness metric sensitive to blur and noise

[{u'author_order': 1, u'affiliation': u'Electrical Engineering Department, University of California at Santa Cruz, 95064, USA', u'full_name': u'Xiang Zhu'}, {u'author_order': 2, u'affiliation': u'Electrical Engineering Department, University of California at Santa Cruz, 95064, USA', u'full_name': u'Peyman Milanfar'}] 2009 International Workshop on Quality of Multimedia Experience, None

A no-reference objective sharpness metric detecting both blur and noise is proposed in this paper. This metric is based on the local gradients of the image and does not require any edge detection. Its value drops either when the test image becomes blurred or corrupted by random noise. It can be thought of as an indicator of the signal to ...


Estimation covariance of measurement fusion on track-to-track problem

[{u'author_order': 1, u'affiliation': u'Nat. Lab. of Ind. Control Technol., Zhejiang Univ., Hangzhou, China', u'full_name': u'Jin Xue-bo'}, {u'author_order': 2, u'affiliation': u'Nat. Lab. of Ind. Control Technol., Zhejiang Univ., Hangzhou, China', u'full_name': u'Sun You-xian'}] TENCON '02. Proceedings. 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering, None

Measurement fusion is an optimal data fusion algorithm. The covariance of measurement fusion is proved to be decided by a function of the measurement matrix and measurement noise covariance. The greater the function is, the less the covariance measurement fusion method can obtain. Therefore, the estimation accuracy increases when the function increase. The results of simulation agree with the theoretical ...


RKH space methods for factoring the covariance operator

[{u'author_order': 1, u'affiliation': u'Dept. of Math. Sci., Clemson Univ., SC, USA', u'full_name': u'R. E. Fennell'}, {u'author_order': 2, u'affiliation': u'Dept. of Math. Sci., Clemson Univ., SC, USA', u'full_name': u'J. A. Reneke'}] [1991 Proceedings] The Twenty-Third Southeastern Symposium on System Theory, None

Given discrete versions of the noise and response covariance functions for a linear hereditary system, an algorithm is presented to obtain an approximation to the system input/output operator. In order that the algorithm apply to a given covariance function, certain mild assumptions have to be made on the input/output operator and the noise process


Maximum entropy power spectrum estimation with uncertainty in correlation measurements

[{u'author_order': 1, u'affiliation': u'Massachusetts Institute of Technology, Cambridge, MA', u'full_name': u'J. -P. Schott'}, {u'author_order': 2, u'full_name': u'J. McClellan'}] IEEE Transactions on Acoustics, Speech, and Signal Processing, 1984

The purpose of this paper is to present a multidimensional MEM algorithm, valid for nonuniformly sampled arrays, which satisfies a "correlation- approximating" constraint. To this end, the correlation matching equality constraints of the usual MEM are replaced by a single inequality constraint whose form is based on a measure of the noise in the given autocovariance function (ACF). In this ...


Rebuttal to "Comments on covariance shaping least-squares estimation"

[{u'author_order': 1, u'affiliation': u'Dept. of Electr. Eng., Technion-Israel Inst. of Technol., Haifa, Israel', u'full_name': u'Y. C. Eldar'}, {u'author_order': 2, u'full_name': u'A. V. Oppenheim'}] IEEE Transactions on Signal Processing, 2004

The author presents a reply to the paper by Zhou (IEEE Trans. Signal Processing, vol.52, no.10, p.2938-9, 2004) which commented on the original paper by Eldar and Oppenheim (IEEE Trans. Signal Processing, vol. 51, p.686-97, 2003 March).


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eLearning

No eLearning Articles are currently tagged "Covariance matrix"

IEEE-USA E-Books

  • Source Separation and Reconstruction of Spatial Audio Using Spectrogram Factorization

    This chapter introduces methods for factorizing the spectrogram of multichannel audio into repetitive spectral objects and apply the introduced models to the analysis of spatial audio and modification of spatial sound through source separation. The purpose of decomposing an audio spectrogram using spectral templates is to learn the underlying structures (audio objects) from the observed data. The chapter discusses two main scenarios such as parameterization of multichannel surround sound and parameterization of microphone array signals. It explains the principles of source separation by time‐frequency filtering using separation masks constructed from the spectrogram models. The chapter introduces a spatial covariance matrix model based on the directions of arrival of sound events and spectral templates, and discusses its relationship to conventional spatial audio signal processing. Source separation using spectrogram factorization models is achieved via time‐ frequency filtering of the original observation short‐time Fourier transform (STFT) by a generalized Wiener filter obtained from the spectrogram model parameters.

  • Covariances in Computer Vision and Machine Learning

    <p>Covariance matrices play important roles in many areas of mathematics, statistics, and machine learning, as well as their applications. In computer vision and image processing, they give rise to a powerful data representation, namely the covariance descriptor, with numerous practical applications.</p> <p>In this book, we begin by presenting an overview of the {it finite- dimensional covariance matrix} representation approach of images, along with its statistical interpretation. In particular, we discuss the various distances and divergences that arise from the intrinsic geometrical structures of the set of Symmetric Positive Definite (SPD) matrices, namely Riemannian manifold and convex cone structures. Computationally, we focus on kernel methods on covariance matrices, especially using the Log-Euclidean distance.</p> <p>We then show some of the latest developments in the generalization of the finite-dimensional covariance matrix representati n to the {it infinite-dimensional covariance operator} representation via positive definite kernels. We present the generalization of the affine-invariant Riemannian metric and the Log-Hilbert-Schmidt metric, which generalizes the Log Euclidean distance. Computationally, we focus on kernel methods on covariance operators, especially using the Log-Hilbert-Schmidt distance. Specifically, we present a two-layer kernel machine, using the Log-Hilbert- Schmidt distance and its finite-dimensional approximation, which reduces the computational complexity of the exact formulation while largely preserving its capability. Theoretical analysis shows that, mathematically, the approximate Log-Hilbert-Schmidt distance should be preferred over the approximate Log- Hilbert-Schmidt inner product and, computationally, it should be preferred over the approximate affine-invariant Riemannian distance.</p> <p>Numerical experiments on image classification demonstrate significant improvements o the infinite-dimensional formulation over the finite-dimensional counterpart. Given the numerous applications of covariance matrices in many areas of mathematics, statistics, and machine learning, just to name a few, we expect that the infinite-dimensional covariance operator formulation presented here will have many more applications beyond those in computer vision.</p>

  • Classical Detection

    This chapter contains sections titled: Formalism of Quantum Information Hypothesis Detection for Collaborative Sensing Sample Covariance Matrix Random Matrices with Independent Rows The Multivariate Normal Distribution Sample Covariance Matrix Estimation and Matrix Compressed Sensing Likelihood Ratio Test

  • Models of MIMO Channels

    This chapter contains sections titled: General classification of MIMO channel models Physical models Analytical models Geometrical phenomenological models On the role of trigonometric polynomials in analysis and simulation of MIMO channels Canonical expansions of bivariate distributions and the structure MIMO channel covariance matrix Bivariate von Mises distribution with correlated transmit and receive sides Bivariate uniform distributions Analytical expression for the diversity measure of an antenna array Effect of AoA/AoD dependency on the SDoF Space‐time covariance function Examples: synthetic data and uniform linear array Approximation of a matrix by a Toeplitz matrix Asymptotic expansions of diversity measure Distributed scattering model

  • Introduction

    This chapter contains sections titled: Vision: "Big Data" Cognitive Radio: System Concepts Spectrum Sensing Interface and Data Structures Mathematical Machinery Sample Covariance Matrix Large Sample Covariance Matrices of Spiked Population Models Random Matrices and Noncommutative Random Variables Principal Component Analysis Generalized Likelihood Ratio Test (GLRT) Bregman Divergence for Matrix Nearness

  • Evaluating Multisensor Classification Performance with Bayesian Networks

    This chapter presents a new analytical approach for quantifying the long¿¿¿run performance of a multisensor, discrete¿¿¿state classification system, under the assumption of independent, asynchronous measurements, and consolidates earlier research. It addresses the problem of evaluating the classification performance of both single¿¿¿ and multisensor classification systems, where one or more sensors observe repeated measurements of a target's features/attributes and compute posterior probability estimates to aid in target identification. The chapter develops a new analytical approach for off¿¿¿line evaluation of the long¿¿¿run classification performance of a single¿¿¿ or a multisensor system for the case of independent sensor measurement. It defines a new approach to quantify the classification performance based on the global classification matrix (GCM) of a classification system. The GCM is analogous to the covariance matrix used in kinematic performance evaluation and is an off¿¿¿line measure that does not depend on the actual sensor measurements.

  • Approaches to High-Dimensional Covariance and Precision Matrix Estimations

    This chapter introduces several recent developments for estimating large covariance and precision matrices without assuming the covariance matrix to be sparse. It explains two methods for covariance estimation: namely covariance estimation via factor analysis, and precision Matrix Estimation and Graphical Models. The low rank plus sparse representation holds on the population covariance matrix. The chapter presents several applications of these methods, including graph estimation for gene expression data, and several financial applications. It then shows how estimating covariance matrices of high- dimensional asset excess returns play a central role in applications of portfolio allocations and in risk management. The chapter explains the factor pricing model, which is one of the most fundamental results in finance. It elucidates estimating risks of large portfolios and large panel test of factor pricing models. The chapter illustrates the recent developments of efficient estimations in panel data models.

  • Vector Radiative Transfer

    Chapter 2 introduces the vector radiative transfer (VRT) theory of random media. It provides the scattering, absorption and extinction coefficients and the phase matrix of non-spherical scatterers in natural media. The first-order Mueller matrix solution of VRT for vegetation canopy model is derived. Polarization indexes, eigen-analysis and entropy are presented. Statistics of multi-look SAR images and covariance matrix are also investigated.

  • Estimation with Unknown Noise Model - Standard Solutions

  • Some Probability and Stochastic Convergence Fundamentals



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