Matrix decomposition

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In the mathematical discipline of linear algebra, a matrix decomposition is a factorization of a matrix into some canonical form. (Wikipedia.org)






Conferences related to Matrix decomposition

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2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting

The joint meeting is intended to provide an international forum for the exchange of information on state of the art research in the area of antennas and propagation, electromagnetic engineering and radio science


2020 59th IEEE Conference on Decision and Control (CDC)

The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.


2020 IEEE 23rd International Conference on Information Fusion (FUSION)

The International Conference on Information Fusion is the premier forum for interchange of the latest research in data and information fusion, and its impacts on our society. The conference brings together researchers and practitioners from academia and industry to report on the latest scientific and technical advances.


2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and severalco-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students, academics and industry researchers.

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conferenceand 27co-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students,academics and industry.

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    computer, vision, pattern, cvpr, machine, learning

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. Main conference plus 50 workshop only attendees and approximately 50 exhibitors and volunteers.

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Topics of interest include all aspects of computer vision and pattern recognition including motion and tracking,stereo, object recognition, object detection, color detection plus many more

  • 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Sensors Early and Biologically-Biologically-inspired Vision, Color and Texture, Segmentation and Grouping, Computational Photography and Video

  • 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics, motion analysis and physics-based vision.

  • 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics,motion analysis and physics-based vision.

  • 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


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Periodicals related to Matrix decomposition

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Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


Antennas and Wireless Propagation Letters, IEEE

IEEE Antennas and Wireless Propagation Letters (AWP Letters) will be devoted to the rapid electronic publication of short manuscripts in the technical areas of Antennas and Wireless Propagation.


Applied Superconductivity, IEEE Transactions on

Contains articles on the applications and other relevant technology. Electronic applications include analog and digital circuits employing thin films and active devices such as Josephson junctions. Power applications include magnet design as well asmotors, generators, and power transmission


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 ...


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Most published Xplore authors for Matrix decomposition

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Xplore Articles related to Matrix decomposition

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Water Change Detection in Natural Dam caused by Earthquake using Pi-SAR fully Polarimetric Data

7th European Conference on Synthetic Aperture Radar, 2008

This paper presents water level change in natural disaster dam, unexpectedly caused by a great earthquake, by Pi-SAR observation. Pi-SAR is a fully polarimetric SAR system operative both in the X- and L-band with high resolution of 1.5 by 1.5 m (X) and 3 by 3 m (L). A modified four-component decomposition of scattering power is applied to retrieve the ...


State space behavior in time-varying biorthogonal filter banks

1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996

Using state space representations of biorthogonal filter banks, it is possible to come up with a compact theory for the transition between two time-invariant filter banks. The transition interval depends on the sizes of the common subspaces spanned by the controllability operators of the decomposition filters and by the observability operators of the reconstruction filters. When the respective operators span ...


Comparison between Huynen and Cameron monostatic parameters

Radio Science, 2012

For coherent targets, Huynen then Cameron proposed a set of characteristic parameters deduced from monostatic full polarimetric scattering matrices. Among these parameters, the two Authors have considered two parameters connected to the same physical properties: orientation and symmetry degree. As the Huynen definition seems to be more intuitive, we will try to compare these parameters by simulations with the aim ...


Ill-Conditioning of non-minimum phase systems

1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996

The typical inverse problem is the recovery of the input, x, given data, y and the knowledge of the system A. Such problems occur frequently in instrumental science. For the Linear Time Invariant (LTT) systems the governing equation can be expressed in matrix form, y=Ax. In this paper the problem of ill- conditioning of non-minimum phase systems and the relation ...


The Singular Value Decomposition

Introduction to Ground Penetrating Radar: Inverse Scattering and Data Processing, None

The kind of method of moments (MoM) used in this chapter is based on point matching in both spatial and frequency domains. The singular value decomposition (SVD) of a rectangular matrix is introduced in the chapter as an extension of the basic theory of the eigenvalues and eigenvectors of a square matrix. So, preliminarily, some reminders about the eigenvalues and ...


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Educational Resources on Matrix decomposition

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IEEE-USA E-Books

  • Water Change Detection in Natural Dam caused by Earthquake using Pi-SAR fully Polarimetric Data

    This paper presents water level change in natural disaster dam, unexpectedly caused by a great earthquake, by Pi-SAR observation. Pi-SAR is a fully polarimetric SAR system operative both in the X- and L-band with high resolution of 1.5 by 1.5 m (X) and 3 by 3 m (L). A modified four-component decomposition of scattering power is applied to retrieve the water surface change in former Yamakoshi village, Niigata, Japan. It is shown that water volume change can be detected effectively by comparing the time series data of volume scattering power image.

  • State space behavior in time-varying biorthogonal filter banks

    Using state space representations of biorthogonal filter banks, it is possible to come up with a compact theory for the transition between two time-invariant filter banks. The transition interval depends on the sizes of the common subspaces spanned by the controllability operators of the decomposition filters and by the observability operators of the reconstruction filters. When the respective operators span the same space, the transition can be made arbitrarily short. If it is zero, then the special case of instantaneous transition is reached.

  • Comparison between Huynen and Cameron monostatic parameters

    For coherent targets, Huynen then Cameron proposed a set of characteristic parameters deduced from monostatic full polarimetric scattering matrices. Among these parameters, the two Authors have considered two parameters connected to the same physical properties: orientation and symmetry degree. As the Huynen definition seems to be more intuitive, we will try to compare these parameters by simulations with the aim to test the real connection of the Huynen parameters to physical properties. This study will show significant differences. However, it is shown that the Huynen parameters correctly represent the orientation and the symmetry degree for nearly symmetric targets. In some cases (real eigenvalues in particular), the two orientation angles can be equal to an ambiguity of 45°, even for asymmetric targets.

  • Ill-Conditioning of non-minimum phase systems

    The typical inverse problem is the recovery of the input, x, given data, y and the knowledge of the system A. Such problems occur frequently in instrumental science. For the Linear Time Invariant (LTT) systems the governing equation can be expressed in matrix form, y=Ax. In this paper the problem of ill- conditioning of non-minimum phase systems and the relation of the phase structure of the system to the singular values of its system matrix is discussed.

  • The Singular Value Decomposition

    The kind of method of moments (MoM) used in this chapter is based on point matching in both spatial and frequency domains. The singular value decomposition (SVD) of a rectangular matrix is introduced in the chapter as an extension of the basic theory of the eigenvalues and eigenvectors of a square matrix. So, preliminarily, some reminders about the eigenvalues and eigenvectors are provided in relationship to matrix inversions. The problem of solving rectangular linear algebraic systems can be dealt with in a regularized way, which requires an extension of the eigenvalue theory; this extension is the SVD. The SVD provides not only a method for the solution of the problem but also a possible method for the analysis the problem. In particular, even if numerically, the SVD can help us to understand the characteristics of the scattering operator.

  • Muscle Coordination, Motor Synergies, and Primitives from Surface EMG

    To investigate neural control strategies, muscle activity must be measured during motor behavior. Recent advances in the investigation of the neural control of movement have led to a re-examination of the mechanisms of sensorimotor integration in the central nervous system (CNS) and in the spinal circuitry in particular. This chapter considers different approaches used to uncover the modular organization of the motor output in human behaviors such as responding to postural perturbations, reaching with the arm, and locomotion, as well as its plasticity and flexibility in movement disorders. It also investigates the strategies that the CNS employs to coordinate the activation of many muscles start from electromyographic (EMG) signals recorded simultaneously from many muscles. Different muscle synergies models are used to decompose the EMG envelopes using appropriate factorization algorithms. The chapter further considers the spatiotemporal organization of the activity patterns of leg and trunk muscles during locomotion.

  • Parallel-cascade adaptive volterra filters

    Adaptive truncated Volterra filters using parallel-cascade structures are discussed in this paper. Parallel-cascade realizations implement higher-order Volterra systems as a parallel and multiplicative combination of lower-order Volterra systems. A normalized LMS adaptive filter for parallel-cascade structures is developed and its performance is evaluated through simulation experiments. The experimental results indicate that the normalized LMS parallel-cascade Volterra filter has superior convergence over several competing structures.

  • An algebraic ICA algorithm for 3 sources and 2 sensors

    In this paper we develop an algebraic algorithm for Independent Component Analysis with 3 complex-valued sources and 2 sensors. First we consider a generalization of an old theorem by Sylvester, which allows us to relate the problem with the approximation of a 4th-order tensor with Hermitean symmetry by a tensor of rank-1. We present an Alternating Least Squares algorithm for the computation of the result.

  • Detection of cisoids in noise

    In this paper, a rank test for detecting the number of cisoids in noise is presented. The method is based on Gaussian Lower triangular-Diagonal-Upper triangular (LDU) decomposition of a Hankel data matrix, and the method is especially useful for short data records.

  • Graphs

    This chapter contains sections titled:Set‐Theoretic Definition of a GraphMatrix Algebra Definition of a GraphThe Bridges of Königsberg GraphSpectral Properties of GraphsTypes of GraphsTopological StructureGraphs in SoftwareExercises



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