Singular value decomposition

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In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix, with many useful applications in signal processing and statistics. (Wikipedia.org)






Conferences related to Singular value decomposition

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2019 IEEE 28th International Symposium on Industrial Electronics (ISIE)

The conference will provide a forum for discussions and presentations of advancements inknowledge, new methods and technologies relevant to industrial electronics, along with their applications and future developments.


2019 IEEE 58th Conference on Decision and Control (CDC)

The CDC is recognized as the premier scientific and engineering 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, systems and control, and related areas.The 58th CDC will feature contributed and invited papers, as well as workshops and may include tutorial sessions.The IEEE CDC is hosted by the IEEE Control Systems Society (CSS) in cooperation with the Society for Industrial and Applied Mathematics (SIAM), the Institute for Operations Research and the Management Sciences (INFORMS), the Japanese Society for Instrument and Control Engineers (SICE), and the European Union Control Association (EUCA).


2019 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 2019, the 26th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

International Geosicence and Remote Sensing Symposium (IGARSS) is the annual conference sponsored by the IEEE Geoscience and Remote Sensing Society (IEEE GRSS), which is also the flagship event of the society. The topics of IGARSS cover a wide variety of the research on the theory, techniques, and applications of remote sensing in geoscience, which includes: the fundamentals of the interactions electromagnetic waves with environment and target to be observed; the techniques and implementation of remote sensing for imaging and sounding; the analysis, processing and information technology of remote sensing data; the applications of remote sensing in different aspects of earth science; the missions and projects of earth observation satellites and airborne and ground based campaigns. The theme of IGARSS 2019 is “Enviroment and Disasters”, and some emphases will be given on related special topics.


2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting

The conference is intended to provide an international forum for the exchange of information on state-of-the-art research in antennas, propagation, electromagnetics, and radio science.


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Periodicals related to Singular value 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.


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


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.


Circuits and Systems II: Express Briefs, IEEE Transactions on

Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.


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Most published Xplore authors for Singular value decomposition

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Xplore Articles related to Singular value decomposition

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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 ...


A closed-form conversion from spherical-wave- to complex-point-source-expansion

Radio Science, 2011

A simple closed-form expression for a complex point source (CPS) beam expansion of an arbitrary electromagnetic field is derived. The expansion process consists of two steps: first, a particular form of the equivalence principle is applied to a sphere enclosing the real sources, and a continuous equivalent electric current distribution is obtained in terms of spherical waves (SW); then, the ...


Virtual Spatial Modulation

IEEE Access, 2016

In this paper, we propose a virtual spatial modulation (VSM) scheme that performs index modulation on the virtual parallel channels resulting from the singular value decomposition of the multi-input-multi-output channels. The VSM scheme conveys information through both the indices of the virtual parallel channels and the M -ary modulated symbols. We derive a closed-form upper bound on the average bit ...


An algorithm for continuous-time state space identification

Proceedings of 1995 34th IEEE Conference on Decision and Control, 1995

We have developed a system identification algorithm to fit continuous-time state-space models to data. The methodology includes a continuous-time operator translation, permitting an algebraic reformulation and the use of subspace and realization algorithms. The new approach has proved effective in identification and modeling of ultrasonic echo applications.


Blind Image Watermarking Technique for Digital Phone Camera

SENSORS, 2006 IEEE, 2006

In today's modern world, digital phone cameras are common daily gadgets that most people have and use. In the event of a traffic accident, a fire accident, or a criminal act, anyone will be able to capture these important moments and use authentic photographs for evidence purposes. Digital watermarking is able to ensure that the digital photographs taken are authentic ...


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Educational Resources on Singular value decomposition

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IEEE-USA E-Books

  • 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.

  • A closed-form conversion from spherical-wave- to complex-point-source-expansion

    A simple closed-form expression for a complex point source (CPS) beam expansion of an arbitrary electromagnetic field is derived. The expansion process consists of two steps: first, a particular form of the equivalence principle is applied to a sphere enclosing the real sources, and a continuous equivalent electric current distribution is obtained in terms of spherical waves (SW); then, the continuous current is extended to complex space and its SW components are properly filtered and sampled to generate the discrete set of CPS. The final result is a compact finite series representation suitable for arbitrary radiated fields, and particularly efficient when the source is highly directional and/or the observation domain is limited to a given angular sector. The robustness of the process is demonstrated by showing its connection with the singular value decomposition of the radiation operator from a complex sphere.

  • Virtual Spatial Modulation

    In this paper, we propose a virtual spatial modulation (VSM) scheme that performs index modulation on the virtual parallel channels resulting from the singular value decomposition of the multi-input-multi-output channels. The VSM scheme conveys information through both the indices of the virtual parallel channels and the M -ary modulated symbols. We derive a closed-form upper bound on the average bit error probability (ABEP), which considers the impact of imperfect channel estimation. Moreover, the asymptotic ABEP is also studied, which characterizes the error floor under imperfect channel estimation and the resulting diversity order as well as the coding gain under perfect channel estimation. Computer simulations verify the analysis and show that the VSM scheme can outperform the existing pre-coding aided spatial modulation schemes under the same spectral efficiency.

  • An algorithm for continuous-time state space identification

    We have developed a system identification algorithm to fit continuous-time state-space models to data. The methodology includes a continuous-time operator translation, permitting an algebraic reformulation and the use of subspace and realization algorithms. The new approach has proved effective in identification and modeling of ultrasonic echo applications.

  • Blind Image Watermarking Technique for Digital Phone Camera

    In today's modern world, digital phone cameras are common daily gadgets that most people have and use. In the event of a traffic accident, a fire accident, or a criminal act, anyone will be able to capture these important moments and use authentic photographs for evidence purposes. Digital watermarking is able to ensure that the digital photographs taken are authentic and indeed taken from a particular phone camera. This paper presents a blind image watermarking technique for digital phone camera. This method is based on singular value decomposition (SVD) and wavelet decomposition. Experimental results show that the proposed technique performs well in security and robustness against JPEG compression.

  • SVD Based Source Cell-Phone Identification

    We propose a method for the identification of the source cellphone from a given image. The sensors, digital image formation pipeline and color filter interpolation algorithms used in different brands of cell-phones make the image unique to the camera. Our method is based on the analysis of strict and relative linear dependance in image rows and columns by the use of singular value decomposition (SVD). We define SVD based features that summarize macro and micro statistical properties of the images. These features are then used with support vector machines for the identification of the image sources. Experimental results show that we can identify 10 different sources with 93% accuracy.

  • Joint Bayesian Estimation of Close Subspaces from Noisy Measurements

    In this letter, we consider two sets of observations defined as subspace signals embedded in noise and we wish to analyze the distance between these two subspaces. The latter entails evaluating the angles between the subspaces, an issue reminiscent of the well-known Procrustes problem. A Bayesian approach is investigated where the subspaces of interest are considered as random with a joint prior distribution (namely a Bingham distribution), which allows the closeness of the two subspaces to be parameterized. Within this framework, the minimum mean-square distance estimator of both subspaces is formulated and implemented via a Gibbs sampler. A simpler scheme based on alternative maximum a posteriori estimation is also presented. The new schemes are shown to provide more accurate estimates of the angles between the subspaces, compared to singular value decomposition based independent estimation of the two subspaces.

  • The Rank Factorization for Central Extended Matrix

    We study the rank factorization and full rank factorization of a special class of centrosymmetric matrices: the central extended matrix which is not only a centrosymmetric matrix but also a row extended matrix. The formula for the rank factorization is obtained, and the relationship of the central extended matrix with mother matrix is also obtained.

  • Face recognition uiing trichotomic combination Of SVD, DF-LDA and LPP

    One of the challenges the face recognition application is facing today is that of the high dimensionality of multivariate data. In this context, this paper proposes to compare the performance of a triumvirate combination of linear dimensionality reduction techniques namely Singular Value Decomposition (SVD) which maximizes the variance of the training vectors, Direct Fractional Linear Discriminant Analysis (DFLDA) that maximizes the ¿between-class¿ scatter while minimizing the ¿within-class¿ scatter and Locality Preserving Projection (LPP) which preserves the local features those unique from its nearest neighbors. The amalgamation containing different ratios is chosen from the features extracted by the three independent techniques mentioned above. Original Face space is projected onto the manifold of chosen basis. The weights obtained from these projections for the probe set are compared with that of the query image using the mean distance classifier. The proposed method has been tested on YALE dataset and the combination in the ratio 3:2:5 showed significant improvement in the efficiency of recognition, with a calculated accuracy of 92.7% on a test set of 165 images.

  • Computational issues arising in models of the inverse problem of electrocardiography

    The authors compare a recently proposed generalized eigensystem approach to more widely used truncated singular value decomposition and zero-order Tikhonov regularization for solving multidimensional elliptic inverse problems. As a test case, the authors use a finite element representation of a homogeneous eccentric spheres model of the inverse problem of electrocardiography. Special attention is paid to numerical issues of accuracy, convergence, and robustness.<<ETX>>



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