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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 46th Photovoltaic Specialists Conference (PVSC)

Photovoltaic materials, devices, systems and related science and technology

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 69th Electronic Components and Technology Conference (ECTC)

premier components, packaging and technology conference

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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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 ...

Broadcasting, IEEE Transactions on

Broadcast technology, including devices, equipment, techniques, and systems related to broadcast technology, including the production, distribution, transmission, and propagation aspects.

Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...

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A high performance multifrontal code for linear solution of structures using multi-core microprocessors

[{u'author_order': 1, u'affiliation': u'Computer Aided Structural Engineering Center, School of Civil and Environmental Engineering Georgia Institute of Technology, Atlanta, Georgia 30332-0355, USA', u'full_name': u'Efe Guney'}, {u'author_order': 2, u'affiliation': u'Computer Aided Structural Engineering Center, School of Civil and Environmental Engineering Georgia Institute of Technology, Atlanta, Georgia 30332-0355, USA', u'full_name': u'Kenneth Will'}] Tsinghua Science and Technology, 2008

A multifrontal code is introduced for the efficient solution of the linear system of equations arising from the analysis of structures. The factorization phase is reduced into a series of interleaved element assembly and dense matrix operations for which the BLAS3 kernels are used. A similar approach is generalized for the forward and back substitution phases for the efficient solution ...

Resilient Filtering for Nonlinear Complex Networks with Multiplicative Noise

[{u'author_order': 1, u'affiliation': u'Department of Systems and Control, Beihang University, Beijing China 100191 (e-mail:', u'full_name': u'Wenling Li'}, {u'author_order': 2, u'affiliation': u'The Seventh Research Division, Beihang University, Beijing, None China 100191 (e-mail:', u'full_name': u'Yingmin Jia'}, {u'author_order': 3, u'affiliation': u'BeijingKeylaboratoryofIntelligentTelecommunicationsSoftwareandMultimedia,School ofComputer and Technology, Beijing University of Posts and Telecommunications, Beijing China 100876 (e-mail:', u'full_name': u'Junping Du'}] IEEE Transactions on Automatic Control, None

This note studies the resilient filtering problem for a class of discrete-time nonlinear complex networks. A novel resilient model is proposed by representing the variations of the filter gain matrix as a multiplicative noise term. By applying the variance-constrained approach to the coupled extended Kalman filter (EKF), an upper bound is derived for the estimation error covariance and such an ...

Blind source separation by simultaneous third-order tensor diagonalization

[{u'author_order': 1, u'affiliation': u'K.U. Leuven - E.E. Dept.- ESAT - SISTA, Kard. Mercierlaan 94, B-3001 Leuven (Heverlee), Belgium', u'full_name': u'Lieven De Lathauwer'}, {u'author_order': 2, u'affiliation': u'K.U. Leuven - E.E. Dept.- ESAT - SISTA, Kard. Mercierlaan 94, B-3001 Leuven (Heverlee), Belgium', u'full_name': u'Bart De Moor'}, {u'author_order': 3, u'affiliation': u'K.U. Leuven - E.E. Dept.- ESAT - SISTA, Kard. Mercierlaan 94, B-3001 Leuven (Heverlee), Belgium', u'full_name': u'Joos Vandewalle'}] 1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996

We develop a technique for Blind Source Separation based on simultaneous diagonalization of (linear combinations of) third-order tensor "slices" of the fourth-order cumulant. It will be shown that, in a Jacobi-type iteration scheme, the computation of an elementary rotation can be reformulated in terms of a simultaneous matrix diagonalization.

Bad data processing when using the coupled measurement model and Takahashi's sparse inverse method

[{u'author_order': 1, u'affiliation': u'Northeastern University, Boston, MA, USA', u'authorUrl': u'', u'full_name': u'Bulent Bilir', u'id': 38228763800}, {u'author_order': 2, u'affiliation': u'Northeastern University, Boston, MA, USA', u'authorUrl': u'', u'full_name': u'Ali Abur', u'id': 37272386300}] IEEE PES Innovative Smart Grid Technologies, Europe, 2014

The paper revisits the computation of residual covariance matrix diagonal entries, which are used for calculating the normalized residuals that are in turn used for bad data identification. It is shown that these entries may be inadvertently computed incorrectly if one uses the commonly accepted implementation of the sparse inverse method due to the numerical cancellations that occur in the ...

A method for computing the information matrix of stationary Gaussian processes

[{u'author_order': 1, u'affiliation': u'Institute\xbb de Telecomunica\xe7\xf5es and D.E.E.C., Institute\xb7 Superior T\xe9cnico', u'full_name': u'Jos\xe9 M. B. Dias'}, {u'author_order': 2, u'affiliation': u'Institute\xbb de Telecomunica\xe7\xf5es and D.E.E.C., Institute\xb7 Superior T\xe9cnico', u'full_name': u'Jos\xe9 M. N. Leit\xe3o'}] 1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996

This paper proposes a new method for the efficient computation of the Fisher information matrix of zero-mean complex stationary Gaussian processes. Its complexity (measured by the number of floating point operations) is smaller than the fastest previously available procedure. The key idea exploited is that the Fisher information matrix depends only on the sum of the diagonals of the inverse ...

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  • Bibliography


  • Symmetrical Components Using Matrix Methods

    The method of symmetrical components has been an important tool for the study of unbalanced three‐phase systems, unsymmetrical short‐circuit currents, models of rotating machines and transmission lines, etc. This chapter studies three‐phase balanced systems, by considering these as single‐phase system. The simplicity of representing a three‐phase as a single phase system is lost for unbalanced systems. There have been two approaches for the study of symmetrical components: A physical description, without going into much mathematical matrix algebra equations. The mathematical approach is adopted in this chapter. Symmetrical component method is a transform. There are three steps that are applicable in any transform for the solution of a problem. The matrix theory can be applied to understand some fundamental aspects of symmetrical components. The chapter also discusses the similarity transformation, symmetrical component transformation, and Clarke component transformation. The significance of symmetrical components is illustrated with an example.

  • References

    Advocate General Jacobs. 1997. Opinion of Mr Advocate General Jacobs delivered on 24 October 1996. Criminal proceedings against Euro Tombesi and Adino Tombesi (C-304/94), Roberto Santella (C-330/94), Giovanni Muzi and others (C-342/94) and Anselmo Savini (C-224/95). European Court.

  • Acknowledgments

    Thank you to all the people who helped to bring this book to reality. I am grateful to my colleagues at New York University's (NYU's) Arthur L. Carter Journalism Institute, my colleagues at the Moore-Sloan Data Science Environment at NYU's Center for Data Science, the faculty and staff at the Tow Center for Digital Journalism at Columbia Journalism School, and my former colleagues at Temple University and the University of Pennsylvania. For reading, consulting on, or otherwise midwifing this manuscript, I am eternally indebted to Elena Lahr-Vivaz, Rosalie Siegel, Jordan Ellenberg, Cathy O'Neil, Miriam Peskowitz, Samira Baird, Lori Tharps, Kira Baker-Doyle, Jane Dmochowski, Josephine Wolff, Solon Barocas, Hanna Wallach, Katy Boss, Janet Alteveer, Leslie Hunt, Elizabeth Hunt, Kay Kinsey, Karen Masse, Stevie Santangelo, Jay Kirk, Claire Wardle, Gita Manaktala, Melinda Rankin, Kathleen Caruso, Kyle Gipson, my writers' group, and the talented team at the MIT Press. It has been an honor to participate in the community of data journalists and news nerds. I would like to thank my colleagues at the annual Computation + Journalism Symposium, everyone on the NICAR-L mailing list, and the team at ProPublica, especially Scott Klein, Derek Willis, and Celeste LeCompte. Special thanks go to Jacob Fenton, Allie Kanik, Andrew Harvard, Chase Davis, Michael Johnston, Jonathan Stray, BC Broussard, Varun D N, and everyone who assisted or advised on Bailiwick. To my family, friends, and extended family, thank you for your help and support while this book gestated. As ever, I am grateful to my husband and son for being extraordinary

  • 9 Direction Finding Techniques for HF Applications

    The bounds described in the previous chapter provide measures of the direction-finding performance that can be expected from an array with a specified geometry and size, at the frequency associated with a particular signal. This chapter covers many of the important methods for direction finding; that is, estimating line of bearing (LOB). Recall from chapter 7 that the arrays of concern are small relative to the propagation distances involved. This observation motivates the view that a single plane wave reaching an array excites all elements with the same amplitude, and if the array elements are not identical, the responses to this excitation will differ. If only one signal is present, all array elements are identical, and element-to-element mutual coupling is modest, phase provides the only clue to signal direction; if the array elements differ in their gain response as a function of arrival angle, then relative gain may also provide clues to the signal direction. One should also note that most receivers perturb the element signals with additive noise, usually with zero mean and Gaussian statistics. This observation informs the analysis presented here, providing a tractable model for estimating an LOB. Finally, it is worth noting that this topic has been the focus of many theoretical and experimental research efforts, leading to the rich discourse of the current chapter.

  • 11 Copy: Steering Vector Methods

    In addition to the direction-finding products associated with an HF sensor, there is strong interest in receiving the signal transmitted over the airwaves and demodulating the intelligence it bears. This topic is covered under the rubric signal estimation; the term copy is often used in reference to this process. The challenges with achieving these objectives at HF include the weak and variable signal strength, the varying character of the ionosphere, and the myriad of interfering signals present in the environment. As outlined early in this text, array antennas have the potential for creating an antenna pattern that provides gain on the signal of interest while placing nulls in those directions associated with unwanted, interfering signals. The design of such patterns is the primary topic of this chapter. Many algorithms on which to base this design are available; the most common and useful at HF are included here in separate sections. The focus is on linear-processing approaches, which determine a weight vector that is applied to the array elements in a vector- summing process; the result is a single-output signal intending to represent the desired signal estimate. Because this structure produces a response that varies spatially, this result is in effect a beam, and the entire process is identified as beamforming.

  • Appendix C: Matrix Multiplication

  • 3 The Urban Metainterface: Territorial Interfaces

    In the app Las calles habladas, or Spoken Streets, by Clara Boj and Diego Diaz (figure 3.1), the user is offered a random map and walking path around their location. The app seemingly functions like an audio guide, where the user listens to news or facts about places along the way. Unlike most audio guides, however, the narrative appears fragmented. The audio track does not contain the voices of the people living there but rather an automated text-to-speech function. The route planning includes not only a suggested path but also Google searches related to the location. Each user movement generates a search and is answered by the reading of debris from the World Wide Web's enormous body of text: phrases from websites, Twitter feeds, or Facebook events appear together with symbols, numbers, and URLs. Sometimes there is a direct and visible linkage to the user's location, as when the user is near a shop that also is listed on a website. At other times the relation is more abstract, and invites the user to speculate on potential narratives, as when a place is linked to a Facebook event or Twitter feed. On rare occasions, there is only a semantic linkage, as when the name of a street or shop is common, and perhaps located in hundreds of cities. The relation between sense and nonsense, between potential narrative and raving incoherent jabber, seems to be central to the experience of using the app. The confrontation with Spoken Streets (i.e., how the web speaks the streets) appears absurd to a human being. It functions almost like a joke or a parody of the ways that sense making functions in other, more conventional applications that, for instance, augment reality by linking locations to the real-time generation of information.

  • Multiple‐Beam Antennas

    This chapter contains sections titled:IntroductionBeamformersLow Sidelobes and Beam InterpolationBeam OrthogonalityAcknowledgmentsReferences

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