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. (

Conferences related to Covariance matrix

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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 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.

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world.

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

The 2019 IEEE International Conference on Communications (ICC) will be held from 20-24 May 2019 at Shanghai International Convention Center, China,conveniently located in the East Coast of China, the region home to many of the world’s largest ICT industries and research labs. Themed“Smart Communications”, this flagship conference of IEEE Communications Society will feature a comprehensive Technical Program including16 Symposia and a number of Tutorials and Workshops. IEEE ICC 2019 will also include an attractive Industry Forum & Exhibition Program featuringkeynote speakers, business and industry pan

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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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Some comments to application of Cognition principle in Wireless Networks

[{u'author_order': 1, u'affiliation': u'Electric Engineering Department (Communications), CINVESTAV-IPN, Av. IPN No. 2508, C.P. 07360, México D.F., México', u'full_name': u'Valeri Kontorovich'}] European Wireless 2012; 18th European Wireless Conference 2012, 2012

Most of the publications related to Cognitive Radio (CR) Wireless Networks are devoted mainly to Spectrum Sensing problems and Resource Allocation for Cognitive Users (SU). So far, there is a high grade of understanding on how to provide solutions for the above mentioned problems as well as for the problems related to them.

Signal Enhancement as Minimization of Relevant Information Loss

[{u'author_order': 1, u'full_name': u'Bernhard C. Geiger'}, {u'author_order': 2, u'full_name': u'Gernot Kubin'}] SCC 2013; 9th International ITG Conference on Systems, Communication and Coding, 2013

We introduce the notion of relevant information loss for the purpose of casting the signal enhancement problem in information-theoretic terms. We show that many algorithms from machine learning can be reformulated using relevant information loss, which allows their application to the aforementioned problem. As a particular example we analyze principle component analysis for dimensionality reduction, discuss its optimality, and show ...

Reduced-order adaptive Kalman filtering for dual-frequency navigation with carrier phase

[{u'author_order': 1, u'affiliation': u'School of Electronic Engineering and Computer Science, Peking University, Beijing, PRC', u'authorUrl': u'', u'full_name': u'Chenxi Lu', u'id': 37599800900}, {u'author_order': 2, u'affiliation': u'School of Electronic Engineering and Computer Science, Peking University, Beijing, PRC', u'authorUrl': u'', u'full_name': u'Yunhua Tan', u'id': 37533831100}, {u'author_order': 3, u'affiliation': u'School of Electronic Engineering and Computer Science, Peking University, Beijing, PRC', u'authorUrl': u'', u'full_name': u'Lezhu Zhou', u'id': 37346096400}] 2011 11th International Symposium on Communications & Information Technologies (ISCIT), 2011

A new adaptive Kalman filtering algorithm for dual-frequency navigation with carrier phase is presented. By reducing the filtering order after full-order initialization, this algorithm saves the extra computational cost brought by carrier phase observations. The improved adaptation of state covariance matrix in reduced-order processing also improves filtering precision and robustness. Finally applications on both static data and kinetic simulations demonstrate ...

Sensitivity of system performance to individual error statistics

[{u'author_order': 1, u'affiliation': u'General Electric Company, Pittsfield, Mass.', u'authorUrl': u'', u'full_name': u'Richard V. Spencer', u'id': 37839319300}] 1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes, 1980

The method of equivalent observation 1 is extended to vector valued constant parameters and to uncorrelated measurement noise sequences. It is then used to determine easily calculated partial derivatives of final covariances with respect to parameter variances. Error matrices and bounds are developed in order to determine the range over which linearity may be assumed.

Shape calibration for a nominally linear equispaced array

[{u'author_order': 1, u'affiliation': u'IRISA, Rennes I Univ., France', u'authorUrl': u'', u'full_name': u'J.J. Fuchs', u'id': 37277690300}] 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1993

The author considers a thin flexible line array of equispaced hydrophones that is towed through the sea, and develops a procedure that allows testing of the straightness of the array. The motion of the towing ship, the currents of the ocean and other forces induce deformations on the array and affect the performance of spatial processing of the data developed ...

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  • Analysis of Contour Motions

    A reliable motion estimation algorithm must function under a wide range of conditions. One regime, which we consider here, is the case of moving objects with contours but no visible texture. Tracking distinctive features such as corners can disambiguate the motion of contours, but spurious features such as T-junctions can be badly misleading. It is difficult to determine the reliability of motion from local measurements, since a full rank covariance matrix can result from both real and spurious features. We propose a novel approach that avoids these points altogether, and derives global motion estimates by utilizing information from three levels of contour analysis: edgelets, boundary fragments and contours. Boundary fragment are chains of orientated edgelets, for which we derive motion estimates from local evidence. The uncertainties of the local estimates are disambiguated after the boundary fragments are properly grouped into contours. The grouping is done by constructing a graphical model and marginalizing it using importance sampling. We propose two equivalent representations in this graphical model, reversible switch variables attached to the ends of fragments and fragment chains, to capture both local and global statistics of boundaries. Our system is successfully applied to both synthetic and real video sequences containing high-contrast boundaries and textureless regions. The system produces good motion estimates along with properly grouped and completed contours.

  • Differential Entropic Clustering of Multivariate Gaussians

    Gaussian data is pervasive and many learning algorithms (e.g., k-means) model their inputs as a single sample drawn from a multivariate Gaussian. However, in many real-life settings, each input object is best described by multiple samples drawn from a multivariate Gaussian. Such data can arise, for example, in a movie review database where each movie is rated by several users, or in time-series domains such as sensor networks. Here, each input can be naturally described by both a mean vector and covariance matrix which parameterize the Gaussian distribution. In this paper, we consider the problem of clustering such input objects, each represented as a multivariate Gaussian. We formulate the problem using an information theoretic approach and draw several interesting theoretical connections to Bregman divergences and also Bregman matrix divergences. We evaluate our method across several domains, including synthetic data, sensor network data, and a statistical debugging application.

  • A Serial Approach to Handling High-Dimensional Measurements in the Sigma-Point Kalman Filter

    Pose estimation is a critical skill in mobile robotics and is often accomplished using onboard sensors and a Kalman filter estimation technique. For systems to run online, computational efficiency of the filter design is crucial, especially when faced with limited computing resources. In this paper, we present a novel approach to serially process high-dimensional measurements in the Sigma-Point Kalman Filter (SPKF), in order to achieve a low computational cost that is linear is the measurement dimension. Although the concept of serially processing measurements has been around for quite some time in the context of the Extended Kalman Filter (EKF), few have considered this approach with the SPKF. At first glance, it may be tempting to apply the SPKF update step serially. However, we prove that without re-drawing sigma points, this ‘naive’ approach cannot guarantee the positive-definiteness of the state covariance matrix (not the case for the EKF). We then introduce a novel method for the Sigma-Point Kalman Filter to process high-dimensional, uncorrelated measurements serially that is algebraically equivalent to processing the measurements in parallel, but still achieves a computational cost linear in the measurement dimension.

  • 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.

  • 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.

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