Covariance matrix
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Back to Top2018 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.
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
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
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Periodicals related to Covariance matrix
Back to TopAudio, 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.
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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Back to TopSome 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'full_name': u'Chenxi Lu'}, {u'author_order': 2, u'affiliation': u'School of Electronic Engineering and Computer Science, Peking University, Beijing, PRC', u'full_name': u'Yunhua Tan'}, {u'author_order': 3, u'affiliation': u'School of Electronic Engineering and Computer Science, Peking University, Beijing, PRC', u'full_name': u'Lezhu Zhou'}] 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'full_name': u'Richard V. Spencer'}] 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'full_name': u'J.J. Fuchs'}] 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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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 representation 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 of 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>
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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