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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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 TopInterference alignment precoder design on Grassmann manifold for cellular system
[{u'author_order': 1, u'affiliation': u'Department of Electrical Engineering and Information Science, University of Science and Technology of China, HeFei, Anhui, 230027, P.R. China', u'full_name': u'Chen Zhang'}, {u'author_order': 2, u'affiliation': u'Department of Electrical Engineering and Information Science, University of Science and Technology of China, HeFei, Anhui, 230027, P.R. China', u'full_name': u'Xu Li'}, {u'author_order': 3, u'affiliation': u'Department of Electrical Engineering and Information Science, University of Science and Technology of China, HeFei, Anhui, 230027, P.R. China', u'full_name': u'Huarui Yin'}, {u'author_order': 4, u'affiliation': u'Department of Electrical Engineering and Information Science, University of Science and Technology of China, HeFei, Anhui, 230027, P.R. China', u'full_name': u'Guo Wei'}] 2012 1st IEEE International Conference on Communications in China (ICCC), None
In this paper, we focus on the interference alignment precoder design for cellular system. Varying from the traditional constrained optimization method, we reformulate the optimization problem on the complex Grassmann manifold and derive a novel steepest descent algorithm to achieve perfect interference alignment. Moreover our proposed algorithm only requires the participation of transmitter. Thus it will alleviate the overhead induced ...
Signal waveform estimation in the presence of uncertainties about the steering vector
[{u'author_order': 1, u'affiliation': u'Dept. of Avionics & Syst., ENSICA, Toulouse, France', u'full_name': u'O. Besson'}, {u'author_order': 2, u'full_name': u'A. Monakov'}, {u'author_order': 3, u'full_name': u'C. Chalus'}] The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, None
The problem of signal waveform estimation using an array of sensors where there exist uncertainties about the steering vector of interest is presented in this paper. This problem occurs in many situations, including arrays undergoing deformations, uncalibrated arrays, scattering around the source, etc. In this paper, we assume that some statistical knowledge about the variations of the steering vector is ...
Efficient square-root algorithms for PDA, IMM and IMMPDA filters
[{u'author_order': 1, u'affiliation': u'Dept. of Electr. & Syst. Eng., Connecticut Univ., Storrs, CT, USA', u'full_name': u'V. Raghavan'}, {u'author_order': 2, u'affiliation': u'Dept. of Electr. & Syst. Eng., Connecticut Univ., Storrs, CT, USA', u'full_name': u'K. R. Pattipati'}, {u'author_order': 3, u'affiliation': u'Dept. of Electr. & Syst. Eng., Connecticut Univ., Storrs, CT, USA', u'full_name': u'Y. Bar-Shalom'}] [1992] Proceedings of the 31st IEEE Conference on Decision and Control, None
Square-root formulations have not been developed for state-of-the-art tracking algorithms. The only exception is the square-root implementation of the PDAF by R. Kenefic (1990). It is shown that there is a substantially better implementation of the square-root probabilistic data association filter (PDAF) than Kenefic's algorithm in terms of both numerical robustness and computational efficiency. On the average, the proposed algorithm ...
Invariant Tests For Spatial Stationarity Using Covariance Structure
[{u'author_order': 1, u'affiliation': u'School of Electrical Engineering, Cornell University', u'full_name': u'S. Bose'}, {u'author_order': 2, u'full_name': u'A. O. Steinhardt'}] IEEE Seventh SP Workshop on Statistical Signal and Array Processing, None
First Page of the Article 
Non-parametric Adaptive Method for Enhancing Image Resolution
[{u'author_order': 1, u'full_name': u'Zhigang Su'}, {u'author_order': 2, u'full_name': u'Kexiang Zhang'}, {u'author_order': 3, u'full_name': u'Yingning Peng'}, {u'author_order': 4, u'full_name': u'Renbiao Wu'}] 2008 Congress on Image and Signal Processing, None
An image resolution enhancing method, which is applied to the two-dimensional (2-D) frequency domain data of the original image, is proposed. The proposed method employs the narrowband filter to filter the partitioned snapshots and utilizes some weight to weight the filter outputs to obtain the amplitude estimate at special image pixel. Due to allowing the sample covariance matrix singular, the ...
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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.
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 representati n 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 o 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>
This chapter contains sections titled: Formalism of Quantum Information Hypothesis Detection for Collaborative Sensing Sample Covariance Matrix Random Matrices with Independent Rows The Multivariate Normal Distribution Sample Covariance Matrix Estimation and Matrix Compressed Sensing Likelihood Ratio Test
This chapter contains sections titled: Vision: "Big Data" Cognitive Radio: System Concepts Spectrum Sensing Interface and Data Structures Mathematical Machinery Sample Covariance Matrix Large Sample Covariance Matrices of Spiked Population Models Random Matrices and Noncommutative Random Variables Principal Component Analysis Generalized Likelihood Ratio Test (GLRT) Bregman Divergence for Matrix Nearness
This chapter contains sections titled: General classification of MIMO channel models Physical models Analytical models Geometrical phenomenological models On the role of trigonometric polynomials in analysis and simulation of MIMO channels Canonical expansions of bivariate distributions and the structure MIMO channel covariance matrix Bivariate von Mises distribution with correlated transmit and receive sides Bivariate uniform distributions Analytical expression for the diversity measure of an antenna array Effect of AoA/AoD dependency on the SDoF Space‐time covariance function Examples: synthetic data and uniform linear array Approximation of a matrix by a Toeplitz matrix Asymptotic expansions of diversity measure Distributed scattering model
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.
Approaches to High-Dimensional Covariance and Precision Matrix Estimations
This chapter introduces several recent developments for estimating large covariance and precision matrices without assuming the covariance matrix to be sparse. It explains two methods for covariance estimation: namely covariance estimation via factor analysis, and precision Matrix Estimation and Graphical Models. The low rank plus sparse representation holds on the population covariance matrix. The chapter presents several applications of these methods, including graph estimation for gene expression data, and several financial applications. It then shows how estimating covariance matrices of high- dimensional asset excess returns play a central role in applications of portfolio allocations and in risk management. The chapter explains the factor pricing model, which is one of the most fundamental results in finance. It elucidates estimating risks of large portfolios and large panel test of factor pricing models. The chapter illustrates the recent developments of efficient estimations in panel data models.
Chapter 2 introduces the vector radiative transfer (VRT) theory of random media. It provides the scattering, absorption and extinction coefficients and the phase matrix of non-spherical scatterers in natural media. The first-order Mueller matrix solution of VRT for vegetation canopy model is derived. Polarization indexes, eigen-analysis and entropy are presented. Statistics of multi-look SAR images and covariance matrix are also investigated.
Estimation with Unknown Noise Model - Standard Solutions
This chapter contains sections titled: * Introduction * Discussion of the Disturbing Noise Assumptions * Properties of the ML Estimator Using a Sample Covariance Matrix * Properties of the GTLS Estimator Using a Sample Covariance Matrix * Properties of the BTLS Estimator Using a Sample Covariance Matrix * Properties of the SUB Estimator Using a Sample Covariance Matrix * Identification in the Presence of Nonlinear Distortions * Illustration and Overview of the Properties * Identification of Parametric Noise Models * Identification in Feedback * Appendixes
Some Probability and Stochastic Convergence Fundamentals
This chapter contains sections titled: * Notations and Definitions * The Covariance Matrix of a Function of a Random Variable * Sample Variables * Mixing Random Variables * Preliminary Example * Definitions of Stochastic Limits * Interrelations between Stochastic Limits * Properties of Stochastic Limits * Laws of Large Numbers * Central Limit Theorems * Properties of Estimators * Cram¿¿¿¿¿¿r-Rao Lower Bound * How to Prove Asymptotic Properties of Estimators? * Pitfalls * Preliminary Example¿¿¿¿¿¿-¿¿¿¿¿¿Continued * Properties of the Noise after a Discrete Fourier Transform * Exercises * Appendixes
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