Conferences related to Gaussian distribution

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2018 14th IEEE International Conference on Signal Processing (ICSP)

ICSP2018 includes sessions on all aspects of theory, design and applications of signal processing. Prospective authors are invited to propose papers in any of the following areas, but not limited to: A. Digital Signal Processing (DSP)B. Spectrum Estimation & ModelingC. TF Spectrum Analysis & WaveletD. Higher Order Spectral AnalysisE. Adaptive Filtering &SPF. Array Signal ProcessingG. Hardware Implementation for Signal ProcessingH Speech and Audio CodingI. Speech Synthesis & RecognitionJ. Image Processing & UnderstandingK. PDE for Image ProcessingL.Video compression &StreamingM. Computer Vision & VRN. Multimedia & Human-computer InteractionO. Statistic Learning & Pattern RecognitionP. AI & Neural NetworksQ. Communication Signal processingR. SP for Internet and Wireless CommunicationsS. Biometrics & AuthentificationT. SP for Bio-medical & Cognitive ScienceU


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

  • 2016 23rd International Conference on Pattern Recognition (ICPR)

    ICPR'2016 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.

  • 2014 22nd International Conference on Pattern Recognition (ICPR)

    ICPR 2014 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.

  • 2012 21st International Conference on Pattern Recognition (ICPR)

    ICPR is the largest international conference which covers pattern recognition, computer vision, signal processing, and machine learning and their applications. This has been organized every two years by main sponsorship of IAPR, and has recently been with the technical sponsorship of IEEE-CS. The related research fields are also covered by many societies of IEEE including IEEE-CS, therefore the technical sponsorship of IEEE-CS will provide huge benefit to a lot of members of IEEE. Archiving into IEEE Xplore will also provide significant benefit to the all members of IEEE.

  • 2010 20th International Conference on Pattern Recognition (ICPR)

    ICPR 2010 will be an international forum for discussions on recent advances in the fields of Computer Vision; Pattern Recognition and Machine Learning; Signal, Speech, Image and Video Processing; Biometrics and Human Computer Interaction; Multimedia and Document Analysis, Processing and Retrieval; Medical Imaging and Visualization.

  • 2008 19th International Conferences on Pattern Recognition (ICPR)

    The ICPR 2008 will be an international forum for discussions on recent advances in the fields of Computer vision, Pattern recognition (theory, methods and algorithms), Image, speech and signal analysis, Multimedia and video analysis, Biometrics, Document analysis, and Bioinformatics and biomedical applications.

  • 2002 16th International Conference on Pattern Recognition


2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE


2018 9th International Particle Accelerator Conference (IPAC)

Topics cover a complete survey of the field of charged particle accelerator science and technology and infrastructure.


2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

The Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) is one of the premier conferences in the wireless research arena and has a long history of bringing together academia, industry and regulatory bodies. Today, it has become one of the IEEE Communication Society’s major conferences in wireless communications and networks. The topics cover the physical layer (PHY) and fundamentals of wireless communications, medium access control (MAC) and cross-layer design, mobile and wireless networks, as well as services, applications, and business.


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Periodicals related to Gaussian distribution

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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


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.


Broadcasting, IEEE Transactions on

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


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.)


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Most published Xplore authors for Gaussian distribution

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Xplore Articles related to Gaussian distribution

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Research and implementation of a real time approach to lip detection in video sequences

[{u'author_order': 1, u'affiliation': u'Sch. of Comput., JiangSu Univ., Zhenjiang, China', u'full_name': u'Jian-Ming Zhang'}, {u'author_order': 2, u'affiliation': u'Sch. of Comput., JiangSu Univ., Zhenjiang, China', u'full_name': u'Liang-Min Wang'}, {u'author_order': 3, u'affiliation': u'Sch. of Comput., JiangSu Univ., Zhenjiang, China', u'full_name': u'De-Jiao Niu'}, {u'author_order': 4, u'affiliation': u'Sch. of Comput., JiangSu Univ., Zhenjiang, China', u'full_name': u'Yong-Zhao Zhan'}] Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693), None

Locating the lip in video sequences is one of the primary steps of the automatic lipreading system. In this paper a new approach to lip detection, which is based on Red Exclusion and Fisher transform, is presented. In this approach, firstly, we locate face region with skin-color model and motion correlation, then trisect the face image and take into account ...


Log-cumulants-based Edgeworth expansion for skew-distributed aggregate interference

[{u'author_order': 1, u'affiliation': u'Department of Signal Theory and Communications, King Juan Carlos University, Madrid, Spain', u'full_name': u'Giancarlo Pastor'}, {u'author_order': 2, u'affiliation': u'Department of Signal Theory and Communications, King Juan Carlos University, Madrid, Spain', u'full_name': u'Inmaculada Mora-Jim\xe9nez'}, {u'author_order': 3, u'affiliation': u'Department of Signal Theory and Communications, King Juan Carlos University, Madrid, Spain', u'full_name': u'Antonio J. Caama\xf1o'}, {u'author_order': 4, u'affiliation': u'Department of Communications and Networking, Aalto University, Espoo, Finland', u'full_name': u'Riku J\xe4ntti'}] 2014 11th International Symposium on Wireless Communications Systems (ISWCS), None

The Edgeworth expansion approximates nearly Gaussian distributions in terms of cumulants. This expansion is developed within the framework of First Kind Statistics, where definitions are derived from the Fourier transform. Alternatively, the framework of Second Kind Statistics offers analogous definitions which are derived from the Mellin transform. Although a formalism with such similarity to the existing definitions cannot lead to ...


Adaptive Gaussian mixture model based on feedback mechanism

[{u'author_order': 1, u'affiliation': u'School of Computer Science & Engineering, South China University of Technology, Guangzhou, China', u'full_name': u'Jinman Luo'}, {u'author_order': 2, u'affiliation': u'School of Computer Science & Engineering, South China University of Technology, Guangzhou, China', u'full_name': u'Juan Zhu'}] 2010 International Conference On Computer Design and Applications, None

Focusing on the traditional Gaussian mixture model suffers from slow learning and lack of accuracy, this paper proposes an adaptive Gaussian mixture model based on feedback mechanism. It models each pixel as an adaptive mixture of Gaussians, uses the information of foreground to advance model update based on feedback mechanism and selects the number of components of Gaussian mixture model ...


A Call Acceptance Algorithm For Gigabit Networks

[{u'author_order': 1, u'affiliation': u'Sterling Federal Systems, Inc.', u'full_name': u'S. M. Srinidhi'}, {u'author_order': 2, u'full_name': u'V. K. Konangi'}] LEOS 1993 Summer Topical Meeting Digest on Optical Microwave Interactions/Visible Semiconductor Lasers/Impact of Fiber Nonlinearities on Lightwave Systems/Hybrid Optoelectronic Integration and Packagi, None

First Page of the Article ![](/xploreAssets/images/absImages/00696915.png)


Fuzzy cluster in credit scoring

[{u'author_order': 1, u'affiliation': u'Sch. of Bus. Adm., South China Univ. of Technol., Guangzhou, China', u'full_name': u'Yu-Zhong Luo'}, {u'author_order': 2, u'full_name': u'Su-Lin Pang'}, {u'author_order': 3, u'full_name': u'Shen-Shan Qiu'}] Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693), None

Nine companies listed on China Stock Exchange by 2000 are chosen and the following six major financial indexes of them are considered: net assets yield, net profit per stock, receivables velocity, stock velocity, floating ratio and asset/debt ratio. Using fuzzy dynamic cluster analysis, this paper classifies these 9 listed companies into three types: Good, Middle and Bad, then two most ...


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Educational Resources on Gaussian distribution

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eLearning

No eLearning Articles are currently tagged "Gaussian distribution"

IEEE-USA E-Books

  • Some Probability Techniques

    This chapter explains different probability techniques used in Tunny work. The discussions presented here are mainly a list of definitions, notations and theorems. It begins with symbols used in symbolic logic, simple probability notations, the laws of probability and some theorems, including Bayes?> theorem, theorem of the weighted average of factors, and theorem of the chain of witnesses. In addition a general formula for sigma in Tunny work and the principle of maximum likelihood are discussed.

  • Bayesian MMSE Error Estimation

    This chapter presents some remarks on application and extension of the Bayesian theory. A minimum mean-square error (MMSE) estimator of the error can be found, in which the uncertainty will manifest itself in a Bayesian framework relative to a space of feature-label distributions and random samples. The standard approach to evaluating the performance of an error estimator is to find its MSE (RMS) for a given feature-label distribution and classification rule. When considering Bayesian MMSE error estimation, there are two sources of randomness: the sampling distribution and uncertainty in the underlying feature-label distribution. For discrete classification the chapter considers an arbitrary number of bins with generalized beta priors. Linear classification of Gaussian distributions and consistency of Bayesian MMSE error estimators are also discussed. Finally, the chapter discusses how a classical ad hoc error estimator is calibrated based on the Bayesian framework.

  • Gaussian Distribution Theory: Univariate Case

    This chapter presents a survey of salient results in the distributional study of error rates of Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) under an assumption of Gaussian populations. It concentrates on results for the separate-sampling population-specific error rates. The chapter presents results for the first moment of the true error and several error estimators for the separate-sampling case. An exact expression for the second-order moments of the true error is also given. The chapter also displays the RMS of the separate-sampling resubstitution and leave-one-out estimators of the overall error rate. It provides exact expressions for the marginal sampling distributions of the classical mixture-sampling resubstitution and leave-one-out estimators and discusses the joint sampling distribution and density of each of these error estimators and the true error in the univariate Gaussian case, and some of its applications, using results published in Zollanvari et al.

  • Simulation Methods for Fractal Processes

    This chapter contains sections titled: Fractional Brownian Motion Fractional Gaussian Noise Regression Models of Traffic Fractal Point Process Fractional Levy Motion and its Application to Network Traffic Modelling Models of Multifractal Network Traffic LRD Traffic Modelling with the Help of Wavelets _M/G/_∞Model References

  • The Normal Random Process: Gaussian Functionals

  • Probability Theory

    The reason why many wireless communication books start from probability theory is that wireless communications deal with uncertainty. If there are no channel impairments by nature, we can receive the transmitted messages without any distortion and do not need to care about probability theory. A random signal cannot be predicted but we may forecast future values from previous events using probability theory. When we consider a signal over time, we find the energy spectral density (ESD) and power spectral density (PSD). A correlation function is used to know the relationship between random variables. When we face a noise that the nature has made electrical component noises or thermal noises, we assume it follows Gaussian distribution because of central limit theorems. This theorem means that the sample average and sum have Gaussian distribution regardless of distribution of each random variable.

  • Outage Models of System Components

    This chapter systematically discusses the outage models of system components. Generally, component outages can be divided into two categories: independent and dependent. The independent outage modes of power system components are categorized as follows: forced outage (repairable forced failure and nonrepairable forced failure), planned outage, semiforced outage, partial failure mode and multiple failure mode. Nonrepairable forced failure includes aging failure and chance failure. The aging failure mode of components should be incorporated in risk evaluation when components approach the end of their lives. Dependent outages can be classified as: common-cause outage, component- group outage, station-originated outage, cascading outage and environment- dependent failure. The common characteristic of all the dependent outage modes is that an outage state includes outages of more than one component. Dependent outages generally produce much more severe consequences than independent outages.

  • Gaussian Distribution Theory: Multivariate Case

    This chapter surveys results in the distributional theory of error rates of the Linear (LDA) and Quadratic Discriminant Analysis (QDA) classification rules under multivariate Gaussian populations, with the aim of obtaining exact or approximate (accurate) expressions for the moments of true and estimated error rates. It focuses on two approaches: small-sample and large sample methods. It examines small-sample methods based on statistical representations for the discriminants defined in the case of Gaussian populations and separate sampling. In this chapter, statistical representations are given for W(Sn0,n1, X) | Y = 0 and W(Sn0,n1, X1) | Y1 = 0 in the case of Anderson's and John's LDA discriminants, as well as the general QDA discriminant, under multivariate Gaussian populations and separate sampling. Finally, the chapter shows how the statistical representations can be useful for Monte-Carlo computation of the error moments and error distribution based on the discriminant distribution.

  • Random Signals

    This chapter provides an overview of integration of the Gaussian probability density function and the Q-function. It explains the weighted sum of random variables and properties of Gaussian variables. The chapter presents the central limit theorem and ensembles average, autocorrelation functions of random processes. It also explores statistical properties of additive white Gaussian noise (AWGN). The chapter provides step-by-step code exercises and instructions to implement execution sequences. The MATLAB command randn(1,b) generates a 1¿¿b vector whose elements are realizations of independent and identically distributed Gaussian random variables with zero mean and unit variance. The chapter summarizes the analytical relationship among the input, the output, and the impulse response of a linear system in the time domain. It is designed to help teach and understand communication systems using a classroom-tested, active learning approach.

  • Statistical Measures of Dependence for Financial Data

    This chapter provides the statistical measures of dependence for financial data. The analysis of financial and econometric data is typified by non- Gaussian multivariate observations that exhibit complex dependencies: heavy- tailed and skewed marginal distributions are commonly encountered; serial dependence, such as autocorrelation and conditional heteroscedasticity. When data are assumed to be jointly Gaussian, all dependence is linear, and therefore only pairwise among the variables. In this setting, Pearson's product-moment correlation coefficient uniquely characterizes the sign and strength of any such dependence. The chapter shows that copulas can be used to model the dependence between random variables. It turns our attention to the dependence structure itself, and when appropriate makes connections to copulas. The chapter describes different types of dependence, and then provides theoretical background.



Standards related to Gaussian distribution

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IEEE Standard for Broadband over Power Line Networks: Medium Access Control and Physical Layer Specifications

The project defines a standard for high-speed (>100 Mbps at the physical layer) communication devices via electric power lines, so-called broadband over power line (BPL) devices. This standard uses transmission frequencies below 100 MHz. It is usable by all classes of BPL devices, including BPL devices used for the first-mile/last-mile connection (<1500 m to the premise) to broadband services as ...



Jobs related to Gaussian distribution

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