Interpolation
41,663 resources related to Interpolation
IEEE Organizations related to Interpolation
Back to TopConferences related to Interpolation
Back to Top2013 Computing in Cardiology Conference (CinC)
Provide a forum for scientists and professionals from the fields of medicine, physics, engineering and computer science to discuss their current research in topics pertaining to computing in clinical cardiology and cardiovascular physiology.
2012 54th International Symposium ELMAR
Image and Video Processing; Multimedia Communications; Speech and Audio Processing; Wireless Commununications; Telecommunications; Antennas and Propagation; eLearning and mLearning; Navigation Systems; Ship Electronic Systems; Power Electronics and Automation; Naval Architecture; Sea Ecology.
2011 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2011)
The scope of the conference includes, but not limited to AI, Artificial Life and Artificial Immune Systems, Cloud Computing, Computer Vision, Data Mining, Fuzzy System, Genetic Algorithms, Information Retrieval, Intelligent Control, Robotics, Machine Learning, Machine Translation, Neural Networks, Rough Set, Systems Biology, Video & Image Processing
2010 6th International Conference on Wireless and Mobile Communications (ICWMC)
The Sixth International Conference on Wireless and Mobile Communications (ICWMC 2010) in Spain follows on the previous events on advanced wireless technologies, wireless networking, and wireless applications.
2010 International Conference on Electrical and Control Engineering (ICECE)
recent advances, new techniques and applications in the field of Electrical Engineering and Automation Control.
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Periodicals related to Interpolation
Back to TopFuzzy Systems, IEEE Transactions on
Theory and application of fuzzy systems with emphasis on engineering systems and scientific applications. (6) (IEEE Guide for Authors) Representative applications areas include:fuzzy estimation, prediction and control; approximate reasoning; intelligent systems design; machine learning; image processing and machine vision;pattern recognition, fuzzy neurocomputing; electronic and photonic implementation; medical computing applications; robotics and motion control; constraint propagation and optimization; civil, chemical and ...
Image Processing, IEEE Transactions on
Signalprocessing aspects of image processing, imaging systems, and image scanning, display, and printing. Includes theory, algorithms, and architectures for image coding, filtering, enhancement, restoration, segmentation, and motion estimation; image formation in tomography, radar, sonar, geophysics, astronomy, microscopy, and crystallography; image scanning, digital halftoning and display, andcolor reproduction.
Multimedia, IEEE Transactions on
The goal of IEEE Transactions on Multimedia is to integrate all aspects of multimedia systems and technology, signal processing, and applications. It will cover various aspects of research in multimedia technology and applications including, but not limited to: circuits, algorithms and macro/microarchitectures, software, detailed design, synchronization, interaction, joint processing and coordination of multimedia and multimodal signals/data, compression, storage, retrieval, communication, ...
Signal Processing, IEEE Transactions on
The technology of transmission, recording, reproduction, processing, and measurement of speech; other audiofrequency waves and other signals by digital, electronic, electrical, acoustic, mechanical, and optical means; the components and systems to accomplish these and related aims; and the environmental, psychological, and physiological factors of thesetechnologies.
Very Large Scale Integration (VLSI) Systems, IEEE Transactions on
Integrated circuits and systems;VLSI based Architecture and applications; highspeed circuits and interconnect; mixedsignal SoC; speed/area/power/noise tradeoffs in CMOS circuits.
Xplore Articles related to Interpolation
Back to TopLars Linsen; Tran Van Long; Paul Rosenthal; Stephan Rosswog IEEE Transactions on Visualization and Computer Graphics, 2008
Data sets resulting from physical simulations typically contain a multitude of physical variables. It is, therefore, desirable that visualization methods take into account the entire multifield volume data rather than concentrating on one variable. We present a visualization approach based on surface extraction from multifield particle volume data. The surfaces segment the data with respect to the underlying multivariate function. ...
A novel approach of resolution enhancement with application in array processing of single snapshot
M. Zhang; W. Yang; L. Li IEEE Transactions on Antennas and Propagation, 1991
An approach based on spatial filter preprocessing is developed to enhance the resolution of current spectrum estimation algorithms and is applied to sensor array processing in the single snapshot case. The effective signaltonoise ratio (SNR) and the accuracy of autocorrelation estimation are significantly improved through the use of this approach. Simulation results are presented to illustrate the improved performance achieved ...
Channel estimation for MCCDMA with compensation of synchronization errors
Yuan Zhang; R. Hoshyar; R. Tafazolli IEEE 60th Vehicular Technology Conference, 2004. VTC2004Fall. 2004, 2004
In this paper, we investigate channel estimation for MCCDMA in the presence of time and frequency synchronization errors. Channel estimation in MCCDMA requires transmission of pilot tones, based on which MMSE interpolation or FFTbased interpolation algorithms are applied. Most channel estimation methods in the literature assume perfect synchronization. However, this condition is not guaranteed in the actual case, and channel ...
Frequencywavenumber domain focusing under linear MIMO array configurations
Xiaodong Zhuge; Alexander Yarovoy 2012 IEEE International Geoscience and Remote Sensing Symposium, 2012
This paper introduces a fast imaging algorithm oriented for linear MIMO array configurations. The image reconstruction process is performed in the frequencywavenumber domain and requires a modified interpolation process among both transmit and receive apertures. The proposed algorithm corrects completely the range curvature in the nearfield and is able to provide high quality images for shortrange targets. The proposed algorithm ...
A fast piecewiselinear implementation of fuzzy controllers
A. Jimenez; F. Matia Fuzzy Information Processing Society Biannual Conference, 1994. Industrial Fuzzy Control and Intelligent Systems Conference, and the NASA Joint Technology Workshop on Neural Networks and Fuzzy Logic,, 1994
This paper discusses how it is possible to develop a fuzzy controller by means of a collection of points (guide points) and a linear interpolation between them, when some conditions are satisfied. The use of the product as tnorm and the bounded sum as snorm, the use of normal and triangular membership functions and an adequate overlapping between them, are ...
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Educational Resources on Interpolation
Back to TopeLearning
Lars Linsen; Tran Van Long; Paul Rosenthal; Stephan Rosswog IEEE Transactions on Visualization and Computer Graphics, 2008
Data sets resulting from physical simulations typically contain a multitude of physical variables. It is, therefore, desirable that visualization methods take into account the entire multifield volume data rather than concentrating on one variable. We present a visualization approach based on surface extraction from multifield particle volume data. The surfaces segment the data with respect to the underlying multivariate function. ...
A novel approach of resolution enhancement with application in array processing of single snapshot
M. Zhang; W. Yang; L. Li IEEE Transactions on Antennas and Propagation, 1991
An approach based on spatial filter preprocessing is developed to enhance the resolution of current spectrum estimation algorithms and is applied to sensor array processing in the single snapshot case. The effective signaltonoise ratio (SNR) and the accuracy of autocorrelation estimation are significantly improved through the use of this approach. Simulation results are presented to illustrate the improved performance achieved ...
Channel estimation for MCCDMA with compensation of synchronization errors
Yuan Zhang; R. Hoshyar; R. Tafazolli IEEE 60th Vehicular Technology Conference, 2004. VTC2004Fall. 2004, 2004
In this paper, we investigate channel estimation for MCCDMA in the presence of time and frequency synchronization errors. Channel estimation in MCCDMA requires transmission of pilot tones, based on which MMSE interpolation or FFTbased interpolation algorithms are applied. Most channel estimation methods in the literature assume perfect synchronization. However, this condition is not guaranteed in the actual case, and channel ...
Frequencywavenumber domain focusing under linear MIMO array configurations
Xiaodong Zhuge; Alexander Yarovoy 2012 IEEE International Geoscience and Remote Sensing Symposium, 2012
This paper introduces a fast imaging algorithm oriented for linear MIMO array configurations. The image reconstruction process is performed in the frequencywavenumber domain and requires a modified interpolation process among both transmit and receive apertures. The proposed algorithm corrects completely the range curvature in the nearfield and is able to provide high quality images for shortrange targets. The proposed algorithm ...
A fast piecewiselinear implementation of fuzzy controllers
A. Jimenez; F. Matia Fuzzy Information Processing Society Biannual Conference, 1994. Industrial Fuzzy Control and Intelligent Systems Conference, and the NASA Joint Technology Workshop on Neural Networks and Fuzzy Logic,, 1994
This paper discusses how it is possible to develop a fuzzy controller by means of a collection of points (guide points) and a linear interpolation between them, when some conditions are satisfied. The use of the product as tnorm and the bounded sum as snorm, the use of normal and triangular membership functions and an adequate overlapping between them, are ...
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IEEEUSA EBooks

A Heuristic Exposition of Wiener's Mathematical Theory of Prediction and Filtering
This chapter contains sections titled: 1 The Autocorrelation Function, 2 The Integral Equation, 3 The Modified Integral Equation, 4 The Factorization Problem, 5 The Functions ψ1, ψ2, and α, 6 The Prediction Operator

It has been the opinion of many that Wiener will be remembered for his Extrapolation long after Cybernetics is forgotten. Indeed few computerscience students would know today what cybernetics is all about, while every communication student knows what Wiener's filter is. The work was circulated as a classified memorandum in 1942, as it was connected with sensitive war time efforts to improve radar communication. This book became the basis for modern communication theory, by a scientist considered one of the founders of the field of artifical intelligence. Combining ideas from statistics and time series analysis, Wiener used Gauss's method of shaping the characteristic of a detector to allow for the maximal recognition of signals in the presence of noise. This method came to be known as the "Wiener filter."

It has been the opinion of many that Wiener will be remembered for his Extrapolation long after Cybernetics is forgotten. Indeed few computerscience students would know today what cybernetics is all about, while every communication student knows what Wiener's filter is. The work was circulated as a classified memorandum in 1942, as it was connected with sensitive war time efforts to improve radar communication. This book became the basis for modern communication theory, by a scientist considered one of the founders of the field of artifical intelligence. Combining ideas from statistics and time series analysis, Wiener used Gauss's method of shaping the characteristic of a detector to allow for the maximal recognition of signals in the presence of noise. This method came to be known as the "Wiener filter."

The projection of light rays onto the retina of the eye forms a two dimensional image, but through combining the stereoscopic aspect of vision with other optical clues by means of some remarkably effective image processing procedures, the viewer is able to perceive threedimensional representations of scenes.From Images to Surfaces proposes and examines a specific imageprocessing procedure to account for this remarkable effecta computational approach that provides a framework for understanding the transformation of a set of images into a representation of the shapes of surfaces visible in a scene. Although much of the analysis is applicable to any visual information processing systembiological or artificialGrimson constrains his final choice of computational algorithms to those that are biologically feasible and consistent with what is known about the human visual system.In order to clarify the analysis, the approach distinguishes three independent levels: the computational theory itself, the algorithms employed, and the underlying implementation of the computation, in this case through the human neural mechanisms. This separation into levels facilitates the generation of specific models from general concepts.This research effort had its origin in a theory of human stereo vision recently developed by David Marr and Tomaso Poggio. Grimson presents a computer implementation of this theory that serves to test its adequacy and provide feedback for the identification of unsuspected problems embedded in it. The author then proceeds to apply and extend the theory in his analysis of surface interpolation through the computational methodology.This methodology allows the activity of the human early visual system to be followed through several stages: the Primal Sketch, in whi ch intensity changes at isolated points on a surface are noted; the Raw 2.5D Sketch, in which surface values at these points are computed; and the Full 2.5D Sketch, in which these valuesncluding stereo and motion perceptionare interpolated over the entire surface. These stages lead to the final 3D Model, in which the threedimensional shapes of objects, in object centered coordinates, are made explicit.

Resume of Fundamental Mathematical Notions
This chapter contains sections titled: 1.00 Fourier Series, 1.01 Orthogonal Functions, 1.02 The Fourier Integral, 1.04 More on the Fourier Integral; Realizability of Filters, 1.1 Generalized Harmonic Analysis, 1.18 Discrete Arrays and Their Spectra, 1.2 Multiple Harmonic Analysis and Coherency Matrices, 1.3 Smoothing Problems, 1.4 Ergodic Theory, 1.5 Brownian Motion, 1.6 Poisson Distributions, 1.7 Harmonic Analysis in the Complex Domain

The Linear Predictor for a Single Time Series
This chapter contains sections titled: 2.01 Formulation of the Problem of the Linear Predictor, 2.02 The Minimization Problem, 2.03 The Factorization Problem, 2.04 The Predictor Formula, 2.1 Examples of Prediction, 2.2 A Limiting Example of Prediction, 2.3 The Prediction of Functions Whose Derivatives Possess Autocorrelation Coefficients, 2.4 Spectrum Lines and Non absolutely Continuous Spectra, 2.5 Prediction by the Linear Combination of Given Operators, 2.6 The Linear Predictor for a Discrete Time Series

Learning Motion Style Synthesis from Perceptual Observations
This paper presents an algorithm for synthesis of human motion in specified styles. We use a theory of movement observation (Laban Movement Analysis) to describe movement styles as points in a multidimensional perceptual space. We cast the task of learning to synthesize desired movement styles as a regression problem: sequences generated via spacetime interpolation of motion capture data are used to learn a nonlinear mapping between animation parameters and movement styles in perceptual space. We demonstrate that the learned model can apply a variety of motion styles to prerecorded motion sequences and it can extrapolate styles not originally included in the training data.

The projection of light rays onto the retina of the eye forms a two dimensional image, but through combining the stereoscopic aspect of vision with other optical clues by means of some remarkably effective image processing procedures, the viewer is able to perceive threedimensional representations of scenes.From Images to Surfaces proposes and examines a specific imageprocessing procedure to account for this remarkable effecta computational approach that provides a framework for understanding the transformation of a set of images into a representation of the shapes of surfaces visible in a scene. Although much of the analysis is applicable to any visual information processing systembiological or artificialGrimson constrains his final choice of computational algorithms to those that are biologically feasible and consistent with what is known about the human visual system.In order to clarify the analysis, the approach distinguishes three independent levels: the computational theory itself, the algorithms employed, and the underlying implementation of the computation, in this case through the human neural mechanisms. This separation into levels facilitates the generation of specific models from general concepts.This research effort had its origin in a theory of human stereo vision recently developed by David Marr and Tomaso Poggio. Grimson presents a computer implementation of this theory that serves to test its adequacy and provide feedback for the identification of unsuspected problems embedded in it. The author then proceeds to apply and extend the theory in his analysis of surface interpolation through the computational methodology.This methodology allows the activity of the human early visual system to be followed through several stages: the Primal Sketch, in whi ch intensity changes at isolated points on a surface are noted; the Raw 2.5D Sketch, in which surface values at these points are computed; and the Full 2.5D Sketch, in which these valuesncluding stereo and motion perceptionare interpolated over the entire surface. These stages lead to the final 3D Model, in which the threedimensional shapes of objects, in object centered coordinates, are made explicit.

The Linear Filter for a Single Time Series
This chapter contains sections titled: 3.0 Formulation of the General Filter Problem, 3.1 Minimization Problem for Filters, 3.2 The Factorization of the Spectrum, 3.3 Prediction and Filtering, 3.4 The Error of Performance of a Filter; Longlag Filters, 3.5 Fillers and Ergodic Theory, 3.6 Computation of Specific Filter Characteristics, 3.7 Lagging Filters, 3.8 The Determination of Lag and Number of Meshes in a Filter, 3.9 Detecting Filters for High Noise Level, 3.91 Filters for Pulses, 3.92 Filters Having Characteristics Linearly Dependent on Given Charaderistics, 3.93 Computation of Filter: Resume

Support Vector Machines on a Budget
The standard Support Vector Machine formulation does not provide its user with the ability to explicitly control the number of support vectors used to define the generated classifier. We present a modified version of SVM that allows the user to set a budget parameter B and focuses on minimizing the loss attained by the B worstclassified examples while ignoring the remaining examples. This idea can be used to derive sparse versions of both L1SVM and L2SVM. Technically, we obtain these new SVM variants by replacing the 1norm in the standard SVM formulation with various interpolationnorms. We also adapt the SMO optimization algorithm to our setting and report on some preliminary experimental results.