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Neuro-wavelet parametric modeling
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000This work describes Neuro-Wavelet Parametric Modeling, a neural-based technique to classify, model and forecast signals or problems which are functions of either time or space. The paper presents the base method and discusses on the selection of the optimal neuro-wavelet network. An industrial application is also presented.
COLLA, Valentina +2 more
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Identification in Parametric Models
Econometrica, 1971A theory of identification is developed for a general stochastic model whose probability law is determined by a finite number of parameters. It is shown under weak regularity conditions that local identifiability of the unknown parameter vector is equivalent to nonsingularity of the information matrix.
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Journal of Electronic Testing, 1995
Assuming that the distribution of path delays introduced by variations in the manufacturing process is exponential instead of gaussian, the interdependence problem between delay-optimization of synthesized networks and parametric yield has been revisited. The result confirms the claim of Williams, Underwood, and Mercer.
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Assuming that the distribution of path delays introduced by variations in the manufacturing process is exponential instead of gaussian, the interdependence problem between delay-optimization of synthesized networks and parametric yield has been revisited. The result confirms the claim of Williams, Underwood, and Mercer.
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Studies in health technology and informatics, 2007
The shape of anatomic objects often depends in complex ways on the shapes and locations of neighboring objects. Shape parameter networks provide an approach for representing shape dependencies and producing multi-object models that share consistent boundary definitions.
Jessica R. Crouch, Andrew Cherry
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The shape of anatomic objects often depends in complex ways on the shapes and locations of neighboring objects. Shape parameter networks provide an approach for representing shape dependencies and producing multi-object models that share consistent boundary definitions.
Jessica R. Crouch, Andrew Cherry
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Parametric Temporal Logic for “Model Measuring”
ACM Transactions on Computational Logic, 1999We extend the standard model checking paradigm of linear temporal logic, LTL, to a “model measuring” paradigm where one can obtain more quantitative information beyond a “Yes/No” answer. For this purpose, we define a parametric temporal logic , PLTL, which allows statements such as “a request p
ALUR R +3 more
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A modeling paradigm incorporating parametric and non-parametric methods
The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2005A novel parametric/non-parametric modeling paradigm was defined and used in characterization of synaptic transmission. In this paradigm, parametric and nonparametric techniques were incorporated in a complementary manner. Non-parametric method was used to generalize experimental data and extract system input/output properties.
D, Song +3 more
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A parametric model for heart sounds
2015 49th Asilomar Conference on Signals, Systems and Computers, 2015This paper presents a novel way to represent heart sound signals by using a harmonic plus noise model (HNM). A set of time-frequency waveforms are selected by the Matching Pursuit algorithm to form the harmonic part. The noise part is obtained after subtracting the harmonic part to the original signal and is modeled as an autorregresive process using ...
Roilhi F. Ibarra +3 more
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Interactive animation of parametric models
The Visual Computer, 1985This paper describes a program which allows parametric models of three-dimensional characters and scenes to be interactively controlled for computer animation. The system attempts to span the two most common approaches to animation: language-driven or programmed and visually-driven or interactive.
Pat Hanrahan, David J. Sturman
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Integrating parametric and non-parametric models for scene labeling
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015We adopt Convolutional Neural Networks (CNN) as our parametric model to learn discriminative features and classifiers for local patch classification. As visually similar pixels are indistinguishable from local context, we alleviate such ambiguity by introducing a global scene constraint.
Bing Shuai +4 more
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Using CFD to Evaluate Natural Ventilation through a 3D Parametric Modeling Approach
Energies, 2021Timea Bejat +2 more
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