Results 281 to 290 of about 14,865,566 (335)
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2012
The purpose of this book is to explain in a rigorous way Stokes’s theorem and to facilitate the student’s use of this theorem in applications. Neither of these aims can be achieved without first agreeing on the notation and necessary background concepts of vector calculus, and therein lies the motivation for our introductory chapter.
Antonio Galbis, Manuel Maestre
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The purpose of this book is to explain in a rigorous way Stokes’s theorem and to facilitate the student’s use of this theorem in applications. Neither of these aims can be achieved without first agreeing on the notation and necessary background concepts of vector calculus, and therein lies the motivation for our introductory chapter.
Antonio Galbis, Manuel Maestre
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2010 IEEE International Conference on Robotics and Automation, 2010
Localization in unknown environments using low-cost sensors remains a challenge. This paper presents a new localization approach that learns the spatial variation of an observed continuous signal. We model the signal as a piece-wise linear function and estimate its parameters using a simultaneous localization and mapping (SLAM) approach.
Jens-Steffen Gutmann +4 more
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Localization in unknown environments using low-cost sensors remains a challenge. This paper presents a new localization approach that learns the spatial variation of an observed continuous signal. We model the signal as a piece-wise linear function and estimate its parameters using a simultaneous localization and mapping (SLAM) approach.
Jens-Steffen Gutmann +4 more
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Results in Mathematics, 1997
Let \(K={\mathbb R}\) or \({\mathbb C}\), \(U\) an open subset of \(K^n\), \(\mu: U\rightarrow K\) and \(f,g: U\rightarrow K^n\) analytic functions, and consider the differential equation \(\dot x =f(x)+\mu(x)g(x)\). The main result of the paper describes how the general solution of this equation is expressible in terms of the solutions of \(\dot x= f ...
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Let \(K={\mathbb R}\) or \({\mathbb C}\), \(U\) an open subset of \(K^n\), \(\mu: U\rightarrow K\) and \(f,g: U\rightarrow K^n\) analytic functions, and consider the differential equation \(\dot x =f(x)+\mu(x)g(x)\). The main result of the paper describes how the general solution of this equation is expressible in terms of the solutions of \(\dot x= f ...
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IEEE Computer Graphics and Applications, 1994
We present two new techniques for rendering 3D vector fields in scientific visualisation. We also present a new technique called texture splats for rendering these volumes efficiently. >
Roger Crawfis +2 more
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We present two new techniques for rendering 3D vector fields in scientific visualisation. We also present a new technique called texture splats for rendering these volumes efficiently. >
Roger Crawfis +2 more
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Proceedings of the 1st International Conference on Internet of Things and Machine Learning, 2017
Conventional classifiers often regard input samples as identically and independently distributed (i.i.d.). This is however not true in many real applications, especially when patterns occur as groups (where each group shares a homogeneous style). Such tasks are also called field classification. By breaking the i.i.d.
Kaizhu Huang +2 more
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Conventional classifiers often regard input samples as identically and independently distributed (i.i.d.). This is however not true in many real applications, especially when patterns occur as groups (where each group shares a homogeneous style). Such tasks are also called field classification. By breaking the i.i.d.
Kaizhu Huang +2 more
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Overparameterized Models for Vector Fields
SIAM Journal on Imaging Sciences, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Keren Rotker +2 more
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Vector Wavelet Thresholding for Vector Field Denoising
IEEE Visualization 2004, 2005Noise reduction is an important preprocessing step for many visualization techniques that make use of feature extraction. We propose a method for denoising 2-D vector fields that are corrupted by additive noise. The method is based on the vector wavelet transform and wavelet coefficient thresholding.
Westenberg, M.A., Ertl, T.
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Learning Implicit Fields for Generative Shape Modeling
Computer Vision and Pattern Recognition, 2018We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes. An implicit field assigns a value
Zhiqin Chen, Hao Zhang
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Vector Analysis and Vector Fields
2004A vector function of a scalar variable is a vector \( {\vec{\text{a}}} \) whose components are real functions of t: \( {\vec{\text{a}}} = {\vec{\text{a}}}(t) = a_{x} (t ) {\vec{\text{e}}}_{x} + a_{y} (t ) {\vec{\text{e}}}_{y} + a_{z} (t ) {\vec{\text{e}}}_{z} .
Ilja N. Bronshtein +3 more
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Controlling Electromagnetic Fields
Science, 2006Using the freedom of design that metamaterials provide, we show how electromagnetic fields can be redirected at will and propose a design strategy. The conserved fields—electric displacement field D, magnetic induction field B, and Poynting vector B—are ...
J. Pendry, D. Schurig, David R. Smith
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