Efficient 3D inversion of magnetic data via octree mesh discretization, space‐filling curves, and wavelets

2010 ◽  
Author(s):  
Kristofer Davis ◽  
Yaoguo Li
Keyword(s):  
Magnetic Data ◽  
Space Filling ◽  
3D Inversion ◽  
Space Filling Curves ◽  
Octree Mesh

scholarly journals On one approach to the approximation of solutions to the direct kinematics problem of parallel manipulators

2020 ◽  
Vol 10 (1) ◽  
pp. 65-70
Author(s):  
Andrei Gorchakov ◽  
Vyacheslav Mozolenko
Keyword(s):  
Neural Network ◽  
Bounded Function ◽  
Parallel Manipulators ◽  
Feedforward Neural Network ◽  
Numerical Implementation ◽  
Continuous Bounded Function ◽  
Space Filling ◽  
Space Filling Curves ◽  
Direct Kinematics

AbstractAny real continuous bounded function of many variables is representable as a superposition of functions of one variable and addition. Depending on the type of superposition, the requirements for the functions of one variable differ. The article investigated one of the options for the numerical implementation of such a superposition proposed by Sprecher. The superposition was presented as a three-layer Feedforward neural network, while the functions of the first’s layer were considered as a generator of space-filling curves (Peano curves). The resulting neural network was applied to the problems of direct kinematics of parallel manipulators.

Computing Space-Filling Curves

2010 ◽  
Vol 50 (2) ◽  
pp. 370-386 ◽  
Author(s):  
P. J. Couch ◽  
B. D. Daniel ◽  
Timothy H. McNicholl
Keyword(s):  
Space Filling ◽  
Space Filling Curves

3D inversion modeling of joint gravity and magnetic data based on a sinusoidal correlation constraint

2019 ◽  
Vol 16 (4) ◽  
pp. 519-529
Author(s):  
Xiu-He Gao ◽  
Sheng-Qing Xiong ◽  
Zhao-Fa Zeng ◽  
Chang-Chun Yu ◽  
Gui-Bin Zhang ◽  
...  
Keyword(s):  
Magnetic Data ◽  
3D Inversion ◽  
Gravity And Magnetic ◽  
Gravity And Magnetic Data

scholarly journals Using Peano–Hilbert space filling curves for fast bidimensional ensemble EMD realization

2012 ◽  
Vol 2012 (1) ◽  
Author(s):  
Paulo Costa ◽  
João Barroso ◽  
Hugo Fernandes ◽  
Leontios J Hadjileontiadis
Keyword(s):  
Hilbert Space ◽  
Space Filling ◽  
Space Filling Curves

Space-Filling Curves

2009 ◽  
pp. 2674-2675
Author(s):  
Mohamed F. Mokbel ◽  
Walid G. Aref
Keyword(s):  
Space Filling ◽  
Space Filling Curves

scholarly journals The Effect of Space-filling Curves on the Efficiency of Hand Gesture Recognition Based on sEMG Signals

2021 ◽  
Vol 12 (1) ◽  
pp. 23-31
Author(s):  
Panagiotis Tsinganos ◽  
Bruno Cornelis ◽  
Jan Cornelis ◽  
Bart Jansen ◽  
Athanassios Skodras
Keyword(s):  
Computer Vision ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Network Architectures ◽  
Hilbert Curve ◽  
Space Filling ◽  
Space Filling Curve ◽  
Space Filling Curves ◽  
Semg Signals

Over the past few years, Deep learning (DL) has revolutionized the field of data analysis. Not only are the algorithmic paradigms changed, but also the performance in various classification and prediction tasks has been significantly improved with respect to the state-of-the-art, especially in the area of computer vision. The progress made in computer vision has produced a spillover in many other domains, such as biomedical engineering. Some recent works are directed towards surface electromyography (sEMG) based hand gesture recognition, often addressed as an image classification problem and solved using tools such as Convolutional Neural Networks (CNN). This paper extends our previous work on the application of the Hilbert space-filling curve for the generation of image representations from multi-electrode sEMG signals, by investigating how the Hilbert curve compares to the Peano- and Z-order space-filling curves. The proposed space-filling mapping methods are evaluated on a variety of network architectures and in some cases yield a classification improvement of at least 3%, when used to structure the inputs before feeding them into the original network architectures.

scholarly journals Neural Network Analysis of Electroencephalograms Graphical Representation

2020 ◽  
pp. short52-1-short52-9
Author(s):  
Aleksandr Bragin ◽  
Vladimir Spitsyn
Keyword(s):  
Hilbert Space ◽  
Measurement Accuracy ◽  
Graphical Representation ◽  
Space Filling ◽  
Good Tool ◽  
Motor Patterns ◽  
Space Filling Curves ◽  
Markov Transition ◽  
Time Signals ◽  
Electroencephalogram Eeg

The article is devoted to the problem of recognition of motor imagery based on electroencephalogram (EEG) signals, which is associated with many difficulties, such as the physical and mental state of a person, measurement accuracy, etc. Artificial neural networks are a good tool in solving this class of problems. Electroencephalograms are time signals, Gramian Angular Fields (GAF), Markov Transition Field (MTF) and Hilbert space-filling curves transformations are used to represent time series as images. The paper shows the possibility of using GAF, MTF and Hilbert space-filling curves EEG signal transforms for recognizing motor patterns, which is further applicable, for example, in building a brain-computer interface.

Sign in / Sign up

Export Citation Format

Share Document