Temat: spline - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Prefiltering in Wavelet Analysis Applying Cubic B-Splines
Autorzy:: Rakowski, W.
Powiązania:: https://bibliotekanauki.pl/articles/226902.pdf
Data publikacji:: 2014
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: wavelet
scaling function
wavelet analysis
wavelet transform
cubic box spline
digital filters
direct cubic B-spline filter
prefiltering
Opis:: Wavelet transform algorithms (Mallat’s algorithm, a trous algorithm) require input data in the form of a sequence of numbers equal to the signal projection coefficients on a space spanned by integer-translated copies of a scaling function. After sampling of the continuous-time signal, it is most frequently possible to compute only approximated values of the signal projection coefficients by choosing a specific signal approximation. Calculation of the signal projection coefficients based on the signal interpolation by means of cubic B-splines is proposed in the paper.
Źródło:: International Journal of Electronics and Telecommunications; 2014, 60, 4; 331-340
2300-1933
Pojawia się w:: International Journal of Electronics and Telecommunications
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Classification of EEG Signals Using Quantum Neural Network and Cubic Spline
Autorzy:: Abdul-Zahra Raheem, M.
AbdulRazzaq Hussein, E.
Powiązania:: https://bibliotekanauki.pl/articles/227206.pdf
Data publikacji:: 2016
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: signals
ERP signals
cubic spline
neural networks
quantum neural network
Opis:: The main aim of this paper is to propose Cubic Spline-Quantum Neural Network (CS-QNN) model for analysis and classification of Electroencephalogram (EEG) signals. Experimental data used here were taken from seven different electrodes. The work has been done in three stages, normalization of the signals, extracting the features by Cubic Spline Technique (CST) and classification using Quantum Neural Network (QNN). The simulation results showed that five types of EEG signals were classified with an average accuracy for seven electrodes that is 94.3% when training 70% of the features while with an average accuracy of 92.84% when training 50% of the features.
Źródło:: International Journal of Electronics and Telecommunications; 2016, 62, 4; 401-408
2300-1933
Pojawia się w:: International Journal of Electronics and Telecommunications
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Topological Fragmentation of Medical 3D Surface Mesh Models for Multi-Hierarchy Anatomical Classification
Autorzy:: Zwettler, G. A.
Backfrieder, W.
Powiązania:: https://bibliotekanauki.pl/articles/226172.pdf
Data publikacji:: 2015
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: 3D surface mesh
functional classification in medicine
multi-hierarchy classification
level of detail
spline-based surface selection
Opis:: High resolution 3D mesh representations of patient anatomy with appendant functional classification are of high importance in the field of clinical education and therapy planning. Thereby, classification is not always possible directly from patient morphology, thus necessitating tool support. In this work a hierarchical mesh data model for multi-hierarchy anatomical classification is introduced, allowing labeling of a patient model according to various medical taxonomies. The classification regions are thereby specified utilizing a spline representation to be placed and deformed by a medical expert at low effort. Furthermore, application of randomized dilation allows conversion of the specified regions on the surface into fragmented and closed sub-meshes, comprising the entire anatomical structure. As proof of concept, the semi-automated classification method is implemented for VTK library and visualization of the multihierarchy anatomical model is validated with OpenGL, successfully extracting sub-meshes of the brain lobes and preparing classification regions according to Brodmann area taxonomy.
Źródło:: International Journal of Electronics and Telecommunications; 2015, 61, 2; 129-136
2300-1933
Pojawia się w:: International Journal of Electronics and Telecommunications
Dostawca treści:: Biblioteka Nauki

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