This analysis uses the Levenberg-Marquardt back propagation artificial neural networks (LM-BP-ANNs) approach to demonstrate mathematical strategy of for simulation MHD Tangent hyperbolic nanofluid (THNF) flow consisting motile microorganisms through a vertically extending surface. The fluid is being investigated in terms exponential heat source/sink, thermal radiation, and magnetic field. modeled equations are relegated ordinary system differential by substituting similarity variables....

The Levenberg-Marquardt (LM) back propagation (BP) artificial neural networks (ANNs) (LM-BP-ANNs) procedure is used in this analysis to show the computational strategy of for simulation magnetohydrodynamics tangent hyperbolic nanofluid flow comprised motile microorganism across a vertical slender stretching surface. fluid were examined under significance chemical reaction, magnetic field, activation energy, and heat source. modeled equations simplified ordinary system differential using...

Traditional deep functional map frameworks are widely used for 3D shape matching; however, many methods fail to adaptively capture the relevant frequency information required estimation in complex scenarios, leading poor performance, especially under significant deformations. To address these challenges, we propose a novel unsupervised learning-based framework, Deep Frequency Awareness Functional Maps (DFAFM), specifically designed tackle diverse shape-matching problems. Our approach...

Unorganized point clouds obtained from 3D shape acquisition devices usually present noise, outliers, and nonuniformities. The proposed framework consolidates unorganized points through an iterative procedure of interlaced downsampling upsampling. Selection operations remove outliers while preserving geometric details. improves the uniformity by moving downsampled particles refining samples. Surface extrapolation fills missed regions. Moreover, adaptive sampling strategy speeds up iterations....

Establishing correspondence between shapes is a very important and active research topic in many domains. Due to the powerful ability of deep learning on geometric data, lots attractive results have been achieved by convolutional neural networks (CNNs). In this paper, we propose novel architecture for shape correspondence, termed Anisotropic Chebyshev spectral CNNs (ACSCNNs), based new extension manifold convolution operator. The extended operators aggregate local features signals set...

Abstract Lattice structures have been widely used in various applications of additive manufacturing due to its superior physical properties. If modeled by triangular meshes, a lattice structure with huge number struts would consume massive memory. This hinders the use large-scale (e.g., design interior solid spatially graded material properties). To solve this issue, we propose memory-efficient method for modeling and slicing adaptive structures. A is represented weighted graph where edge...

A large number of 3D spectral descriptors have been proposed in the literature, which act as an essential component for deformable shape matching and related applications. An outstanding descriptor should desirable natures including high-level descriptive capacity, cheap storage, robustness to a set nuisances. It is, however, unclear are more suitable particular application. This paper fills gap by comprehensively evaluating nine state-of-the-art on ten popular datasets well perturbations...

A geodesic distance-based approach synthesizes natural facial animations using radial basis function (RBF) interpolation. The consists of two parts: the distance calculation and RBF method works in real time, which is important computer games online chatting.

In traditional deep functional maps for non-rigid shape correspondence, estimating a map including high-frequency information requires enough linearly independent features via the least square method, which is prone to be violated in practice, especially at an early stage of training, or costly post-processing, e.g. ZoomOut. this paper, we propose novel method called RFMNet (Robust Deep Functional Map Networks), jointly considers training stability and more geometric than previous works. We...

Abstract In this paper, we present a powerful spectral shape descriptor for analysis, named Anisotropic Spectral Manifold Wavelet Descriptor (ASMWD). We proposed novel manifold harmonic signal processing tool termed Transform (ASMWT) first. ASMWT allows to comprehensively analyse signals from multiple wavelet diffusion directions on local regions of the with series low‐pass and band‐pass frequency filters in each direction. Based coefficients very simple signal, ASMWD is efficiently...

The functional map framework has proven to be extremely effective for representing dense correspondences between deformable shapes. A key step in this is formulate suitable preservation constraints encode the geometric information that must preserved by unknown map. For issue, we construct novel and powerful determine map, where multiscale spectral manifold wavelets are required at each scale correspondingly. Such allow us extract significantly more than previous methods, especially those...

Four new quartic rational Said-Ball-like basis functions, which include the cubic Said-Ball functions as a special case, are constructed in this paper. The is applied to generate class of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msup><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math>continuous Hermite interpolation splines with local tension shape parameters. error estimate expression of...

Pedestrians, motorist, and cyclist remain the victims of poor vision negligence human drivers, especially in night. Millions people die or sustain physical injury yearly as a result traffic accidents. Detection recognition road markings play vital role many applications such surveillance autonomous driving. In this study, we have trained nighttime road-marking detection model using NIR camera images. We modified VGG-16 base network state-of-the-art faster R-CNN algorithm by multilayer...