scholarly journals The Hierarchical Subspace Iteration Method for Laplace–Beltrami Eigenproblems

2022 ◽  
Vol 41 (2) ◽  
pp. 1-14
Author(s):  
Ahmad Nasikun ◽  
Klaus Hildebrandt
Keyword(s):  
Shape Analysis ◽  
Iteration Method ◽  
State Of The Art ◽  
Beltrami Operator ◽  
Vector Spaces ◽  
Preconditioned Conjugate Gradients ◽  
Subspace Iteration ◽  
Previous Level ◽  
Coarse To Fine ◽  
Number Of Iterations

Sparse eigenproblems are important for various applications in computer graphics. The spectrum and eigenfunctions of the Laplace–Beltrami operator, for example, are fundamental for methods in shape analysis and mesh processing. The Subspace Iteration Method is a robust solver for these problems. In practice, however, Lanczos schemes are often faster. In this article, we introduce the Hierarchical Subspace Iteration Method (HSIM) , a novel solver for sparse eigenproblems that operates on a hierarchy of nested vector spaces. The hierarchy is constructed such that on the coarsest space all eigenpairs can be computed with a dense eigensolver. HSIM uses these eigenpairs as initialization and iterates from coarse to fine over the hierarchy. On each level, subspace iterations, initialized with the solution from the previous level, are used to approximate the eigenpairs. This approach substantially reduces the number of iterations needed on the finest grid compared to the non-hierarchical Subspace Iteration Method. Our experiments show that HSIM can solve Laplace–Beltrami eigenproblems on meshes faster than state-of-the-art methods based on Lanczos iterations, preconditioned conjugate gradients, and subspace iterations.

Intermittent Failures in High Pin Count Packaging

2006 ◽  
Author(s):  
Ramesh Varma ◽  
Richard Brooks ◽  
Ronald Twist ◽  
James Arnold ◽  
Cleston Messick
Keyword(s):  
Failure Analysis ◽  
Shape Analysis ◽  
State Of The Art ◽  
Process Variation ◽  
High Reliability ◽  
Temperature Cycling ◽  
Assembly Process ◽  
System Failures ◽  
Industrial Grade ◽  
Corrective Actions

Abstract In a prequalification effort to evaluate the assembly process for the industrial grade high pin count devices for use in a high reliability application, one device exhibited characteristics that, without corrective actions and/or extensive screening, may lead to intermittent system failures and unacceptable reliability. Five methodologies confirmed this conclusion: (1) low post-decapsulation wire pull results; (2) bond shape analysis showed process variation; (3) Failure Analysis (FA) using state of the art equipment determined the root causes and verified the low wire pull results; (4) temperature cycling parts while monitoring, showed intermittent failures, and (5) parts tested from other vendors using the same techniques passed all limits.

scholarly journals TransET: Knowledge Graph Embedding with Entity Types

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1407
Author(s):  
Peng Wang ◽  
Jing Zhou ◽  
Yuzhang Liu ◽  
Xingchen Zhou
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Score Function ◽  
Graph Embedding ◽  
Vector Spaces ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Low Dimensional

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.

scholarly journals ART-UP: A Novel Method for Generating Scanning-Robust Aesthetic QR Codes

2021 ◽  
Vol 17 (1) ◽  
pp. 1-23
Author(s):  
Mingliang Xu ◽  
Qingfeng Li ◽  
Jianwei Niu ◽  
Hao Su ◽  
Xiting Liu ◽  
...  
Keyword(s):  
State Of The Art ◽  
Visual Quality ◽  
Qr Code ◽  
Quick Response ◽  
Estimation Model ◽  
Qr Codes ◽  
Excellent Performance ◽  
Novel Method ◽  
Coarse To Fine

Quick response (QR) codes are usually scanned in different environments, so they must be robust to variations in illumination, scale, coverage, and camera angles. Aesthetic QR codes improve the visual quality, but subtle changes in their appearance may cause scanning failure. In this article, a new method to generate scanning-robust aesthetic QR codes is proposed, which is based on a module-based scanning probability estimation model that can effectively balance the tradeoff between visual quality and scanning robustness. Our method locally adjusts the luminance of each module by estimating the probability of successful sampling. The approach adopts the hierarchical, coarse-to-fine strategy to enhance the visual quality of aesthetic QR codes, which sequentially generate the following three codes: a binary aesthetic QR code, a grayscale aesthetic QR code, and the final color aesthetic QR code. Our approach also can be used to create QR codes with different visual styles by adjusting some initialization parameters. User surveys and decoding experiments were adopted for evaluating our method compared with state-of-the-art algorithms, which indicates that the proposed approach has excellent performance in terms of both visual quality and scanning robustness.

scholarly journals A Termination Criterion for Probabilistic Point Clouds Registration

Signals ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 159-173
Author(s):  
Simone Fontana ◽  
Domenico Giorgio Sorrenti
Keyword(s):  
State Of The Art ◽  
Point Clouds ◽  
Computational Time ◽  
Termination Criteria ◽  
Practical Usefulness ◽  
Local Point ◽  
Termination Criterion ◽  
Probabilistic Point ◽  
Excessive Number ◽  
Number Of Iterations

Probabilistic Point Clouds Registration (PPCR) is an algorithm that, in its multi-iteration version, outperformed state-of-the-art algorithms for local point clouds registration. However, its performances have been tested using a fixed high number of iterations. To be of practical usefulness, we think that the algorithm should decide by itself when to stop, on one hand to avoid an excessive number of iterations and waste computational time, on the other to avoid getting a sub-optimal registration. With this work, we compare different termination criteria on several datasets, and prove that the chosen one produces very good results that are comparable to those obtained using a very large number of iterations, while saving computational time.

Dynamic Characteristics of a Long-Span Cable-Stayed Bridge

2011 ◽  
Vol 480-481 ◽  
pp. 1496-1501
Author(s):  
Liu Hui
Keyword(s):  
Finite Element ◽  
Dynamic Characteristics ◽  
Iteration Method ◽  
Natural Frequencies ◽  
Element Model ◽  
Vibration Modes ◽  
Cable Stayed Bridge ◽  
Long Span ◽  
Subspace Iteration ◽  
Floating System

In order to study the dynamic characteristics of a super-long-span cable-stayed bridge which is semi-floating system, the spatial finite element model of this cable-stayed bridge was established in ANSYS based on the finite element theory.Modal solution was conducted using subspace iteration method, and natural frequencies and vibration modes were obtained.The dynamic characteristics of this super-long-span cable-stayed bridge were then analyzed.Results showed that the super-long-span cable-stayed bridge of semi-floating system has long basic cycle, low natural frequencies, dense modes and intercoupling vibration modes.

scholarly journals Visual Navigation for Recovering an AUV by Another AUV in Shallow Water

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1889 ◽  
Author(s):  
Shuang Liu ◽  
Hongli Xu ◽  
Yang Lin ◽  
Lei Gao
Keyword(s):  
Shallow Water ◽  
Pose Estimation ◽  
Field Experiments ◽  
State Of The Art ◽  
Autonomous Underwater Vehicles ◽  
Visual Navigation ◽  
Ambient Light ◽  
Robust Detection ◽  
Novel Method ◽  
Coarse To Fine

Autonomous underwater vehicles (AUVs) play very important roles in underwater missions. However, the reliability of the automated recovery of AUVs has still not been well addressed. We propose a vision-based framework for automatically recovering an AUV by another AUV in shallow water. The proposed framework contains a detection phase for the robust detection of underwater landmarks mounted on the docking station in shallow water and a pose-estimation phase for estimating the pose between AUVs and underwater landmarks. We propose a Laplacian-of-Gaussian-based coarse-to-fine blockwise (LCB) method for the detection of underwater landmarks to overcome ambient light and nonuniform spreading, which are the two main problems in shallow water. We propose a novel method for pose estimation in practical cases where landmarks are broken or covered by biofouling. In the experiments, we show that our proposed LCB method outperforms the state-of-the-art method in terms of remote landmark detection. We then combine our proposed vision-based framework with acoustic sensors in field experiments to demonstrate its effectiveness in the automated recovery of AUVs.

scholarly journals Rule-Guided Compositional Representation Learning on Knowledge Graphs

2020 ◽  
Vol 34 (03) ◽  
pp. 2950-2958
Author(s):  
Guanglin Niu ◽  
Yongfei Zhang ◽  
Bo Li ◽  
Peng Cui ◽  
Si Liu ◽  
...  
Keyword(s):  
State Of The Art ◽  
Representation Learning ◽  
Vector Spaces ◽  
Semantic Structure ◽  
Completion Task ◽  
Joint Embedding ◽  
Semantic Associations ◽  
Structured Information ◽  
Low Dimensional ◽  
Embedding Methods

Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces. Early KG embedding methods only pay attention to structured information encoded in triples, which would cause limited performance due to the structure sparseness of KGs. Some recent attempts consider paths information to expand the structure of KGs but lack explainability in the process of obtaining the path representations. In this paper, we propose a novel Rule and Path-based Joint Embedding (RPJE) scheme, which takes full advantage of the explainability and accuracy of logic rules, the generalization of KG embedding as well as the supplementary semantic structure of paths. Specifically, logic rules of different lengths (the number of relations in rule body) in the form of Horn clauses are first mined from the KG and elaborately encoded for representation learning. Then, the rules of length 2 are applied to compose paths accurately while the rules of length 1 are explicitly employed to create semantic associations among relations and constrain relation embeddings. Moreover, the confidence level of each rule is also considered in optimization to guarantee the availability of applying the rule to representation learning. Extensive experimental results illustrate that RPJE outperforms other state-of-the-art baselines on KG completion task, which also demonstrate the superiority of utilizing logic rules as well as paths for improving the accuracy and explainability of representation learning.

scholarly journals Fast Finger Vein Recognition Based on Sparse Matching Algorithm under a Multicore Platform for Real-Time Individuals Identification

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1167
Author(s):  
Ruber Hernández-García ◽  
Ricardo J. Barrientos ◽  
Cristofher Rojas ◽  
Wladimir E. Soto-Silva ◽  
Marco Mora ◽  
...  
Keyword(s):  
Real Time ◽  
State Of The Art ◽  
Individual Identification ◽  
Affine Transformations ◽  
Private Companies ◽  
Time Efficiency ◽  
Finger Vein ◽  
Vein Recognition ◽  
Coarse To Fine ◽  
Finger Vein Recognition

Nowadays, individual identification is a problem in many private companies, but also in governmental and public order entities. Currently, there are multiple biometric methods, each with different advantages. Finger vein recognition is a modern biometric technique, which has several advantages, especially in terms of security and accuracy. However, image deformations and time efficiency are two of the major limitations of state-of-the-art contributions. In spite of affine transformations produced during the acquisition process, the geometric structure of finger vein images remains invariant. This consideration of the symmetry phenomena presented in finger vein images is exploited in the present work. We combine an image enhancement procedure, the DAISY descriptor, and an optimized Coarse-to-fine PatchMatch (CPM) algorithm under a multicore parallel platform, to develop a fast finger vein recognition method for real-time individuals identification. Our proposal provides an effective and efficient technique to obtain the displacement between finger vein images and considering it as discriminatory information. Experimental results on two well-known databases, PolyU and SDUMLA, show that our proposed approach achieves results comparable to deformation-based techniques of the state-of-the-art, finding statistical differences respect to non-deformation-based approaches. Moreover, our method highly outperforms the baseline method in time efficiency.

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