Edge Matching Research Articles

Retracted: Development of Bulk Metallic Glasses and their Composites by Additive Manufacturing – Evolution, Challenges and a Proposed Novel Solution

Bulk metallic glasses (BMGs) and their composites (BMGMCs) have emerged as competitive materials for structural engineering applications exhibiting superior tensile strength, hardness along with very large elastic strain limit. However, they suffer from lack of ductility and subsequent low toughness due to the inherent brittleness of the glassy structure which makes them amenable to failure without appreciable yielding. Various mechanisms and methods have been proposed to counter this effect out of which, recently Additive Manufacturing has gained widespread attention. It is proposed that additive manufacturing can overcome these difficulties in single step due to inherent existence of very high cooling rate in the process which is essential for glass formation. This, when coupled with careful selection of alloy chemistry is proposed to be the best solution to fabricate near net shape parts in a single step with excellent properties. In this report, an effort has been made to describe one possible route to achieve this. Solidification processing employing carefully selected inoculants based on edge to edge matching technique along with the carefuly controlled inoculation procedure is proposed to reflect upon enhanced mechanical properties. It is hypothesized that number density, size and distribution of ductile crystalline phase would best be able to improve microstructure and hence properties. This is meant to be controlled by manipulating type, size and the amount of inoculants. The proposed methodology is claimed to bear maximum potential.

Multi-frame stereo matching with edges, planes, and superpixels

We present a multi-frame narrow-baseline stereo matching algorithm based on extracting and matching edges across multiple frames. Edge matching allows us to focus on the important features at the very beginning, and deal with occlusion boundaries as well as untextured regions. Given the initial sparse matches, we fit overlapping local planes to form a coarse, over-complete representation of the scene. After breaking up the reference image in our sequence into superpixels, we perform a Markov random field optimization to assign each superpixel to one of the plane hypotheses. Finally, we refine our continuous depth map estimate using a piecewise-continuous variational optimization. Our approach successfully deals with depth discontinuities, occlusions, and large textureless regions, while also producing detailed and accurate depth maps. We show that our method out-performs competing methods on high-resolution multi-frame stereo benchmarks and is well-suited for view interpolation applications.

Open-independent, Open-locating-dominating Sets in Complementary Prism Graphs

For a finite, simple, undirected graph G = (V (G),E(G)), an open-dominating set S ⊆ V (G) is such that every vertex in G has at least one neighbor in S. An open-independent, open-locating-dominating set S ⊆ V (G) (OLDOIND-set for short) is such that no two vertices in G have the same set of neighbors in S and each vertex in S is open-dominated exactly once by S. The problem of deciding whether or not a given graph has an OLDOIND-set is known to be NP-complete. The complementary prism of G is the graph GG‾, formed from the disjoint union of G and its complement G‾ by adding the edges of a perfect matching between the corresponding vertices of G and G‾. We provided a logarithmic lower bound on the size of an OLDOIND-set in any graph. Various properties of and bounds on OLDOIND-sets in complementary prisms were presented and the cases of cliques, paths and cycles have been completely solved. It has been shown that for any graph with girth at least five, it can be decided in polynomial time whether or not its complementary prism has an OLD-OIND-set (and also the set can be found in polynomial time if it exists).

Quantitative classification of vortical flows based on topological features using graph matching.

Vortical flow patterns generated by swimming animals or flow separation (e.g. behind bluff objects such as cylinders) provide important insight to global flow behaviour such as fluid dynamic drag or propulsive performance. The present work introduces a new method for quantitatively comparing and classifying flow fields using a novel graph-theoretic concept, called a weighted Gabriel graph, that employs critical points of the velocity vector field, which identify key flow features such as vortices, as graph vertices. The edges (connections between vertices) and edge weights of the weighted Gabriel graph encode local geometric structure. The resulting graph exhibits robustness to minor changes in the flow fields. Dissimilarity between flow fields is quantified by finding the best match (minimum difference) in weights of matched graph edges under relevant constraints on the properties of the edge vertices, and flows are classified using hierarchical clustering based on computed dissimilarity. Application of this approach to a set of artificially generated, periodic vortical flows demonstrates high classification accuracy, even for large perturbations, and insensitivity to scale variations and number of periods in the periodic flow pattern. The generality of the approach allows for comparison of flows generated by very different means (e.g. different animal species).

Microstructure and mechanical properties of Mg-Gd-Y-Sm-Al alloy and analysis of grain refinement and strengthening mechanism

The effects of Sm on the microstructure and mechanical properties of Mg-11Gd-2Y-0.6Al alloy were investigated by X-ray diffraction, optical microscopy, scanning electron microscopy, energy dispersive spectrometry and high resolution transmission electron microscopy. Based on the theory of edge–edge matching and electronegativity theory, the mechanism of grain refinement is discussed. The strengthening mechanism is expounded conveniently from fine grain strengthening, coherent strengthening, precipitation strengthening and grain boundary strengthening. The results show that the microstructure of Mg-11Gd-2Y-0.6Al alloy is mainly composed of α-Mg matrix, Mg5Gd and Mg24Y5 phases. The addition of Sm forms Mg41Sm5 phase in the alloy and refines the alloy. The addition of Sm significantly improves the mechanical properties of the alloy at room and high temperatures. When the addition of Sm is 3 wt%, the tensile strengths of the alloy at room temperature and high temperature (200 °C) reach the maximum value 292 and 321 MPa, respectively. The fracture mode of the alloy at different temperatures is mainly brittle fracture and intercrystalline fracture.

Fusion of Ship Perceptual Information for Electronic Navigational Chart and Radar Images based on Deep Learning

Superimposing Electronic Navigational Chart (ENC) data on marine radar images can enrich information for navigation. However, direct image superposition is affected by the performance of various instruments such as Global Navigation Satellite Systems (GNSS) and compasses and may undermine the effectiveness of the resulting information. We propose a data fusion algorithm based on deep learning to extract robust features from radar images. By deep learning in this context we mean employing a class of machine learning algorithms, including artificial neural networks, that use multiple layers to progressively extract higher level features from raw input. We first exploit the ability of deep learning to perform target detection for the identification of marine radar targets. Then, image processing is performed on the identified targets to determine reference points for consistent data fusion of ENC and marine radar information. Finally, a more intelligent fusion algorithm is built to merge the marine radar and electronic chart data according to the determined reference points. The proposed fusion is verified through simulations using ENC data and marine radar images from real ships in narrow waters over a continuous period. The results suggest a suitable performance for edge matching of the shoreline and real-time applicability. The fused image can provide comprehensive information to support navigation, thus enhancing important aspects such as safety.

Efficient control of band gap energy and optical properties of titania thin films for solar cell applications

Titanium dioxide, also known as titania is an important semiconducting oxide that has gained prominence in recent years for applications in photoelectrochemical/dye-sensitized solar cells. TiO2 is at present preferred over oxides such as ZnO or SnO2 for applications in dye-sensitized solar cell, because of its mesoporous structure, which offers high surface area for efficient dye loading. It is also important that the band gap of the metal oxide can be adjusted for efficient electron injection from most available commercial dyes since the generation of voltage in the device depends mainly on the band edge matching. Herein, TiO2 thin films were deposited in a polyvinyl pyrolidon medium by the chemical bath deposition technique at 65 °C. The inclusion of lead ions during the film formation process led to structural modification of the thin films of titania – anatase (TiO2) and subsequent formation of structurally disordered Pb-doped TiO2 thin films. The resulting films retain the high band gap energy of crystalline anatase and substantially tuned in the range, 2.85–3.26 ± 0.05 eV by the Pb2+ inclusion.

The preparation of high quality heterostructure photonic crystals by using pulse anodization and their enhanced photocatalytic property

The TiO2 nanotube heterostructure photonic crystals (HPCs) with high quality photonic band gap (PBG) and more photonic band edges have been prepared by using the pulse anodization with compensation voltage. It is found that when the hetero-structure is introduced on the top of the whole HPC, its structure becomes more uniform and more band edges with high quality PBG appear in the whole visible range, which greatly enhance the light absorption of PC photocatalyst due to the higher photonic state density, and further improve its photocatalytic performance. Furthermore, the matching of PBG red and blue edges with the RhB absorption peak also has effect on the photocatalytic performance. And the HPC has two closely adjoining PBGs and their band edges are simultaneously matched with the RhB absorption peak, which make the HPC have the strongest photocatalytic enhancement.

The Discussion on Interior Design Mode Based on 3D Virtual Vision Technology

Three dimensional virtual vision technologies improves the realistic effect of interior design, improves the rationality of interior design, and guides interior decoration and graphic design. In this paper, MultiGen Creator 3D modeling technology is used to reconstruct the indoor landscape and form the plane image of interior design. At the same time, the edge matching method is used to divide and decompose the features of the interior design image, and the 3D vision of the interior design is reconstructed with the key points of the interior design. In addition, through the adaptive tracking and rendering technology, the fidelity and space utilization efficiency of the room plane design are improved. The simulation results show that 3D virtual vision technology has better guidance for interior design, visual effect and landscape color fusion.

Graded Betti numbers of crown edge ideals

An n-crown on 2n vertices is a graph obtained from complete bipartite graph by removing edges of a perfect matching. Given a finite simple graph G, one can associate a simplicial complex . In this article, we use combinatorial data from the associated simplicial complex of the crown graph and give formulas to find all Betti numbers of edge ideals of .

A Novel Method for Image Segmentation Based on Simplified Pulse Coupled Neural Network and Gbest Led Gravitational Search Algorithm

This paper proposed a novel image segmentation method based on simplified pulse coupled neural network (SPCNN), which was optimized by gbest led gravitational search algorithm (GLGSA) that combined gravitational search algorithm (GSA) with gbest agent memory ability. To evaluate the performance of GLGSA, we applied it to 23 standard benchmark functions and compared with GSA and GGSA. The results showed that the GLGSA had better performance in term of convergence and avoidance of local minima. Besides, in order to improve the accuracy of segmentation, the fitness function consisted of cross entropy parameter, edge matching, and noise control. To verify the efficiency of our method, we compared it with the state-of-the-art algorithms, such as Otsu, GA Renyi, and PSO-PCNN, using the gray nature images from the Berkeley segmentation dataset. Finally, the subjective visual analysis and quantitative analysis that included the uniformity measure, region contrast measure, structural similarity, and comprehensive evaluation were used to evaluate the segmented images. The comparison results demonstrated that our proposed method could get better segmentation results.

Three-dimensional image reconstruction method for human maxillofacial defect images based on block sample feature matching

In traditional methods, template matching of human face image sample and edge contour segmentation is ignored, and the application is susceptible to the robustness of skin colour features and pose changes, resulting in poor accurate three-dimensional reconstruction and recognition of human maxillofacial defect images. Therefore, a three-dimensional image reconstruction method for human maxillofacial defect images based on block sample feature matching is proposed. A feature matching template of human maxillofacial images is constructed, and template matching and three-dimensional edge contour segmentation are performed to the sample images through the blocking technique. The simulation results show that this method has good feature detection and detection ability in three-dimensional reconstruction of human maxillofacial defect images and can provide high accuracy in three-dimensional reconstruction.

Machine Learning Approach-Based Gamma Distribution for Brain Tumor Detection and Data Sample Imbalance Analysis

Recently, artificial intelligence applications in magnetic resonance imaging have been applied in several clinical studies. The analysis of brain tumors without human intervention is considered a significant area of research because the extracted brain images need to be optimized using a segmentation algorithm that is highly resilient to noise and cluster size sensitivity problems and automatically detects the region of interest (ROI). In this paper, an improved orthogonal gamma distribution-based machine-learning approach is used to analyze the under- segments and over-segments of brain tumor regions to automatically detect abnormalities in the ROI. Further data imbalances due to improper edge matching in the abnormal region is sampled by matching the edge coordinates and sensitivity, and the selectivity parameters are measured using the machine learning algorithm. The benchmark medical image database was collected and analyzed to validate the efficiency and accuracy of the optimal automatic detection in tumor and non-tumor regions. The mean error rate of the algorithm was determined using a mathematical formulation. The system is evaluated based on experimental results that showed the method of orthogonal gamma distribution with the machine learning approach attained an accuracy of 99.55% in detecting brain tumors. This research contributes to the field of brain abnormality detection and analysis without human intervention in the health care sector.

Efficient Matching of Multi-Modal Sensing Nodes for Collaborative Sense Optimization of Composite Events

Composite events are sense maximizes collaboration through multiple sensors. Efficient matching of multi-modal sensing nodes in multi-composite events is always a thorny problem. In this paper, the composite event sensing model is first proposed, and then the collaborative-sense problem of multi-modal sensing nodes is translated into a binary matching problem. For these multi-class sensors and multi-class compound events scene, a pruning-grafting and parallel strategy be adopted, which can speed up the traversal speed and find the maximum matching edge quickly. For multi-nodes selection, the distance of the composite event constraints into binarily weighted matching. A collaborative-sense intelligent matching algorithm is suggested. It takes collaborative in various kinds of nodes matching combining with the distribution of the composite event itself around the nodes. Combined with the random distribution of various sensor nodes and composite events, the matching rate of some sensor nodes is sacrificed for the overall event efficiency. Compare to parallel algorithms, it has another effect on perceived efficiency. Finally, by comparing with other algorithms, CSSMA and other proposed algorithms have a certain advantage in the inclusive sense efficiency. In terms of composite events collaborative-sense, this work has nice theoretical significance and practical value.

Analogies between the Crossing Number and the Tangle Crossing Number

Tanglegrams are special graphs that consist of a pair of rooted binary trees with the same number of leaves, and a perfect matching between the two leaf-sets. These objects are of use in phylogenetics and are represented with straight-line drawings where the leaves of the two plane binary trees are on two parallel lines and only the matching edges can cross. The tangle crossing number of a tanglegram is the minimum number of crossings over all such drawings and is related to biologically relevant quantities, such as the number of times a parasite switched hosts.Our main results for tanglegrams which parallel known theorems for crossing numbers are as follows. The removal of a single matching edge in a tanglegram with $n$ leaves decreases the tangle crossing number by at most $n-3$, and this is sharp. Additionally, if $\gamma(n)$ is the maximum tangle crossing number of a tanglegram with $n$ leaves, we prove $\frac{1}{2}\binom{n}{2}(1-o(1))\le\gamma(n)<\frac{1}{2}\binom{n}{2}$. For an arbitrary tanglegram $T$, the tangle crossing number, $\mathrm{crt}(T)$, is NP-hard to compute (Fernau et al. 2005). We provide an algorithm which lower bounds $\mathrm{crt}(T)$ and runs in $O(n^4)$ time. To demonstrate the strength of the algorithm, simulations on tanglegrams chosen uniformly at random suggest that the tangle crossing number is at least $0.055n^2$ with high probabilty, which matches the result that the tangle crossing number is $\Theta(n^2)$ with high probability (Czabarka et al. 2017).

Class of weighted graphs with strong anti-reciprocal eigenvalue property

Let G be a graph with a unique perfect matching and be the collection of all positive weight functions defined on the edge set of G such that each weight function assigns weight 1 to each matching edge and a positive weight to each non-matching edge of G. Given , let denote the weighted graph obtained from G corresponding to the weight function w. Let denote the adjacency matrix of . We say satisfies the anti-reciprocal eigenvalue property (property (−R)) if for each eigenvalue λ of , its anti-reciprocal is also an eigenvalue of . Furthermore, if λ and have the same multiplicities, then we say satisfies the strong anti-reciprocal eigenvalue property (property (−SR)). In this paper, the graphs with property (−SR) in the class of graphs with unique perfect matching are investigated and it is shown that a weighted graph with a unique perfect matching satisfies property (−SR) for each if and only if it is a corona graph.

A Simplified Three-Isocenter VMAT for Craniospinal Irradiation

Craniospinal irradiation (CSI) has become an important treatment method for Central nervous system (CNS) relapse of hematological malignancies. However, conventional CSI is technically very challenging and field edge matching is needed because of the mechanical limitations of standard linear accelerators. We have developed a simple Linac based volumetric modulated arc therapy (VMAT) technique for CSI. The aim of this study is to report initial clinical experience of CSI-VMAT for CNS relapse of hematological malignancies and compare dose homogeneity and dose to organs at risk (OAR) with conventional three-dimensional conformal radiotherapy (3D-CRT) technique. Seven patients with CNS relapse of hematological malignancies were treated with CSI using a novel VMAT technique and seven patients were previously underwent conventional 3D-CRT technique. A dose of 22.5 Gy was prescribed to be delivered in 15 fractions. Planning target volume (PTV) included the whole brain and spinal canal to the S3 vertebral level. Patient is positioned supine and immobilized using a face mask and Vac-Lock in the body. The VMAT plan used three full arcs, each with a unique isocenter (Brain, Superior Spine, Inferior Spine). Spinal two arcs turned off lateral beams (225-315o and 45-135o) to minimize lung dose. Adjacent fields were intentionally overlapped to eliminate the issues of beam edge matching, and the optimization process smoothly integrated the dose inside the overlapped junction. The 3D-CRT plan used edge matching field sets and shifted junctions three times. Comparing the VMAT plan with the 3D-CRT plan, the average simplified homogeneity index (HI=Dmax/Dmin) of PTV was 1.19 ± 0.11 and 1.46 ± 0.13, respectively. Mean doses to avoidable OAR were significantly decreased (for the heart: 5.3 Gy and 11.1 Gy, for the esophagus: 11.6 Gy and 19.7 Gy, for the parotid glands: 8.8 Gy and 20.5 Gy, using VMAT and 3D-CRT, respectively). The lung V5, V10 and Dmean were 28.3%, 5.4%, 3.4 Gy, in VMAT, 20.1%, 15.3 %, 4.6 Gy, in 3D-CRT. The doses of lung except V5 were lower in VMAT. Short term follow-up evaluations revealed no unexpected toxicity associated with this novel VMAT technique. We have developed a simplified three-isocenter Linac based VMAT approach for CSI. The dose to the brain and spine is significantly improved, compared to the conventional 3D-CRT. The single VMAT plan is easier to manage. This new technique simplifies the set up and eliminates the need for shifting junctions during course of CSI treatment and saves OAR of patients with CNS relapse of hematological malignancies.

CsTaWO6 as photoelectron platform by carbon nitride sensitizing toward enhanced charge carrier separation and photocatalytic activity

A series of CsTaWO6/g-C3N4 composite photocatalysts were successfully prepared aiming to regulate the charge kinetics for spatial separating the photoinduced electrons and holes under visible light irradiation, which was ascribed to the matched band edge alignment of CsTaWO6 and g-C3N4. Careful characterization on the as-prepared photocatalysts indicated the co-existent of g-C3N4 and CsTaWO6 in the composite. Because of electronic structure match and fine interfacial contact, CsTaWO6 anchored with g-C3N4 can enhance the visible light harvesting capacity and the photocatalytic activity toward hydrogen evolution was greatly improved with an optimized evolution rate of 7.5 μmol·g−1·h−1 and an excellent photocatalytic stability was achieved. The enhancement of photocatalytic activity was attributed to the fact that photoinduced electrons in the conduction band of g-C3N4 can be efficiently injected into the conduction band of CsTaWO6, leading to spatial charge separation between g-C3N4 and CsTaWO6, which can be confirmed by incident photocurrent responses and electrochemical impedance curves.

COMPARISON OF GEOMETRIC CORRECTION SCHEMES FOR GEOSTATIONARY OCEAN COLOR IMAGER SLOTS WITHOUT GCPS

Abstract. Currently, the geometric correction process of GOCI (Geostationary Ocean Color Imager) image is performed by matching slot images against shorelines and utilizing the matching results as GCPs (Ground Control Point). However, there are several GOCI slots without shorelines and for such slots acquiring GCPs is not easy. The purpose of this paper is to compare several alternative geometric correction schemes applicable to the slots without GCPs. We analyzed three schemes. The first scheme is to apply the correction angle of the same slot in the most recent dataset. The second scheme is to apply correction angle of the previous slot in the current dataset. And the last scheme is to apply correction angle of the slot with the largest number of GCPs in the same time dataset. Overall process for comparing the quality of the three geometric correction schemes consisted of the following steps. Firstly, using ephemeris metadata of GOCI Level 1A, we established initial sensor model, which defines geometry relationship between ground coordinate system and image coordinate system of a GOCI image. And then, by matching edge detected from GOCI slot images and shoreline landmark chips, we obtained GCPs. Using these GCPs, we calculated correction angle of each slot. After then, through the three schemes, we conducted precision sensor modeling. Among three schemes, geometric correction applying the previous slot correction angle showed the best quality. The average RMSE of this scheme was about 1.4 km, which was quite close to geometric correction quality applying correction angles from GCPs.

Real-time scene background initialization based on spatio-temporal neighborhood exploration

In this paper, we address the problem of scene background initialization to define a background model free from foreground objects. The complexity of this task resides in the continuous clutter of the scene by moving and stationary objects. To face this challenge, we propose a robust real-time iterative model completion method based on online block-level processing to initialize the background with low computational cost. First, temporal data analysis is conducted to cluster similar blocks. Meanwhile, a two-folded inter-block spatial neighborhood exploration is performed. It aims to capture relationships among neighboring clusters and reduce the number of candidate clusters employed in the next phase. Then, a smoothness analysis between neighboring locations is performed to iteratively reconstruct the background based on a newly proposed edge matching metric and an inter-block color discontinuity. Extensive evaluations of the proposed approach on the public Scene Background Initialization 2015 dataset and on the Scene Background Modeling Contest 2016 dataset revealed a performance superior or comparable to state-of-the-art methods.