Journal articles on the topic 'Computer matching'

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1

OSUMI, Masayuki. "Computer Color Matching System." Journal of the Japan Society of Colour Material 80, no. 12 (2007): 530–36. http://dx.doi.org/10.4011/shikizai1937.80.530.

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2

CUTLER, A. E. "A New Colour-matching Computer." Journal of the Society of Dyers and Colourists 81, no. 12 (October 22, 2008): 601–8. http://dx.doi.org/10.1111/j.1478-4408.1965.tb02636.x.

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3

CHENG, EDDIE, RANDY JIA, and DAVID LU. "MATCHING PRECLUSION AND CONDITIONAL MATCHING PRECLUSION FOR AUGMENTED CUBES." Journal of Interconnection Networks 11, no. 01n02 (March 2010): 35–60. http://dx.doi.org/10.1142/s0219265910002726.

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The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost-perfect matchings. For many interconnection networks, the optimal sets are precisely those incident to a single vertex. Recently, the conditional matching preclusion number of a graph was introduced to look for obstruction sets beyond those incident to a single vertex. It is defined to be the minimum number of edges whose deletion results in a graph with no isolated vertices that has neither perfect matchings nor almost-perfect matchings. In this paper, we find this number and classify all optimal sets for the augmented cubes, a class of networks designed as an improvement of the hypercubes.
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MAO, YAPING, and EDDIE CHENG. "A Concise Survey of Matching Preclusion in Interconnection Networks." Journal of Interconnection Networks 19, no. 03 (September 2019): 1940006. http://dx.doi.org/10.1142/s0219265919400061.

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The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost-perfect matchings. There are other related parameters and generalization including the strong matching preclusion number, the conditional matching preclusion number, the fractional matching preclusion number, and so on. In this survey, we give an introduction on the general topic of matching preclusion.
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5

LÜ, HUAZHONG, and TINGZENG WU. "Fractional Matching Preclusion for Restricted Hypercube-Like Graphs." Journal of Interconnection Networks 19, no. 03 (September 2019): 1940010. http://dx.doi.org/10.1142/s0219265919400103.

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The restricted hypercube-like graphs, variants of the hypercube, were proposed as desired interconnection networks of parallel systems. The matching preclusion number of a graph is the minimum number of edges whose deletion results in the graph with neither perfect matchings nor almost perfect matchings. The fractional perfect matching preclusion and fractional strong perfect matching preclusion are generalizations of the matching preclusion. In this paper, we obtain fractional matching preclusion number and fractional strong matching preclusion number of restricted hypercube-like graphs, which extend some known results.
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6

CHENG, EDDIE, DAVID LU, and BRIAN XU. "STRONG MATCHING PRECLUSION OF PANCAKE GRAPHS." Journal of Interconnection Networks 14, no. 02 (June 2013): 1350007. http://dx.doi.org/10.1142/s0219265913500072.

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The strong matching preclusion number of a graph is the minimum number of vertices and edges whose deletion results in a graph that has neither perfect matchings nor almost-perfect matchings. This is an extension of the matching preclusion problem that was introduced by Park and Ihm. In this paper, we examine the properties of pancake graphs by finding its strong matching preclusion number and categorizing all optimal solutions.
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Chen, Ciping. "Matchings and matching extensions in graphs." Discrete Mathematics 186, no. 1-3 (May 1998): 95–103. http://dx.doi.org/10.1016/s0012-365x(97)00182-9.

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8

WEI, XIAQI, SHURONG ZHANG, and WEIHUA YANG. "Matching Preclusion for Enhanced Pyramid Networks." Journal of Interconnection Networks 19, no. 03 (September 2019): 1940009. http://dx.doi.org/10.1142/s0219265919400097.

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The matching preclusion number of a graph is the minimum number of edges whose deletion leaves the resulting graph that has neither perfect matchings nor almost perfect matchings. This concept was introduced as a measure of robustness in the event of edge failure in interconnection networks. The pyramid network is one of the important networks applied in parallel and distributed computer systems. Chen et al. in 2004 proposed a new hierarchy structure, called the enhanced pyramid network, by replacing each mesh in a pyramid network with a torus. An enhanced pyramid network of n layers is denoted by EPM(n). In this paper, we prove that the matching preclusion number of EPM(n) is 9 where n ≥ 4.
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9

Oh, Won-suk, John Pogoncheff, and William J. O’Brien. "Digital Computer Matching of Tooth Color." Materials 3, no. 6 (June 18, 2010): 3694–99. http://dx.doi.org/10.3390/ma3063694.

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10

Fysh, Matthew C., and Markus Bindemann. "Human-Computer Interaction in Face Matching." Cognitive Science 42, no. 5 (June 28, 2018): 1714–32. http://dx.doi.org/10.1111/cogs.12633.

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11

Aljawad, Dr Mohamed Saleh, Dr Mohamed Hlaeel, Abdul-Mohaimin Abbas Dawood, Alya‘a Mahmood Ali, and Hiba Ala‘a Nsaeef. "Matching Well Test Data with Computer Model." Journal of Petroleum Research and Studies 7, no. 2 (May 6, 2021): 144–61. http://dx.doi.org/10.52716/jprs.v7i2.194.

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This project concerning with matching the well test data for one of Buzurgan wells the objective of make matching is to see if the observed data of the well test as same as the calculated one by using the mathematical model we made by using computer program.The well test data was available for matching was consist of three build up test two of them have a record for the well head pressure and bottom hole pressure and one just contain a record for the well head pressure so after matching we can make correlation to find the bottom hole pressure for the test haven‘t BHP values .By using Eclipse program we build a mathematical model for the well BU-6The model consist of six layer (MA, MB11, MB12, MB21, MC1and MC2) and we take r=1, theta=10,we use the available data in Buzurgan field reports and then we enter the well test data and see the result of matching between the observed and the calculated one.From the matching we see that there was good matching between the two data, the matching was for the production and the bottomhole flowing pressure and the two was matched with the observed one.In order to make the matching very well we make change in permeability and increase its value by 20% and this change was very good to the matching of the production data and we also change the skin factor to -3.6 and that effect on the pressure matching and make it very well.
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12

Anantapantula, Sai, Christopher Melekian, and Eddie Cheng. "Matching Preclusion for the Shuffle-Cubes." Parallel Processing Letters 28, no. 03 (September 2018): 1850012. http://dx.doi.org/10.1142/s0129626418500123.

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The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost perfect matchings. A graph is maximally matched if its matching preclusion number is equal to its minimum degree, and is super matched if the matching preclusion number can only be achieved by deleting all edges incident to a single vertex. In this paper, we determine the matching preclusion number and classify the optimal matching preclusion sets for the shuffle-cube graphs, a variant of the well-known hypercubes.
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13

Zhang, Shuangshuang, Yuzhi Xiao, Xia Liu, and Jun Yin. "A Short Note of Strong Matching Preclusion for a Class of Arrangement Graphs." Parallel Processing Letters 30, no. 01 (March 2020): 2050001. http://dx.doi.org/10.1142/s0129626420500012.

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The strong matching preclusion number of a graph is the minimum number of vertices and edges whose deletion results in a graph that has neither perfect matchings nor almost perfect matchings. The strong matching preclusion is a well-studied measure for the network invulnerability in the event of edge failure. In this paper, we obtain the strong matching preclusion number for a class of arrangement graphs and categorize their the strong matching preclusion set, which are a supplement of known results.
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14

Yang, Hong Ying, Ge Zhang, and Jin Li Zhou. "Effect of Computer Color Matching System in Laboratory." Advanced Materials Research 655-657 (January 2013): 3–6. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.3.

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Computer color matching technology has many advantages for coloration processes theoretically. However, in practice, many manufacturers would rather employ experienced color matching engineer with very high salary than buying computer color matching instrument. There are many reasons for this phenomenon. This paper checks the effect of Datacolor SF600 color measuring and matching system in laboratory. The experiment show that the color matching result is much better than that of a green hand person, but far from that being expected. The reasons are discussed and new theory is expected to be developed to give a fundamental solution.
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15

An, Yan Ming, Lian Qin He, Guo Zhong Zhao, Ming Fei An, and Yue Zhen. "Study of Computer Aided History Matching Technology." Advanced Materials Research 271-273 (July 2011): 275–80. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.275.

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On the basic of studying the present technical situation of home and abroad about computer aided History Matching of the reservoir numerical simulation and digesting, absorbing technology, we studied and optimized highly effective algorithm fit for the aided History Matching. At the same time, we designed the software interface and frame and function module, developed the independent aided History Matching software named CAPHE, thus formed aided history methods suitable for our independent reservoir simulator-PBRS. By using of the software in practical some oil simulation blocks, CAPHE can significantly increase History Matching efficiency in the reservoir simulation.
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16

LI, YALAN, CHENGFU YE, MIAOLIN WU, and PING HAN. "Fractional Matching Preclusion for Möbius Cubes." Journal of Interconnection Networks 19, no. 04 (December 2019): 1950007. http://dx.doi.org/10.1142/s0219265919500075.

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Let F be an edge subset and F′ a subset of vertices and edges of a graph G. If G − F and G − F′ have no fractional perfect matchings, then F is a fractional matching preclusion (FMP) set and F′ is a fractional strong matching preclusion (FSMP) set of G. The FMP (FSMP) number of G is the minimum size of FMP (FSMP) sets of G. In this paper, we study the fractional matching preclusion number and the fractional strong matching preclusion number for the Möbius cube MQn. In adddition, all the optimal fractional strong preclusion sets of these graphs are categorized.
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17

Baste, Julien, Binh-Minh Bui-Xuan, and Antoine Roux. "Temporal matching." Theoretical Computer Science 806 (February 2020): 184–96. http://dx.doi.org/10.1016/j.tcs.2019.03.026.

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18

Eker, S. M. "Associative-commutative matching via bipartite graph matching." Computer Journal 38, no. 5 (May 1, 1995): 381–99. http://dx.doi.org/10.1093/comjnl/38.5.381.

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19

OSUMI, Masayuki. "Computer Color Matching System and Color Communication." Journal of the Japan Society of Colour Material 71, no. 2 (1998): 130–41. http://dx.doi.org/10.4011/shikizai1937.71.130.

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20

OSUMI, Masayuki. "Computer Color Matching System Employing Fuzzy Concepts." Journal of Japan Society for Fuzzy Theory and Systems 8, no. 6 (1996): 999–1006. http://dx.doi.org/10.3156/jfuzzy.8.6_17.

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21

SHI Yi-le, 施逸乐, 王辉 WANG Hui, 吴琼 WU Qiong, 李勇 LI Yong, and 金洪震 JIN Hong-zhen. "Color Matching of Color Computer-generated Holography." ACTA PHOTONICA SINICA 42, no. 1 (2013): 104–9. http://dx.doi.org/10.3788/gzxb20134201.0104.

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22

Blake, Richard E., and Algimantas Juozapavicius. "Convergent matching for model-based computer vision." Pattern Recognition 36, no. 2 (February 2003): 527–34. http://dx.doi.org/10.1016/s0031-3203(02)00059-6.

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23

Cleveland, Theodore G. "Type-Curve Matching Using a Computer Spreadsheet." Ground Water 34, no. 3 (May 1996): 554–62. http://dx.doi.org/10.1111/j.1745-6584.1996.tb02038.x.

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24

REICHMAN, NANCY. "Computer Matching: Toward Computerized Systems of Regulation." Law & Policy 9, no. 4 (October 1987): 387–415. http://dx.doi.org/10.1111/j.1467-9930.1987.tb00417.x.

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25

Lafitte, Jean-Louis. "Qualitatively matching computer architecture with Turing machine." ACM SIGARCH Computer Architecture News 31, no. 3 (June 2003): 33–41. http://dx.doi.org/10.1145/882105.882111.

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26

Oyarzun Laura, Cristina, and Klaus Drechsler. "Computer assisted matching of anatomical vessel trees." Computers & Graphics 35, no. 2 (April 2011): 299–311. http://dx.doi.org/10.1016/j.cag.2010.12.009.

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27

Seah, Hock Soon, and Tian Feng. "Computer-assisted coloring by matching line drawings." Visual Computer 16, no. 5 (June 1, 2000): 289–304. http://dx.doi.org/10.1007/s003719900068.

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28

Sebe, Nicu, Qi Tian, Michael S. Lew, and Thomas S. Huang. "Similarity Matching in Computer Vision and Multimedia." Computer Vision and Image Understanding 110, no. 3 (June 2008): 309–11. http://dx.doi.org/10.1016/j.cviu.2008.04.001.

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29

Camps, Octavia I., Linda G. Shapiro, and Robert M. Haralick. "A probabilistic matching algorithm for computer vision." Annals of Mathematics and Artificial Intelligence 10, no. 1-2 (March 1994): 85–124. http://dx.doi.org/10.1007/bf01530945.

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30

Blake, Richard E. "Symbolic approximation for computer vision." Nonlinear Analysis: Modelling and Control 3 (December 3, 1998): 31–41. http://dx.doi.org/10.15388/na.1998.3.0.15255.

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The need for a definition of discrete convergence in matching problems, which are essential in computer vision, is described and the fundamental properties of Scott lattice theory are outlined. Three data types: relational graphs, graph match_table and constraints, are considered and partial orderings are exhibited for them. A matching iteration consistent with the theory is sketched.
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31

Barbosa, Davi M. J., Julien Cretin, Nate Foster, Michael Greenberg, and Benjamin C. Pierce. "Matching lenses." ACM SIGPLAN Notices 45, no. 9 (September 27, 2010): 193–204. http://dx.doi.org/10.1145/1932681.1863572.

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32

Cseh, Ágnes, and Telikepalli Kavitha. "Popular Matchings in Complete Graphs." Algorithmica 83, no. 5 (January 25, 2021): 1493–523. http://dx.doi.org/10.1007/s00453-020-00791-7.

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AbstractOur input is a complete graph G on n vertices where each vertex has a strict ranking of all other vertices in G. The goal is to construct a matching in G that is popular. A matching M is popular if M does not lose a head-to-head election against any matching $$M'$$ M ′ : here each vertex casts a vote for the matching in $$\{M,M'\}$$ { M , M ′ } in which it gets a better assignment. Popular matchings need not exist in the given instance G and the popular matching problem is to decide whether one exists or not. The popular matching problem in G is easy to solve for odd n. Surprisingly, the problem becomes $$\texttt {NP}$$ NP -complete for even n, as we show here. This is one of the few graph theoretic problems efficiently solvable when n has one parity and $$\texttt {NP}$$ NP -complete when n has the other parity.
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33

Lo, Allan. "Existences of rainbow matchings and rainbow matching covers." Discrete Mathematics 338, no. 11 (November 2015): 2119–24. http://dx.doi.org/10.1016/j.disc.2015.05.015.

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34

WANG, SHIYING, YANLING WANG, and MUJIANGSHAN WANG. "Connectivity and Matching Preclusion for Leaf-Sort Graphs." Journal of Interconnection Networks 19, no. 03 (September 2019): 1940007. http://dx.doi.org/10.1142/s0219265919400073.

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A multiprocessor system and interconnection network have a underlying topology, which is usually presented by a graph, where nodes represent processors and links represent communication links between processors. The (conditional) matching preclusion number of a graph is the minimum number of edges whose deletion leaves the resulting graph (with no isolated vertices) that has neither perfect matchings nor almost perfect matchings. In this paper, we prove that (1) the connectivity (edge connectivity) of the leaf-sort graph CFn is [Formula: see text] for odd n and [Formula: see text] for even n; (2) CFn is super edge-connected; (3) the matching preclusion number of CFn is [Formula: see text] for odd n and [Formula: see text] for even n; (4) the conditional matching preclusion number of CFn is 3n – 5 for odd n and n ≥ 3, and 3n – 6 for even n and n ≥ 4.
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35

Zhao, Ping, Yong Kui Li, and Shan Liang Xie. "Composite Measurement Method Based on Computer Vision Technology." Advanced Materials Research 418-420 (December 2011): 2118–21. http://dx.doi.org/10.4028/www.scientific.net/amr.418-420.2118.

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Based on analyzing current methods of acquiring point clouds data in reversing engineering, considering the features of structure light scanning and binocular stereovision, we proposed a composite measuring method based on vision technology, which combines the advantages of the two measurement methods in together, and established the measuring system. The greatest advantage of this method is that the edge data were obtained by binocular vision technology, and the internal data were obtained by Structure light method. In binocular vision, stereo matching was realized with the combination of epipolar constraint, corner feature matching and area matching, which could improve location precision and matching speed of feature point. An application example shows the composite measuring method is feasible.
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CHENG, EDDIE, and LÁSZLÓ LIPTÁK. "CONDITIONAL MATCHING PRECLUSION FOR (n,k)-STAR GRAPHS." Parallel Processing Letters 23, no. 01 (March 2013): 1350004. http://dx.doi.org/10.1142/s0129626413500047.

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The matching preclusion number of an even graph G, denoted by mp (G), is the minimum number of edges whose deletion leaves the resulting graph without perfect matchings. The conditional matching preclusion number of an even graph G, denoted by mp 1(G), is the minimum number of edges whose deletion leaves the resulting graph with neither perfect matchings nor isolated vertices. The class of (n,k)-star graphs is a popular class of interconnection networks for which the matching preclusion number and the classification of the corresponding optimal solutions were known. However, the conditional version of this problem was open. In this paper, we determine the conditional matching preclusion for (n,k)-star graphs as well as classify the corresponding optimal solutions via several new results. In addition, an alternate proof of the results on the matching preclusion problem will also be given.
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Li, Haisheng, Long Lai, Li Chen, Cheng Lu, and Qiang Cai. "The Prediction in Computer Color Matching of Dentistry Based on GA+BP Neural Network." Computational and Mathematical Methods in Medicine 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/816719.

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Although the use of computer color matching can reduce the influence of subjective factors by technicians, matching the color of a natural tooth with a ceramic restoration is still one of the most challenging topics in esthetic prosthodontics. Back propagation neural network (BPNN) has already been introduced into the computer color matching in dentistry, but it has disadvantages such as unstable and low accuracy. In our study, we adopt genetic algorithm (GA) to optimize the initial weights and threshold values in BPNN for improving the matching precision. To our knowledge, we firstly combine the BPNN with GA in computer color matching in dentistry. Extensive experiments demonstrate that the proposed method improves the precision and prediction robustness of the color matching in restorative dentistry.
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38

Kim, Jinil, Peter Eades, Rudolf Fleischer, Seok-Hee Hong, Costas S. Iliopoulos, Kunsoo Park, Simon J. Puglisi, and Takeshi Tokuyama. "Order-preserving matching." Theoretical Computer Science 525 (March 2014): 68–79. http://dx.doi.org/10.1016/j.tcs.2013.10.006.

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39

Butman, Ayelet, Noa Lewenstein, and J. Ian Munro. "Permuted scaled matching." Theoretical Computer Science 638 (July 2016): 27–32. http://dx.doi.org/10.1016/j.tcs.2016.02.036.

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40

Nielsen, Christoffer Rosenkilde, Flemming Nielson, and Hanne Riis Nielson. "Cryptographic Pattern Matching." Electronic Notes in Theoretical Computer Science 168 (February 2007): 91–107. http://dx.doi.org/10.1016/j.entcs.2006.08.024.

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41

Birkedal, Lars, Troels Christoffer Damgaard, Arne John Glenstrup, and Robin Milner. "Matching of Bigraphs." Electronic Notes in Theoretical Computer Science 175, no. 4 (July 2007): 3–19. http://dx.doi.org/10.1016/j.entcs.2007.04.013.

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42

Muthukrishnan, S., and H. Ramesh. "String Matching under a General Matching Relation." Information and Computation 122, no. 1 (October 1995): 140–48. http://dx.doi.org/10.1006/inco.1995.1144.

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43

Barr, John. "Decade matching." ACM Inroads 9, no. 4 (November 2018): 107–8. http://dx.doi.org/10.1145/3230685.

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44

Shimizu, Hirofumi, Janet S. Twyman, and Jun-ichi Yamamoto. "Computer-based sorting-to-matching in identity matching for young children with developmental disabilities." Research in Developmental Disabilities 24, no. 3 (May 2003): 183–94. http://dx.doi.org/10.1016/s0891-4222(03)00028-3.

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45

HIRAYAMA, Tohoru. "Recent Trend of Computer Colour Matching for Paints." Journal of the Japan Society of Colour Material 65, no. 11 (1992): 692–99. http://dx.doi.org/10.4011/shikizai1937.65.692.

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46

SUZUKA, Masakazu. "Recent Trend of Computer Colour Matching for Dyes." Journal of the Japan Society of Colour Material 65, no. 11 (1992): 700–710. http://dx.doi.org/10.4011/shikizai1937.65.700.

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47

Leifer, Richard. "Matching Computer-Based Information Systems with Organizational Structures." MIS Quarterly 12, no. 1 (March 1988): 63. http://dx.doi.org/10.2307/248805.

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48

van Geel, Mieke. "THE COMPUTER MATCHING AND PRIVACY ACT OF 1988." Tilburg Law Review 2, no. 1 (January 1, 1992): 41–48. http://dx.doi.org/10.1163/221125992x00034.

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49

Karbasi, A., S. Moradian, and S. Asiaban. "Computer Color Matching Procedures for Mass Colored Polypropylene." Polymer-Plastics Technology and Engineering 47, no. 10 (September 29, 2008): 1024–31. http://dx.doi.org/10.1080/03602550802355248.

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50

Song, Zhijun, Jun Yu, Changle Zhou, and Meng Wang. "Automatic cartoon matching in computer-assisted animation production." Neurocomputing 120 (November 2013): 397–403. http://dx.doi.org/10.1016/j.neucom.2012.08.051.

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