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Relevant bibliographies by topics / Euclidean distances

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Academic literature on the topic 'Euclidean distances'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Euclidean distances"

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Cubukcu, K. Mert, and Hatcha Taha. "Are Euclidean Distance and Network Distance Related ?" Environment-Behaviour Proceedings Journal 1, no. 4 (2016): 167. http://dx.doi.org/10.21834/e-bpj.v1i4.137.

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Although spatial distance is a very important concept for a wide variety of disciplines including social, natural, and information sciences, the methods used to measure spatial distance are not directly expressed and fully explained. In this study, we calculate and compare Euclidean distances and network distances for 10 randomly selected European cities. On the contrary to the findings reported in past research, we find that there is not a global straight forward relation between the Euclidian distance and network distance.© 2016. The Authors. Published for AMER ABRA by e-International Publis

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AICHHOLZER, OSWIN, FRANZ AURENHAMMER, DANNY Z. CHEN, D. T. LEE, and EVANTHIA PAPADOPOULOU. "SKEW VORONOI DIAGRAMS." International Journal of Computational Geometry & Applications 09, no. 03 (1999): 235–47. http://dx.doi.org/10.1142/s0218195999000169.

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On a tilted plane T in three-space, skew distances are defined as the Euclidean distance plus a multiple of the signed difference in height. Skew distances may model realistic environments more closely than the Euclidean distance. Voronoi diagrams and related problems under this kind of distances are investigated. A relationship to convex distance functions and to Euclidean Voronoi diagrams for planar circles is shown, and is exploited for a geometric analysisis and a plane-sweep construction of Voronoi diagrams on T. An output-sensitive algorithm running in time O(n log h) is developed, where

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Rodríguez-Larralde, Alvaro. "Genetic distance estimated through surname frequencies of 37 counties from the state of Lara, Venezuela." Journal of Biosocial Science 25, no. 1 (1993): 101–10. http://dx.doi.org/10.1017/s0021932000020344.

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SummaryGenetic distances between all possible pairs of counties (n = 37) in the state of Lara, Venezuela were calculated using surname frequencies and the Euclidean distance as estimator. In general, Euclidean distances were smaller between counties closer together, and the product moment correlation between geographic and Euclidean distances was 0·49 (p < 0.02). The results suggest that, in Lara, geographic distance has been an important determinant of genetic structure, although topography and roadways also have had an important influence upon this structure.

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Fearn, Tom. "Mahalanobis and Euclidean Distances." NIR news 21, no. 1 (2010): 12–14. http://dx.doi.org/10.1255/nirn.1167.

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Hajdu, András, Lajos Hajdu, and Robert Tijdeman. "Approximation of the Euclidean Distance by Chamfer Distances." Acta Cybernetica 20, no. 3 (2012): 399–417. http://dx.doi.org/10.14232/actacyb.20.3.2012.3.

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Karachurina, Liliya B., and Nikita V. Mkrtchyan. "The experience of calculating distances between different types of settlements in Russia to assess the range of population migration." Vestnik of Saint Petersburg University. Earth Sciences 68, no. 3 (2023): 418–42. http://dx.doi.org/10.21638/spbu07.2023.301.

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The problem of measuring distances in migration is not trivial, but important, for example, for delimiting the concepts of population migration and housing mobility. In conditions of limited access to detailed spatial data, researchers solve this issue in different ways. Only a few countries, such as Sweden, have the ability to calculate migration distances between point locations using Euclidean distance (in a straight line). In this article, as applied to Russia, the measure of correspondence between Euclidean distances and real distances along transport routes is investigated. For this purp

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Vijay, Sujith. "Eleven Euclidean distances are enough." Journal of Number Theory 128, no. 6 (2008): 1655–61. http://dx.doi.org/10.1016/j.jnt.2007.08.016.

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Sinha, Hari Om Sharan. "Enhancement of “Technique for Order Preference by Similarity to Ideal Solution” Approach for Evaluating the Web Sources to Select as External Source for Web Warehousing." International Journal of Natural Computing Research 6, no. 1 (2017): 1–16. http://dx.doi.org/10.4018/ijncr.2017010101.

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The main concern of this paper is to evaluate the web sources, which are to be selected as external data sources for web warehousing. In order to identify the web sources, they are evaluated on the ground of their multiple features. For it, Multi Criteria Decision Making (MCDM) approach has been used. Here, among all the MCDM approach, the focus is on “Technique for Order Preference by Similarity to Ideal Solution” (TOPSIS) approach and proposing an enhancement in this method. The conventional TOPSIS approach uses Euclidean Distance to measure the similarity. Here, Jeffrey Divergence has been

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Guzmán Naranjo, Matías, and Gerhard Jäger. "Euclide, the crow, the wolf and the pedestrian: distance metrics for linguistic typology." Open Research Europe 3 (July 2, 2024): 104. http://dx.doi.org/10.12688/openreseurope.16141.2.

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It is common for people working on linguistic geography, language contact and typology to make use of some type of distance metric between lects. However, most work so far has either used Euclidean distances, or geodesic distance, both of which do not represent the real separation between communities very accurately. This paper presents two datasets: one on walking distances and one on topographic distances between over 8700 lects across all macro-areas. We calculated walking distances using Open Street Maps data, and topographic distances using digital elevation data. We evaluate these distan

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Marques, Samuel de França, Renan Favero, and Cira Souza Pitombo. "Should We Account for Network Distances or Anisotropy in the Spatial Estimation of Missing Traffic Data?" TRANSPORTES 31, no. 1 (2023): e2822. http://dx.doi.org/10.58922/transportes.v31i1.2822.

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In light of the unavailability of traffic volume data for all road segments, the scientific literature proposes estimating this variable using spatial interpolators. However, most of the methods found use the Euclidean distance between the database points as a proximity measure, in addition to ignoring the anisotropy of the phenomenon. Thus, the objective of the present study was to apply Ordinary Kriging (OK) with network distances and anisotropic OK in traffic volume data on highways in the state of São Paulo (Brazil), comparing its results to the traditional isotropic approach with Euclidea

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Dissertations / Theses on the topic "Euclidean distances"

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Alvarez, Milagros. "Tradeoff Analysis and Evaluation in the Management of Forests for Multiple Uses. The use of Euclidean distances as a Decision Support Tool." Fogler Library, University of Maine, 2002. http://www.library.umaine.edu/theses/pdf/AlvarezM2002.pdf.

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Самородов, Б. В. "Врахування компетентностей експертів при рейтингуванні банків за допомогою таксонометричного методу". Thesis, Українська академія банківської справи Національного банку України, 2011. http://essuir.sumdu.edu.ua/handle/123456789/62203.

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Важливим питанням при рейтингуванні банку є питання урахування компетентностей експертів, які беруть участь у наданні оцінок важливості обраних показників діяльності банків.<br>An important issue when re-rating the bank is the issue of taking into account the competencies of experts involved in the assessment of the importance of the selected indicators of the banks.

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Wright, Mark William. "The extended Euclidean distance transform." Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388344.

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Hirata, Tomio. "VLSI Algorithm for Euclidean Distance Transform." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2004. http://hdl.handle.net/2237/10354.

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Chilakamarri, Kiran Babu. "Unit-distance graphs in Euclidean spaces /." The Ohio State University, 1990. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487678444256871.

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Gustafsson, Lukas. "The Euclidean Distance Degree of Conics." Thesis, KTH, Matematik (Avd.), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252533.

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The Euclidean Distance Degree (EDD) of a variety is the number of critical points of the squared distance function of a general point outside the variety. In this thesis we give a classification of conics based on their EDD, originally attributed to Cayley. We show that circles and parabolas have EDD 2 and 3 respectively while all other conics have EDD 4. We reduce the computation of the EDD to finding solutions of the determinant of a certain generalized matrix, called the hyperdeterminant of type 2 × 3 × 3. This determinant is computed using the celebrated Schläfli decomposition.<br>The Eucl

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Tuncbilek, Cihan H. "A Squared-Euclidean distance location-allocation problem." Thesis, Virginia Tech, 1990. http://hdl.handle.net/10919/42358.

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none available<br>This thesis is concerned with the analysis of a squared-Euclidean distance location-allocation problem with balanced transportation constraints, where the costs are directly proportional to distances and the amount shipped. The problem is shown to be equivalent to maximizing a convex, quadratic function subject to transportation constraints. A branch and bound algorithm is developed that utilizes a specialized, tight, linear programming representation to compute strong upper bounds. These bounds are shown to substantially dominate several other upper bounds that are derived u

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Torelli, Julio Cesar. ""Implementação paralela da transformada de distância euclidiana exata"." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-21102005-132225/.

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Transformada de distância euclidiana (TDE) é a operação que converte uma imagem binária composta de pontos de objeto e de fundo em outra, chamada mapa de distâncias euclidianas, onde o valor armazenado em cada ponto corresponde à menor distância euclidiana entre este ponto e o fundo da imagem. A TDE é muito utilizada em visão computacional, análise de imagens e robótica, mas é uma transformação muito demorada, principalmente em imagens 3-D. Neste trabalho são utilizados dois tipos de computadores paralelos, (i) multiprocessadores simétricos (SMPs) e (ii) agregados de computadores, para reduzi

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Bai, Shuanghua. "Numerical methods for constrained Euclidean distance matrix optimization." Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/401542/.

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This thesis is an accumulation of work regarding a class of constrained Euclidean Distance Matrix (EDM) based optimization models and corresponding numerical approaches. EDM-based optimization is powerful for processing distance information which appears in diverse applications arising from a wide range of fields, from which the motivation for this work comes. Those problems usually involve minimizing the error of distance measurements as well as satisfying some Euclidean distance constraints, which may present enormous challenge to the existing algorithms. In this thesis, we focus on problems

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Gomes, Tânia Tenório. "Rede ARTMAP Euclidiana utilizada na solução do problema de previsão de cargas elétricas." Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/152580.

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Submitted by TÂNIA TENÓRIO GOMES (taniatgs@yahoo.com.br) on 2018-01-23T18:19:50Z No. of bitstreams: 1 Tania_dissertação_dee_22_12_2017.pdf: 2284683 bytes, checksum: 58edd8d14052f9f162a3d091782c28c3 (MD5)<br>Approved for entry into archive by Cristina Alexandra de Godoy null (cristina@adm.feis.unesp.br) on 2018-01-24T11:18:07Z (GMT) No. of bitstreams: 1 gomes_tt_me_ilha.pdf: 2284683 bytes, checksum: 58edd8d14052f9f162a3d091782c28c3 (MD5)<br>Made available in DSpace on 2018-01-24T11:18:07Z (GMT). No. of bitstreams: 1 gomes_tt_me_ilha.pdf: 2284683 bytes, checksum: 58edd8d14052f9f162a3d091782c

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Books on the topic "Euclidean distances"

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Mukhopadhyay, Jayanta. Approximation of Euclidean Metric by Digital Distances. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9901-9.

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Li, Yuying. A Newton acceleration of the Weiszfeld algorithm for minimizing the sum of Euclidean distances. Cornell Theory Center, Cornell University, 1995.

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Liberti, Leo, and Carlile Lavor. Euclidean Distance Geometry. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60792-4.

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Ragnemalm, Ingemar. The Euclidean distance transform. Univ., 1993.

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Hanjoul, Pierre. Theoretical market areas under Euclidean distance. Institute of Mathematical Geography, 1988.

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Koolen, Jacobus Hendricus. Euclidean representations and substructures of distance-regular graphs. Technische Universiteit Eindhoven, 1996.

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Alfakih, Abdo Y. Euclidean Distance Matrices and Their Applications in Rigidity Theory. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97846-8.

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Garibaldi, Julia. The Erdös distance problem. American Mathematical Society, 2010.

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Ibragimov, Zair. Topics in several complex variables: First USA-Uzbekistan Conference on Analysis and Mathematical Physics, May 20-23, 2014, California State University, Fullerton, California. American Mathematical Society, 2016.

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Mukhopadhyay, Jayanta. Approximation of Euclidean Metric by Digital Distances. Springer Singapore Pte. Limited, 2020.

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Book chapters on the topic "Euclidean distances"

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Matoušek, Jiří. "Are these distances Euclidean?" In The Student Mathematical Library. American Mathematical Society, 2010. http://dx.doi.org/10.1090/stml/053/07.

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Camiz, Sergio. "Comparison of Euclidean Approximations of non-Euclidean Distances." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60126-2_18.

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Borg, Ingwer, and Patrick Groenen. "Scalar Products and Euclidean Distances." In Springer Series in Statistics. Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2711-1_17.

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Camiz, Sergio, and Georges Le Calvé. "Recent Experimentation on Euclidean Approximations of Biased Euclidean Distances." In Advances in Classification and Data Analysis. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-59471-7_10.

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Mukhopadhyay, Jayanta. "Digital Distances: Classes and Hierarchies." In Approximation of Euclidean Metric by Digital Distances. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9901-9_2.

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Mukhopadhyay, Jayanta. "Linear Combination of Digital Distances." In Approximation of Euclidean Metric by Digital Distances. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9901-9_5.

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Mukhopadhyay, Jayanta. "Geometry, Space, and Metrics." In Approximation of Euclidean Metric by Digital Distances. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9901-9_1.

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Mukhopadhyay, Jayanta. "Error Analysis: Analytical Approaches." In Approximation of Euclidean Metric by Digital Distances. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9901-9_3.

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Mukhopadhyay, Jayanta. "Error Analysis: Geometric Approaches." In Approximation of Euclidean Metric by Digital Distances. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9901-9_4.

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Mukhopadhyay, Jayanta. "Conclusion." In Approximation of Euclidean Metric by Digital Distances. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9901-9_6.

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Conference papers on the topic "Euclidean distances"

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Khan, Muneeb Sami, Zunnurain Hussain, Muhammad Irtaza Amaad, et al. "Movie Recommendation System Using Euclidean Distance." In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0. IEEE, 2024. http://dx.doi.org/10.1109/otcon60325.2024.10688032.

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Kundu, Chandra, Abiy Tasissa, and HanQin Cai. "Structured Sampling for Robust Euclidean Distance Geometry." In 2025 59th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2025. https://doi.org/10.1109/ciss64860.2025.10944739.

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Memoli, Facundo. "Gromov-Hausdorff distances in Euclidean spaces." In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE, 2008. http://dx.doi.org/10.1109/cvprw.2008.4563074.

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Sayeed, Farrukh, M. Hanmandlu, A. Q. Ansari, and Shantaram Vasikarla. "Iris Recognition Using Segmental Euclidean Distances." In 2011 Eighth International Conference on Information Technology: New Generations (ITNG). IEEE, 2011. http://dx.doi.org/10.1109/itng.2011.96.

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Mondal, Sudip, and Khaled N. Salama. "Efficient enumeration of 2D Euclidean distances." In 2008 International Conference on Microelectronics - ICM. IEEE, 2008. http://dx.doi.org/10.1109/icm.2008.5393537.

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Wan-Jui Lee, Robert P. W. Duin, Alessandro Ibba, and Marco Loog. "An experimental study on combining Euclidean distances." In 2010 2nd International Workshop on Cognitive Information Processing (CIP). IEEE, 2010. http://dx.doi.org/10.1109/cip.2010.5604238.

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Nilsson, Peter, and Erik Hertz. "Ultra low power hardware for computing Squared Euclidean Distances." In 2011 European Conference on Circuit Theory and Design (ECCTD). IEEE, 2011. http://dx.doi.org/10.1109/ecctd.2011.6043600.

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Singh, Mahesh K., Narendra Singh, and A. K. Singh. "Speaker's Voice Characteristics and Similarity Measurement using Euclidean Distances." In 2019 International Conference on Signal Processing and Communication (ICSC). IEEE, 2019. http://dx.doi.org/10.1109/icsc45622.2019.8938366.

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Cohen, Liron, Tansel Uras, Shiva Jahangiri, Aliyah Arunasalam, Sven Koenig, and T. K. Satish Kumar. "The FastMap Algorithm for Shortest Path Computations." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/198.

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We present a new preprocessing algorithm for embedding the nodes of a given edge-weighted undirected graph into a Euclidean space. The Euclidean distance between any two nodes in this space approximates the length of the shortest path between them in the given graph. Later, at runtime, a shortest path between any two nodes can be computed with an A* search using the Euclidean distances as heuristic. Our preprocessing algorithm, called FastMap, is inspired by the data-mining algorithm of the same name and runs in near-linear time. Hence, FastMap is orders of magnitude faster than competing appr

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Rowekamp, Jan Henrik. "Fast thresholding of high dimensional Euclidean distances using binary squaring." In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7900111.

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Reports on the topic "Euclidean distances"

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Hurwitz, Arnon, and Adrian Hood. Gear Anomaly Detection Using a Matrix Profile Index: Fixed-Axis Gearbox with Cracked Gear: An Application Using Euclidean Distances. DEVCOM Army Research Laboratory, 2022. http://dx.doi.org/10.21236/ad1167705.

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Ratliff, Raymond D., and Sylvia R. Mori. Squared Euclidean distance: a statistical test to evaluate plant community change. U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station, 1993. http://dx.doi.org/10.2737/psw-rn-416.

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Lasko, Kristofer, Francis O’Neill, and Elena Sava. Automated mapping of land cover type within international heterogenous landscapes using Sentinel-2 imagery with ancillary geospatial data. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49367.

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A near-global framework for automated training data generation and land cover classification using shallow machine learning with low-density time series imagery does not exist. This study presents a methodology to map nine-class, six-class, and five-class land cover using two dates of a Sentinel-2 granule across seven international sites. The approach uses a series of spectral, textural, and distance decision functions combined with modified ancillary layers to create binary masks from which to generate a balanced set of training data applied to a random forest classifier. For the land cover m

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