Relevant bibliographies by topics / Multidimensional Hypersphere

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  1. Journal articles
  2. Dissertations / Theses

Academic literature on the topic 'Multidimensional Hypersphere'

Author: Grafiati
Published: 6 September 2023
Last updated: 31 July 2025

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Journal articles on the topic "Multidimensional Hypersphere"

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XU, JINQUAN, ERIC S. CARLSON, and VISHAL V. VORA. "Multidimensional Finite Differencing (MDFD) with Hypersphere-Close-Pack Grids." Chemical Engineering Communications 192, no. 8 (2005): 984–1016. http://dx.doi.org/10.1080/009864490517296.

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Sokolov, E. N. "Four-dimensional color space." Behavioral and Brain Sciences 20, no. 2 (1997): 207–8. http://dx.doi.org/10.1017/s0140525x9747142x.

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Multidimensional scaling of subjective color differences has shown that color stimuli are located on a hypersphere in four-dimensional space. The semantic space of color names is isomorphic with perceptual color space. A spherical four-dimensional space revealed in monkeys and fish suggests the primacy of common neuronal basis.
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Terada, Yoshikazu, and Hiroshi Yadohisa. "Multidimensional scaling with the nested hypersphere model for percentile dissimilarities." Procedia Computer Science 6 (2011): 364–69. http://dx.doi.org/10.1016/j.procs.2011.08.067.

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Reddy, Ravula Muralidhar, and N. Naveen Kumar. "Network Based Feature Extraction Method for Fraud Detection Using Label Propagation." International Journal for Research in Applied Science and Engineering Technology 12, no. 7 (2024): 71–82. http://dx.doi.org/10.22214/ijraset.2024.63525.

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Abstract: Nowadays, judging the current transaction based on user history transactions is an important detection method. However, different users have different transaction behaviors, when all users use the same limit to judge whether the transaction is abnormal, it will result in higher misjudgment for some users. Aiming at the above problems, this paper proposes an individual behavior transaction detection method based on hypersphere model. In this model, considering multiple dimensions of normal historical transaction records, the characteristics of user’s transaction behavior is generated
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Kovalov, Sergiy, and Oleksandr Mostovenko. "SOME PROPERTIES OF THE HYPERSPHERE IN N-DIMENSIONAL SPACE." APPLIED GEOMETRY AND ENGINEERING GRAPHICS, no. 100 (May 24, 2021): 153–61. http://dx.doi.org/10.32347/0131-579x.2021.100.153-161.

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The study of the properties of surfaces contributes to the expansion of their use in solving various practical problems, especially if such properties can be generalized to manifolds of n-dimensional space. The most thoroughly studied are the properties of the simplest surfaces, including the properties of a sphere. That is why the simplest surfaces are most often used in practice. Each property not covered in the existing literature expands the indicated possibilities. Therefore, the purpose of this article is to identify the properties of the hypersphere unknown from the literature.
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Maeda, Satoshi, Yu Watanabe, and Koichi Ohno. "A scaled hypersphere interpolation technique for efficient construction of multidimensional potential energy surfaces." Chemical Physics Letters 414, no. 4-6 (2005): 265–70. http://dx.doi.org/10.1016/j.cplett.2005.08.063.

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Unver, Mustafa, and Nihal Erginel. "Clustering applications of IFDBSCAN algorithm with comparative analysis." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 6099–108. http://dx.doi.org/10.3233/jifs-189082.

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Density Based Spatial Clustering of Application with Noise (DBSCAN) is one of the mostly preferred algorithm among density based clustering approaches in unsupervised machine learning, which uses epsilon neighborhood construction strategy in order to discover arbitrary shaped clusters. DBSCAN separates dense regions from low density regions and simultaneously assigns points that lie alone as outliers to unearth the hidden cluster patterns in the datasets. DBSCAN identifies dense regions by means of core point definition, detection of which are strictly dependent on input parameter definitions:
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Wu, Guangjun, Bingqing Zhu, Jun Li, Yong Wang, and Yungang Jia. "H2SA-ALSH: A Privacy-Preserved Indexing and Searching Schema for IoT Data Collection and Mining." Wireless Communications and Mobile Computing 2022 (April 18, 2022): 1–12. http://dx.doi.org/10.1155/2022/9990193.

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Currently, smart devices of Internet of Things generate massive amount of data for different applications. However, it will expose sensitive information to external users in the process of IoT data collection, transmission, and mining. In this paper, we propose a novel indexing and searching schema based on homocentric hypersphere and similarity-aware asymmetric LSH (H2SA-ALSH) for privacy-preserved data collection and mining over IoT environments. The H2SA-ALSH collects multidimensional data objects and indexes their features according to the Euclidean norm and cosine similarity. Additionally
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Karbauskaitė, Rasa, and Gintautas Dzemyda. "Optimization of the Maximum Likelihood Estimator for Determining the Intrinsic Dimensionality of High–Dimensional Data." International Journal of Applied Mathematics and Computer Science 25, no. 4 (2015): 895–913. http://dx.doi.org/10.1515/amcs-2015-0064.

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AbstractOne of the problems in the analysis of the set of images of a moving object is to evaluate the degree of freedom of motion and the angle of rotation. Here the intrinsic dimensionality of multidimensional data, characterizing the set of images, can be used. Usually, the image may be represented by a high-dimensional point whose dimensionality depends on the number of pixels in the image. The knowledge of the intrinsic dimensionality of a data set is very useful information in exploratory data analysis, because it is possible to reduce the dimensionality of the data without losing much i
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Sharmin, Dmitrii V., Tamara N. Sharmina, and Valentin G. Sharmin. "Curvature Tensor of the n-Surface and Its Spherical Image in En+k." UNIVERSITY NEWS. NORTH-CAUCASIAN REGION. NATURAL SCIENCES SERIES, no. 1 (217) (March 31, 2023): 29–34. http://dx.doi.org/10.18522/1026-2237-2023-1-29-34.

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The main task of classical multidimensional differential geometry is to study the properties of various n-surfaces. Often these studies use torsion coefficients that are defined for any n-surface with codimension k > 1 in (n + k)-dimensional Euclidean space. For hypersurfaces, the torsion coefficients are not defined.Another important concept used to study the properties of n-surfaces is the spherical Gaussian mapping. The Gaussian mapping defined on submanifolds of Euclidean and pseudo-Euclidean spaces allows one to study the external properties of a submanifold immersed in a Eucli
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Dissertations / Theses on the topic "Multidimensional Hypersphere"

1

Bhat, Sandeep. "Load Modulated Arrays for Multi-antenna Wireless Communications." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4422.

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Conventional multi-antenna transmitters use a separate RF chain (consisting of DACs, mixers, filters) and a power amplifier (PA) for each antenna element, and use modulation alphabets such as QAM/PSK for transmission. The consequences of this are, first, the RF hardware complexity, size, and cost increase with the number of antennas, and second, the linearity requirements for higher order QAM and OFDM transmissions affect the power efficiency of the amplifier in each RF chain. Load modulated array (LMA) is emerging as a promising multi-antenna transmission architecture that alleviates the afor
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Journal articles
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