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Journal articles on the topic 'Data synthesis'

Author: Grafiati

Published: 28 June 2021

Last updated: 27 April 2022

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1

Simran, Simran, and Parminder Singh Jassal. "Synthesis of 64-Bit Triple Data Encryption Standard Algorithm using VHDL." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 775–78. http://dx.doi.org/10.31142/ijtsrd14159.

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2

Hawkins, Peter, Alex Aiken, Kathleen Fisher, Martin Rinard, and Mooly Sagiv. "Data representation synthesis." ACM SIGPLAN Notices 47, no. 6 (August 6, 2012): 38. http://dx.doi.org/10.1145/2345156.1993504.

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3

Hawkins, Peter, Alex Aiken, Kathleen Fisher, Martin Rinard, and Mooly Sagiv. "Data representation synthesis." ACM SIGPLAN Notices 46, no. 6 (June 4, 2011): 38–49. http://dx.doi.org/10.1145/1993316.1993504.

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4

Stok, Leon. "Data path synthesis." Integration 18, no. 1 (December 1994): 1–71. http://dx.doi.org/10.1016/0167-9260(94)90011-6.

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5

Carlson, Rolf, and Björn Granström. "Data-driven multimodal synthesis." Speech Communication 47, no. 1-2 (September 2005): 182–93. http://dx.doi.org/10.1016/j.specom.2005.02.015.

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6

Hawkins, Peter, Alex Aiken, Kathleen Fisher, Martin Rinard, and Mooly Sagiv. "Concurrent data representation synthesis." ACM SIGPLAN Notices 47, no. 6 (August 6, 2012): 417–28. http://dx.doi.org/10.1145/2345156.2254114.

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7

Gordon, Mae O. "Synthesis of Available Data." Journal of Glaucoma 2, Supplement A (1993): 27???28. http://dx.doi.org/10.1097/00061198-199300021-00013.

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8

Campbell, Nick. "Data‐driven speech synthesis." Journal of the Acoustical Society of America 105, no. 2 (February 1999): 1029–30. http://dx.doi.org/10.1121/1.424923.

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9

Feldman, Kenneth A., William H. Yeaton, and Paul M. Wortman. "Issues in Data Synthesis." Journal of the American Statistical Association 81, no. 393 (March 1986): 253. http://dx.doi.org/10.2307/2288004.

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10

Ferná ndez-Carrasco, Lucia, and Jordi Rius. "Synthesis and crystal structure determination of hydrated potassium dawsonite from powder diffraction data." European Journal of Mineralogy 18, no. 1 (March 6, 2006): 99–104. http://dx.doi.org/10.1127/0935-1221/2006/0018-0099.

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11

Vijay Rane, Charushila, and Sandip Raosaheb Patil. "Data Embeddable Texture Synthesis with Fast Data Extraction." HELIX 10, no. 4 (August 31, 2020): 83–89. http://dx.doi.org/10.29042/2020-10-4-83-89.

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12

Chujai, Pasapitch, Kedkarn Chaiyakhan, Nittaya Kerdprasop, and Kittisak Kerdprasop. "The One-Versus-One Classification Technique Based on Data Synthesis with Appropriate Distant Neighbors." International Journal of Information and Electronics Engineering 9, no. 2 (June 2019): 43–49. http://dx.doi.org/10.18178/ijiee.2019.9.2.703.

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13

Hájek, Michal, and Petra Hájková. "Environmental determinants of variation in Czech Calthion wet meadows: a synthesis of phytosociological data." Phytocoenologia 34, no. 1 (March 16, 2004): 33–54. http://dx.doi.org/10.1127/0340-269x/2004/0034-0033.

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14

Inamdar, Suphiya P., and Suhas B. Bhagate. "Secret Data Embedding using Texture Synthesis." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 11 (November 30, 2017): 158. http://dx.doi.org/10.23956/ijarcsse.v7i11.496.

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A steganography is an art of hiding confidential data into digital media such as image, audio, video etc. The proposed of system using steganography using reversible texture synthesis. Texture synthesis uses the concept of patch which represents an image block of source texture where its size is user specified. A texture synthesis process resamples a smaller texture image, and provides a new image with arbitrary size and shape. Instead of using an existing cover image to hide messages, the algorithm conceals the source texture image and embeds secret messages using the process of texture synthesis. This allows extracting the secret messages and source texture from a stego synthetic texture. The approach offers some advantages. First, the scheme offers the embedding capacity that is proportional to the size of the stego texture image. Second, the reversible capability inherited from this scheme provides functionality, which allows recovery of the source texture. And third, there will be no image distortion since size of new texture image is user specified.

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15

Oppe, Mark, Maiwenn Al, and Maureen Rutten-van Mölken. "Comparing Methods of Data Synthesis." PharmacoEconomics 29, no. 3 (March 2011): 239–50. http://dx.doi.org/10.2165/11539870-000000000-00000.

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16

Taylor, Sam, Doug A. Edwards, Luis A. Plana, and Luis A. Tarazona D. "Asynchronous Data-Driven Circuit Synthesis." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 18, no. 7 (July 2010): 1093–106. http://dx.doi.org/10.1109/tvlsi.2009.2020168.

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17

Krajewski, Witold F., and Konstantine P. Georgakakos. "Synthesis of radar rainfall data." Water Resources Research 21, no. 5 (May 1985): 764–68. http://dx.doi.org/10.1029/wr021i005p00764.

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18

Choi, Y.-H. "Synthesis of pipelined data paths." Computer-Aided Design 24, no. 1 (January 1992): 36–40. http://dx.doi.org/10.1016/0010-4485(92)90089-s.

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19

Chen, C. H. "Data path synthesis in digital electronics. II. Bus synthesis." IEEE Transactions on Aerospace and Electronic Systems 32, no. 1 (January 1996): 16–33. http://dx.doi.org/10.1109/7.481246.

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20

Armoogum, Jimmy, and Jennifer Dill. "Workshop Synthesis: Sampling Issues, Data Quality & Data Protection." Transportation Research Procedia 11 (2015): 60–65. http://dx.doi.org/10.1016/j.trpro.2015.12.006.

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21

Schwab, R. G., T. Pimpl, H. Schukow, A. Stolle, and D. K. Breitinger. "Compounds of the crandallite-type: Synthesis, properties and thermodynamic data of pure crandallite and woodhouseite." Neues Jahrbuch für Mineralogie - Monatshefte 2004, no. 9 (August 20, 2004): 385–409. http://dx.doi.org/10.1127/0028-3649/2004/2004-0385.

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22

Chen, Chien-In Henry, and Gerald Sobelman. "Cluster Partitioning Techniques for Data Path Synthesis." VLSI Design 1, no. 3 (January 1, 1994): 181–92. http://dx.doi.org/10.1155/1994/58689.

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New, efficient algorithms for the automated synthesis of buses in data path design are presented. Modifications to the technique of Generalized Clique Partitioning (GCP) are discussed which lead to better designs and reduced computation time in large synthesis problems. The new approach, Weighted Cluster Partitioning (WCP), eliminates the need for backtracking. The algorithm guides the process of bus formation by assigning a higher weight to those interconnection units that should be combined first. The operation of the WCP algorithm is clearly demonstrated using a detailed example.A modified priority ordering in the selection of the candidate pair is also discussed which can improve the performance of GCP and WCP. We demonstrate that GCP II performs better than GCP, while WCP II consistently produces the best results of all these algorithms on a set of large synthesis examples.

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23

Pruseth, Kamal L., Biswajit Mishra, and Heinz-J. Bernhardt. "The minerals boulangerite, falkmanite and Cu-free meneghinite: synthesis, new powder diffraction data and stability relations." European Journal of Mineralogy 13, no. 2 (March 30, 2001): 411–19. http://dx.doi.org/10.1127/0935-1221/01/0013-0411.

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24

Raab, Gillian M., Beata Nowok, and Chris Dibben. "Practical Data Synthesis for Large Samples." Journal of Privacy and Confidentiality 7, no. 3 (February 2, 2018): 67–97. http://dx.doi.org/10.29012/jpc.v7i3.407.

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We describe results on the creation and use of synthetic data that were derived in the context of a project to make synthetic extracts available for users of the UK Longitudinal Studies. A critical review of existing methods of inference from large synthetic data sets is presented. We introduce new variance estimates for use with large samples of completely synthesised data that do not require them to be generated from the posterior predictive distribution derived from the observed data and can be used with a single synthetic data set. We make recommendations on how to synthesise data based on these results. The practical consequences of these results are illustrated with an example from the Scottish Longitudinal Study.

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25

Lv, Pei, Mingliang Xu, Bailin Yang, Mingyuan Li, and Bing Zhou. "Data-driven humanlike reaching behaviors synthesis." Neurocomputing 177 (February 2016): 26–32. http://dx.doi.org/10.1016/j.neucom.2015.10.118.

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26

Otori, H., and S. Kuriyama. "Texture Synthesis for Mobile Data Communications." IEEE Computer Graphics and Applications 29, no. 6 (November 2009): 74–81. http://dx.doi.org/10.1109/mcg.2009.127.

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27

Chapman, Piers, and Worth D. Nowlin. "Ocean data synthesis offers research opportunities." Eos, Transactions American Geophysical Union 81, no. 10 (March 7, 2000): 102–7. http://dx.doi.org/10.1029/00eo00065.

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28

Bohg, Jeannette, Antonio Morales, Tamim Asfour, and Danica Kragic. "Data-Driven Grasp Synthesis—A Survey." IEEE Transactions on Robotics 30, no. 2 (April 2014): 289–309. http://dx.doi.org/10.1109/tro.2013.2289018.

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29

Erwin, Paul Campbell, Margaret Knight, Jennifer Graham, Alan V. Kalos, Louise A. Kent, Michael Glenn, Rebecca J. Rayman, Erin Read, and Stephanie Welch. "Data Synthesis in Community Health Assessment." Journal of Public Health Management and Practice 19, no. 5 (2013): 468–74. http://dx.doi.org/10.1097/phh.0b013e31828000f7.

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30

Launay, S., P. Mahé, and M. Quarton. "Synthesis and crystal data for K3Nb3O6Si2O7." Powder Diffraction 9, no. 2 (June 1994): 96–97. http://dx.doi.org/10.1017/s0885715600014032.

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The silicate compound, K3Nb3O6Si2O7, has been synthesized by solid-state reaction. Single crystals were obtained; they crystallized with the hexagonal space group P2c. Refined unit-cell parameters are: a = 9.0377(3), c=8.0465(7)Å, V=569.18(5)Å3, Z=2, Dm = 3.84(4), Dx=3.851. Powder diffraction data are reported.

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31

Wang, Yuepeng, Rushi Shah, Abby Criswell, Rong Pan, and Isil Dillig. "Data migration using datalog program synthesis." Proceedings of the VLDB Endowment 13, no. 7 (March 2020): 1006–19. http://dx.doi.org/10.14778/3384345.3384350.

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32

Hawkins, Peter, Martin Rinard, Alex Aiken, Mooly Sagiv, and Kathleen Fisher. "An introduction to data representation synthesis." Communications of the ACM 55, no. 12 (December 2012): 91–99. http://dx.doi.org/10.1145/2380656.2380677.

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33

Ren, Y., and C. F. Beards. "On substructure synthesis with FRF data." Journal of Sound and Vibration 185, no. 5 (September 1995): 845–66. http://dx.doi.org/10.1006/jsvi.1995.0421.

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34

Howard, A. Q., and J. L. Kretzschmar. "Synthesis of EM geophysical tomographic data." Proceedings of the IEEE 74, no. 2 (1986): 353–60. http://dx.doi.org/10.1109/proc.1986.13462.

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35

Ravitz, Orr. "Data-driven computer aided synthesis design." Drug Discovery Today: Technologies 10, no. 3 (September 2013): e443-e449. http://dx.doi.org/10.1016/j.ddtec.2013.01.005.

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36

Aghdasi, F. "Controller synthesis using data-driven clocks." Microelectronics Journal 26, no. 5 (July 1995): 449–61. http://dx.doi.org/10.1016/0026-2692(95)98947-p.

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37

Heberling, J. Mason, Joseph T. Miller, Daniel Noesgaard, Scott B. Weingart, and Dmitry Schigel. "Data integration enables global biodiversity synthesis." Proceedings of the National Academy of Sciences 118, no. 6 (February 1, 2021): e2018093118. http://dx.doi.org/10.1073/pnas.2018093118.

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The accessibility of global biodiversity information has surged in the past two decades, notably through widespread funding initiatives for museum specimen digitization and emergence of large-scale public participation in community science. Effective use of these data requires the integration of disconnected datasets, but the scientific impacts of consolidated biodiversity data networks have not yet been quantified. To determine whether data integration enables novel research, we carried out a quantitative text analysis and bibliographic synthesis of >4,000 studies published from 2003 to 2019 that use data mediated by the world’s largest biodiversity data network, the Global Biodiversity Information Facility (GBIF). Data available through GBIF increased 12-fold since 2007, a trend matched by global data use with roughly two publications using GBIF-mediated data per day in 2019. Data-use patterns were diverse by authorship, geographic extent, taxonomic group, and dataset type. Despite facilitating global authorship, legacies of colonial science remain. Studies involving species distribution modeling were most prevalent (31% of literature surveyed) but recently shifted in focus from theory to application. Topic prevalence was stable across the 17-y period for some research areas (e.g., macroecology), yet other topics proportionately declined (e.g., taxonomy) or increased (e.g., species interactions, disease). Although centered on biological subfields, GBIF-enabled research extends surprisingly across all major scientific disciplines. Biodiversity data mobilization through global data aggregation has enabled basic and applied research use at temporal, spatial, and taxonomic scales otherwise not possible, launching biodiversity sciences into a new era.

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38

Quist, C. F., L. Lindegren, and S. Söderhjelm. "Aperture Synthesis of Hipparcos Transit Data." Highlights of Astronomy 11, no. 1 (1998): 549. http://dx.doi.org/10.1017/s1539299600022036.

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The ESA Hipparcos astrometry satellite did not contain an imaging detector (such as a CCD) but used a modulating grid of alternating transparent and opaque bands to modulate the light from a given object. The Fourier coefficients of the modulated detector signal were the basis for the astrometric and photometric processing leading to the Hipparcos Catalogue. These coefficients are included (for a subset of the stars) in the CD-ROM version of the Hipparcos Catalogue as the ‘Hipparcos Transit Data’. In this work, the Transit Data are used in a completely different and novel application: the modulation components from a single scan are considered to be equivalent to the interference fringes from an interferometer oriented along the scan direction and having baselines of 0, ≃ 10 and ≃ 20 cm and a wavelength of 550 nm. Using all the scans collected over the mission lifetime, it is possible to use the complex visibilities (containing bothamplitude and phase information) calculated from the Transit Data to createaperture synthesis images of various type of objects such as visual or orbiting binary systems. More information, including sample images, can be found in: Quist C.F., Lindegren L., Söderhjelm S., 1997, Using HipparcosTransit Data for Aperture Synthesis Imaging, Hipparcos Venice ‘97 Symposium, ESA SP-402 (in press).

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39

Le, Tran Nguyen, Jens Lundell, Fares J. Abu-Dakka, and Ville Kyrki. "Deformation-Aware Data-Driven Grasp Synthesis." IEEE Robotics and Automation Letters 7, no. 2 (April 2022): 3038–45. http://dx.doi.org/10.1109/lra.2022.3146551.

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40

Gray, A. D. "Self-calibration of M.O.S.T. data." International Astronomical Union Colloquium 131 (1991): 202–5. http://dx.doi.org/10.1017/s0252921100013312.

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AbstractThe Molonglo Observatory Synthesis Telescope (MOST), located near Canberra, Australia, is an east-west earth-rotation synthesis array which, unlike conventional synthesis arrays, generates multiple real-time fan-beams and forms images by the back-projection of the recorded intensities rather than by Fourier inversion of visibilities. The images produced in this fashion are often marred by the presence of radial artefacts emanating from strong sources due to residual calibration errors. An algorithm devised to self-calibrate these errors is discussed.

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41

Alam, M. Jahangir, Benoit Dostie, Jörg Drechsler, and Lars Vilhuber. "Applying data synthesis for longitudinal business data across three countries." Statistics in Transition New Series 21, no. 4 (2020): 212–36. http://dx.doi.org/10.21307/stattrans-2020-039.

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42

Du, Han, Martin Manns, Erik Herrmann, and Klaus Fischer. "Joint Angle Data Representation for Data Driven Human Motion Synthesis." Procedia CIRP 41 (2016): 746–51. http://dx.doi.org/10.1016/j.procir.2015.12.096.

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43

Sallier, Kenza. "Toward more user-centric data access solutions: Producing synthetic data of high analytical value by data synthesis1." Statistical Journal of the IAOS 36, no. 4 (November 25, 2020): 1059–66. http://dx.doi.org/10.3233/sji-200682.

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Under the Modernization programme Statistics Canada has recently undertaken, the Agency is to put forward data access solutions that present greater analytical value to Canadians while maintaining its core values of protecting confidentiality of respondents’ information. One avenue currently explored is Data Synthesis as a means of delivering synthetic data with high analytical value to users. At the time of writing, Statistics Canada has publicly released synthetic versions of two different datasets related to census, mortality and cancer information. In both cases, synthetic data were generated using the R package synthpop. This paper describes the use of Data Synthesis as a proof of concept for modernizing Statistics Canada’s data access solutions.

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44

Pratsri, Sajeewan, Prachyanun Nilsook, and Panita Wannapiroon. "Synthesis of Data Science Competency for Higher Education Students." International Journal of Education and Information Technologies 16 (January 31, 2022): 101–9. http://dx.doi.org/10.46300/9109.2022.16.11.

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The research aims to Data Science Performance Synthesis for Higher Education Students and Data Science Performance Suitability Assessment for Higher Education Students. The research instruments include 1) data science performance synthesis tables, 2) expert interviews in data science performance assessments, 3) expert questionnaires to assess the consistency of data science performance. Analytical methods include 1) analyzing the frequency obtained from the content analysis table, 2) synthesis of content from interviews, 3) analyzing performance consistency, and components of data science performance, from data science synthesis for higher education students, finding that data performance for higher education students consists of five performances: 1) programming skills, 2)elementary statistics, 3) fundamentals of data science, 4) data preparation, and 5) Big data analytics.

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45

Raupach, M. R., P. J. Rayner, D. J. Barrett, R. S. DeFries, M. Heimann, D. S. Ojima, S. Quegan, and C. C. Schmullius. "Model-data synthesis in terrestrial carbon observation: methods, data requirements and data uncertainty specifications." Global Change Biology 11, no. 3 (March 2005): 378–97. http://dx.doi.org/10.1111/j.1365-2486.2005.00917.x.

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46

Chan, Jenq-Tzong H. "Control System Synthesis Based on Plant Test Data." Journal of Dynamic Systems, Measurement, and Control 117, no. 4 (December 1, 1995): 484–89. http://dx.doi.org/10.1115/1.2801104.

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A correlation equation is established between open-loop test data and the desired closed-loop system characteristics permitting control system synthesis to be done on the basis of a numerical approach using experimental data. The method is applicable when the system is linear-time-invariant and open-loop stable. The major merits of the algorithm are two-fold: 1) Arbitrary placement of the closed-loop system equation is possible, and 2) explicit knowledge of an open-loop system model is not needed for the controller synthesis.

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47

Tianxing, Man, Vasily Osipov, Alexander I. Vodyaho, Sergey Lebedev, and Nataly Zhukova. "Distributed Technical Object Model Synthesis Based on Monitoring Data." International Journal of Knowledge and Systems Science 10, no. 3 (July 2019): 27–43. http://dx.doi.org/10.4018/ijkss.2019070103.

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Practically all human activities depend on technical systems, which consists of a multitude of dynamically distributed objects. In order to control these systems, it is necessary to build and periodically rebuild models of objects, which consist of elements and connections between them and describes the object's state in time and space. Due to a large amount of monitoring data, the problem of automation of object model synthesis arises. By now the most work is done by experts. Analysis of the works from the related areas has shown that methods for the automated synthesis of object models based on link discovering do not exist. An approach for the automated synthesis of object models based on content extracted from messages received from monitoring systems is proposed. A context describing synthesis process conditions is supposed to be considered. The approach is illustrated with an example.

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48

Velo, A., F. F. Pérez, X. Lin, R. M. Key, T. Tanhua, M. de la Paz, S. van Heuven, S. Jutterström, and A. F. Ríos. "CARINA data synthesis project: pH data scale unification and cruise adjustments." Earth System Science Data Discussions 2, no. 1 (October 13, 2009): 421–75. http://dx.doi.org/10.5194/essdd-2-421-2009.

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Abstract. Data on carbon and carbon-relevant hydrographic and hydrochemical parameters from previously non-publicly available cruise data sets in the Artic Mediterranean Seas (AMS), Atlantic and Southern Ocean have been retrieved and merged to a new database: CARINA (CARbon IN the Atlantic). These data have gone through rigorous quality control (QC) procedures to assure the highest possible quality and consistency. The data for most of the measured parameters in the CARINA database were objectively examined in order to quantify systematic differences in the reported values, i.e. secondary quality control. Systematic biases found in the data have been corrected in the data products, i.e. three merged data files with measured, calculated and interpolated data for each of the three CARINA regions; AMS, Atlantic and Southern Ocean. Out of a total of 188 cruise entries in the CARINA database, 59 reported pH measured values. Here we present details of the secondary QC on pH for the CARINA database. Procedures of quality control, including crossover analysis between cruises and inversion analysis of all crossover data are briefly described. Adjustments were applied to the pH values for 21 of the cruises in the CARINA dataset. With these adjustments the CARINA database is consistent both internally as well as with GLODAP data, an oceanographic data set based on the World Hydrographic Program in the 1990s. Based on our analysis we estimate the internal accuracy of the CARINA pH data to be 0.005 pH units. The CARINA data are now suitable for accurate assessments of, for example, oceanic carbon inventories and uptake rates and for model validation.

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49

Velo, A., F. F. Pérez, X. Lin, R. M. Key, T. Tanhua, M. de la Paz, A. Olsen, S. van Heuven, S. Jutterström, and A. F. Ríos. "CARINA data synthesis project: pH data scale unification and cruise adjustments." Earth System Science Data 2, no. 1 (May 11, 2010): 133–55. http://dx.doi.org/10.5194/essd-2-133-2010.

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Abstract. Data on carbon and carbon-relevant hydrographic and hydrochemical parameters from 188 previously non-publicly available cruise data sets in the Artic Mediterranean Seas (AMS), Atlantic Ocean and Southern Ocean have been retrieved and merged to a new database: CARINA (CARbon IN the Atlantic Ocean). These data have gone through rigorous quality control (QC) procedures to assure the highest possible quality and consistency. The data for most of the measured parameters in the CARINA database were objectively examined in order to quantify systematic differences in the reported values. Systematic biases found in the data have been corrected in the data products, three merged data files with measured, calculated and interpolated data for each of the three CARINA regions; AMS, Atlantic Ocean and Southern Ocean. Out of a total of 188 cruise entries in the CARINA database, 59 reported pH measured values. All reported pH data have been unified to the Sea-Water Scale (SWS) at 25 °C. Here we present details of the secondary QC of pH in the CARINA database and the scale unification to SWS at 25 °C. The pH scale has been converted for 36 cruises. Procedures of quality control, including crossover analysis between cruises and inversion analysis are described. Adjustments were applied to the pH values for 21 of the cruises in the CARINA dataset. With these adjustments the CARINA database is consistent both internally as well as with the GLODAP data, an oceanographic data set based on the World Hydrographic Program in the 1990s. Based on our analysis we estimate the internal consistency of the CARINA pH data to be 0.005 pH units. The CARINA data are now suitable for accurate assessments of, for example, oceanic carbon inventories and uptake rates, for ocean acidification assessment and for model validation.

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50

Manns, Martin, Michael Otto, and Markus Mauer. "Measuring Motion Capture Data Quality for Data Driven Human Motion Synthesis." Procedia CIRP 41 (2016): 945–50. http://dx.doi.org/10.1016/j.procir.2015.12.068.

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