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Artículos de revistas sobre el tema "Data processing"

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Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 25 de julio de 2024

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1

Mehraj, Nadiya y Harveen Kour. "Data Processing Through Image Processing using Gaussian Minimum Shift Keying". International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (31 de octubre de 2018): 977–81. http://dx.doi.org/10.31142/ijtsrd18819.

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2

Rossmann, Michael G. y Cornelis G. van Beek. "Data processing". Acta Crystallographica Section D Biological Crystallography 55, n.º 10 (1 de octubre de 1999): 1631–40. http://dx.doi.org/10.1107/s0907444999008379.

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X-ray diffraction data processing proceeds through indexing, pre-refinement of camera parameters and crystal orientation, intensity integration, post-refinement and scaling. TheDENZOprogram has set new standards for autoindexing, but no publication has appeared which describes the algorithm. In the development of the newData Processing Suite(DPS), one of the first aims has been the development of an autoindexing procedure at least as powerful as that used byDENZO. The resultant algorithm will be described. Another major problem which has arisen in recent years is scaling and post-refinement of data from different images when there are few, if any, full reflections. This occurs when the mosaic spread approaches or exceeds the angle of oscillation, as is usually the case for frozen crystals. A procedure which is able to obtain satisfactory results for such a situation will be described.

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3

Volkova, T., E. Furta, O. Dmitrieva y I. Shabalina. "Pattern Building Methods in Genetic Data Processing". Journal on Selected Topics in Nano Electronics and Computing 1, n.º 2 (junio de 2014): 2–6. http://dx.doi.org/10.15393/j8.art.2014.3041.

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4

Dayalan, Muthu. "MapReduce: Simplified Data Processing on Large Cluster". International Journal of Research and Engineering 5, n.º 5 (abril de 2018): 399–403. http://dx.doi.org/10.21276/ijre.2018.5.5.4.

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5

Starukhin, Yaroslav y Vladimir Diukarev. "AUTOMATION OF TEXT DATA PROCESSING USING NLP". American Journal of Engineering and Technology 6, n.º 7 (1 de julio de 2024): 24–39. http://dx.doi.org/10.37547/tajet/volume06issue07-04.

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This study aims to develop an automated system for processing scientific texts using advanced NLP techniques. The methodology integrates classical NLP methods with deep learning approaches, employing SciBERT for text classification, LDA for topic modeling, and a modified TextRank algorithm for keyword extraction. Results demonstrate high accuracy in document classification (F1-score of 0.92), effective topic identification, and precise keyword extraction. The developed web interface showcases the system's practical applicability. This research contributes to the field by presenting a comprehensive solution for scientific text analysis, combining state-of-the-art language models with established NLP techniques. The study's novelty lies in its tailored approach to scientific literature, addressing the unique challenges of domain-specific language and complex content structure in academic texts.

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6

Zasuhina, Ol'ga, Egor Ershov, Leonid Golovatiukov y Grigory Shitenkov. "BIG DATA PROCESSING TECHNOLOGY". Bulletin of the Angarsk State Technical University 1, n.º 16 (27 de diciembre de 2022): 98–100. http://dx.doi.org/10.36629/2686-777x-2022-1-16-98-100.

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7

Gnip, P. y S. Kafka. "Using technology of data collection and data processing in precision farming". Agricultural Economics (Zemědělská ekonomika) 49, No. 9 (2 de marzo de 2012): 419–26. http://dx.doi.org/10.17221/5426-agricecon.

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Data collection, data processing, data presentation and data application in the System of Precision farming guarantee a success of this system in the market. Difficulties of technologies, which are currently and continually involved in this system, argue against its practical using by farmers. In this case, service company wants to create a suitable environment not only for data collection, but also for the high quality of the information distribution to customers. One of such tools is the MapServer placed on Internet web sites.

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8

KRYVENCHUK, Yurii y Mykhailo-Yurii KHANAS. "ALGORITHM OF DATA MINING AND PROCESSING OF RELATED DATA IN SOCIAL NETWORKS". Herald of Khmelnytskyi National University. Technical sciences 311, n.º 4 (agosto de 2022): 115–18. http://dx.doi.org/10.31891/2307-5732-2022-311-4-115-118.

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We live in a time of rapid growth of information technology, which is firmly entrenched in our daily lives. It is simply impossible to imagine a modern person without social networks, because they perform a communicative and informational function, namely: communication, information retrieval, news exchange, etc. Five hundred million tweets are posted daily, making Twitter a major social media platform from which topical information on events can be extracted. So, there is a lot of information available to the user, which is difficult to identify something specific and necessary in the usual way viewing. Accordingly, there is a need for technologies that can quickly process large amounts of data and highlight only the information that is useful to a particular user. This technology called recommender systems. It automatically suggest items to users that might be interesting for them. Due to the desire to unite people with common interests, it is relevant to develop a recommendation system based on social networks that help in personification of the user and compilation of his psychotype using his profile. The paper has description and results of the creation of recommendation system. The basis of this work is one of the algorithms used in recommendation systems – the recommendation system is based on content filtering. It analyzes users’ Twitter posts and calculates their interests. If we consider all the words, our model will not have good results and do not pay attention to what is important to use. Therefore, the most important step is always filtering data, so the number one task is to speed up the time of filtering text and retrieving data from the social network for further processing. The feature of this system is that this algorithm uses parallel calculations and frequency analysis of the text.

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9

Nikolova, Evgeniya, Mariya Monova-Zheleva y Yanislav Zhelev. "Personal Data Processing in a Digital Educational Environment". Mathematics and Informatics LXV, n.º 4 (30 de agosto de 2022): 365–78. http://dx.doi.org/10.53656/math2022-4-4-per.

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New technologies provide innovative spaces for cooperation and communication between employers and employees, citizens and structures, educators, and learners. Data protection issues have always been key to education providers, but the proliferation of online learning forms and formats poses new and unique challenges in this regard. When introducing a new technology that involves the collection of sensitive data, the General Data Protection Regulation (GDPR) of the European Parliament and the Council of the European Union requires the identification and mitigation of all risks that could lead to the misuse of personal data. The article discusses some critical points regarding the application of GDPR in online learning. The goal of this article is to investigate the vulnerabilities to personal data security during online learning and to identify methods that schools and universities may apply to ensure that personal data are kept private while students utilize online platforms to learn. For the purposes of the research, the published privacy, and data protection policies of all Bulgarian universities as well as papers on how universities could adapt to the new EU General Data Protection Regulation were revised and analysed. Best practices of some foreign universities in this regard were studied as well.

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10

MARTYNIUK, Tatiana, Andrii KOZHEMIAKO, Bohdan KRUKIVSKYI y Antonina BUDA. "ASSOCIATIVE OPERATIONS BASED ON DIFFERENCE-SLICE DATA PROCESSING". Herald of Khmelnytskyi National University. Technical sciences 311, n.º 4 (agosto de 2022): 159–63. http://dx.doi.org/10.31891/2307-5732-2022-311-4-159-163.

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Associative operations are effectively used to solve such application problems as sorting, searching for certain features, and identifying extreme (maximum/minimum) elements in data sets. Thus, determining the maximum number as a result of sorting a numerical array is an acceptable operation in implementing the competition mechanism in neural networks. In addition, determining the average number in a numerical series by sorting significantly speeds up the process of median filtering of images and signals. In this case, the implementation of median filtering requires the use of sorting with the ranking of the elements of the number array. This paper analyses the possibilities of associative operations implementing the elements of a vector (one-dimensional) array of numbers based on processing by difference slices (DS). A simplified description of DS processing with a selection of the common part of the elements of the vector and the difference slice formed from its elements is given. In addition, elements of the binary mask matrix are used as an example of a topological feature matrix. The proposed approach allows for the formation of the ranks of the elements of the initial vector, as a result of sorting in ascending order of their numerical values. The paper shows a schematic representation of the process of DS processing, as well as an example of DS processing of a number vector in the form of a table, which shows the formation sequence of numbers of the sorted array and the ranks of numbers of the initial array. Therefore, the proposed use of topological features allows to determine the comparative relations between the elements of the numerical array in the process of spatially distributed DS processing, as well as to confirm the versatility of this approach.

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11

Stefanowicz, Bogdan y Marek Cierpiał-Wolan. "Data processing errors". Wiadomości Statystyczne. The Polish Statistician 60, n.º 9 (28 de septiembre de 2015): 23–29. http://dx.doi.org/10.5604/01.3001.0014.8296.

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The article highlights the need to broaden the analysis of the quality of the survey results, taking into account the negative impact of certain operations of so-called editing input data, such as checking their accuracy and correction of errors. In the conclusions it underlines the need to extend the programs for academic lectures in statistics for analysis of the impact of processing operations on the quality of the results.

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12

Jaworski, John y Elizabeth Bliss. "Data Processing Mathematics". Mathematical Gazette 71, n.º 458 (diciembre de 1987): 334. http://dx.doi.org/10.2307/3617092.

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13

OHE, Shuzo. "Statistical Data Processing". Journal of the Japan Society of Colour Material 67, n.º 9 (1994): 590–95. http://dx.doi.org/10.4011/shikizai1937.67.590.

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14

Sarychev, Dmitriy S. "Lidar data processing". SAPR i GIS avtomobilnykh dorog, n.º 1(2) (2014): 16–19. http://dx.doi.org/10.17273/cadgis.2014.1.4.

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15

Pu, Wenjing. "Standardized Data Processing". Transportation Research Record: Journal of the Transportation Research Board 2338, n.º 1 (enero de 2013): 44–57. http://dx.doi.org/10.3141/2338-06.

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16

Ahlswede, R. y P. Lober. "Quantum data processing". IEEE Transactions on Information Theory 47, n.º 1 (2001): 474–78. http://dx.doi.org/10.1109/18.904565.

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17

Ahituv, Niv, Yeheskel Lapid y Seev Neumann. "Processing encrypted data". Communications of the ACM 30, n.º 9 (septiembre de 1987): 777–80. http://dx.doi.org/10.1145/30401.30404.

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18

Hagaman, Edward W., Jeffrey C. Hoch y Alan S. Stern. "NMR Data Processing". Radiation Research 147, n.º 2 (febrero de 1997): 272. http://dx.doi.org/10.2307/3579432.

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19

Scherr, A. L. "Distributed data processing". IBM Systems Journal 38, n.º 2.3 (1999): 354–74. http://dx.doi.org/10.1147/sj.382.0354.

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20

Satoh, Ichiro. "Pervasive Data Processing". Procedia Computer Science 63 (2015): 16–23. http://dx.doi.org/10.1016/j.procs.2015.08.307.

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21

Bouchachia, Abdelhamid. "Online data processing". Neurocomputing 126 (febrero de 2014): 116–17. http://dx.doi.org/10.1016/j.neucom.2013.05.008.

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22

Cameron, David G. "Advanced data processing". Mikrochimica Acta 93, n.º 1-6 (enero de 1987): 229–39. http://dx.doi.org/10.1007/bf01201692.

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23

Gough, T. G. "Data Processing Methods". Data Processing 27, n.º 5 (junio de 1985): 51. http://dx.doi.org/10.1016/0011-684x(85)90145-5.

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24

Richards, B. "Data processing mathematics". Data Processing 28, n.º 3 (abril de 1986): 162. http://dx.doi.org/10.1016/0011-684x(86)90015-8.

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25

Lepper, AM. "Data Processing Budgets". Data Processing 28, n.º 2 (marzo de 1986): 103. http://dx.doi.org/10.1016/0011-684x(86)90114-0.

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26

Campbell-Kelly, Martin. "Victorian data processing". Communications of the ACM 53, n.º 10 (octubre de 2010): 19–21. http://dx.doi.org/10.1145/1831407.1831417.

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27

Starck, J. L., A. Abergel, H. Aussel, M. Sauvage, R. Gastaud, A. Claret, X. Desert, C. Delattre y E. Pantin. "ISOCAM data processing". Astronomy and Astrophysics Supplement Series 134, n.º 1 (enero de 1999): 135–48. http://dx.doi.org/10.1051/aas:1999129.

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28

McIntyre, D. J. O. "NMR data processing". NMR in Biomedicine 12, n.º 6 (octubre de 1999): 405–6. http://dx.doi.org/10.1002/(sici)1099-1492(199910)12:6<405::aid-nbm590>3.0.co;2-c.

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29

Görlitz, L., B. H. Menze, B. M. Kelm y F. A. Hamprecht. "Processing spectral data". Surface and Interface Analysis 41, n.º 8 (agosto de 2009): 636–44. http://dx.doi.org/10.1002/sia.3066.

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30

Rostek, Katarzyna. "Data Analytical Processing in Data Warehouses". Foundations of Management 2, n.º 1 (1 de enero de 2010): 99–116. http://dx.doi.org/10.2478/v10238-012-0023-x.

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Data Analytical Processing in Data Warehouses The article presents issues connected with processing information from data warehouses (the analytical enterprise databases) and two basic types of analytical data processing in data warehouse. The genesis, main definitions, scope of application and real examples from business implementations will be described for each type of analysis. There will be presented copyrighted method of knowledge discovering in databases, together with practical guidelines for its proper and effective use in the enterprise.

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31

Hwa Choi, Hyun, Kangho Kim y Seung Jo Bae. "A Remote Memory System for High Performance Data Processing". International Journal of Future Computer and Communication 4, n.º 1 (febrero de 2015): 50–54. http://dx.doi.org/10.7763/ijfcc.2015.v4.354.

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32

Osborn, Wendy. "Unbounded Spatial Data Stream Query Processing using Spatial Semijoins". Journal of Ubiquitous Systems and Pervasive Networks 15, n.º 02 (1 de marzo de 2021): 33–41. http://dx.doi.org/10.5383/juspn.15.02.005.

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In this paper, the problem of query processing in spatial data streams is explored, with a focus on the spatial join operation. Although the spatial join has been utilized in many proposed centralized and distributed query processing strategies, for its application to spatial data streams the spatial join operation has received very little attention. One identified limitation with existing strategies is that a bounded region of space (i.e., spatial extent) from which the spatial objects are generated needs to be known in advance. However, this information may not be available. Therefore, two strategies for spatial data stream join processing are proposed where the spatial extent of the spatial object stream is not required to be known in advance. Both strategies estimate the common region that is shared by two or more spatial data streams in order to process the spatial join. An evaluation of both strategies includes a comparison with a recently proposed approach in which the spatial extent of the data set is known. Experimental results show that one of the strategies performs very well at estimating the common region of space using only incoming objects on the spatial data streams. Other limitations of this work are also identified.

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33

Proshin, A. A., A. M. Matveev, A. V. Kashnitskiy y M. A. Burtsev. "Satellite data efficient processing with dynamic block archive access". Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 17, n.º 6 (2020): 56–60. http://dx.doi.org/10.21046/2070-7401-2020-17-6-56-60.

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34

Penížek, V. y L. Borůvka. "Processing of conventional soil survey data using geostatistical methods". Plant, Soil and Environment 50, No. 8 (10 de diciembre de 2011): 352–57. http://dx.doi.org/10.17221/4043-pse.

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The aim of this study is to find a suitable treatment of conventional soil survey data for geostatistical exploitation. Different aims and methods of a conventional soil survey and the geostatistics can cause some problems. The spatial variability of clay content and pH for an area of 543 km<sup>2</sup> was described by variograms. First the original untreated data were used. Then the original data were treated to overcome the problems that arise from different aims of conventional soil survey and geostatistical approaches. Variograms calculated from the original data, both for clay content and pH, showed a big portion of nugget variability caused by a few extreme values. Simple exclusion of data representing some specific soil units (local extremes, non-zonal soils) did not bring almost any improvement. Exclusion of outlying values from the first three lag classes that were the most influenced due to a relatively big portion of these extreme values provided much better results. The nugget decreased from pure nugget to 50% of the sill variability for clay content and from 81 to 23% for pH.

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35

MILOSAN, Ioan. "STATISTICAL PROCESSING OF EXPERIMENTAL DATA USING ANALYSIS OF VARIANCE". SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE 18, n.º 1 (24 de junio de 2016): 489–96. http://dx.doi.org/10.19062/2247-3173.2016.18.1.67.

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36

More, Prof Vijay, Ms Ankita Shetty y Ms Aishwarya Mapara Mr Rahul Ghuge Mr Rohit Sharma. "Employee Data Mining Based on Text and Image Processing". International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (30 de abril de 2018): 379–81. http://dx.doi.org/10.31142/ijtsrd10791.

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37

Ch, Bilal Hussain. "Securing Cloud Data with the Application of Image Processing". International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (31 de octubre de 2018): 297–301. http://dx.doi.org/10.31142/ijtsrd18454.

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38

Maruddani, Baso y Efri Sandi. "The Development of Ground Penetrating Radar (GPR) Data Processing". International Journal of Machine Learning and Computing 9, n.º 6 (diciembre de 2019): 768–73. http://dx.doi.org/10.18178/ijmlc.2019.9.6.871.

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39

Ummatovich, Eshonqulov Sherzod. "DATA FILTERING IN THE IMAGE PROCESSING TOOLBOX(IPT) ENVIRONMENT". American Journal of Applied Science and Technology 4, n.º 3 (1 de marzo de 2024): 24–28. http://dx.doi.org/10.37547/ajast/volume04issue03-05.

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Analysis of the Aydar-Arnasoy lake system in the environment of IPT. A brief hydrogeological description of the Aydar-Arnasoy lake system. Digital filtering of the Aydar-Arnasoy lake system image. Development of a digital model of the image of the Aydar-Arnasoy lake system using discrete Fourier transformation.

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40

Skoropad, Pylyp y Andrii Yuras. "MACHINE LEARNING METHODS IN THERMOMETERS’ DATA EXTRACTION AND PROCESSING". Measuring Equipment and Metrology 85, n.º 2 (2024): 40–45. http://dx.doi.org/10.23939/istcmtm2024.02.040.

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Research focuses on developing an all-encompassing algorithm for efficiently extracting, processing, and analyz- ing data about thermometers. The examination involves the application of a branch of artificial intelligence, in particular machine learning (ML) methods, as a means of automating processes. Such methods facilitate the identification and aggregation of pertinent data, the detection of gaps, and the conversion of unstructured text into an easily analyzable structured format. The paper details the employment of reinforcement learning for the automatic extraction of information from diverse resources, natural language pro- cessing for analysis of textual values, and the decision tree method for discerning patterns within the data.

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41

., A. Hassini, N. Benabadji . y A. H. Belbachir . "AVHRR Data Sensor Processing". Journal of Applied Sciences 6, n.º 11 (15 de mayo de 2006): 2501–5. http://dx.doi.org/10.3923/jas.2006.2501.2505.

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42

Lukesh, Susan, J. D. Richards y N. S. Ryan. "Data Processing in Archaeology". American Journal of Archaeology 90, n.º 4 (octubre de 1986): 476. http://dx.doi.org/10.2307/506039.

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43

Shao, Wei y Ron Balliet. "NMR Logging Data Processing". Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description 63, n.º 3 (1 de junio de 2022): 300–338. http://dx.doi.org/10.30632/pjv63n3-2022a3.

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An overview of NMR data processing is provided with more emphasis on the algorithms and interpretation methods commonly used in everyday NMR logging practices. For many of these algorithms and methods discussed in this paper, either enough technical details or field examples are given for better understanding NMR data processing and interpretation, but more focus is given to the algorithms’ applicable scope and the related issues encountered in the practice of NMR logging interpretation. The latest developments in NMR data processing and interpretation are also discussed.

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44

Rowland,, Robert J., J. D. Richards y N. S. Ryan. "Data Processing in Archaeology". Classical World 80, n.º 1 (1986): 61. http://dx.doi.org/10.2307/4349991.

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45

Saleh, Eyad, Ahmad Alsa'deh, Ahmad Kayed y Christoph Meinel. "Processing Over Encrypted Data". ACM SIGMOD Record 45, n.º 3 (6 de diciembre de 2016): 5–16. http://dx.doi.org/10.1145/3022860.3022862.

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46

Tang, Qiu-hua, Xing-hua Zhou, Zhong-chen Liu y AND De-wen DU. "Processing Multibeam Backscatter Data". Marine Geodesy 28, n.º 3 (julio de 2005): 251–58. http://dx.doi.org/10.1080/01490410500204595.

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47

Stozhkov, Yu I., S. V. Viktorov, A. A. Kvashnin, A. N. Kvashnin y V. I. Logachev. "PAMELA spectrometer data processing". Bulletin of the Lebedev Physics Institute 43, n.º 3 (marzo de 2016): 102–7. http://dx.doi.org/10.3103/s1068335616030040.

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48

Taniguchi, D. K. y R. L. Hagrman. "Processing flight test data". IEEE Potentials 10, n.º 3 (octubre de 1991): 55–57. http://dx.doi.org/10.1109/45.127649.

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49

Barnes, Gary y John Lumley. "Processing gravity gradient data". GEOPHYSICS 76, n.º 2 (marzo de 2011): I33—I47. http://dx.doi.org/10.1190/1.3548548.

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As the demand for high-resolution gravity gradient data increases and surveys are undertaken over larger areas, new challenges for data processing have emerged. In the case of full-tensor gradiometry, the processor is faced with multiple derivative measurements of the gravity field with useful signal content down to a few hundred meters’ wavelength. Ideally, all measurement data should be processed together in a joint scheme to exploit the fact that all components derive from a common source. We have investigated two methods used in commercial practice to process airborne full-tensor gravity gradient data; the methods result in enhanced, noise-reduced estimates of the tensor. The first is based around Fourier operators that perform integration and differentiation in the spatial frequency domain. By transforming the tensor measurements to a common component, the data can be combined in a way that reduces noise. The second method is based on the equivalent-source technique, where all measurements are inverted into a single density distribution. This technique incorporates a model that accommodates low-order drift in the measurements, thereby making the inversion less susceptible to correlated time-domain noise. A leveling stage is therefore not required in processing. In our work, using data generated from a geologic model along with noise and survey patterns taken from a real survey, we have analyzed the difference between the processed data and the known signal to show that, when considering the Gzz component, the modified equivalent-source processing method can reduce the noise level by a factor of 2.4. The technique has proven useful for processing data from airborne gradiometer surveys over mountainous terrain where the flight lines tend to be flown at vastly differing heights.

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50

Sobala, G. M. y A. T. R. Axon. "Data Processing in Endoscopy". Endoscopy 24, n.º 01/02 (enero de 1992): 167–68. http://dx.doi.org/10.1055/s-2007-1010456.

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