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Journal articles on the topic 'Automatic data processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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1

Mott, Paul, T. W. Sammis, and Robert Jackson. "Automatic weather data collection and processing." Computers and Electronics in Agriculture 7, no. 4 (December 1992): 337–45. http://dx.doi.org/10.1016/s0168-1699(05)80014-6.

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2

Melichar, Martin, and Dana Kubátová. "Processing Data from Automatic Measurement Device." Procedia Engineering 100 (2015): 899–906. http://dx.doi.org/10.1016/j.proeng.2015.01.447.

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3

Белашев, Борис Залманович, Михаил Юрьевич Нилов, Boris Belashev, and Michail Nilov. "Georadar data processing by an automatic algorithm." Proceedings of the Karelian Research Centre of the Russian Academy of Sciences, no. 7 (June 30, 2020): 19. http://dx.doi.org/10.17076/mat1221.

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4

Badalyan, V. G., A. Kh Vopilkin, S. A. Dolenko, Yu V. Orlov, and I. G. Persiantsev. "Data-processing algorithms for automatic operation of ultrasonic systems with coherent data processing." Russian Journal of Nondestructive Testing 40, no. 12 (December 2004): 791–800. http://dx.doi.org/10.1007/s11181-005-0108-7.

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5

Skoropad, Pylyp, and Andrii Yuras. "MACHINE LEARNING METHODS IN THERMOMETERS’ DATA EXTRACTION AND PROCESSING." Measuring Equipment and Metrology 85, no. 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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Velkavrh, Igor, Katharina Dimovski, Fevzi Kafexhiu, and Thomas Wright. "Enhanced Efficiency in Coefficient of Friction Evaluation through Automated Data Processing." Tribologie und Schmierungstechnik 70, no. 3 (August 14, 2023): 22–26. http://dx.doi.org/10.24053/tus-2023-0014.

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In this work, an approach towards automated extraction and evaluation of static and kinematic coefficients of friction is presented. By replacing manual evaluation with an automated process, this approach yields promising results, simplifies data handling, and saves significant time. The methodology shows potential for application to a wide range of experimental data and provides advanced processing capabilities using the extracted and mathematically evaluated data points. However, for data with high signal- to-noise ratios, automatic detection still requires further optimization to improve accuracy.

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Oh, Hyeju, Sungbeom Jo, Keeyoung Choi, Eun-Jung Roh, and Byung-Ryong Kang. "Automatic Processing Techniques of Rotorcraft Flight Data Using Data Mining." Journal of the Korean Society for Aeronautical & Space Sciences 46, no. 10 (October 31, 2018): 823–32. http://dx.doi.org/10.5139/jksas.2018.46.10.823.

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8

Selmer, Knut S. "Council of Europe Convention on automatic data processing." Medical Informatics 14, no. 3 (January 1989): 211–14. http://dx.doi.org/10.3109/14639238908994995.

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Bellino, A., L. Garibaldi, and A. Godio. "An automatic method for data processing of seismic data in tunneling." Journal of Applied Geophysics 98 (November 2013): 243–53. http://dx.doi.org/10.1016/j.jappgeo.2013.09.007.

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10

Szymczyk, Piotr, Sylwia Tomecka-Suchoń, and Magdalena Szymczyk. "Neural Networks as a Tool for Georadar Data Processing." International Journal of Applied Mathematics and Computer Science 25, no. 4 (December 1, 2015): 955–60. http://dx.doi.org/10.1515/amcs-2015-0068.

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Abstract In this article a new neural network based method for automatic classification of ground penetrating radar (GPR) traces is proposed. The presented approach is based on a new representation of GPR signals by polynomials approximation. The coefficients of the polynomial (the feature vector) are neural network inputs for automatic classification of a special kind of geologic structure—a sinkhole. The analysis and results show that the classifier can effectively distinguish sinkholes from other geologic structures.

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11

Lagerev, D. G., and E. A. Makarova. "FEATURES OF PRELIMINARY PROCESSING OF SEMI-STRUCTURED MEDICAL DATA IN RUSSIAN FOR USE IN ENSEMBLES OF DATA MINING MODELS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 193 (July 2020): 44–54. http://dx.doi.org/10.14489/vkit.2020.07.pp.044-054.

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The paper considers the problem of integration, processing and mining of poorly structured data of medical information systems in order to make managerial decisions in healthcare. The problems of medical data are described, such as the lack of a sufficient structure, a large number of abbreviations characteristic of specific nosologies, the complexity of the automatic semantic interpretation of some fields. The authors demonstrated an approach to the search and disclosure of abbreviation in texts, based on a combination of machine and human processing. The method proposed by the authors, based on a hybrid approach combining the strengths of machine and human processing, made it possible to increase the number of abbreviations found by automatic methods by 21 %, and also opened up to 55 % of cases in the automated mode (with a probability of correctness above 70 %) and significantly reduce the time spent by specialists in processing the remaining reductions. Further research will be aimed at solving the following problems associated with the processing and specificity of medical data, such as a large number of spelling errors, specific grammatical constructions. Using a hybrid approach to preprocessing poorly structured data will increase the efficiency of management decisions in the field of healthcare by reducing the time spent by experts on their creation and support. The hybrid approach to the preprocessing of text data in Russian can be applied in other subject areas. However, it may be necessary to adjust the technique to the specifics of the processed data.

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Lagerev, D. G., and E. A. Makarova. "FEATURES OF PRELIMINARY PROCESSING OF SEMI-STRUCTURED MEDICAL DATA IN RUSSIAN FOR USE IN ENSEMBLES OF DATA MINING MODELS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 193 (July 2020): 44–54. http://dx.doi.org/10.14489/vkit.2020.07.pp.044-054.

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The paper considers the problem of integration, processing and mining of poorly structured data of medical information systems in order to make managerial decisions in healthcare. The problems of medical data are described, such as the lack of a sufficient structure, a large number of abbreviations characteristic of specific nosologies, the complexity of the automatic semantic interpretation of some fields. The authors demonstrated an approach to the search and disclosure of abbreviation in texts, based on a combination of machine and human processing. The method proposed by the authors, based on a hybrid approach combining the strengths of machine and human processing, made it possible to increase the number of abbreviations found by automatic methods by 21 %, and also opened up to 55 % of cases in the automated mode (with a probability of correctness above 70 %) and significantly reduce the time spent by specialists in processing the remaining reductions. Further research will be aimed at solving the following problems associated with the processing and specificity of medical data, such as a large number of spelling errors, specific grammatical constructions. Using a hybrid approach to preprocessing poorly structured data will increase the efficiency of management decisions in the field of healthcare by reducing the time spent by experts on their creation and support. The hybrid approach to the preprocessing of text data in Russian can be applied in other subject areas. However, it may be necessary to adjust the technique to the specifics of the processed data.

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13

Береснев, Е. М., and П. А. Беспалова. "Analysis of fast data processing methods." Экономика и предпринимательство, no. 5(142) (August 21, 2022): 307–10. http://dx.doi.org/10.34925/eip.2022.142.5.057.

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Для организации мониторинга конвейера обработки данных, помимо контроля отдельных этапов обработки, тестирования и восстановления отдельных комплексов, планируется создать автоматический мониторинг состояния и работоспособности всего конвейера. Важно включить возможность автоматического запуска тестов, проверки качества данных и описания метаданных. Необходимо получать проактивную информацию об инцидентах при выполнении этапов обработки данных, чтобы система могла автоматически предпринимать корректирующие действия. Все инциденты должны быть зарегистрированы. В идеальном сценарии использования данных любой потребитель без вмешательства специалистов может найти необходимые данные, обновить их и проверить гипотезу об их использовании. В дальнейшем выбранная технология получения и использования данных включается в автоматический сервис этого потребителя. To organize monitoring of the data processing pipeline, in addition to monitoring individual stages of processing, testing and restoring individual complexes, it is planned to create automatic monitoring of the state and performance of the entire pipeline. It is important to enable the ability to automatically run tests, check data quality and describe metadata. It is necessary to receive proactive information about incidents during data processing steps so that the system can automatically take corrective actions. All incidents must be recorded. In an ideal use case for data, any consumer without the intervention of specialists can find the necessary data, update it and test a hypothesis about its use. In the future, the selected technology for obtaining and using data is included in the automatic service of this consumer.

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14

Zhong, Li Juan, and Jing Lin Tong. "Research of Automatic Camshaft Detection and Data Processing Technology." Applied Mechanics and Materials 42 (November 2010): 404–7. http://dx.doi.org/10.4028/www.scientific.net/amm.42.404.

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The traditional optical-mechanical measurement instrument as well as manual data processing methods has unable to meet the efficient and accurate detection requirements for camshaft. In this article, the principle of automatic camshaft detection technology and the control system’s structure are introduced. The data acquisition system independent of movement controller and the data processing method of "all the sensitive" and evaluation method are introduced sequent.

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15

Xiaojie, Sun, Gu Pengfei, Xu Cong, and Li Zhaoqin. "Research on Automatic Processing of Worn Tread Test Data." Journal of Physics: Conference Series 1881, no. 4 (April 1, 2021): 042058. http://dx.doi.org/10.1088/1742-6596/1881/4/042058.

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16

Lukin, V. V., S. K. Abramov, N. N. Ponomarenko, S. S. Krivenko, M. L. Uss, Benoit Vozel, Kacem Chehdi, Karen O. Egiazarian, and J. T. Astola. "APPROACHES TO AUTOMATIC DATA PROCESSING IN HYPERSPECTRAL REMOTE SENSING." Telecommunications and Radio Engineering 73, no. 13 (2014): 1125–39. http://dx.doi.org/10.1615/telecomradeng.v73.i13.10.

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17

Canepa, G., O. Bergem, and N. G. Pace. "A new algorithm for automatic processing of bathymetric data." IEEE Journal of Oceanic Engineering 28, no. 1 (January 2003): 62–77. http://dx.doi.org/10.1109/joe.2002.808204.

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18

Kang, Dong-Il, Hong-Suh Park, In-Woo Han, G. Valyavin, Byeong-Cheol Lee, and Kang-Min Kim. "DEVELOPMENT OF AN AUTOMATIC PROCESSING PROGRAM FOR BOES DATA." Publications of The Korean Astronomical Society 20, no. 1 (December 31, 2005): 97–107. http://dx.doi.org/10.5303/pkas.2005.20.1.097.

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19

Nepeina, K. "Automatic post processing algorithm for passive seismic monitoring data." Journal of Physics: Conference Series 510 (May 15, 2014): 012007. http://dx.doi.org/10.1088/1742-6596/510/1/012007.

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20

Shutler, J. D., T. J. Smyth, P. E. Land, and S. B. Groom. "A near‐real time automatic MODIS data processing system." International Journal of Remote Sensing 26, no. 5 (March 2005): 1049–55. http://dx.doi.org/10.1080/01431160412331299244.

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21

Mack, Harry, and Eugene Smart. "Automatic Processing of Multi-Array Long-Period Seismic Data." Geophysical Journal of the Royal Astronomical Society 35, no. 1-3 (September 15, 2009): 215–24. http://dx.doi.org/10.1111/j.1365-246x.1973.tb02423.x.

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22

Adamo, Francesco, Filippo Attivissimo, Attilio Di Nisio, and Maurizio Spadavecchia. "An automatic document processing system for medical data extraction." Measurement 61 (February 2015): 88–99. http://dx.doi.org/10.1016/j.measurement.2014.10.032.

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23

Lavender, S. J., and S. B. Groom. "Technical note The SeaWiFS Automatic Data Processing System (SeaAPS)." International Journal of Remote Sensing 20, no. 6 (January 1999): 1051–56. http://dx.doi.org/10.1080/014311699212830.

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24

Schaudt, K., and G. Pawelec. "Automatic collection, transfer, and processing of cellular proliferation data." Journal of Immunological Methods 138, no. 2 (April 1991): 305. http://dx.doi.org/10.1016/0022-1759(91)90180-n.

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25

Jungmair, Michael, and Jana Giceva. "Declarative Sub-Operators for Universal Data Processing." Proceedings of the VLDB Endowment 16, no. 11 (July 2023): 3461–74. http://dx.doi.org/10.14778/3611479.3611539.

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Data processing systems face the challenge of supporting increasingly diverse workloads efficiently. At the same time, they are already bloated with internal complexity, and it is not clear how new hardware can be supported sustainably. In this paper, we aim to resolve these issues by proposing a unified abstraction layer based on declarative sub-operators in addition to relational operators. By exposing this layer to users, they can express their non-relational workloads declaratively with sub-operators. Furthermore, the proposed sub-operators decouple the semantic implementation of operators from the efficient imperative implementation, reducing the implementation complexity for relational operators. Finally, through fine-grained automatic optimizations, the declarative sub-operators allow for automatic morsel-driven parallelism. We demonstrate the benefits not only by providing a specific set of sub-operators but also implementing them in a compiling query engine. With thorough evaluation and analysis, we show that we can support a richer set of workloads while retaining the development complexity low and being competitive in performance even with specialized systems.

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Parsons, Aaron D., Stephen W. T. Price, Nicola Wadeson, Mark Basham, Andrew M. Beale, Alun W. Ashton, J. Frederick W. Mosselmans, and Paul D. Quinn. "Automatic processing of multimodal tomography datasets." Journal of Synchrotron Radiation 24, no. 1 (January 1, 2017): 248–56. http://dx.doi.org/10.1107/s1600577516017756.

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With the development of fourth-generation high-brightness synchrotrons on the horizon, the already large volume of data that will be collected on imaging and mapping beamlines is set to increase by orders of magnitude. As such, an easy and accessible way of dealing with such large datasets as quickly as possible is required in order to be able to address the core scientific problems during the experimental data collection. Savu is an accessible and flexible big data processing framework that is able to deal with both the variety and the volume of data of multimodal and multidimensional scientific datasets output such as those from chemical tomography experiments on the I18 microfocus scanning beamline at Diamond Light Source.

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27

Widyaningrum, E., and B. G. H. Gorte. "CHALLENGES AND OPPORTUNITIES: ONE STOP PROCESSING OF AUTOMATIC LARGE-SCALE BASE MAP PRODUCTION USING AIRBORNE LIDAR DATA WITHIN GIS ENVIRONMENT. CASE STUDY: MAKASSAR CITY, INDONESIA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 365–69. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-365-2017.

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LiDAR data acquisition is recognized as one of the fastest solutions to provide basis data for large-scale topographical base maps worldwide. Automatic LiDAR processing is believed one possible scheme to accelerate the large-scale topographic base map provision by the Geospatial Information Agency in Indonesia. As a progressive advanced technology, Geographic Information System (GIS) open possibilities to deal with geospatial data automatic processing and analyses. Considering further needs of spatial data sharing and integration, the one stop processing of LiDAR data in a GIS environment is considered a powerful and efficient approach for the base map provision. The quality of the automated topographic base map is assessed and analysed based on its completeness, correctness, quality, and the confusion matrix.

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Scotto, Carlo. "The Automatic Processing of Ionospheric Data: New Possibilities Even with Old Data?" American Journal of Space Science 3, no. 1 (January 1, 2015): 1–2. http://dx.doi.org/10.3844/ajssp.2015.1.2.

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29

Hitesh Chaitanyaswami, Hitesh Chaitanyaswami. "Data Processing from Bank Cheques by Utilising Split Attribute Character Analysis and Multi Branch Network Forest Classifier Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 12, no. 1 (January 1, 2024): 145–51. http://dx.doi.org/10.17762/ijritcc.v12i1.9777.

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Bank cheques are primarily used for conducting financial transactions, resulting in their substantial daily processing volumes worldwide. The automation of the whole process of recognising and verifying cheques has the potential to significantly reduce both the time and expenditures associated with cheque execution. The field of automatic bank cheque processing system is now gaining prominence in the realm of computer vision, image processing, pattern recognition, machine learning, and deep learning. The study places particular emphasis on the sequential processes involved in the automated bank Cheque processing system, including picture capture, pre-processing, and extraction and identification. This article provides an overview of the sequential processes included in the automated data extraction system. This research aims to propose strategies for the automated processing of bank cheque images via the use of Split Attribute character analysis and Multi branch network forest classifier. The study indicates that the recommended technique demonstrates satisfactory performance by achieving high levels of accuracy, precision, recall, and F score.

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30

Monaco, Stéphanie, Elspeth Gordon, Matthew W. Bowler, Solange Delagenière, Matias Guijarro, Darren Spruce, Olof Svensson, et al. "Automatic processing of macromolecular crystallography X-ray diffraction data at the ESRF." Journal of Applied Crystallography 46, no. 3 (May 15, 2013): 804–10. http://dx.doi.org/10.1107/s0021889813006195.

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The development of automated high-intensity macromolecular crystallography (MX) beamlines at synchrotron facilities has resulted in a remarkable increase in sample throughput. Developments in X-ray detector technology now mean that complete X-ray diffraction datasets can be collected in less than one minute. Such high-speed collection, and the volumes of data that it produces, often make it difficult for even the most experienced users to cope with the deluge. However, the careful reduction of data during experimental sessions is often necessary for the success of a particular project or as an aid in decision making for subsequent experiments. Automated data reduction pipelines provide a fast and reliable alternative to user-initiated processing at the beamline. In order to provide such a pipeline for the MX user community of the European Synchrotron Radiation Facility (ESRF), a system for the rapid automatic processing of MX diffraction data from single and multiple positions on a single or multiple crystals has been developed. Standard integration and data analysis programs have been incorporated into the ESRF data collection, storage and computing environment, with the final results stored and displayed in an intuitive manner in the ISPyB (information system for protein crystallography beamlines) database, from which they are also available for download. In some cases, experimental phase information can be automatically determined from the processed data. Here, the system is described in detail.

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31

Broersen, Piet M. T. "Automatic spectral analysis with missing data." Digital Signal Processing 16, no. 6 (November 2006): 754–66. http://dx.doi.org/10.1016/j.dsp.2006.01.001.

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32

Zagul’s’ka, O. B. "THE MAIN DIRECTIONS OF THEMATIC PROCESSING OF REMOTE SENSING DATA IN RUSSIA: LANDSCAPE ASPECT." Odesa National University Herald. Geography and Geology 19, no. 4(23) (March 20, 2015): 38–47. http://dx.doi.org/10.18524/2303-9914.2014.4(23).39287.

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Purpose. Publications of the Russian scientific last years in the field of thematic processing aero- and space images about use for the solution of problems of a landscape science are analysed. Methodology. The publications of the Russian scientists specified in the list of references were a source of information. Finding. In Russia achieved decent success in the field of automatic interpretation of earth cover. One of the leading places in the structure of remote sensing occupies the working of techniques for the study of vegetation. Landscape complex as a whole object of research by the methods of remote sensing in recent years become rare. Elementary landscape complexes are determined by images of vegetation, the system of landscape standards for training of the automated systems is created. The technique of interpretation of landscapes is developed for the applied purposes. Works in which landscape approach is applied meet. A number of works has an indicative orientation. Among priority a question of the automated allocation in images of compound natural, natural-anthropogenous and anthropogenous structures. The technique of recognition of images according to multispectral and hyperspectral remote sensing is actively developed. Results. In Russia the technique of automatic landscape mapping on aero- and to satellite images isn’t developed yet. Progress has been made only in the decoding of the individual components of landscapes, particularly vegetation.

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33

Hou, Tingting, and Zamira Madina. "Automatic Classification of Basic Nursing Teaching Resources Based on the Fusion of Multiple Neural Networks." Mathematical Problems in Engineering 2022 (February 21, 2022): 1–7. http://dx.doi.org/10.1155/2022/7176111.

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Automatic classification is one of the hot topics in the field of information retrieval and natural language processing, but it still faces many problems to be solved. The classic automated classification approach has a sluggish classification speed and poor processing accuracy for resources with a large quantity of data. Based on this, an automated classification approach based on the integration of various neural networks for fundamental nursing teaching materials was presented. The automatic classification method of teaching resources was designed by extracting the characteristics of teaching resources, establishing the model of multiple neural network integration, and designing the classification index of basic nursing teaching resources. The experimental findings suggest that this technique has higher chi-square test parameters and better outcomes for the automated classification of large instructional materials than the classic rough set automatic classification method.

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Ma, Xiaoya, Zhaoqian Gong, Feng Zhang, Shun Wang, Xiaojun Liu, and Guangyou Fang. "An Automatic Drift-Measurement-Data-Processing Method with Digital Ionosondes." Remote Sensing 14, no. 19 (September 21, 2022): 4710. http://dx.doi.org/10.3390/rs14194710.

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Drift detection is one of the important detection modes in a digital ionosonde system. In this paper, a new data processing method is presented for boosting the automatic and high-quality drift measurement, which is helpful for long-term ionospheric observation, and has been successfully applied to the Chinese Academy of Sciences, Digital Ionosonde (CAS-DIS). Based on Doppler interferometry principle, this method can be successively divided into four constraint steps: extracting the stable echo data; restricting the ionospheric detection region; extracting the reliable reflection cluster, including Doppler filtering and coarse clustering analysis; and calculating the drift velocity. Ordinary wave (O-wave) data extraction, complementary code pulse compression and other data preprocessing techniques are used to improve the signal-to-noise ratio (SNR) of echo data. For the purpose of eliminating multiple echoes, the ionospheric region is determined by combining the optimal height range and detection frequencies obtained from the ionogram. Successively, Doppler filtering and coarse clustering analysis extract reliable reflection clusters. Finally, the weighting factor is brought in, and then weighted least-squares (WLS) is used to fit the drift velocity. The entire data processing process can be implemented automatically without constantly changing parameter settings due to changes in external conditions. This is the first time coarse clustering analysis has been used to extract the paracentral reflection cluster to eliminate scattered reflection points and outer reflection clusters, which further reduces the impacts of external conditions on parameter settings and improves the ability of automatic drift measurement. Compared with the previous method possessed by Digisonde Protable Sounder 4D (DPS4D), the new method can achieve comparable drift detection precision and results even with fewer reflection points. In 2021–2022, several experiments on F region drift detection were carried out in Hainan, China. Results indicate that drift velocities fitted by the new method have diurnal variation and change more gently; the trends of drift velocities fitted by the new method and the previous method are semblable; and this new method can be widely applied to digital ionosondes.

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35

Jung, Jaehoon, Cyrill Stachniss, and Changjae Kim. "Automatic Room Segmentation of 3D Laser Data Using Morphological Processing." ISPRS International Journal of Geo-Information 6, no. 7 (July 7, 2017): 206. http://dx.doi.org/10.3390/ijgi6070206.

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36

Skarzynski, T., M. Meyer, and P. Stec. "New version ofCrysAlisProoptimized for automatic macromolecular data collection and processing." Acta Crystallographica Section A Foundations of Crystallography 67, a1 (August 22, 2011): C660—C661. http://dx.doi.org/10.1107/s0108767311083280.

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37

Lukić, Tatjana. "Automatic computer based data processing as a special investigation activity." Glasnik Advokatske komore Vojvodine 78, no. 9 (2006): 602–15. http://dx.doi.org/10.5937/gakv0611602l.

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The application of special investigation activities is characteristic for proactive investigations related to the organized crime. One of these special investigation activities, that became more and more important, is a computer based automatic data processing which was introduced in Serbia by the new Law on Criminal Procedure. The subject matter of this paper is legal solution in Serbia related to this special investigation activity. Apart from that, the author demonstrated its comparative regulation by analyzing legal basis for its application, terms and means of its approval, duration, use of computer excerpts as evidence in the criminal procedure and supervision over its legality. The author paid special attention to the personal data processing on the international level and issue of criminal investigation analytics in combating terrorism.

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Hernando, M. Elena, Gema García-Sáez, Iñaki Martínez-Sarriegui, Agustín Rodríguez-Herrero, Carmen Pérez-Gandía, Mercedes Rigla, Alberto de Leiva, Ismael Capel, Belén Pons, and Enrique J. Gómez. "Automatic Data Processing to Achieve a Safe Telemedical Artificial Pancreas." Journal of Diabetes Science and Technology 3, no. 5 (September 2009): 1039–46. http://dx.doi.org/10.1177/193229680900300507.

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Background: The use of telemedicine for diabetes care has evolved over time, proving that it contributes to patient self-monitoring, improves glycemic control, and provides analysis tools for decision support. The timely development of a safe and robust ambulatory artificial pancreas should rely on a telemedicine architecture complemented with automatic data analysis tools able to manage all the possible high-risk situations and to guarantee the patient's safety. Methods: The Intelligent Control Assistant system (INCA) telemedical artificial pancreas architecture is based on a mobile personal assistant integrated into a telemedicine system. The INCA supports four control strategies and implements an automatic data processing system for risk management (ADP-RM) providing short-term and medium-term risk analyses. The system validation comprises data from 10 type 1 pump-treated diabetic patients who participated in two randomized crossover studies, and it also includes in silico simulation and retrospective data analysis. Results: The ADP-RM short-term risk analysis prevents hypoglycemic events by interrupting insulin infusion. The pump interruption has been implemented in silico and tested for a closed-loop simulation over 30 hours. For medium-term risk management, analysis of capillary blood glucose notified the physician with a total of 62 alarms during a clinical experiment (56% for hyperglycemic events). The ADP-RM system is able to filter anomalous continuous glucose records and to detect abnormal administration of insulin doses with the pump. Conclusions: Automatic data analysis procedures have been tested as an essential tool to achieve a safe ambulatory telemedical artificial pancreas, showing their ability to manage short-term and medium-term risk situations.

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Xu, Yang, Brighter Agyemang, Shuai Wu, and Ming Liu. "Procedure Graph Model for Automatic RFID Data Processing Service Management." IEEE Internet of Things Journal 4, no. 3 (June 2017): 713–22. http://dx.doi.org/10.1109/jiot.2017.2661326.

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Saze, Takuya, Masahiro Etoh, Chizuo Mori, and Kunihide Nishizawa. "Automatic activity measurement and data processing system using image analyzer." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 443, no. 2-3 (April 2000): 578–85. http://dx.doi.org/10.1016/s0168-9002(99)01160-2.

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Sarout, Joël, Mohamed Ferjani, and Yves Guéguen. "A semi-automatic processing technique for elastic-wave laboratory data." Ultrasonics 49, no. 4-5 (May 2009): 452–58. http://dx.doi.org/10.1016/j.ultras.2008.12.001.

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El Mohadab, Mohamed, Belaid Bouikhalene, and Said Safi. "Automatic CV processing for scientific research using data mining algorithm." Journal of King Saud University - Computer and Information Sciences 32, no. 5 (June 2020): 561–67. http://dx.doi.org/10.1016/j.jksuci.2018.07.002.

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Eftimie, Eugenia, and E. Segal. "Basic language programs for automatic processing non-isothermal kinetic data." Thermochimica Acta 111 (February 1987): 359–67. http://dx.doi.org/10.1016/0040-6031(87)88063-2.

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INOUE, Makoto, Masaaki OHTEKI, Akira IWAMATSU, and Shuichiro YOKOTA. "Automatic Observation System of Electrical Prospecting and Its Data Processing." Geoinformatics 2, no. 1 (1991): 65–83. http://dx.doi.org/10.6010/geoinformatics1990.2.1_65.

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Llirbat, F., J. P. Matsumoto, E. Simon, J. P. Berroir, I. Herlin, and H. Yahia. "Using Scientific Workflow Techniques for Automatic Processing of Environmental Data." Systems Analysis Modelling Simulation 42, no. 11 (January 2002): 1601–13. http://dx.doi.org/10.1080/716067178.

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Calder, B. R., and L. A. Mayer. "Automatic processing of high-rate, high-density multibeam echosounder data." Geochemistry, Geophysics, Geosystems 4, no. 6 (June 2003): n/a. http://dx.doi.org/10.1029/2002gc000486.

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Martínez, Joaquín, Alex Soria-Medina, Pedro Arias, and Alzir Felippe Buffara-Antunes. "Automatic processing of Terrestrial Laser Scanning data of building façades." Automation in Construction 22 (March 2012): 298–305. http://dx.doi.org/10.1016/j.autcon.2011.09.005.

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Liu, Yuanjie, Yude Yu, Xiaoguang Zhou, and Chong Wang. "A new automatic threshold selecting criteria for spectroscopy data processing." Chemometrics and Intelligent Laboratory Systems 161 (February 2017): 8–14. http://dx.doi.org/10.1016/j.chemolab.2016.12.006.

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Jeffries, David, Irfan Zaidi, Bouke de Jong, Martin J. Holland, and David J. C. Miles. "Analysis of flow cytometry data using an automatic processing tool." Cytometry Part A 73A, no. 9 (September 2008): 857–67. http://dx.doi.org/10.1002/cyto.a.20611.

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Li, Chang You, and Hong Zheng Li. "Information Processing of the Broken Tape of Flight Data Recorder." Applied Mechanics and Materials 58-60 (June 2011): 2402–6. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2402.

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For the requirement of the department for aviation accident investigation, a computer information system of processing the broken tape of flight data recorder has been designed. The main principles of the system are that magnetic track images are first preprocessed using image processing techniques, furthermore, the position coordinates of tracks are obtained using image recognizing techniques, finally, the data corresponding to track coordinates are obtained by encoding the track coordinates using the serial-parallel scanning method that is specially designed. The accurate ratio of automatic encoding is 99.3%. The times of automatic encoding are reduced to proximate 5 seconds from about 4 hours of two operators for one frame track image. This system is significant for aviation accident investigations.

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