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Готові списки джерел за темами / Electronic data processing

Добірка наукової літератури з теми "Electronic data processing"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 6 вересня 2023

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Статті в журналах з теми "Electronic data processing":

1

Spalevic, Zaklina, Milos Ilic, and Dusan Jerotijevic. "Electronic monitoring devices and data processing." Ekonomika 62, no. 4 (2016): 155–66. http://dx.doi.org/10.5937/ekonomika1604155s.

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2

Jacobson, Robert V. "Electronic Data Processing Security—An Overview." International Journal of Network Management 6, no. 2 (March 1996): 77–93. http://dx.doi.org/10.1002/(sici)1099-1190(199603/04)6:2<77::aid-nem184>3.0.co;2-7.

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3

Mutiarni, Rita. "Implementasi Electronic Data Processing Pada Koperasi Wanita." Eksis: Jurnal Riset Ekonomi dan Bisnis 12, no. 2 (October 20, 2017): 135–48. http://dx.doi.org/10.26533/eksis.v12i2.203.

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4

M.Essa, Youssef, Gamal ATTIYA, Ayman El-Sayed, and Ahmed ElMahalawy. "Data processing platforms for electronic health records." Health and Technology 8, no. 4 (January 31, 2018): 271–80. http://dx.doi.org/10.1007/s12553-018-0219-5.

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5

Makalov, A. O., V. A. Smirnov, and A. V. Prokhortsov. "Electronic Auscultation System: Processing of Auscultatory Data." Proceedings of the Southwest State University. Series: IT Management, Computer Science, Computer Engineering. Medical Equipment Engineering 13, no. 2 (August 3, 2023): 137–52. http://dx.doi.org/10.21869/2223-1536-2023-13-2-137-152.

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Анотація:

The purpose of research is to increase the diversity of electronic auscultation system designs with measured characteristics and testing. The series of articles includes the development of a model of the electronic auscultation system, the design of an electronic stethoscope, the manufacture of an experimental sample, the development of a methodology for measuring the amplitude-frequency characteristics of electronic and classical stethoscopes, testing of the proposed models and methods, analysis of auscultative data. The article considers mathematical methods of primary analysis of auscultative data. Recordings of normal and hard breathing were used for the experiment. Frequency, time-frequency and autocorrelation analysis of respiratory sounds was performed. Methods. The research was based on the theory of digital signal processing. The study uses auscultative data obtained from an experimental sample of an electronic auscultation system. Electronic records of hard (pathological) and normal human breathing over the left middle lung were used. Respiratory noises were converted into digital form with the following parameters: sampling frequency fd = 48 kHz; bit depth n = 24 bits; number of channels 1. To analyze the frequency information content of the recordings, their spectra were constructed. A fast Fourier transform was used to calculate the values in the spectra. Results. In the presented work, the frequency characteristics of recordings of hard and normal breathing are analyzed. The values of autocorrelation functions are obtained. An author-regression model of the process of normal breathing is obtained. The problem of analytical determination of the model order remains open and requires a separate solution. The resulting model allows you to generate an equivalent breathing noise of inhalation or exhalation when a signal with a uniform is applied to its input. Conclusion. A sample of the electronic auscultation system has been tested, methods of simplified primary analysis of auscultative data have been proposed. The difference in the results of the treatment of hard and normal breathing has no statistical significance due to the small sample.

6

Lagutina, I. V. "ELECTRONIC LABOUR BOOKS AND DATA PROTECTION." Наукові праці Національного університету “Одеська юридична академія” 28, no. 29 (January 26, 2022): 129–35. http://dx.doi.org/10.32837/npnuola.v28i29.725.

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Lagutina I. V. Electronic labour book and data protection. – Article. The article considers the protection of personal data as a modern and active law that provides a system of checks and balances to protect the individual if his personal data is processed. Data processing must meet the basic requirements of personal data protection, namely independent control and respect for the rights of the personal data subject. The development of legislation on personal data protection has been marked by a significant expansion of the rights of their subjects and the legal regulation of all transactions with information from collection to destruction. With the adoption of the Law of Ukraine “On Personal Data Protection” of June 1, 2010, a regulatory framework for the protection of personal data in national legal practice was created. It is emphasized that the right to protection of personal data is not absolute; it may be restricted as necessary to satisfy the general interest or to protect the rights and freedoms of others. The right to data protection is often interlinked with other rights, such as freedom of expression and the right to receive and impart information. Any type of information can be personal data, provided that the information relates to an identified person or a person who can be identified. Personal data are processed legally if they meet one of the following criteria: processing is carried out with the consent of the personal data subject; data processing is required by contractual relationship; data processing is necessary for the controller to comply with a legal obligation; data processing is required to comply with the vital interests of personal data subjects or others; data processing is necessary to perform the task in the public interest; the legitimate interests of the controllers or other persons are the basis for processing, but only if they are not outweighed by the interests or fundamental rights of the data subjects. It is necessary to develop a sectoral mechanism for the protection of personal data of employees under labour legislation of Ukraine, as the Law of Ukraine “On Personal Data Protection” does not take into account the peculiarities of personal data protection of employees as subjects of labour relations.

7

Rushinek, A., and S. F. Rushinek. "Accounting Systems and the Electronic Data Processing Function." Management Decision 23, no. 4 (April 1985): 35–44. http://dx.doi.org/10.1108/eb001383.

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8

Yu, Qian, and Abhijit Shanbhag. "Electronic Data Processing for Error and Dispersion Compensation." Journal of Lightwave Technology 24, no. 12 (December 2006): 4514–25. http://dx.doi.org/10.1109/jlt.2006.886065.

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9

Srivastava, A. K., K. K. Shukla, and S. K. Srivastava. "Exploring neuro-genetic processing of electronic nose data." Microelectronics Journal 29, no. 11 (November 1998): 921–31. http://dx.doi.org/10.1016/s0026-2692(98)00056-1.

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10

Byrne, Edmund. "The Two-Tiered Ethics of Electronic Data Processing." Society for Philosophy and Technology Quarterly Electronic Journal 2, no. 1 (1996): 18–27. http://dx.doi.org/10.5840/techne1996214.

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Дисертації з теми "Electronic data processing":

1

Ostroumov, Ivan Victorovich. "Magnetic field data processing with personal electronic device." Thesis, Polit. Challenges of science today: International Scientific and Practical Conference of Young Researchers and Students, April 6–8, 2016 : theses. – К., 2016. – 83p, 2016. http://er.nau.edu.ua/handle/NAU/26649.

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2

Romig, Phillip R. "Parallel task processing of very large datasets." [Lincoln, Neb. : University of Nebraska-Lincoln], 1999. http://international.unl.edu/Private/1999/romigab.pdf.

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3

Rehfuss, Paul Stephen. "Parallelism in contextual processing /." Full text open access at:, 1999. http://content.ohsu.edu/u?/etd,272.

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4

Parker, Greg. "Robust processing of diffusion weighted image data." Thesis, Cardiff University, 2014. http://orca.cf.ac.uk/61622/.

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Анотація:

The work presented in this thesis comprises a proposed robust diffusion weighted magnetic resonance imaging (DW-MRI) pipeline, each chapter detailing a step designed to ultimately transform raw DW-MRI data into segmented bundles of coherent fibre ready for more complex analysis or manipulation. In addition to this pipeline we will also demonstrate, where appropriate, ways in which each step could be optimized for the maxillofacial region, setting the groundwork for a wider maxillofacial modelling project intended to aid surgical planning. Our contribution begins with RESDORE, an algorithm designed to automatically identify corrupt DW-MRI signal elements. While slower than the closest alternative, RESDORE is also far more robust to localised changes in SNR and pervasive image corruptions. The second step in the pipeline concerns the retrieval of accurate fibre orientation distribution functions (fODFs) from the DW-MRI signal. Chapter 4 comprises a simulation study exploring the application of spherical deconvolution methods to `generic' fibre; finding that the commonly used constrained spherical harmonic deconvolution (CSHD) is extremely sensitive to calibration but, if handled correctly, might be able to resolve muscle fODFs in vivo. Building upon this information, Chapter 5 conducts further simulations and in vivo image experimentation demonstrating that this is indeed the case, allowing us to demonstrate, for the first time, anatomically plausible reconstructions of several maxillofacial muscles. To complete the proposed pipeline, Chapter 6 then introduces a method for segmenting whole volume streamline tractographies into anatomically valid bundles. In addition to providing an accurate segmentation, this shape-based method does not require computationally expensive inter-streamline comparisons employed by other approaches, allowing the algorithm to scale linearly with respect to the number of streamlines within the dataset. This is not often true for comparison based methods which in the best case scale in higher linear time but more often by O(N2) complexity.

5

Robinson, Patrick Glen. "Distributed Linda : design, development, and characterization of the data subsystem /." Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-07102009-040417/.

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6

Snowdon, Jane Louise. "Workflow control for surges from a batch work station." Diss., Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/25100.

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7

Lee, J. J. "The object-oriented database and processing of electronic warfare data." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1996. http://handle.dtic.mil/100.2/ADA303112.

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8

Tucker, Peter A. "Punctuated data streams /." Full text open access at:, 2005. http://content.ohsu.edu/u?/etd,255.

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9

Gottemukkala, Vibby. "Scalability issues in distributed and parallel databases." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/8176.

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10

Lewis, Tony. "Electronic data interchange in the construction industry." Thesis, Loughborough University, 1998. https://dspace.lboro.ac.uk/2134/11183.

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Анотація:

The aim of this research is to improve the efficiency of the construction process through the application of electronic data interchange (EDI). This thesis describes the development and application of EDI messages. The messages described are targeted to provide a means for transferring construction specific information during the construction process. The definition of electronic data interchange and its technical issues are first described. The nature of EDI, replacing paper based communication with electronic messages, impacts on the way in which business is conducted, and also has far reaching legal implications due to the reliance of many legal systems on paper documents and signatures. The business and legal implications are therefore discussed in detail. The application of EDI in the construction industry is investigated by means of a literature review. This work is furthered by a longitudinal study of the construction industry's application of EDI, which consisted of two surveys at a five year interval. A model of the information flows within the traditional construction process is developed to assist in the identification of information flows suitable for EDI. A methodology for message development was produced. The methodology was then applied to develop a description data model that could be utilised in the existing bill of quantity and trading cycle messages. The bill of quantity message set was at a stage ready for trial. To determine the issues related to implementation specifically in the construction industry a trial implementation of this message set was undertaken. The official implementation undertaken by EDICON is described. Software was also developed to undertake the trial. This software was tested and proved the message set developed was suitable for the transfer of bill of quantity related information during a construction project. The factors causing the failure of the implementation of the bill of quantities message set are discussed. A number of these factors are considered valid for all construction project information flows. Finally, the use of shared project models to re-engineer construction information tasks is recommended as a means of achieving significant benefit from electronic data exchange in the construction process.

Більше джерел

Книги з теми "Electronic data processing":

1

Canada. Translation Bureau. Terminology Directorate., ed. Glossary: Electronic data processing. 2nd ed. Ottawa: Communication Services, Translation Bureau = Service des communications, Bureau des traductions, 1985.

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2

Lester, Graham C. Data processing. London: Pitman, 1988.

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3

Lester, Graham C. Data processing. 3rd ed. London: Pitman, 1988.

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4

Kimberley, Paul. Electronic data interchange. New York: McGraw-Hill, 1991.

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5

Gildersleeve, Thomas Robert. Data processing project management. 2nd ed. New York: Van Nostrand Reinhold, 1985.

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6

Floyd, Nancy A. Essentials of data processing. St. Louis: Times-Mirror/Mosby College Pub., 1987.

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7

N, Bordage, Buendia Laurent, Gagnon Rénard, and Canada. Translation Bureau. Terminology and Linguistic Services Branch., eds. Glossary, electronic data processing = Lexique informatique. Ottawa: Department of the Secretary of State, Translation Bureau, 1987.

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8

Montana. Legislature. Office of the Legislative Auditor. EDP audit: Electronic data processing survey. Helena, Mont: The Office, 1991.

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9

Hodgson, J. P. E. Data processing tests: Including the certificate in data processing examination. New York: Arco Pub., 1985.

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10

Miller, Howard W. Electronic vaulting. Byfield, MA (136 Orchard St., Byfield 01922-1605): Information Technology Institute, 1994.

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Частини книг з теми "Electronic data processing":

1

Walsh, Vincent. "Electronic Data Processing." In Computer Literacy, 75–78. London: Macmillan Education UK, 1985. http://dx.doi.org/10.1007/978-1-349-07674-1_13.

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2

Malley, Brian, Daniele Ramazzotti, and Joy Tzung-yu Wu. "Data Pre-processing." In Secondary Analysis of Electronic Health Records, 115–41. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43742-2_12.

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3

Younes, Hamoud, Mohamad Alameh, Ali Ibrahim, Mostafa Rizk, and Maurizio Valle. "Efficient Algorithms for Embedded Tactile Data Processing." In Electronic Skin, 113–38. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338062-6.

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4

de Smit, Jacob. "Electronic Data Processing in Strategic Planning." In Operations Research Proceedings, 256. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73778-7_61.

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5

Schuster, Alfons, Simon McCaughey, and Roy Sterritt. "From Data to Intelligent Agents, Directions in a Data Intensive Data Processing Project." In Electronic Business and Education, 147–63. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1497-8_6.

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6

Osta, Mario, Ali Ibrahim, and Maurizio Valle. "Approximate Arithmetic Circuits for Energy Efficient Data Processing in Electronic Skin." In Electronic Skin, 139–61. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338062-7.

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7

Engel, Robert, Worarat Krathu, Marco Zapletal, Christian Pichler, Wil M. P. van der Aalst, and Hannes Werthner. "Process Mining for Electronic Data Interchange." In Lecture Notes in Business Information Processing, 77–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23014-1_7.

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8

Srivastava, Samarth, Oshi Varma, and M. Gayathri. "Electronic Invoicing Using Image Processing and NLP." In Innovative Data Communication Technologies and Application, 843–56. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7167-8_62.

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9

Ibrahim, Ali, Luca Noli, Hussein Chible, and Maurizio Valle. "Embedded Electronic Systems for Tactile Data Processing." In Lecture Notes in Electrical Engineering, 17–23. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47913-2_3.

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10

Härder, Theo, and Norbert Ritter. "Transaction-Based Design Data Processing in the PRIMA Framework." In Electronic Design Automation Frameworks, 3–12. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-0-387-34880-3_1.

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Тези доповідей конференцій з теми "Electronic data processing":

1

Tian, Bing, and Kun Liu. "Electronic Map Data Processing and Optimization." In 2010 International Conference on E-Product E-Service and E-Entertainment (ICEEE 2010). IEEE, 2010. http://dx.doi.org/10.1109/iceee.2010.5661625.

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2

Spalević, Žaklina, Miloš Ilić, and Petar Spalević. "Electronic monitoring devices and data processing." In Synthesis 2015. Belgrade, Serbia: Singidunum University, 2015. http://dx.doi.org/10.15308/synthesis-2015-242-247.

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3

Lee, JeongHun, and OkSam Chae. "Multimedia data processing algorithm development environment (MADE)." In Electronic Imaging '99, edited by Robert F. Erbacher, Philip C. Chen, and Craig M. Wittenbrink. SPIE, 1999. http://dx.doi.org/10.1117/12.342835.

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4

Shaposhnik, Alexey V., and Pavel V. Moskalev. "Processing Electronic Nose Data Using Artificial Neural Networks." In 2020 4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR). IEEE, 2020. http://dx.doi.org/10.1109/dcnair50402.2020.9216931.

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5

zhang, Huan, and Hongli Tai. "Several Data Analysis And Processing of Electronic Nose Data Preprocessing Subsystem." In 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2021. http://dx.doi.org/10.1109/iaeac50856.2021.9390785.

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6

Iskhakova, Anastasia. "Processing of Big Data Streams in Intelligent Electronic Data Analysis Systems." In Proceedings of the VIth International Workshop 'Critical Infrastructures: Contingency Management, Intelligent, Agent-Based, Cloud Computing and Cyber Security' (IWCI 2019). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/iwci-19.2019.3.

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7

Sitnik, Robert, Artur Filipczak, Marcin Witkowski, Walter Rapp, and Bart Haex. "4D data processing for dynamic human body analysis." In Electronic Imaging 2006, edited by Brian D. Corner, Peng Li, and Matthew Tocheri. SPIE, 2006. http://dx.doi.org/10.1117/12.643213.

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8

Raman, Rajeev. "Image processing data flow in digital cameras." In Photonics West '98 Electronic Imaging, edited by George M. Williams, Jr. SPIE, 1998. http://dx.doi.org/10.1117/12.304589.

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9

Lay-Ekuakille, Aime, and Amerigo Trotta. "Processing stabilometric data for electronic knee: Training and calibration." In 2010 IEEE Instrumentation & Measurement Technology Conference Proceedings. IEEE, 2010. http://dx.doi.org/10.1109/imtc.2010.5488104.

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10

Liu, Yingjie, Qingqing Bian, Jie Tong, Hao Wen, and Chunyi Zhan. "Data Processing Based on Glomerular Microcircuits for Electronic Noses." In 2021 40th Chinese Control Conference (CCC). IEEE, 2021. http://dx.doi.org/10.23919/ccc52363.2021.9549686.

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Звіти організацій з теми "Electronic data processing":

1

Overberg, Mark E., Kent Martin Geib, Darwin Keith Serkland, Alan Yuan-Chun Hsu, Gordon Arthur Keeler, and Patrick Sean Finnegan. Electronic/photonic interfaces for ultrafast data processing. Office of Scientific and Technical Information (OSTI), September 2008. http://dx.doi.org/10.2172/940521.

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2

Avis, William. Drivers, Barriers and Opportunities of E-waste Management in Africa. Institute of Development Studies (IDS), December 2021. http://dx.doi.org/10.19088/k4d.2022.016.

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Анотація:

Population growth, increasing prosperity and changing consumer habits globally are increasing demand for consumer electronics. Further to this, rapid changes in technology, falling prices and consumer appetite for better products have exacerbated e-waste management challenges and seen millions of tons of electronic devices become obsolete. This rapid literature review collates evidence from academic, policy focussed and grey literature on e-waste management in Africa. This report provides an overview of constitutes e-waste, the environmental and health impacts of e-waste, of the barriers to effective e-waste management, the opportunities associated with effective e-waste management and of the limited literature available that estimate future volumes of e-waste. Africa generated a total of 2.9 million Mt of e-waste, or 2.5 kg per capita, the lowest regional rate in the world. Africa’s e-waste is the product of Local and imported Sources of Used Electronic and Electrical Equipment (UEEE). Challenges in e-waste management in Africa are exacerbated by a lack of awareness, environmental legislation and limited financial resources. Proper disposal of e-waste requires training and investment in recycling and management technology as improper processing can have severe environmental and health effects. In Africa, thirteen countries have been identified as having a national e-waste legislation/policy.. The main barriers to effective e-waste management include: Insufficient legislative frameworks and government agencies’ lack of capacity to enforce regulations, Infrastructure, Operating standards and transparency, illegal imports, Security, Data gaps, Trust, Informality and Costs. Aspirations associated with energy transition and net zero are laudable, products associated with these goals can become major contributors to the e-waste challenge. The necessary wind turbines, solar panels, electric car batteries, and other "green" technologies require vast amounts of resources. Further to this, at the end of their lifetime, they can pose environmental hazards. An example of e-waste associated with energy transitions can be gleaned from the solar power sector. Different types of solar power cells need to undergo different treatments (mechanical, thermal, chemical) depending on type to recover the valuable metals contained. Similar issues apply to waste associated with other energy transition technologies. Although e-waste contains toxic and hazardous metals such as barium and mercury among others, it also contains non-ferrous metals such as copper, aluminium and precious metals such as gold and copper, which if recycled could have a value exceeding 55 billion euros. There thus exists an opportunity to convert existing e-waste challenges into an economic opportunity.

3

Hanwell, Marcus. OPEN SOURCE PLATFORM FOR LIVE PROCESSING OF HIGH DATA RATE ELECTRON MICROSCOPY. Office of Scientific and Technical Information (OSTI), March 2020. http://dx.doi.org/10.2172/1602474.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.

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Modlo, Yevhenii O., Serhiy O. Semerikov, Stanislav L. Bondarevskyi, Stanislav T. Tolmachev, Oksana M. Markova, and Pavlo P. Nechypurenko. Methods of using mobile Internet devices in the formation of the general scientific component of bachelor in electromechanics competency in modeling of technical objects. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3677.

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An analysis of the experience of professional training bachelors of electromechanics in Ukraine and abroad made it possible to determine that one of the leading trends in its modernization is the synergistic integration of various engineering branches (mechanical, electrical, electronic engineering and automation) in mechatronics for the purpose of design, manufacture, operation and maintenance electromechanical equipment. Teaching mechatronics provides for the meaningful integration of various disciplines of professional and practical training bachelors of electromechanics based on the concept of modeling and technological integration of various organizational forms and teaching methods based on the concept of mobility. Within this approach, the leading learning tools of bachelors of electromechanics are mobile Internet devices (MID) – a multimedia mobile devices that provide wireless access to information and communication Internet services for collecting, organizing, storing, processing, transmitting, presenting all kinds of messages and data. The authors reveals the main possibilities of using MID in learning to ensure equal access to education, personalized learning, instant feedback and evaluating learning outcomes, mobile learning, productive use of time spent in classrooms, creating mobile learning communities, support situated learning, development of continuous seamless learning, ensuring the gap between formal and informal learning, minimize educational disruption in conflict and disaster areas, assist learners with disabilities, improve the quality of the communication and the management of institution, and maximize the cost-efficiency. Bachelor of electromechanics competency in modeling of technical objects is a personal and vocational ability, which includes a system of knowledge, skills, experience in learning and research activities on modeling mechatronic systems and a positive value attitude towards it; bachelor of electromechanics should be ready and able to use methods and software/hardware modeling tools for processes analyzes, systems synthesis, evaluating their reliability and effectiveness for solving practical problems in professional field. The competency structure of the bachelor of electromechanics in the modeling of technical objects is reflected in three groups of competencies: general scientific, general professional and specialized professional. The implementation of the technique of using MID in learning bachelors of electromechanics in modeling of technical objects is the appropriate methodic of using, the component of which is partial methods for using MID in the formation of the general scientific component of the bachelor of electromechanics competency in modeling of technical objects, are disclosed by example academic disciplines “Higher mathematics”, “Computers and programming”, “Engineering mechanics”, “Electrical machines”. The leading tools of formation of the general scientific component of bachelor in electromechanics competency in modeling of technical objects are augmented reality mobile tools (to visualize the objects’ structure and modeling results), mobile computer mathematical systems (universal tools used at all stages of modeling learning), cloud based spreadsheets (as modeling tools) and text editors (to make the program description of model), mobile computer-aided design systems (to create and view the physical properties of models of technical objects) and mobile communication tools (to organize a joint activity in modeling).

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Electronic Data Processing (EDP) Section - c.1965. Reserve Bank of Australia, September 2022. http://dx.doi.org/10.47688/rba_archives_pn-004508.

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Federal Information Processing Standards Publication: electronic data interchange (EDI). Gaithersburg, MD: National Institute of Standards and Technology, 1996. http://dx.doi.org/10.6028/nist.fips.161-2.

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Premises - Head Office - Electronic Data Processing (EDP) Section, 1969. Reserve Bank of Australia, September 2022. http://dx.doi.org/10.47688/rba_archives_pn-004822.

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Staff at Work - Electronic Data Processing (EDP) Section - September 1968. Reserve Bank of Australia, September 2022. http://dx.doi.org/10.47688/rba_archives_pn-004746.

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Premises - Head Office - Electronic Data Processing (EDP) Section, 1969 (copy b). Reserve Bank of Australia, September 2022. http://dx.doi.org/10.47688/rba_archives_pn-004823.

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