Relevant bibliographies by topics / Automatic data processing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Automatic data processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Automatic data processing"

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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Automatic data processing"

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余銘龍 and Ming-lung Yu. "Automatic processing of Chinese language bank cheques." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B31225548.

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Hoyt, Matthew Ray. "Automatic Tagging of Communication Data." Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc149611/.

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Globally distributed software teams are widespread throughout industry. But finding reliable methods that can properly assess a team's activities is a real challenge. Methods such as surveys and manual coding of activities are too time consuming and are often unreliable. Recent advances in information retrieval and linguistics, however, suggest that automated and/or semi-automated text classification algorithms could be an effective way of finding differences in the communication patterns among individuals and groups. Communication among group members is frequent and generates a significant amount of data. Thus having a web-based tool that can automatically analyze the communication patterns among global software teams could lead to a better understanding of group performance. The goal of this thesis, therefore, is to compare automatic and semi-automatic measures of communication and evaluate their effectiveness in classifying different types of group activities that occur within a global software development project. In order to achieve this goal, we developed a web-based component that can be used to help clean and classify communication activities. The component was then used to compare different automated text classification techniques on various group activities to determine their effectiveness in correctly classifying data from a global software development team project.
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Lee, Hiu-wing Doris, and 李曉穎. "A study of automatic expansion of Chinese abbreviations." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B31609338.

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Ikei, Mitsuru. "Automatic program restructuring for distributed memory multicomputers." Full text open access at:, 1992. http://content.ohsu.edu/u?/etd,191.

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張少能 and Siu-nang Bruce Cheung. "A theory of automatic language acquisition." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B31233521.

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Josifovski, Ljubomir. "Robust automatic speech recognition with missing and unreliable data." Thesis, University of Sheffield, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275021.

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Wang, Wei. "Automatic Chinese calligraphic font generation with machine learning technology." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950605.

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Wong, Angela Sai On. "A fully automatic analytic approach to budget-constrained system upgrade." Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/26670.

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This thesis describes the development of a software package to upgrade computer systems. The package, named OPTIMAL, solves the following problem: given an existing computer system and its workload, a budget, and the costs and descriptions of available upgrade alternatives for devices in the system, what is the most cost-effective way of upgrading and tuning the system to produce the optimal system throughput? To enhance the practicality of OPTIMAL, the research followed two criteria: i) input required by OPTIMAL must be system and workload characteristics directly measurable from the system under consideration; ii) other than gathering the appropriate input data, the package must be completely automated and must not require any specialized knowledge in systems performance evaluation to interpret the results. The output of OPTIMAL consists of the optimal system throughput under the budget constraint, the workload and system configuration (or upgrade strategy) that provide such throughput, and the cost of the upgrade. Various optimization techniques, including saturation analysis and fine tuning, have been applied to enhance the performance of OPTIMAL.
Science, Faculty of
Computer Science, Department of
Graduate
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Morgan, Clifford Owen. "Development of computer aided analysis and design software for studying dynamic process operability." Thesis, Georgia Institute of Technology, 1986. http://hdl.handle.net/1853/10187.

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Paithoonwattanakij, Kitti. "Automatic pattern recognition techniques for geometrical correction on satellite data." Thesis, University of Dundee, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293190.

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Books on the topic "Automatic data processing"

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1947-, Impedovo S., Wang Patrick S-P, and Bunke Horst, eds. Automatic bankcheck processing. Singapore: World Scientific, 1997.

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United States Army Soldier Support Institute. Automatic data processing management handbook. 4th ed. [Ft. Benjamin Harrison, IN]: The Institute, 1985.

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United States Army Soldier Support Institute. Automatic data processing management handbook. 3rd ed. [Washington, D.C.?]: U.S. Army Soldier Support Institute, 1985.

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Pêcheux, Michel. Automatic discourse analysis. Amsterdam: Rodopi, 1995.

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Lee, Jae Y. Automatic retrieval of ATR data. College Station, Tex: Texas Transportation Institute, Texas A&M System, 1986.

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Bolle, Ruud, and Nalini K. Ratha. Automatic fingerprint recognition systems. New York: Springer, 2004.

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Leningradskiĭ institut informatiki i avtomatizat͡sii, ed. Informat͡sionnye problemy avtomatizat͡sii: Sbornik nauchnykh trudov. Leningrad: LIIAN, 1988.

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United States. Defense Logistics Agency., ed. DLA career intern training program for automatic data processing. Alexandria, Va: Dept. of Defense, Defense Logistics Agency, 1985.

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Magee, Colin. Automatic exchange betting: Automating the betting process - from strategy to execution. London: High Stakes, 2008.

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Cristina, Sánchez Mabel, ed. Data processing and reconciliation for chemical process operations. San Diego: Academic Press, 2000.

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Book chapters on the topic "Automatic data processing"

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Weik, Martin H. "automatic data processing." In Computer Science and Communications Dictionary, 82. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_1084.

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Weik, Martin H. "automatic data processing equipment." In Computer Science and Communications Dictionary, 82. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_1085.

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Weik, Martin H. "automatic data processing system." In Computer Science and Communications Dictionary, 83. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_1086.

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Elavarasi, S., and G. Suseendran. "Automatic Robot Processing Using Speech Recognition System." In Data Management, Analytics and Innovation, 185–95. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9949-8_14.

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Schuecker, P., H. Horstmann, and C. C. Volkmer. "Automatic Processing of Very Low-Dispersion Spectra." In Data Analysis in Astronomy II, 109–16. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2249-8_10.

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Nguyen, Thai Son, Hoang Thanh Duong, and Khanh Duy Nguyen. "Automatic Generation of Course Schedules Using Genetic Algorithm." In Artificial Intelligence in Data and Big Data Processing, 37–45. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97610-1_4.

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Chaurasiya, Satish, Neelu Nihalani, and Durgesh Mishra. "Novel Approach for Automatic Cataract Detection Using Image Processing." In Data Science and Security, 403–11. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2211-4_36.

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Adsett, Connie R., and Yannick Marchand. "A Comparison of Data-Driven Automatic Syllabification Methods." In String Processing and Information Retrieval, 174–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03784-9_17.

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Słota, Renata, Łukasz Skitał, Darin Nikolow, and Jacek Kitowski. "Algorithms for Automatic Data Replication in Grid Environment." In Parallel Processing and Applied Mathematics, 707–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752578_85.

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Mishra, Abhijit, and Pushpak Bhattacharyya. "Automatic Extraction of Cognitive Features from Gaze Data." In Cognitively Inspired Natural Language Processing, 153–69. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1516-9_7.

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Conference papers on the topic "Automatic data processing"

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Halliwell, N. A. "Particle image velocimetry: Automatic data processing." In ICALEO® ‘88: Proceedings of the Optical Methods in Flow & Particle Diagnostics Conference. Laser Institute of America, 1988. http://dx.doi.org/10.2351/1.5057977.

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Meyers, Bennet E., Elpiniki Apostolaki-Iosifidou, and Laura T. Schelhas. "Solar Data Tools: Automatic Solar Data Processing Pipeline." In 2020 IEEE 47th Photovoltaic Specialists Conference (PVSC). IEEE, 2020. http://dx.doi.org/10.1109/pvsc45281.2020.9300847.

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Li, Bingcheng. "Network dynamics based sensor data processing." In Automatic Target Recognition XXX, edited by Timothy L. Overman, Riad I. Hammoud, and Abhijit Mahalanobis. SPIE, 2020. http://dx.doi.org/10.1117/12.2558194.

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Dupre, Jean-Christophe, and Alexis Lagarde. "Automatic data processing of speckle fringe pattern." In Second Intl Conf on Photomechanics and Speckle Metrology: Speckle Techniques, Birefringence Methods, and Applications to Solid Mechanics. SPIE, 1991. http://dx.doi.org/10.1117/12.49526.

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Mari, J. L., and P. Gavin. "Automatic processing of full‐waveform sonic data." In SEG Technical Program Expanded Abstracts 1991. Society of Exploration Geophysicists, 1991. http://dx.doi.org/10.1190/1.1889101.

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Ostroumov, Ivan, Nataliia Kuzmenko, and Olena Kyzymchuk. "Automatic Dependent Surveillance-Broadcast Trajectory Data Processing." In 2022 IEEE 16th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET). IEEE, 2022. http://dx.doi.org/10.1109/tcset55632.2022.9767058.

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Twaty, Muaz, Amine Ghrab, and Sabri Skhiri. "GraphOpt: a Framework for Automatic Parameters Tuning of Graph Processing Frameworks." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006320.

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Sugiura, Toki, and Hiromitsu Nishizaki. "Automatic Exploration of Optimal Data Processing Operations for Sound Data Augmentation Using Improved Differentiable Automatic Data Augmentation." In INTERSPEECH 2023. ISCA: ISCA, 2023. http://dx.doi.org/10.21437/interspeech.2023-202.

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Oye, V., M. Roth, and L. Jahren. "Automatic Real-Time Processing of Induced Microseismic Data." In 64th EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, 2002. http://dx.doi.org/10.3997/2214-4609-pdb.5.p236.

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Piip, V. B., and A. N. Naumov. "Automatic processing and interpretation of engineering seismic data." In Geophysics of the 21st Century - The Leap into the Future. European Association of Geoscientists & Engineers, 2003. http://dx.doi.org/10.3997/2214-4609-pdb.38.f191.

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Reports on the topic "Automatic data processing"

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Washington, William N. Outsourcing Automatic Data Processing Requirements and Support. Fort Belvoir, VA: Defense Technical Information Center, January 1999. http://dx.doi.org/10.21236/ada372857.

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Washington, William N. Outsourcing Automatic Data Processing Requirements and Support. Fort Belvoir, VA: Defense Technical Information Center, July 1997. http://dx.doi.org/10.21236/ada327808.

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Johnston, B. L. Addendum, automatic data processing (ADP) security plan, Revision 1. ADP facility number: PNL-63. Office of Scientific and Technical Information (OSTI), June 1989. http://dx.doi.org/10.2172/10118490.

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Pavlicheva, E. N., V. P. Meshalkin, and N. S. CHikunov. Algorithm for processing text data for an automatic classification problem using the word2vec method. OFERNIO, February 2021. http://dx.doi.org/10.12731/ofernio.2021.24759.

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Dannemann Dugick, Fransiska, Sarah Albert, Gil Averbuch, and Stephen Arrowsmith. Utilizing the Dynamic Networks Data Processing and Analysis Experiment (DNE18) to Establish Methodologies for the Comparison of Automatic Infrasonic Signal Detectors. Office of Scientific and Technical Information (OSTI), November 2021. http://dx.doi.org/10.2172/1832306.

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Cook, Samantha, Marissa Torres, Nathan Lamie, Lee Perren, Scott Slone, and Bonnie Jones. Automated ground-penetrating-radar post-processing software in R programming. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45621.

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Ground-penetrating radar (GPR) is a nondestructive geophysical technique used to create images of the subsurface. A major limitation of GPR is that a subject matter expert (SME) needs to post-process and interpret the data, limiting the technique’s use. Post-processing is time-intensive and, for detailed processing, requires proprietary software. The goal of this study is to develop automated GPR post-processing software, compatible with Geophysical Survey Systems, Inc. (GSSI) data, in open-source R programming. This would eliminate the need for an SME to process GPR data, remove proprietary software dependencies, and render GPR more accessible. This study collected GPR profiles by using a GSSI SIR4000 control unit, a 100 MHz antenna, and a Trimble GPS. A standardized method for post-processing data was then established, which includes static data removal, time-zero correction, distance normalization, data filtering, and stacking. These steps were scripted and automated in R programming, excluding data filtering, which was used from an existing package, RGPR. The study compared profiles processed using GSSI software to profiles processed using the R script developed here to ensure comparable functionality and output. While an SME is currently still necessary for interpretations, this script eliminates the need for one to post-process GSSI GPR data.
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Puckett, Philip L. Shipboard Non-Tactical Automated Data Processing (SNAP) Resystemization: An Alternate Approach. Fort Belvoir, VA: Defense Technical Information Center, March 1990. http://dx.doi.org/10.21236/ada227287.

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Bates, C. Richards, Melanie Chocholek, Clive Fox, John Howe, and Neil Jones. Scottish Inshore Fisheries Integrated Data System (SIFIDS): Work package (3) final report development of a novel, automated mechanism for the collection of scallop stock data. Edited by Mark James and Hannah Ladd-Jones. Marine Alliance for Science and Technology for Scotland (MASTS), 2019. http://dx.doi.org/10.15664/10023.23449.

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[Extract from Executive Summary] This project, aimed at the development of a novel, automated mechanism for the collection of scallop stock data was a sub-part of the Scottish Inshore Fisheries Integrated Data Systems (SIFIDS) project. The project reviewed the state-of-the-art remote sensing (geophysical and camera-based) technologies available from industry and compared these to inexpensive, off-the -shelf equipment. Sea trials were conducted on scallop dredge sites and also hand-dived scallop sites. Data was analysed manually, and tests conducted with automated processing methods. It was concluded that geophysical acoustic technologies cannot presently detect individual scallop but the remote sensing technologies can be used for broad scale habitat mapping of scallop harvest areas. Further, the techniques allow for monitoring these areas in terms of scallop dredging impact. Camera (video and still) imagery is effective for scallop count and provide data that compares favourably with diver-based ground truth information for recording scallop density. Deployment of cameras is possible through inexpensive drop-down camera frames which it is recommended be deployed on a wide area basis for further trials. In addition, implementation of a ‘citizen science’ approach to wide area recording is suggested to increase the stock assessment across the widest possible variety of seafloor types around Scotland. Armed with such data a full, statistical analysis could be completed and data used with automated processing routines for future long-term monitoring of stock.
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Berney, Ernest, Andrew Ward, and Naveen Ganesh. First generation automated assessment of airfield damage using LiDAR point clouds. Engineer Research and Development Center (U.S.), March 2021. http://dx.doi.org/10.21079/11681/40042.

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This research developed an automated software technique for identifying type, size, and location of man-made airfield damage including craters, spalls, and camouflets from a digitized three-dimensional point cloud of the airfield surface. Point clouds were initially generated from Light Detection and Ranging (LiDAR) sensors mounted on elevated lifts to simulate aerial data collection and, later, an actual unmanned aerial system. LiDAR data provided a high-resolution, globally positioned, and dimensionally scaled point cloud exported in a LAS file format that was automatically retrieved and processed using volumetric detection algorithms developed in the MATLAB software environment. Developed MATLAB algorithms used a three-stage filling technique to identify the boundaries of craters first, then spalls, then camouflets, and scaled their sizes based on the greatest pointwise extents. All pavement damages and their locations were saved as shapefiles and uploaded into the GeoExPT processing environment for visualization and quality control. This technique requires no user input between data collection and GeoExPT visualization, allowing for a completely automated software analysis with all filters and data processing hidden from the user.
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Dudley, J. P., and S. V. Samsonov. Système de traitement automatisé du gouvernement canadien pour la détection des variations et l'analyse des déformations du sol à partir des données de radar à synthèse d'ouverture de RADARSAT-2 et de la mission de la Constellation RADARSAT : description et guide de l'utilisateur. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329134.

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Remote sensing using Synthetic Aperture Radar (SAR) offers powerful methods for monitoring ground deformation from both natural and anthropogenic sources. Advanced analysis techniques such as Differential Interferometric Synthetic Aperture Radar (DInSAR), change detection, and Speckle Offset Tracking (SPO) provide sensitive measures of ground movement. With both the RADARSAT-2 and RADARSAT Constellation Mission (RCM) SAR satellites, Canada has access to a significant catalogue of SAR data. To make use of this data, the Canada Centre for Mapping and Earth Observation (CCMEO) has developed an automated system for generating standard and advanced deformation products from SAR data using both DInSAR and SPO methods. This document provides a user guide for this automated processing system.
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