Готові списки джерел за темами / Data processing

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Добірка наукової літератури з теми "Data processing"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 31 липня 2024

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Статті в журналах з теми "Data processing"

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

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Rossmann, Michael G., and Cornelis G. van Beek. "Data processing." Acta Crystallographica Section D Biological Crystallography 55, no. 10 (October 1, 1999): 1631–40. http://dx.doi.org/10.1107/s0907444999008379.

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

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Volkova, T., E. Furta, O. Dmitrieva, and I. Shabalina. "Pattern Building Methods in Genetic Data Processing." Journal on Selected Topics in Nano Electronics and Computing 1, no. 2 (June 2014): 2–6. http://dx.doi.org/10.15393/j8.art.2014.3041.

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Dayalan, Muthu. "MapReduce: Simplified Data Processing on Large Cluster." International Journal of Research and Engineering 5, no. 5 (April 2018): 399–403. http://dx.doi.org/10.21276/ijre.2018.5.5.4.

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Starukhin, Yaroslav, and Vladimir Diukarev. "AUTOMATION OF TEXT DATA PROCESSING USING NLP." American Journal of Engineering and Technology 6, no. 7 (July 1, 2024): 24–39. http://dx.doi.org/10.37547/tajet/volume06issue07-04.

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

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Zasuhina, Ol'ga, Egor Ershov, Leonid Golovatiukov, and Grigory Shitenkov. "BIG DATA PROCESSING TECHNOLOGY." Bulletin of the Angarsk State Technical University 1, no. 16 (December 27, 2022): 98–100. http://dx.doi.org/10.36629/2686-777x-2022-1-16-98-100.

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

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

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KRYVENCHUK, Yurii, and Mykhailo-Yurii KHANAS. "ALGORITHM OF DATA MINING AND PROCESSING OF RELATED DATA IN SOCIAL NETWORKS." Herald of Khmelnytskyi National University. Technical sciences 311, no. 4 (August 2022): 115–18. http://dx.doi.org/10.31891/2307-5732-2022-311-4-115-118.

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

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Nikolova, Evgeniya, Mariya Monova-Zheleva, and Yanislav Zhelev. "Personal Data Processing in a Digital Educational Environment." Mathematics and Informatics LXV, no. 4 (August 30, 2022): 365–78. http://dx.doi.org/10.53656/math2022-4-4-per.

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

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MARTYNIUK, Tatiana, Andrii KOZHEMIAKO, Bohdan KRUKIVSKYI, and Antonina BUDA. "ASSOCIATIVE OPERATIONS BASED ON DIFFERENCE-SLICE DATA PROCESSING." Herald of Khmelnytskyi National University. Technical sciences 311, no. 4 (August 2022): 159–63. http://dx.doi.org/10.31891/2307-5732-2022-311-4-159-163.

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

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Дисертації з теми "Data processing"

Long, Christopher C. "Data Processing for NASA's TDRSS DAMA Channel." International Foundation for Telemetering, 1996. http://hdl.handle.net/10150/611474.

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International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California
Presently, NASA's Space Network (SN) does not have the ability to receive random messages from satellites using the system. Scheduling of the service must be done by the owner of the spacecraft through Goddard Space Flight Center (GSFC). The goal of NASA is to improve the current system so that random messages, that are generated on board the satellite, can be received by the SN. The messages will be requests for service that the satellites control system deems necessary. These messages will then be sent to the owner of the spacecraft where appropriate action and scheduling can take place. This new service is known as the Demand Assignment Multiple Access system (DAMA).

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Sun, Wenjun. "Parallel data processing for semistructured data." Thesis, London South Bank University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.434394.

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Giordano, Manfredi. "Autonomic Big Data Processing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14837/.

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Apache Spark è un framework open source per la computazione distribuita su larga scala, caratterizzato da un engine in-memory che permette prestazioni superiori a soluzioni concorrenti nell’elaborazione di dati a riposo (batch) o in movimento (streaming). In questo lavoro presenteremo alcune tecniche progettate e implementate per migliorare l’elasticità e l’adattabilità del framework rispetto a modifiche dinamiche nell’ambiente di esecuzione o nel workload. Lo scopo primario di tali tecniche è di permettere ad applicazioni concorrenti di condividere le risorse fisiche disponibili nell’infrastruttura cluster sottostante in modo efficiente. Il contesto nel quale le applicazioni distribuite vengono eseguite difficilmente può essere considerato statico: le componenti hardware possono fallire, i processi possono interrompersi, gli utenti possono allocare risorse aggiuntive in modo imprevedibile nel tentativo di accelerare la computazione o di allegerire il carico di lavoro. Infine, non soltanto le risorse fisiche ma anche i dati in input possono variare di dimensione e complessità durante l’esecuzione, così che sia dati sia risorse non possano essere considerati statici. Una configurazione immutabile del cluster non riuscirà a ottenere la migliore efficienza possibile per tutti i differenti carichi di lavoro. Ne consegue che un framework per il calcolo distribuito che sia "consapevole" delle modifiche ambientali e delle modifiche al workload e che sia in grado di adattarsi a esse puo risultare piu performante di un framework che permetta unicamente configurazioni statiche. Gli esperimenti da noi compiuti con applicazioni Big Data altamente parallelizzabili mostrano come il costo della soluzione proposta sia minimo e come la nostra version di Spark più dinamica e adattiva possa portare a benefici in termini di flessibilità, scalabilità ed efficienza.

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Rydell, Joakim. "Advanced MRI Data Processing." Doctoral thesis, Linköping : Department of Biomedical Engineering, Linköpings universitet, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10038.

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Irick, Nancy. "Post Processing Data Analysis." International Foundation for Telemetering, 2009. http://hdl.handle.net/10150/606091.

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ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada
Once the test is complete, the job of the Data Analyst has begun. Files from the various acquisition systems are collected. It is the job of the analyst to put together these files in a readable format so the success or failure of the test can be attained. This paper will discuss the process of breaking down these files, comparing data from different systems, and methods of presenting the data.

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Castro, Fernandez Raul. "Stateful data-parallel processing." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/31596.

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Democratisation of data means that more people than ever are involved in the data analysis process. This is beneficial - it brings domain-specific knowledge from broad fields - but data scientists do not have adequate tools to write algorithms and execute them at scale. Processing models of current data-parallel processing systems, designed for scalability and fault tolerance, are stateless. Stateless processing facilitates capturing parallelisation opportunities and hides fault tolerance. However, data scientists want to write stateful programs - with explicit state that they can update, such as matrices in machine learning algorithms - and are used to imperative-style languages. These programs struggle to execute with high-performance in stateless data-parallel systems. Representing state explicitly makes data-parallel processing at scale challenging. To achieve scalability, state must be distributed and coordinated across machines. In the event of failures, state must be recovered to provide correct results. We introduce stateful data-parallel processing that addresses the previous challenges by: (i) representing state as a first-class citizen so that a system can manipulate it; (ii) introducing two distributed mutable state abstractions for scalability; and (iii) an integrated approach to scale out and fault tolerance that recovers large state - spanning the memory of multiple machines. To support imperative-style programs a static analysis tool analyses Java programs that manipulate state and translates them to a representation that can execute on SEEP, an implementation of a stateful data-parallel processing model. SEEP is evaluated with stateful Big Data applications and shows comparable or better performance than state-of-the-art stateless systems.

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Nyström, Simon, and Joakim Lönnegren. "Processing data sources with big data frameworks." Thesis, KTH, Data- och elektroteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188204.

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Big data is a concept that is expanding rapidly. As more and more data is generatedand garnered, there is an increasing need for efficient solutions that can be utilized to process all this data in attempts to gain value from it. The purpose of this thesis is to find an efficient way to quickly process a large number of relatively small files. More specifically, the purpose is to test two frameworks that can be used for processing big data. The frameworks that are tested against each other are Apache NiFi and Apache Storm. A method is devised in order to, firstly, construct a data flow and secondly, construct a method for testing the performance and scalability of the frameworks running this data flow. The results reveal that Apache Storm is faster than Apache NiFi, at the sort of task that was tested. As the number of nodes included in the tests went up, the performance did not always do the same. This indicates that adding more nodes to a big data processing pipeline, does not always result in a better performing setup and that, sometimes, other measures must be made to heighten the performance.
Big data är ett koncept som växer snabbt. När mer och mer data genereras och samlas in finns det ett ökande behov av effektiva lösningar som kan användas föratt behandla all denna data, i försök att utvinna värde från den. Syftet med detta examensarbete är att hitta ett effektivt sätt att snabbt behandla ett stort antal filer, av relativt liten storlek. Mer specifikt så är det för att testa två ramverk som kan användas vid big data-behandling. De två ramverken som testas mot varandra är Apache NiFi och Apache Storm. En metod beskrivs för att, för det första, konstruera ett dataflöde och, för det andra, konstruera en metod för att testa prestandan och skalbarheten av de ramverk som kör dataflödet. Resultaten avslöjar att Apache Storm är snabbare än NiFi, på den typen av test som gjordes. När antalet noder som var med i testerna ökades, så ökade inte alltid prestandan. Detta visar att en ökning av antalet noder, i en big data-behandlingskedja, inte alltid leder till bättre prestanda och att det ibland krävs andra åtgärder för att öka prestandan.

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Mai, Luo. "Towards efficient big data processing in data centres." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/64817.

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Large data processing systems require a high degree of coordination, and exhibit network bottlenecks due to massive communication data. This motivates my PhD study to propose system control mechanisms that improve monitoring and coordination, and efficient communication methods by bridging applications and networks. The first result is Chi, a new control plane for stateful streaming systems. Chi has a control loop that embeds control messages in data channels to seamlessly monitor and coordinate a streaming pipeline. This design helps monitor system and application-specific metrics in a scalable manner, and perform complex modification with on-the-fly data. The behaviours of control messages are customisable, thus enabling various control algorithms. Chi has been deployed into production systems, and exhibits high performance and scalability in test-bed experiments. With effective coordination, data-intensive systems need to remove network bottlenecks. This is important in data centres as their networks are usually over-subscribed. Hence, my study explores an idea that bridges applications and networks for accelerating communication. This idea can be realised (i) in the network core through a middlebox platform called NetAgg that can efficiently execute application-specific aggregation functions along busy network paths, and (ii) at network edges through a server network stack that provides powerful communication primitives and traffic management services. Test-bed experiments show that these methods can improve the communication of important analytics systems. A tight integration of applications and networks, however, requires an intuitive network programming model. My study thus proposes a network programming framework named Flick. Flick has a high-level programming language for application-specific network services. The services are compiled to dataflows and executed by a high-performance runtime. To be production-friendly, this runtime can run in commodity network elements and guarantee fair resource sharing among services. Flick has been used for developing popular network services, and its performance is shown in real-world benchmarks.

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Mueller, Guenter. "DIGITAL DATA RECORDING: NEW WAYS IN DATA PROCESSING." International Foundation for Telemetering, 2000. http://hdl.handle.net/10150/606505.

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International Telemetering Conference Proceedings / October 23-26, 2000 / Town & Country Hotel and Conference Center, San Diego, California
With the introduction of digital data recorders new ways of data processing have been developed. The three most important improvements are discussed in this paper: A) By processing PCM Data from a digital recorder by using the SCSI-Interface our ground station has developed software to detect the synchronization pattern of the PCM data and then perform software frame decommutation. Many advantages will be found with this method. B) New digital recorders already use the CCSDS Standard as the internal recording format. Once this technique is implemented in our ground station’s software and becomes part of our software engineering team’s general know-how, the switch to CCSDS telemetry in the future will require no quantum leap in effort. C) Digital recorders offer a very new application: Writing data to a digital tape in the recorder’s own format, allows the replay of data using the recorder’s interfaces; i.e. writing vibration data from the host system to tape, using the analog format of the digital recorder, allows the analysis of the data either in analog form, using the analog interface of the recorder, or in digital form.

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Macias, Filiberto. "Real Time Telemetry Data Processing and Data Display." International Foundation for Telemetering, 1996. http://hdl.handle.net/10150/611405.

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International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California
The Telemetry Data Center (TDC) at White Sands Missile Range (WSMR) is now beginning to modernize its existing telemetry data processing system. Modern networking and interactive graphical displays are now being introduced. This infusion of modern technology will allow the TDC to provide our customers with enhanced data processing and display capability. The intent of this project is to outline this undertaking.

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Книги з теми "Data processing"

Bourque, Linda, and Virginia Clark. Processing Data. 2455 Teller Road, Newbury Park California 91320 United States of America: SAGE Publications, Inc., 1992. http://dx.doi.org/10.4135/9781412985499.

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Anderson, Ronald Gordon. Data processing. 7th ed. London: Macdonald and Evans, 1990.

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Bingham, John. Data Processing. London: Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-19938-9.

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

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

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Technicians, Association of Accounting. Data processing. London: Financial Training, 1993.

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Bingham, John E. Data processing. 2nd ed. Basingstoke: Macmillan, 1989.

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Bingham, John E. Data processing. 2nd ed. London: Macmillan, 1989.

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Godse, Jay. Ruby Data Processing. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3474-7.

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Chang, Chein-I. Hyperspectral Data Processing. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118269787.

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Частини книг з теми "Data processing"

Hofmann-Wellenhof, Bernhard, Herbert Lichtenegger, and James Collins. "Data processing." In Global Positioning System, 179–227. Vienna: Springer Vienna, 1992. http://dx.doi.org/10.1007/978-3-7091-5126-6_9.

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Raggatt, Peter. "Data Processing." In Principles and Practice of Immunoassay, 190–218. London: Palgrave Macmillan UK, 1991. http://dx.doi.org/10.1007/978-1-349-11234-0_7.

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Singh, Pramod. "Data Processing." In Learn PySpark, 17–48. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4961-1_2.

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Kitchin, Christopher Robert. "Data Processing." In Telescopes and Techniques, 163–67. London: Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3370-4_10.

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Tinnefeld, Christian. "Data Processing." In Building a Columnar Database on RAMCloud, 63–66. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20711-7_5.

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Johnson, Chris F. A., and Jayant Varma. "Data Processing." In Pro Bash Programming, 161–81. Berkeley, CA: Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-0121-3_13.

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Hofmann-Wellenhof, Bernhard, Herbert Lichtenegger, and James Collins. "Data processing." In Global Positioning System, 201–80. Vienna: Springer Vienna, 1997. http://dx.doi.org/10.1007/978-3-7091-3297-5_9.

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Hofmann-Wellenhof, Bernhard, Herbert Lichtenegger, and James Collins. "Data processing." In Global Positioning System, 199–253. Vienna: Springer Vienna, 1994. http://dx.doi.org/10.1007/978-3-7091-3311-8_9.

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Akrill, Tim, and Stephen Osmond. "Data Processing." In Physics A Level, 195–206. London: Macmillan Education UK, 1991. http://dx.doi.org/10.1007/978-1-349-13852-4_8.

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Kitchin, Chris. "Data Processing." In Telescopes and Techniques, 193–206. London: Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-0023-2_10.

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Тези доповідей конференцій з теми "Data processing"

Hassan, Syed Minhaj, Sudhakar Yalamanchili, and Saibal Mukhopadhyay. "Near Data Processing." In MEMSYS '15: International Symposium on Memory Systems. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2818950.2818952.

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Ribeiro, Marcela X., Mônica R. P. Ferreira, Caetano Traina, and Agma J. M. Traina. "Data pre-processing." In the 5th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1456223.1456277.

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Satoh, Ichiro. "Edge Data Processing." In 2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA). IEEE, 2016. http://dx.doi.org/10.1109/waina.2016.96.

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Voronkov, Maxim A., Ben Humphreys, Daniel Mitchell, and Matthew Whiting. "ASKAP data processing." In 2015 1st URSI Atlantic Radio Science Conference (URSI AT-RASC). IEEE, 2015. http://dx.doi.org/10.1109/ursi-at-rasc.2015.7303169.

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Spence, J. "Data processing challenges." In IET Seminar on Smart Metering - Gizmo or Revolutionary Technology? IEE, 2008. http://dx.doi.org/10.1049/ic:20080121.

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Gyurjyan, V., A. Bartle, C. Lukashin, S. Mancilla, R. Oyarzun, and A. Vakhnin. "Component based dataflow processing framework." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363971.

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Rainey, Bryan, and David F. Gleich. "Massive graph processing on nanocomputers." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840992.

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Mège, Alexandre, Patrice Gonzalez, Clément Coggiola, and Louise Yu. "SMOS-HR Correlator Architecture Processing Study." In 2023 European Data Handling & Data Processing Conference (EDHPC). IEEE, 2023. http://dx.doi.org/10.23919/edhpc59100.2023.10396375.

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A.K., Shaboyan. "AUTOMATION OF DATA PROCESSING BIG DATA." In "INNOVATIVE TECHNOLOGIES IN SCIENCE AND EDUCATION". ДГТУ-Принт, 2021. http://dx.doi.org/10.23947/itno.2021.21-22.

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Анотація:

Today, data is one of the most important components of the life of society and of every person. The modern stage of development of society is characterized by a constant increase in the volume of data that comes from many different sources. The structure and composition of this data is often not defined. There is such a large amount of information that it is impossible to solve the task without the intervention of technology. So, Big Data technology is a set of tools, approaches and methods for processing both structured and unstructured data of a huge size for their further use.

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Esslinger, Malte, and Grzegorz Adamiuk. "The Universal Processing Module – Standardised Hardware for State of the Art Radar Data Conversion and Data Processing." In 2023 European Data Handling & Data Processing Conference (EDHPC). IEEE, 2023. http://dx.doi.org/10.23919/edhpc59100.2023.10396196.

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Звіти організацій з теми "Data processing"

Casasent, David. Optical Data Processing. Fort Belvoir, VA: Defense Technical Information Center, October 1985. http://dx.doi.org/10.21236/ada174465.

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Conlin, Jeremy L., and Andrej Trkov. Nuclear Data Processing. IAEA Nuclear Data Section, November 2018. http://dx.doi.org/10.61092/iaea.c7t6-j2x8.

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SEA TECHNOLOGY ARLINGTON VA. Communications, Telemetry, Data Processing. Fort Belvoir, VA: Defense Technical Information Center, May 1998. http://dx.doi.org/10.21236/ada417821.

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Andrews, Elisabeth. RACORO aerosol data processing. Office of Scientific and Technical Information (OSTI), October 2011. http://dx.doi.org/10.2172/1028128.

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- UC BERKELEY, M. SHEATS. ADVANCED DATA PROCESSING FOR VOLUMETRIC COMPUTED TOMOGRAPHY DATA. Office of Scientific and Technical Information (OSTI), August 2001. http://dx.doi.org/10.2172/784592.

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Feng, Ya-Chien, Alyssa Matthews, Marqi Rocque, Mindy Deng, Timothy Wendler, Karen Johnson, Eddie Schuman, et al. TRACER Radar b1 Data Processing: Corrections, Calibrations, and Processing Report. Office of Scientific and Technical Information (OSTI), March 2024. http://dx.doi.org/10.2172/2326212.

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Paterno, Marc, and Chris Green. Processing Contexts for Experimental HEP Data. Office of Scientific and Technical Information (OSTI), February 2017. http://dx.doi.org/10.2172/1422188.

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Macduff, M., and D. Egan. ACRF Data Collection and Processing Infrastructure. Office of Scientific and Technical Information (OSTI), December 2004. http://dx.doi.org/10.2172/1020559.

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Fields, Erik, Karen Tracey, and D. R. Watts. Inverted Echo Sounder Data Processing Report. Fort Belvoir, VA: Defense Technical Information Center, May 1991. http://dx.doi.org/10.21236/ada237576.

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Kennelly, Maureen, Karen Tracey, and D. R. Watts. Inverted Echo Sounder Data Processing Manual. Fort Belvoir, VA: Defense Technical Information Center, June 2007. http://dx.doi.org/10.21236/ada477328.

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