Relevant bibliographies by topics / Electronic data processing – Distributed processing

Contents

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

Academic literature on the topic 'Electronic data processing – Distributed processing'

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

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Journal articles on the topic "Electronic data processing – Distributed processing"

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A. M., Chernykh. "Blockchain and Processing of Judicial Data." Rossijskoe pravosudie, no. 9 (August 23, 2021): 54–62. http://dx.doi.org/10.37399/issn2072-909x.2021.9.54-62.

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. Improving the electronic document management system of the judicial system requires the use of new information technologies. Conducting trials with guaranteed protection of documentary data of all participants in the trial from changes or loss will reduce the corruption component, increase mutual confidence of the parties involved in the litigation in documents. An system analysis was made of the possibility of using a distributed registry of databases and building on its basis a secure document exchange network using blockchain technology. The work defines the directions of interaction of information resources of federal state systems and the information system of justice on the blockchain platform in the interests of solving the problems of ensuring the openness of user services of the judicial system and the security of legal data of participants in the trial. A conceptual-logical model of interaction of information resources of the parties with increased requirements for mutual trust based on blockchain technology for maintaining a distributed data register and the ability to process multidimensional data (numerical, text, graphic, coordinate, etc.) with a high degree of information security for a long time.
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Nazemi, Sepideh, Kin K. Leung, and Ananthram Swami. "Distributed Optimization Framework for In-Network Data Processing." IEEE/ACM Transactions on Networking 27, no. 6 (December 2019): 2432–43. http://dx.doi.org/10.1109/tnet.2019.2953581.

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Omar, Hoger Khayrolla, and Alaa Khalil Jumaa. "Distributed big data analysis using spark parallel data processing." Bulletin of Electrical Engineering and Informatics 11, no. 3 (June 1, 2022): 1505–15. http://dx.doi.org/10.11591/eei.v11i3.3187.

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Nowadays, the big data marketplace is rising rapidly. The big challenge is finding a system that can store and handle a huge size of data and then processing that huge data for mining the hidden knowledge. This paper proposed a comprehensive system that is used for improving big data analysis performance. It contains a fast big data processing engine using Apache Spark and a big data storage environment using Apache Hadoop. The system tests about 11 Gigabytes of text data which are collected from multiple sources for sentiment analysis. Three different machine learning (ML) algorithms are used in this system which is already supported by the Spark ML package. The system programs were written in Java and Scala programming languages and the constructed model consists of the classification algorithms as well as the pre-processing steps in a figure of ML pipeline. The proposed system was implemented in both central and distributed data processing. Moreover, some datasets manipulation manners have been applied in the system tests to check which manner provides the best accuracy and time performance. The results showed that the system works efficiently for treating big data, it gains excellent accuracy with fast execution time especially in the distributed data nodes.
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R.Kennady, Et al. "A Scalable and Economical Method for Distributed Data Processing." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 2 (February 25, 2023): 198–201. http://dx.doi.org/10.17762/ijritcc.v11i2.9832.

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This research paper presents a distributed data processing approach that involves the establishment of virtual machines, the creation of a distributed system, and the processing of data to obtain desired results. The proposed method aims to provide a simple and cost-effective solution for distributed data processing, with the ability to scale infrastructure according to the specific needs. Furthermore, a distributed data processing system is introduced, comprising virtual machines equipped with specialized software to facilitate the establishment of the distributed system. The method offers practical advantages in terms of implementation simplicity, reduced infrastructure costs, and improved resource utilization.
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Benediktsson, Jon Atli, and Zebin Wu. "Distributed Computing for Remotely Sensed Data Processing [Scanning the Section]." Proceedings of the IEEE 109, no. 8 (August 2021): 1278–81. http://dx.doi.org/10.1109/jproc.2021.3094335.

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Atakishchev, O. I., M. V. Belov, I. S. Zakharov, and A. V. Nikolaev. "Specific Features of Parallel Asynchronous Data Processing in Distributed GIS." Telecommunications and Radio Engineering 64, no. 3 (2005): 167–75. http://dx.doi.org/10.1615/telecomradeng.v64.i3.10.

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Nokleby, Matthew, Haroon Raja, and Waheed U. Bajwa. "Scaling-Up Distributed Processing of Data Streams for Machine Learning." Proceedings of the IEEE 108, no. 11 (November 2020): 1984–2012. http://dx.doi.org/10.1109/jproc.2020.3021381.

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Li, Xin, Huayan Yu, Ligang Yuan, and Xiaolin Qin. "Query Optimization for Distributed Spatio-Temporal Sensing Data Processing." Sensors 22, no. 5 (February 23, 2022): 1748. http://dx.doi.org/10.3390/s22051748.

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The unprecedented development of Internet of Things (IoT) technology produces humongous amounts of spatio-temporal sensing data with various geometry types. However, processing such datasets is often challenging due to high-dimensional sensor data geometry characteristics, complex anomalistic spatial regions, unique query patterns, and so on. Timely and efficient spatio-temporal querying significantly improves the accuracy and intelligence of processing sensing data. Most existing query algorithms show their lack of supporting spatio-temporal queries and irregular spatial areas. In this paper, we propose two spatio-temporal query optimization algorithms based on SpatialHadoop to improve the efficiency of query spatio-temporal sensing data: (1) spatio-temporal polygon range query (STPRQ), which aims to find all records from a polygonal location in a time interval; (2) spatio-temporal k nearest neighbors query (STkNNQ), which directly searches the query point’s k closest neighbors. To optimize the STkNNQ algorithm, we further propose an adaptive iterative range optimization algorithm (AIRO), which can optimize the iterative range of the algorithm according to the query time range and avoid querying irrelevant data partitions. Finally, extensive experiments based on trajectory datasets demonstrate that our proposed query algorithms can significantly improve query performance over baseline algorithms and shorten response time by 81% and 35.6%, respectively.
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Szmajduch, Magdalena. "Data and Task Scheduling in Distributed Computing Environments." Journal of Telecommunications and Information Technology, no. 4 (December 30, 2014): 71–78. http://dx.doi.org/10.26636/jtit.2014.4.1049.

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Data-aware scheduling in today’s large-scale heterogeneous environments has become a major research and engineering issue. Data Grids (DGs), Data Clouds (DCs) and Data Centers are designed for supporting the processing and analysis of massive data, which can be generated by distributed users, devices and computing centers. Data scheduling must be considered jointly with the application scheduling process. It generates a wide family of global optimization problems with the new scheduling criteria including data transmission time, data access and processing times, reliability of the data servers, security in the data processing and data access processes. In this paper, a new version of the Expected Time to Compute Matrix (ETC Matrix) model is defined for independent batch scheduling in physical network in DG and DC environments. In this model, the completion times of the computing nodes are estimated based on the standard ETC Matrix and data transmission times. The proposed model has been empirically evaluated on the static grid scheduling benchmark by using the simple genetic-based schedulers. A simple comparison of the achieved results for two basic scheduling metrics, namely makespan and average flowtime, with the results generated in the case of ignoring the data scheduling phase show the significant impact of the data processing model on the schedule execution times.
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Sestok, C. K., M. R. Said, and A. V. Oppenheim. "Randomized data selection in detection with applications to distributed signal processing." Proceedings of the IEEE 91, no. 8 (August 2003): 1184–98. http://dx.doi.org/10.1109/jproc.2003.814922.

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Dissertations / Theses on the topic "Electronic data processing – Distributed processing"

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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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Bodorik, Peter Carleton University Dissertation Engineering Electrical. "Query processing strategies in a distributed data base." Ottawa, 1985.

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Cline, George E. "A control framework for distributed (parallel) processing environments." Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-12042009-020227/.

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Ravindran, K. "Reliable client-server communication in distributed programs." Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/27514.

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Remote procedure call (RPC) and shared variable are communication abstractions which allow the various processes of a distributed program, often modelled as clients and servers, to communicate with one another across machine boundaries. A key requirement of the abstractions is to mask the machine and communication failures that may occur during the client-server communications. In practice, many distributed applications can inherently tolerate failures under certain situations. If such application layer information is available to the client-server communication layer (RPC and shared variable), the failure masking algorithms in the communication layer may relax the constraints under which the algorithms may have to operate if the information is not available. The relaxation significantly simplifies the algorithms and the underlying message transport layer and allows formulation of efficient algorithms. This application-driven approach forms the backbone of the failure masking techniques described in the thesis, as outlined below: Orphan handling in RPCs: Using the application-driven approach, the thesis introduces a new technique of adopting the orphans caused by failures during RPCs. The adoption technique is preferable to orphan killing because orphan killing wastes any work already completed and requires rollback which may be expensive and sometimes not meaningful. The thesis incorporates orphan adoption into two schemes of replicating a server: i) Primary-secondary scheme in which one of the replicas of the server acts as the primary and executes RPCs from clients while the other replicas stand by as secondaries. When the primary fails, one of the secondaries becomes the primary, restarts the server execution from the most recent checkpoint and adopts the orphan, ii) Replicated execution scheme in which an RPC on the server is executed by more than one replica of the server. When any of the replicas fails, the orphan generated by the failure is adopted by the surviving replicas. Both schemes employ call re-executions by servers based on the application-level idempotency properties of the calls. Access to shared variables: Contemporary distributed programs deal with a new class of shared variables such as information on name bindings, distributed load and leadership within a service group. Since the consistency constraints on such system variables need not be as strong as those for user data, the access operations on the variables may be made simpler using this application layer information. Along this direction, the thesis introduces an abstraction, which we call application-driven shared variable, to govern access operations on the variables. The algorithms for the access operations on a variable use intra-server group communication and enforce consistency of the variable to the extent required by the application. The thesis describes complete communication models incorporating the application-driven approach to mask failures.
Science, Faculty of
Computer Science, Department of
Graduate
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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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Setiowijoso, Liono. "Data Allocation for Distributed Programs." PDXScholar, 1995. https://pdxscholar.library.pdx.edu/open_access_etds/5102.

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This thesis shows that both data and code must be efficiently distributed to achieve good performance in a distributed system. Most previous research has either tried to distribute code structures to improve parallelism or to distribute data to reduce communication costs. Code distribution (exploiting functional parallelism) is an effort to distribute or to duplicate function codes to optimize parallel performance. On the other hand, data distribution tries to place data structures as close as possible to the function codes that use it, so that communication cost can be reduced. In particular, dataflow researchers have primarily focused on code partitioning and assignment. We have adapted existing data allocation algorithms for use with an existing dataflow-based system, ParPlum. ParPlum allows the execution of dataflow graphs on networks of workstations. To evaluate the impact of data allocation, we extended ParPlum to more effectively handle data structures. We then implemented tools to extract from dataflow graphs information that is relevant to the mapping algorithms and fed this information to our version of a data distribution algorithm. To see the relation between code and data parallelism we added optimization to optimize the distribution of the loop function components and the data structure access components. All of these are done automatically without programmer or user involvement. We ran a number of experiments using matrix multiplication as our workload. We used different numbers of processors and different existing partitioning and allocation algorithm. Our results show that automatic data distribution greatly improves the performance of distributed dataflow applications. For example, with 15 x 15 matrices, applying data distribution speeds up execution about 80% on 7 machines. Using data distribution and our code-optimizations on 7 machines speeds up execution over the base case by 800%. Our work shows that it is possible to make efficient use of distributed networks with compiler support and shows that both code mapping and data mapping must be considered to achieve optimal performance.
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Bernabéu-Aubán, José Manuel. "Location finding algorithms for distributed systems." Diss., Georgia Institute of Technology, 1988. http://hdl.handle.net/1853/32951.

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Navaratnam, Srivallipuranandan. "Reliable group communication in distributed systems." Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/26505.

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This work describes the design and implementation details of a reliable group communication mechanism. The mechanism guarantees that messages will be received by all the operational members of the group or by none of them (atomicity). In addition, the sequence of messages will be the same at each of the recipients (order). The message ordering property can be used to simplify distributed database systems and distributed processing algorithms. The proposed mechanism continues to operate despite process, host and communication link failures (survivability). Survivability is essential in fault-tolerant applications.
Science, Faculty of
Computer Science, Department of
Graduate
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Torres-Rojas, Francisco Jose. "Scalable approximations to causality and consistency of distributed objects." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/9155.

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Algire, Martin. "Distributed multi-processing for high performance computing." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=31180.

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Parallel computing can take many forms. From a user's perspective, it is important to consider the advantages and disadvantages of each methodology. The following project attempts to provide some perspective on the methods of parallel computing and indicate where the tradeoffs lie along the continuum. Problems that are parallelizable enable researchers to maximize the computing resources available for a problem, and thus push the limits of the problems that can be solved. Solving any particular problem in parallel will require some very important design decisions to be made. These decisions may dramatically affect portability, performance, and cost of implementing a software solution to the problem. The results gained from this work indicate that although performance improvements are indeed possible---they are heavily dependent on the application in question and may require much more programming effort and expertise to implement.
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Books on the topic "Electronic data processing – Distributed processing"

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J, Mullender Sape, ed. Distributed systems. 2nd ed. New York: ACM Press, 1993.

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IFIP/IEEE International Conference on Open Distributed Processing and Distributed Platforms (1997 Toronto, Ont.). Open distributed processing and distributed platforms: Proceedings of the IFIP/IEEE international conference on Open Distributed Processing and Distributed Platforms : 26-30 May 1997, Toronto, Canada. London: Chapman & Hall on behalf of the International Federation for Information Processing (IFIP), 1997.

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Varshney, Pramod K. Distributed detection and data fusion. Edited by Burrus C. S. Berlin: Springer, 1996.

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Varshney, Pramod K. Distributed detection and data fusion. Edited by Burrus C. S. New York: Springer, 1997.

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Crowcroft, Jon. Open distributed systems. London: UCL Press, 1995.

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Langsford, Alwyn. Distributed systems management. Wokingham, Eng: Addison-Wesley, 1993.

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Liu, M. L. Distributed computing: Principles and applications. Boston: Pearson/Addison Wesley, 2004.

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Bal, H. E. Programming distributed systems. Summit, NJ, USA: Silicon Press, 1990.

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T, Brazier F. M., Johansen D, and Institute of Electrical and Electronics Engineers., eds. Distributed open systems. Los Alamitos, Calif: IEEE Computer Society Press, 1994.

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Varshney, Pramod K. Distributed Detection and Data Fusion. New York, NY: Springer New York, 1997.

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Book chapters on the topic "Electronic data processing – Distributed processing"

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Bingham, John. "Distributed Systems." In Data Processing, 245–55. London: Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-19938-9_19.

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Buchanan, W. J. "Distributed Processing." In The Handbook of Data Communications and Networks, 83–110. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4020-7870-5_5.

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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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Weik, Martin H. "distributed data processing." In Computer Science and Communications Dictionary, 443. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_5393.

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Sakr, Sherif, Marcin Wylot, Raghava Mutharaju, Danh Le Phuoc, and Irini Fundulaki. "Distributed RDF Query Processing." In Linked Data, 51–83. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73515-3_4.

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Özsu, M. Tamer. "Distributed XML Processing." In Advances in Data and Web Management, 1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00672-2_1.

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Özsu, M. Tamer, and Patrick Valduriez. "Big Data Processing." In Principles of Distributed Database Systems, 449–518. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26253-2_10.

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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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Weik, Martin H. "distributed data processing network." In Computer Science and Communications Dictionary, 443. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_5394.

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Weik, Martin H. "distributed data processing system." In Computer Science and Communications Dictionary, 443. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_5395.

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Conference papers on the topic "Electronic data processing – Distributed processing"

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Zhang, Yongli, Tailin Han, Xingye Liu, and Siqi Tian. "Shock wave signal distributed acquisition and data processing technology." In 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2017. http://dx.doi.org/10.1109/iaeac.2017.8054027.

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Plattner, Markus, Sabine Ott, Sebastian Albrecht, Jintin Tran, Christopher Mandla, Jan-Christoph Tenzer, Thomas Schanz, et al. "ESA Athena WFI Onboard Electronics - Distributed Control and Data Processing." In 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2020. http://dx.doi.org/10.23919/date48585.2020.9116484.

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Munerman, Victor, and Daniel Munerman. "Realization of Distributed Data Processing on the Basis of Container Technology." In 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). IEEE, 2019. http://dx.doi.org/10.1109/eiconrus.2019.8656766.

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Furuichi, Tetsuo, and Hiroshi Mineno. "Distributed data processing method for next generation IoT system." In 2016 IEEE 5th Global Conference on Consumer Electronics. IEEE, 2016. http://dx.doi.org/10.1109/gcce.2016.7800441.

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Dai, Qinglong, and Jin Qian. "A Distributed Stream Data Processing Platform Design and Implementation in Smart Cities." In 2020 IEEE 3rd International Conference on Electronic Information and Communication Technology (ICEICT). IEEE, 2020. http://dx.doi.org/10.1109/iceict51264.2020.9334234.

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Kamenskikh, I. S., D. M. Sinelnikov, D. S. Kalintsev, A. A. Kozlov, M. M. Rovnyagin, and D. A. Shulga. "Software development framework for a distributed storage and GPGPU data processing infrastructure." In 2016 IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW). IEEE, 2016. http://dx.doi.org/10.1109/eiconrusnw.2016.7448158.

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Jiang, Bing, and Kun Huang. "The Design of Cloud Computing Platform for Massive Data Processing of Distributed Photovoltaic Power." In 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/meic-15.2015.161.

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Zheng, Ting, and Chuan He. "Design of real-time distributed data collection and analysis system." In International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), edited by Valentina E. Balas and Zeashan Hameed Khan. SPIE, 2023. http://dx.doi.org/10.1117/12.2682274.

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Ananth, K. R., Hari Shankar Punna, Krishnamoorthy Selvaraj, Rajagopal K, Vaishali Mahajan, and Sakthivel S. "Green IoT Edge Computing Towards Sustainable and Distributed Data Processing." In 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). IEEE, 2023. http://dx.doi.org/10.1109/upcon59197.2023.10434734.

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Chen, Yihui, and Xue Tang. "Computer Vision and Image Processing Under Big Data Analysis." In 2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). IEEE, 2024. http://dx.doi.org/10.1109/icdcece60827.2024.10548841.

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Reports on the topic "Electronic data processing – Distributed processing"

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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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Igor V. Terekhov. Distributed processing and analysis of physics data in the D0 SAM system at Fermilab. Office of Scientific and Technical Information (OSTI), August 2001. http://dx.doi.org/10.2172/785150.

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Ramchandran, Kannan, and Kristofer Pister. Sensor Webs of SmartDust: Distributed Signal Processing/Data Fusion/Inferencing in Large Microsensor Arrays. Fort Belvoir, VA: Defense Technical Information Center, March 2004. http://dx.doi.org/10.21236/ada422190.

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Varastehpour, Soheil, Hamid Sharifzadeh, and Iman Ardekani. A Comprehensive Review of Deep Learning Algorithms. Unitec ePress, 2021. http://dx.doi.org/10.34074/ocds.092.

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Deep learning algorithms are a subset of machine learning algorithms that aim to explore several levels of the distributed representations from the input data. Recently, many deep learning algorithms have been proposed to solve traditional artificial intelligence problems. In this review paper, some of the up-to-date algorithms of this topic in the field of computer vision and image processing are reviewed. Following this, a brief overview of several different deep learning methods and their recent developments are discussed.
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Modlo, Yevhenii O., Serhiy O. Semerikov, Pavlo P. Nechypurenko, Stanislav L. Bondarevskyi, Olena M. Bondarevska, and Stanislav T. Tolmachev. The use of mobile Internet devices in the formation of ICT component of bachelors in electromechanics competency in modeling of technical objects. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3264.

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Computer simulation of technical objects and processes is one of the components of the system of professional training of a modern electromechanics engineer. It has been established that despite the fact that mobile Internet devices (MID) are actively used by electrical engineers, the methods of using them in the process of bachelor in electromechanics training is considered only in some domestic scientific studies. The article highlights the components of the methods of using MID in the formation of the ICT component of the competence of the bachelor in electromechanics in modeling of technical objects, providing for students to acquire basic knowledge in the field of Computer Science and modern ICT and skills to use programming systems, math packages, subroutine libraries, and the like. For processing tabular data, it is proposed to use various freely distributed tools that do not significantly differ in functionality, such as Google Sheets, Microsoft Excel, for processing text data – QuickEdit Text Editor, Google Docs, Microsoft Word. For 3D-modeling and viewing the design and technological documentation, the proposed comprehensive use of Autodesk tools in the training process.
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Leathers, Emily, Clayton Thurmer, and Kendall Niles. Encryption for edge computing applications. Engineer Research and Development Center (U.S.), May 2024. http://dx.doi.org/10.21079/11681/48596.

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As smart sensors and the Internet of Things (IoT) exponentially expand, there is an increased need for effective processing solutions for sensor node data located in the operational arena where it can be leveraged for immediate decision support. Current developments reveal that edge computing, where processing and storage are performed close to data generation locations, can meet this need (Ahmed and Ahmed 2016). Edge computing imparts greater flexibility than that experienced in cloud computing architectures (Khan et al. 2019). Despite these benefits, the literature highlights open security issues in edge computing, particularly in the realm of encryption. A prominent limitation of edge devices is the hardware’s ability to support the computational complexity of traditional encryption methodologies (Alwarafy et al. 2020). Furthermore, encryption on the edge poses challenges in key management, the process by which cryptographic keys are transferred and stored among devices (Zeyu et al. 2020). Though edge computing provides reduced latency in data processing, encryption mechanism utilization reintroduces delay and can hinder achieving real-time results (Yu et al. 2018). The IoT is composed of a wide range of devices with a diverse set of computational capabilities, rendering a homogeneous solution for encryption impractical (Dar et al. 2019). Edge devices are often deployed in operational locations that are vulnerable to physical tampering and attacks. Sensitive data may be compromised if not sufficiently encrypted or if keys are not managed properly. Furthermore, the distributed nature and quantity of edge devices create a vast attack surface that can be compromised in other ways (Xiao et al. 2019). Understanding established mechanisms and exploring emerging methodologies for encryption reveals potential solutions for developing a robust solution for edge computing applications. The purpose of this document is to detail the current research for encryption methods in the edge computing space and highlight the major challenges associated with executing successful encryption on the edge.
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Borgwardt, Stefan, Walter Forkel, and Alisa Kovtunova. Finding New Diamonds: Temporal Minimal-World Query Answering over Sparse ABoxes. Technische Universität Dresden, 2019. http://dx.doi.org/10.25368/2023.223.

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Lightweight temporal ontology languages have become a very active field of research in recent years. Many real-world applications, like processing electronic health records (EHRs), inherently contain a temporal dimension, and require efficient reasoning algorithms. Moreover, since medical data is not recorded on a regular basis, reasoners must deal with sparse data with potentially large temporal gaps. In this paper, we introduce a temporal extension of the tractable language ELH⊥, which features a new class of convex diamond operators that can be used to bridge temporal gaps. We develop a completion algorithm for our logic, which shows that entailment remains tractable. Based on this, we develop a minimal-world semantics for answering metric temporal conjunctive queries with negation. We show that query answering is combined first-order rewritable, and hence in polynomial time in data complexity.
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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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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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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.
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Tanny, Josef, Gabriel Katul, Shabtai Cohen, and Meir Teitel. Micrometeorological methods for inferring whole canopy evapotranspiration in large agricultural structures: measurements and modeling. United States Department of Agriculture, October 2015. http://dx.doi.org/10.32747/2015.7594402.bard.

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Original objectives and revisions The original objectives as stated in the approved proposal were: (1) To establish guidelines for the use of micrometeorological techniques as accurate, reliable and low-cost tools for continuous monitoring of whole canopy ET of common crops grown in large agricultural structures. (2) To adapt existing methods for protected cultivation environments. (3) To combine previously derived theoretical models of air flow and scalar fluxes in large agricultural structures (an outcome of our previous BARD project) with ET data derived from application of turbulent transport techniques for different crops and structure types. All the objectives have been successfully addressed. The study was focused on both screenhouses and naturally ventilated greenhouses, and all proposed methods were examined. Background to the topic Our previous BARD project established that the eddy covariance (EC) technique is suitable for whole canopy evapotranspiration measurements in large agricultural screenhouses. Nevertheless, the eddy covariance technique remains difficult to apply in the farm due to costs, operational complexity, and post-processing of data – thereby inviting alternative techniques to be developed. The subject of this project was: 1) the evaluation of four turbulent transport (TT) techniques, namely, Surface Renewal (SR), Flux-Variance (FV), Half-order Time Derivative (HTD) and Bowen Ratio (BR), whose instrumentation needs and operational demands are not as elaborate as the EC, to estimate evapotranspiration within large agricultural structures; and 2) the development of mathematical models able to predict water savings and account for the external environmental conditions, physiological properties of the plant, and structure properties as well as to evaluate the necessary micrometeorological conditions for utilizing the above turbulent transfer methods in such protected environments. Major conclusions and achievements The major conclusions are: (i) the SR and FV techniques were suitable for reliable estimates of ET in shading and insect-proof screenhouses; (ii) The BR technique was reliable in shading screenhouses; (iii) HTD provided reasonable results in the shading and insect proof screenhouses; (iv) Quality control analysis of the EC method showed that conditions in the shading and insect proof screenhouses were reasonable for flux measurements. However, in the plastic covered greenhouse energy balance closure was poor. Therefore, the alternative methods could not be analyzed in the greenhouse; (v) A multi-layered flux footprint model was developed for a ‘generic’ crop canopy situated within a protected environment such as a large screenhouse. The new model accounts for the vertically distributed sources and sinks within the canopy volume as well as for modifications introduced by the screen on the flow field and microenvironment. The effect of the screen on fetch as a function of its relative height above the canopy is then studied for the first time and compared to the case where the screen is absent. The model calculations agreed with field experiments based on EC measurements from two screenhouse experiments. Implications, both scientific and agricultural The study established for the first time, both experimentally and theoretically, the use of four simple TT techniques for ET estimates within large agricultural screenhouses. Such measurements, along with reliable theoretical models, will enable the future development of lowcost ET monitoring system which will be attainable for day-to-day use by growers in improving irrigation management.
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