Relevant bibliographies by topics / Real-time data processing

Contents

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

Academic literature on the topic 'Real-time data processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 June 2025

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Journal articles on the topic "Real-time data processing"

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Martha, Ranjith. "Real-Time Data Ingestion for Big Data Processing." International Journal of Science and Research (IJSR) 14, no. 2 (February 27, 2025): 570–72. https://doi.org/10.21275/sr25209075243.

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Seenivasan, Dhamotharan. "Real-Time Data Processing with Streaming ETL." International Journal of Science and Research (IJSR) 12, no. 11 (November 27, 2023): 2185–92. https://doi.org/10.21275/sr24619000026.

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Patrick Bell, Denis, Eliasu Tambominyi, and Yang Chunting. "Real-Time Stream Processing of Big Data." International Journal of Science and Research (IJSR) 10, no. 3 (March 27, 2021): 1247–52. https://doi.org/10.21275/sr21320045639.

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Karan, Patel, Sakaria Yash, and Bhadane Chetashri. "Real Time Data Processing Frameworks." International Journal of Data Mining & Knowledge Management Process (IJDKP) 5, no. 5 (September 12, 2019): 49–63. https://doi.org/10.5281/zenodo.3406010.

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On a business level, everyone wants to get hold of the business value and other organizational advantages that big data has to offer. Analytics has arisen as the primitive path to business value from big data. Hadoop is not just a storage platform for big data; it’s also a computational and processing platform for business analytics. Hadoop is, however, unsuccessful in fulfilling business requirements when it comes to live data streaming. The initial architecture of Apache Hadoop did not solve the problem of live stream data mining. In summary, the traditional approach of big data being co-relational to Hadoop is false; focus needs to be given on business value as well. Data Warehousing, Hadoop and stream processing complement each other very well. In this paper, we have tried reviewing a few frameworks and products which use real time data streaming by providing modifications to Hadoop.
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Vennamaneni, Pradeep Rao. "Real-Time Financial Data Processing Using Apache Spark and Kafka." International journal of data science and machine learning 05, no. 01 (May 9, 2025): 137–69. https://doi.org/10.55640/ijdsml-05-01-16.

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The financial services industry is transforming batch processing to real-time, AI-driven architectures. This article looks at how the frameworks Apache Kafka and Apache Spark are used as bases for building scalable and low-latency, fault-tolerant data pipelines, meeting the special requirements of the financial sector. These real-time applications include high-frequency trading, fraud detection, compliance monitoring, and customer engagement. They are made possible through these open-source platforms that publicly ingest, process, and make decisions. Integrating cloud-native infrastructure—using Kubernetes, service mesh, and container orchestration—ensures elasticity, security, and regulatory alignment. Large language models (LLMs) are now being entrenched into micro services for decision support, regulatory reporting automation, and the automation of client interactions. The article also contains detailed architectural guidance on how to integrate Kafka and Spark, tips for improving Kafka Spark performance, and best practices around observability and DevSecOps. Real-time stream processing combined with AI-driven analysis serves as a real-world use case for trade surveillance. The future impact of emerging trends such as edge-native computing, federated learning, and decentralized finance is also examined. Strategic recommendations to CTOs and architects for developing secure, AI-native, and future-proof financial systems are presented to close.
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Patel, Karan, Yash Sakaria, and Chetashri Bhadane. "Real Time Data Processing Framework." International Journal of Data Mining & Knowledge Management Process 5, no. 5 (September 30, 2015): 49–63. http://dx.doi.org/10.5121/ijdkp.2015.5504.

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Achanta, Mounica. "The Impact of Real - Time Data Processing on Business Decision - making." International Journal of Science and Research (IJSR) 13, no. 7 (July 5, 2024): 400–404. http://dx.doi.org/10.21275/sr24708033511.

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K Singhal, Dhruv. "Real-Time Data Processing and Analysis in MIS: Challenges and Solutions." International Journal of Science and Research (IJSR) 13, no. 4 (April 5, 2024): 1295–98. http://dx.doi.org/10.21275/sr24415195628.

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Matai, Puneet, and Abir Bhatia. "Architecting for Real - Time Analytics: Leveraging Stream Processing and Data Warehousing Integration." International Journal of Science and Research (IJSR) 13, no. 9 (September 5, 2024): 1586–90. http://dx.doi.org/10.21275/sr24925170923.

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Mehendale, Pushkar. "Survey on Real-Time Data Processing in Finance Using Machine Learning Techniques." International Journal of Science and Research (IJSR) 8, no. 7 (July 5, 2019): 1910–13. http://dx.doi.org/10.21275/sr24810081140.

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Dissertations / Theses on the topic "Real-time data processing"

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Ostroumov, Ivan Victorovich. "Real time sensors data processing." Thesis, Polit. Challenges of science today: XIV International Scientific and Practical Conference of Young Researchers and Students, April 2–3, 2014 : theses. – К., 2014. – 35p, 2014. http://er.nau.edu.ua/handle/NAU/26582.

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Sensor it is the most powerful part of any system. Aviation industry is the plase where milions of sensors is be used for difetrent purpuses. Othe wery important task of avionics equipment is data transfer between sensors to processing equipment. Why it is so important to transmit data online into MatLab? Nowadays rapidly are developing unmanned aerial vehicles. If we can transmit data from UAV sensors into MatLab, then we can process it and get the desired information about UAV. Of course we have to use the most chipiest way to data transfer. Today everyone in the world has mobile phone. Many of them has different sensors, such as: pressure sensor, temperature sensor, gravity sensor, gyroscope, rotation vector sensor, proximity sensor, light sensor, orientation sensor, magnetic field sensor, accelerometer, GPS receiver and so on. It will be cool if we can use real time data from cell phone sensors for some navigation tasks. In our work we use mobile phone Samsung Galaxy SIII with all sensors which are listed above except temperature sensor. There are existing many programs for reading and displaying data from sensors, such as: “Sensor Kinetics”, “Sensors”, “Data Recording”, “Android Sensors Viewer”. We used “Data Recording”. For the purpose of transmitting data from cell phone there are following methods: - GPRS (Mobile internet); - Bluetooth; - USB cable; - Wi-Fi. After comparing this methods we analyzed that GPRS is uncomfortable for us because we should pay for it, Bluetooth has small coverage, USB cable has not such portability as others methods. So we decided that Wi-Fi is optimal method on transmitting data for our goal
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White, Allan P., and Richard K. Dean. "Real-Time Test Data Processing System." International Foundation for Telemetering, 1989. http://hdl.handle.net/10150/614650.

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International Telemetering Conference Proceedings / October 30-November 02, 1989 / Town & Country Hotel & Convention Center, San Diego, California<br>The U.S. Army Aviation Development Test Activity at Fort Rucker, Alabama needed a real-time test data collection and processing capability for helicopter flight testing. The system had to be capable of collecting and processing both FM and PCM data streams from analog tape and/or a telemetry receiver. The hardware and software was to be off the shelf whenever possible. The integration was to result in a stand alone telemetry collection and processing system.
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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<br>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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Dowling, Jason, John Welling, Loral Aerosys, Kathy Nanzetta, Toby Bennett, and Jeff Shi. "ACCELERATING REAL-TIME SPACE DATA PACKET PROCESSING." International Foundation for Telemetering, 1995. http://hdl.handle.net/10150/608429.

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International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada<br>NASA’s use of high bandwidth packetized Consultative Committee for Space Data Systems (CCSDS) telemetry in future missions presents a great challenge to ground data system developers. These missions, including the Earth Observing System (EOS), call for high data rate interfaces and small packet sizes. Because each packet requires a similar amount of protocol processing, high data rates and small packet sizes dramatically increase the real-time workload on ground packet processing systems. NASA’s Goddard Space Flight Center has been developing packet processing subsystems for more than twelve years. Implementations of these subsystems have ranged from mini-computers to single-card VLSI multiprocessor subsystems. The latter subsystem, known as the VLSI Packet Processor, was first deployed in 1991 for use in support of the Solar Anomalous & Magnetospheric Particle Explorer (SAMPEX) mission. An upgraded version of this VMEBus card, first deployed for Space Station flight hardware verification, has demonstrated sustained throughput of up to 50 Megabits per second and 15,000 packets per second. Future space missions including EOS will require significantly higher data and packet rate performance. A new approach to packet processing is under development that will not only increase performance levels by at least a factor of six but also reduce subsystem replication costs by a factor of five. This paper will discuss the development of a next generation packet processing subsystem and the architectural changes necessary to achieve a thirty-fold improvement in the performance/price of real-time packet processing.
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Liu, Guangtian. "An event service architecture in distributed real-time systems /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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Dreibelbis, Harold N., Dennis Kelsch, and Larry James. "REAL-TIME TELEMETRY DATA PROCESSING and LARGE SCALE PROCESSORS." International Foundation for Telemetering, 1991. http://hdl.handle.net/10150/612912.

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International Telemetering Conference Proceedings / November 04-07, 1991 / Riviera Hotel and Convention Center, Las Vegas, Nevada<br>Real-time data processing of telemetry data has evolved from a highly centralized single large scale computer system to multiple mini-computers or super mini-computers tied together in a loosely coupled distributed network. Each mini-computer or super mini-computer essentially performing a single function in the real-time processing sequence of events. The reasons in the past for this evolution are many and varied. This paper will review some of the more significant factors in that evolution and will present some alternatives to a fully distributed mini-computer network that appear to offer significant real-time data processing advantages.
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Feather, Bob, and Michael O’Brien. "OPEN ARCHITECTURE SYSTEM FOR REAL TIME TELEMETRY DATA PROCESSING." International Foundation for Telemetering, 1991. http://hdl.handle.net/10150/612934.

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International Telemetering Conference Proceedings / November 04-07, 1991 / Riviera Hotel and Convention Center, Las Vegas, Nevada<br>There have been many recent technological advances in small computers, graphics stations, and system networks. This has made it possible to build highly advanced distributed processing systems for telemetry data acquisition and processing. Presently there is a plethora of vendors marketing powerful new network workstation hardware and software products. Computer vendors are rapidly developing new products as new technology continues to emerge. It is becoming difficult to procure and install a new computer system before it has been made obsolete by a competitor or even the same vendor. If one purchases the best hardware and software products individually, the system can end up being composed of incompatible components from different vendors that do not operate as one integrated homogeneous system. If one uses only hardware and software from one vendor in order to simplify system integration, the system will be limited to only those products that the vendor chooses to develop. To truly take advantage of the rapidly advancing computer technology, today’s telemetry systems should be designed for an open systems environment. This paper defines an optimum open architecture system designed around industry wide standards for both hardware and software. This will allow for different vendor’s computers to operate in the same distributed networked system, and will allow software to be portable to the various computers and workstations in the system while maintaining the same user interface. The open architecture system allows for new products to be added as they become available to increase system performance and capability in a truly heterogeneous system environment.
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Dahan, Michael. "RTDAP: Real-Time Data Acquisition, Processing and Display System." International Foundation for Telemetering, 1989. http://hdl.handle.net/10150/614629.

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International Telemetering Conference Proceedings / October 30-November 02, 1989 / Town & Country Hotel & Convention Center, San Diego, California<br>This paper describes a data acquisition, processing and display system which is suitable for various telemetry applications. The system can be connected either to a PCM encoder or to a telemetry decommutator through a built-in interface and can directly address any channel from the PCM stream for processing. Its compact size and simplicity allow it to be used in the flight line as a test console, in mobile stations as the main data processing system, or on-board test civil aircrafts for in-flight monitoring and data processing.
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Spina, Robert. "Real time maze traversal /." Online version of thesis, 1989. http://hdl.handle.net/1850/10566.

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Ghosh, Kaushik. "Speculative execution in real-time systems." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/8174.

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Books on the topic "Real-time data processing"

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1951-, Halang Wolfgang A., Stoyenko Alexander D. 1962-, North Atlantic Treaty Organization. Scientific Affairs Division., and NATO Advanced Study Institute on Real Time Computing (1992 : Sint Maarten, Netherlands Antilles), eds. Real time computing. Berlin: Springer-Verlag, 1994.

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Jan, Wikander, and Svensson Bertil 1954-, eds. Real-time systems in mechatronic applications. Boston, Mass: Kluwer Academic Publishers, 1998.

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Krishna, C. M. Real-time systems. New York: McGraw-Hill, 1997.

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Lee, Y. H. Readings in real-time systems. Los Alamitos, Calif: IEEE Computer Society Press, 1993.

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1945-, Brown Christopher M., and Terzopoulos Demetri, eds. Real-time computer vision. Cambridge, [England]: Cambridge University Press, 1995.

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Brett, Tjaden, and Welch Lonnie R, eds. Real-time system security. New York: Nova Science Pub., 2003.

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Armstrong, Philip N. Data rearrangement and real-time computation. Santa Monica, CA: Rand Corp., 1993.

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-P, Tsai Jeffrey J., ed. Distributed real-time systems: Monitoring, visualization, debugging, and analysis. New York: Wiley, 1996.

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1958-, Haines Eric, ed. Real-time rendering. Natick, Mass: A K Peters, 1999.

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Motus, L. Timing analysis of real-time software. Oxford: Pergamon, 1994.

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Book chapters on the topic "Real-time data processing"

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Fournier, Fabiana, and Inna Skarbovsky. "Real-Time Data Processing." In Big Data in Bioeconomy, 147–56. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_11.

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AbstractTo remain competitive, organizations are increasingly taking advantage of the high volumes of data produced in real time for actionable insights and operational decision-making. In this chapter, we present basic concepts in real-time analytics, their importance in today’s organizations, and their applicability to the bioeconomy domains investigated in the DataBio project. We begin by introducing key terminology for event processing, and motivation for the growing use of event processing systems, followed by a market analysis synopsis. Thereafter, we provide a high-level overview of event processing system architectures, with its main characteristics and components, followed by a survey of some of the most prominent commercial and open source tools. We then describe how we applied this technology in two of the DataBio project domains: agriculture and fishery. The devised generic pipeline for IoT data real-time processing and decision-making was successfully applied to three pilots in the project from the agriculture and fishery domains. This event processing pipeline can be generalized to any use case in which data is collected from IoT sensors and analyzed in real-time to provide real-time alerts for operational decision-making.
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Weik, Martin H. "real-time data processing." In Computer Science and Communications Dictionary, 1423. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_15596.

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Bingham, John. "On-Line and Real Time Systems." In Data Processing, 239–44. London: Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-19938-9_18.

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Wingerath, Wolfram, Norbert Ritter, and Felix Gessert. "General-Purpose Stream Processing." In Real-Time & Stream Data Management, 57–74. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10555-6_5.

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Attoui, Ammar. "Principles of Real-Time Data Processing." In Practitioner Series, 175–237. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0463-6_5.

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Paterson, M. "Real-Time Data Processing for SuperCOSMOS." In Astrophysics and Space Science Library, 141–45. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2472-0_19.

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Wiederhold, Gio, and Paul D. Clayton. "Processing Biological Data in Real Time." In M. D. Computing: Benchmark Papers, 107–16. New York, NY: Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4612-4710-4_13.

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Yadav, Vinit. "Real-Time Analytics with Storm." In Processing Big Data with Azure HDInsight, 143–72. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2869-2_7.

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Zhao, Bo, Cheng Cheng, Yuxin Cai, and Tang Zhiwei. "Real-Time Image Processing System." In Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019), 1965–70. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1468-5_232.

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Fox, Geoffrey C., Mehmet S. Aktas, Galip Aydin, Hasan Bulut, Harshawardhan Gadgil, Sangyoon Oh, Shrideep Pallickara, Marlon E. Pierce, Ahmet Sayar, and Gang Zhai. "Grids for Real Time Data Applications." In Parallel Processing and Applied Mathematics, 320–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752578_39.

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Conference papers on the topic "Real-time data processing"

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Garimella, Sarvesh, and Douglas Franz. "Satellite edge AI for automated training data collection, transfer learning, and synthetic training data generation." In Real-Time Image Processing and Deep Learning 2025, edited by Nasser Kehtarnavaz and Mukul V. Shirvaikar, 11. SPIE, 2025. https://doi.org/10.1117/12.3053567.

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Sun, Xiaoyang, Feng Wang, Yong Wang, and Shi Li. "Data processing for EAST remote participation." In 2016 IEEE-NPSS Real Time Conference (RT). IEEE, 2016. http://dx.doi.org/10.1109/rtc.2016.7543126.

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Kaixin, Shen, Honglei An, Huang Yongshan, Wei Qing, and Ma HongXu. "Visual Real-time Data Processing." In 2020 Chinese Control And Decision Conference (CCDC). IEEE, 2020. http://dx.doi.org/10.1109/ccdc49329.2020.9164097.

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Vinitski, S., U. Szumowski, and R. H. Griffey. "Real time NMR data processing." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1988. http://dx.doi.org/10.1109/iembs.1988.94544.

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Makowski, D., A. Mielczarek, P. Perek, A. Napieralski, L. Butkowski, J. Branlard, M. Fenner, H. Schlarb, and B. Yang. "High-speed data processing module for LLRF." In 2014 IEEE-NPSS Real Time Conference (RT). IEEE, 2014. http://dx.doi.org/10.1109/rtc.2014.7097409.

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Gu, Minhao, Kejun Zhu, Fei Li, and Wei Shen. "TaskRouter: A newly designed online data processing framework." In 2016 IEEE-NPSS Real Time Conference (RT). IEEE, 2016. http://dx.doi.org/10.1109/rtc.2016.7543088.

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Barrera, E., M. Ruiz, S. Lopez, D. Machon, and J. Vega. "PXI-based architecture for real time data acquisition and distributed dynamical data processing." In 14th IEEE-NPSS Real Time Conference, 2005. IEEE, 2005. http://dx.doi.org/10.1109/rtc.2005.1547509.

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Mousessian, Ardvas, and Christina Vuu. "Near real time data processing system." In Optical Engineering + Applications, edited by Philip E. Ardanuy and Jeffery J. Puschell. SPIE, 2008. http://dx.doi.org/10.1117/12.800641.

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Durbin, Phillip, Curt Tilmes, Brian Duggan, and Bigyani Das. "OMI Near Real Time data processing." In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5651380.

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Svingos, Christoforos, Theofilos Mailis, Herald Kllapi, Lefteris Stamatogiannakis, Yannis Kotidis, and Yannis Ioannidis. "Real time processing of streaming and static information." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840631.

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Reports on the topic "Real-time data processing"

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Fiori, R. A. D., K. Reiter, D. Galeschuk, T. Ghosal, and N. Olfert. Near real-time processing of NRCan riometer data. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/332078.

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Owechko, Yuri, and Bernard Soffer. Real-Time Implementation of Nonlinear Optical Data Processing Functions. Fort Belvoir, VA: Defense Technical Information Center, November 1990. http://dx.doi.org/10.21236/ada233521.

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Beer, Randall D. Neural Networks for Real-Time Sensory Data Processing and Sensorimotor Control. Fort Belvoir, VA: Defense Technical Information Center, June 1992. http://dx.doi.org/10.21236/ada251567.

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Beer, Randall D. Neural Networks for Real-Time Sensory Data Processing and Sensorimotor Control. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada259120.

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Roth, Christopher J., Nelson A. Bonito, Maurice F. Tautz, and Eugene C. Courtney. CHAWS Data Processing and Analysis Tools in Real-Time and Postflight Environments. Fort Belvoir, VA: Defense Technical Information Center, September 1998. http://dx.doi.org/10.21236/ada381118.

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Birkemeier, William, Kent Hathaway, Michael Forte, Katherine Brodie, Patrick Dickhudt, and Annika O'Dea. Field Research Facility long-term data. Engineer Research and Development Center (U.S.), April 2025. https://doi.org/10.21079/11681/49713.

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The U.S. Army Corps of Engineers has been collecting long-term coastal process data at the Engineer Research and Development Center, Field Research Facility in Duck, NC since 1974. These data include meteorological, oceanographic, topographic, and bathymetric data. Oceanographic and meteorologic data are collected and processed as real-time data and follow Qartod (https://ioos.noaa.gov/project/qartod/) real-time data quality control standards. Dune lidar data including both DEMs and hydrodynamic data are collected and processed in real. These data are stored and publicly served in netCDF format (https://www.unidata.ucar.edu/software/netcdf/) from a Thredds server (https://www.unidata.ucar.edu/software/tds/) located at https://chldata.erdc.dren.mil/thredds/catalog/frf/catalog.html. Data are organized by data type and in most cases separated down to the individual gauge. For an individual gauge, data are stored in monthly netCDF files. Data collection, processing, maintenance, and dissemination is supported by the USACE Coastal Field Data Collection Program.
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Desai, Jairaj, Rahul Suryakant Sakhare Sakhare, Justin Mahlberg, Jijo K. Mathew, Howell Li, and Darcy M. Bullock. Implementation of Enhanced Probe Data (CANBUS) for Tactical Workzone and Winter Operations Management. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317643.

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For over a decade, segment-based probe data has been extensively used by transportation stakeholders for monitoring mobility on Indiana roadways. However, enhanced probe data from connected vehicles includes a richer dataset that can provide more detailed real-time and after-action reviews. This enhanced data includes detailed vehicle trajectories, at 3s resolution, and “event data.” This event data is near real-time and includes hard-braking events, hard-acceleration events, weather-related data, including wiper activations and some seat belt usage data. This project developed a set of methodologies and resulting visualizations that enables the use of emerging connected vehicle data in operational decision-making on work zone management and winter operations activities. Each month approximately 13 billion connected vehicle records are ingested for Indiana. During peak periods, approximately 625,000 records per minute are ingested. Without substantial processing, this large data set is “data-rich, information-poor.” This study developed techniques to rapidly assign relevant data to interstate segments so that visual graphics could be efficiently generated. This provided the ability for both real-time monitoring as well as after action assessment to identify opportunities to improve both work zone operations and winter operation activities. The summaries derived from these datasets have helped promote effective actionable dialog among agencies, contractors, and public safety colleagues towards the overarching goal of improving interstate safety and mobility.
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Navarro, Luke, Shea Hammond, and Richard Johansen. Sensor fusion for aerial robotic system. Engineer Research and Development Center (U.S.), April 2025. https://doi.org/10.21079/11681/49701.

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As uncrewed aerial vehicle (drone) use expands across industries so also does the complexity of sensor payloads. At present, there are no commercially available products for the management and fusion of multisensor data. Sensor Fusion for Aerial Robotic Systems (SFARS) is a sensor agnostic, modular platform for intelligent multisensor data fusion and processing. At the time of writing, SFARS exists as a root codebase, a PC application for processing of previously collected drone data and as a prototype hardware platform for real-time drone deployment. This report serves as a technical users guide to the design, development, and implementation of the suite of SFARS software.
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9

Kong, Zhihao, and Na Lu. Field Implementation of Concrete Strength Sensor to Determine Optimal Traffic Opening Time. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317724.

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In the fast-paced and time-sensitive fields of construction and concrete production, real-time monitoring of concrete strength is crucial. Traditional testing methods, such as hydraulic compression (ASTM C 39) and maturity methods (ASTM C 1074), are often laborious and challenging to implement on-site. Building on prior research (SPR 4210 and SPR 4513), we have advanced the electromechanical impedance (EMI) technique for in-situ concrete strength monitoring, crucial for determining safe traffic opening times. These projects have made significant strides in technology, including the development of an IoT-based hardware system for wireless data collection and a cloud-based platform for efficient data processing. A key innovation is the integration of machine learning tools, which not only enhance immediate strength predictions but also facilitate long-term projections vital for maintenance and asset management. To bring this technology to practical use, we collaborated with third-party manufacturers to set up a production line for the sensor and datalogger assembly. The system was extensively tested in various field scenarios, including pavements, patches, and bridge decks. Our refined signal processing algorithms, benchmarked against a mean absolute percentage error (MAPE) of 16%, which is comparable to the ASTM C39 interlaboratory variance of 14%, demonstrate reliable accuracy. Additionally, we have developed a comprehensive user manual to aid field engineers in deploying, connecting, and maintaining the sensing system, paving the way for broader implementation in real-world construction settings.
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10

Selvaraju, Ragul, SHABARIRAJ SIDDESWARAN, and Hariharan Sankarasubramanian. The Validation of Auto Rickshaw Model for Frontal Crash Studies Using Video Capture Data. SAE International, September 2020. http://dx.doi.org/10.4271/2020-28-0490.

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Despite being Auto rickshaws are the most important public transportation around Asian countries and especially in India, the safety standards and regulations have not been established as much as for the car segment. The Crash simulations have evolved to analyze the vehicle crashworthiness since crash experimentations are costly. The work intends to provide the validation for an Auto rickshaw model by comparing frontal crash simulation with a random head-on crash video. MATLAB video processing tool has been used to process the crash video, and the impact velocity of the frontal crash is obtained. The vehicle modelled in CATIA is imported in the LS-DYNA software simulation environment to perform frontal crash simulation at the captured speed. The simulation is compared with the crash video at 5, 25, and 40 milliseconds respectively. The comparison shows that the crash pattern of simulation and real crash video are similar in detail. Thus the modelled Auto-rickshaw can be used in the future to validate the real-time crash for providing the scope of improvement in Three-wheeler safety.
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