Abstract
This paper is devoted to a unified approach to monitoring data generated by various electronic devices that are based on programmable microcontrollers. We suggest that communication between visualization systems and target devices be automatically tuned by retrieving the description of the input/output data structure from the firmware of the devices. For this purpose, we propose an ontology-based generator of firmware parsers. In our approach, the ontology that describes the syntax of input/output statements of different programming languages and the generator of firmware parsers become an essential part of the visualization system. Next, we propose to enrich the visualization pipeline with a data filtering stage. To make the filtering and rendering stages highly configurable, we use data flow diagrams (DFDs) that define data transformation. To enable the user to compose these diagrams, we develop a special high-level graphical editor. The description of DFD nodes is stored in the ontological knowledge base of the visualization system. To specify the nodes in ontological notation, we use ontologies of semantic filters, visual objects, and graphical scenes. We implement the proposed approach in the SciVi multiplatform client-server scientific visualization system and test its new capabilities by monitoring the orientation and light direction sensors.
Similar content being viewed by others
REFERENCES
Tractica, Consumer robotics. http://www.tractica.com/research/consumer-robotics.
Brown, E., Who needs the Internet of Things? http://www.linux.com/news/who-needs-internet-things.
Yi, Man Li R., Ching, Yu Li H., Kei, Mak C., and Beiqi, Tang T., Sustainable smart home and home automation: Big data analytics approach, Int. J. Smart Home, 2016, vol. 10, no. 8, pp. 177–198.
Jamadar, S., Applications of smart and interactive textiles. http://textilelearner.blogspot.ru/2013/04/applications-of-smart-and-interactive.html.
Zhupikov, Yu.Yu., Robotics as a factor of forming the research competence of students in the framework of extracurricular activities, Sbornik tezisov 6-oi Vserossiiskoi konferentsii “Sovremennoe tekhnologicheskoe obuchenie: ot komp’yutera k robotu” (Proc. 6th All-Russ. Conf. Modern Technological Education: From a Computer to a Robot), St. Petersburg, 2016, pp. 18–19.
Ryabinin, K. and Chuprina, S., Development of ontology-based multiplatform adaptive scientific visualization system, J. Comput. Sci., 2015, vol. 10, pp. 370–381.
Ryabinin, K.V., Chuprina, S.I., and Bortnikov, A.Yu., Automated adaptation of scientific visualization systems to the specifics of various data sources, Nauchn. vizualizatsiya, 2016, vol. 8, no. 4, pp. 1–14.
Ryabinin, K.V. and Chuprina, S.I., A unified approach to adapt scientific visualization systems to third-party solvers, Program. Comput. Software, 2016, vol. 42, no. 6, pp. 347–355.
Solov’ev, V.I. and Shabalov, P.G., Inertial navigation systems, in Uchebnoe posobie (Tutorial), Samara: Samar. Gos. Nats. Issled. Aerokosm. Univ. Koroleva, 2011.
Brown, E., Linux and open source hardware for IoT. http://www.linux.com/news/linux-and-open-source-hardware-iot.
Kalman, R.E., A new approach to linear filtering and prediction problems, J. Basic Eng., 1960, no. 82.
Kushner, D., The making of Arduino. http://spectrum.ieee.org/geek-life/hands-on/the-making-of-arduino.
Author information
Authors and Affiliations
Corresponding authors
Additional information
Translated by Yu. Kornienko
Rights and permissions
About this article
Cite this article
Ryabinin, K.V., Chuprina, S.I. Using Scientific Visualization Systems to Automate Monitoring of Data Generated by Lightweight Programmable Electronic Devices. Program Comput Soft 44, 278–285 (2018). https://doi.org/10.1134/S0361768818040102
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S0361768818040102