Universal hardware and software system of signal converting for integrated sensor devices implementation

Article Sidebar

Download article PDF
Published: 22-12-2020
DOI: https://doi.org/10.33108/visnyk_tntu2020.04.106
Keywords:
sensor, Data Fusion, signal converter, programmable system

Main Article Content

Hryhorii Barylo
Lviv Polytechnic National University, Lviv, Ukraine
Oksana Boyko
Lviv Polytechnic National University, Lviv, Ukraine
Ihor Helzhynskyy
Lviv Polytechnic National University, Lviv, Ukraine
Roman Holyaka
Lviv Polytechnic National University, Lviv, Ukraine
Tetyana Marusenkova
Lviv Polytechnic National University, Lviv, Ukraine

Abstract

The problem of developing a universal signal converter for the construction of integrated sensors in data fusion concept is solved. Considering the requirements of modern microcircuit technique, in particular for sensory devices of the Internet of Things, the signal path of the synthesized sensors is implemented based on PSoC of 5LP Family Cypress. The testing of the developed system was carried out in the process of realization the integrated sensors of thermal analysis, optoelectronics, magnetic tracking and impedance spectroscopy

Article Details

Issue

Vol. 100 No. 4 (2020)

Section

Articles
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

1. Mapa L. M., Golin A. F., Costa C. C., Bianchi R. F., The use of complex impedance spectroscopy measurements for improving strain sensor performance, Sensors and Actuators A: Physical, 293 (2019), 101–107. DOI: https://doi.org/10.1016/j.sna.2019.02.001

2. Charsley E., Price D., Hunter N., Gabbott P., Kett V., Gaisford S., Parkes G., Principles of thermal analysis and calorimetry, Royal society of chemistry, 2019.

3. Prabowo B. A., Su L. C., Chang Y. F., Lai H. C., Chiu N. F., Liu K. C., Performance of white organic lightemitting diode for portable optical biosensor, Sensors and Actuators B: Chemical, 222 (2016), 1058–1065. DOI: https://doi.org/10.1016/j.snb.2015.09.059

4. Patonis P., Patias P., Tziavos I.N., Rossikopoulos D., A methodology for the performance evaluation of low-cost accelerometer and magnetometer sensors in geomatics applications, Geo -spatial information science, 21 (2018), 139–148. DOI: https://doi.org/10.1080/10095020.2018.1424085

5. Stępień P., Pulka J., Serowik M., Białowiec A. Thermogravimetric and calorimetric characteristics of alternative fuel in terms of its use in low-temperature pyrolysis. Waste and Biomass Valorization, 10 (2019), No. 6, 1669–1677. DOI: https://doi.org/10.1007/s12649-017-0169-6

6. Boyko O., Barylo G., Holyaka R., Hotra Z., Ilkanych K., Development of signal converter of thermal sensors based on combination of thermal and capacity research methods, Eastern-European Journal of Enterprise Technologies, 4/9 (2018), No. 94, 36–42. DOI: https://doi.org/10.15587/1729-4061.2018.139763

7. Boyko O., Holyaka R. Hotra Z., Fechan A., Ivanyuk H., Chaban O., Zyska T., Shedreyeva I. Functionally integrated sensors of thermal quantities based on optocoupler, Proceeding of SPIE, 10808 (2018), 1080812 -1 – 1080812-6.

8. Wang J., Pei L., Wang J., Ruan Z., Zheng J., Li J., Ning T. Magnetic field and temperature dual-parameter sensor based on magnetic fluid materials filled photonic crystal fiber. Optics Express, 28 (2020), No. 2, 1456–1471. DOI: https://doi.org/10.1364/OE.377116

9. Barylo G. I., Boyko O. V., Holyaka R. L., Marusenkova T. A., Prudyus I. N., Fabirovskyy S.E. Signal transducer of functionally integrated thermomagnetic sensors, Visnyk NTUU KPI Seriia – Radiotekhnika Radioaparatobuduvannia, 76 (2019), 63–71. DOI: https://doi.org/10.20535/RADAP.2019.76.63-71

10. Xiao F. Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy, Information Fusion, 46 (2019), 23–32. DOI: https://doi.org/10.1016/j.inffus.2018.04.003

11. Jaeger R. C., Blalock T. N. Microelectronic circuit design (5 th ed.). McGraw-Hill Education, (2016).

12. Kolluri N., Klapperich C. M., Cabodi M. Towards lab-on-a-chip diagnostics for malaria elimination. Lab on a Chip, 18 (2018), No. 1, 75–94. DOI: https://doi.org/10.1039/C7LC00758B

13. Bassi A., Bauer M., Fiedler M., Kramp Th. Kranenburg R. V., Lange S., Meissner S. Enabling things to talk. Designing IoT solutions with the IoT Architectural Reference Model. Springer-Verlag GmbH, (2013). DOI: https://doi.org/10.1007/978-3-642-40403-0

14. PSoC® 5LP: CY8C52LP Family Datasheet: Programmable System-on-Chip. http://www.cypress.com/ documentation/datasheets/psoc-5lp-cy8c52lp-family-datasheet-programmable-system-chip-psoc.

15. Boyko O., Hotra O. Improvement of dynamic characteristics of thermoresistive transducers with controlled heating, Przegląd elektrotechniczny, 5 (2019), 110–113. DOI: https://doi.org/10.15199/48.2019.05.27