The system of MEMS sensors data streaming and signal quality analysis

• Autorzy: Fabiański B. , Nowopolski K. , Wicher B.

Identyfikatory

DOI

10.21008/j.1508-4248.2016.0028

• Języki publikacji: EN

Abstrakty

EN

In the article, a dedicated testing environment for MEMS sensors is presented. The system serve real–time measurements from several, different interfaced sensors, what gives opportunity to collect the data and – furthermore – its off–line analysis. To complete the main challenge what is MEMS ICs integration in one platform, a special hardware layer is applied together with operational algorithms. Two low–level boards are connected to the embedded server by RS–485 lines. This data server translates RS–485 signals and communicates with dedicated PC program by an Ethernet interface. Such a solution made possible to parallel streaming, archive, and analyze of data in a convenient way. The architecture and operational algorithms of individual components, such as complex synchronization methods in the data streaming process is described. Proper system design is verified by presenting selected signal waveforms grabbed in an experimental tests. In the end introduced two signal quality indicators resulting in comparison of different MEMS ICs. Summary table of computed indicators is shown with its analysis.

Słowa kluczowe

EN

MEMS technology; accelerometers; data streaming; digital signal processing; data acquisition; STM32L microcontrollers

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Computer Applications in Electrical Engineering
• Rocznik: 2016
• Tom: Vol. 14
• Strony: 328--339
• Opis fizyczny: Bibliogr. 10 poz., rys., tab.

Twórcy

autor

Fabiański B.

• Poznań University of Technology

autor

Nowopolski K.

• Poznań University of Technology

autor

Wicher B.

• Poznań University of Technology

Bibliografia

• [1] Fabianski B., Nowopolski K., Wicher B., “Selected algorithms of MEMS accelerometers signal processing in burglary detector application” Poznan University of Technology Academic Journals, vol. 87, 2016, pp. 301–312, ISSN: 1897–0737.
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• [7] Song Y., Shin S., Kim S., Lee D., and Lee K.H., “Speed Estimation From a Tri–axial Accelerometer Using Neural Networks,” in 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, EMBS 2007, 2007, pp. 3224–3227.
• [8] Lim J.G., Kim S.–Y., and Kwon D.–S., “Neural network based motion segmentation for accelerometer applications,” in 2011 IEEE Symposium on 3D User Interfaces (3DUI), 2011, pp. 109–110.
• [9] Fabianski B., “Embedded System of Critical Information Management” Poznan University of Technology Academic Journals. Electrical Engineering No. 76 (2013), pp. 143–149.
• [10] “STM32L052R8 Ultra–low power ARM Cortex–M0+ MCU with 64 Kbytes Flash, 32 MHz CPU, USB – STMicroelectronics.” [Online]. Available: http://www.st.com/web/catalog/mmc/FM141/SC1169/SS1817/LN1844/PF254256 [Accessed: 11–Feb–2016].

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).