The system of streaming and analysis of signals from MEMS accelerometers

• Autorzy: Fabiański B. , Nowopolski K. , Wicher B.
• Konferencja: Computer Applications in Electrical Engineering (18-19.04.2016 ; Poznań, Polska)
• Języki publikacji: EN

Abstrakty

EN

In the article, a dedicated testing environment for MEMS acceleration sensors is shown. The system is able to collect data from multiple devices with different physical interfaces, send them through parallel streaming, archive, and analyze it. The architecture and operational algorithms of individual components, such as complex synchronization methods in the data streaming process is described. This data streaming is finally realized by Ethernet interface which becomes a bridge between the PC system running the dedicated application and the sensor board. In the last section of the article, quality indicators of acceleration sensors signals are presented. These indicators indicate primarily a useful signal to noise ratio with respect to the measurement resolution.

Słowa kluczowe

EN

MEMS technology; gravity sensors; data streaming; digital signal processing; data acquisition; STM32L microcontrollers

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Poznan University of Technology Academic Journals. Electrical Engineering
• Rocznik: 2016
• Tom: No. 87
• Strony: 301--312
• Opis fizyczny: Bibliogr. 9 poz., rys.

Twórcy

autor

Fabiański B.

• Poznan University of Technology

autor

Nowopolski K.

• Poznan University of Technology

autor

Wicher B.

• Poznan University of Technology

Bibliografia

• [1] Getman A., Cooper C.D., Key G., Zhou H., Frankle N., Detection of mobile machine damage using accelerometer data and prognostic health monitoring techniques, in IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems, 2009. CIVVS ’09, 2009, pp. 101-104.
• [2] Iorgulescu M., Beloiu R., Popescu M.O., Vibration monitoring for diagnosis of electrical equipment’s faults, in 2010 12th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM), 2010, pp. 493-499.
• [3] Cheng W.-C., Jhan D.-M., Triaxial Accelerometer-Based Fall Detection Method Using a Self-Constructing Cascade-AdaBoost-SVM Classifier, IEEE J. Biomed. Health Inform., vol. 17, no. 2, Mar. 2013, pp. 411-419.
• [4] Vakulya G., Simon G., Low power accelerometer based intrusion and tamper detector, in Multi-Conference on Systems, Signals Devices (SSD), 2014 11th International, 2014, pp. 1-6.
• [5] Ahmed V., Ladhake S.A., Novel Ultra Low Cost Remote Monitoring System for Home Automation Using Cell Phone, in 2011 International Conference on Computational Intelligence and Communication Networks (CICN), 2011, pp. 569-573.
• [6] Song Y., Shin S., Kim S., Lee D., 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.
• [7] Lim J.G., Kim S.-Y., Kwon D.-S., Neural network based motion segmentation for accelerometer applications, in 2011 IEEE Symposium on 3D User Interfaces (3DUI), 2011, pp. 109-110.
• [8] 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].
• [9] Fabiański B., Embedded System of Critical Information Management. Poznan University of Technology Academic Journals. Electrical Engineering No. 76 (2013), pp. 143-149.

Uwagi

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