Burglary detection based on accelometric data using selected signal processing algorithms

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

Identyfikatory

DOI

10.21008/j.1508-4248.2016.0027

• Języki publikacji: EN

Abstrakty

EN

The paper presents two approaches to the problem of burglary detection. The first one utilizes direct signal processing, while the other – artificial neural network (ANN). Both algorithms are compared in real operating conditions. The implementation of the algorithms was performed in a portable, battery operating devices that can be easily attached to the door. For direct comparison, two identical devices including several MEMS accelerometers and 32 bit microcontroller have been used – each with one algorithm implemented. The goal of using artificial neural network algorithm was to improve the performance of the burglary detection system in comparison to classical direct signal processing. The structure of ANN and required pre – processing of the input data, is presented and discussed as well. The article also describes the research system required to collecting the data for ANN training and to directly compare both algorithms. Finally, the results of behavior of the classification methods in real actual conditions is discussed.

Słowa kluczowe

EN

MEMS; accelerometer sensor; data streaming; DSP; low–power MCU; alarm system; artificial neural network

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Computer Applications in Electrical Engineering
• Rocznik: 2016
• Tom: Vol. 14
• Strony: 313--327
• Opis fizyczny: Bibliogr. 9 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] Finkbeiner S., “MEMS for automotive and consumer electronics,” in Solid–State Device Research Conference (ESSDERC), 2013 Proceedings of the European, 2013, pp. 9–14.
• [2] Perlmutter M. and Robin L., “High–performance, low cost inertial MEMS: A market in motion!” in Position Location and Navigation Symposium (PLANS), 2012 IEEE/ION, 2012, pp. 225–229.
• [3] “STM32L052R8 Ultra–low power ARM Cortex–M0+ MCU with 64 Kbytes Flash, 32 MHz CPU, USB – STMicroelectronics,” 09–Feb–2016. [Online]. Available:http://www.st.com/web/catalog/mmc/FM141/SC1169/SS1817/LN184.
• [4] Fabiański B., Nowopolski K., and Wicher B., “The System of Streaming and Analysis of Signals from MEMS Accelerometers,” Poznan University of Technology Academic Journals, Vol. 87, 2016, pp. 267–278, ISSN 1897–0737.
• [5] Awasthi S. and Joshi A., “MEMS accelerometer based system for motion analysis,” in 2015 2nd International Conference on Electronics and Communication Systems (ICECS), 2015, pp. 762–767.
• [6] Fabianski B., “Embedded System of Critical Information Management,” Poznan University of Technology Academic Journals, vol. 76, pp. 143–149, 2013.
• [7] Chen Y.–Y., Chang S.–J., Huang C.–Y., and Hsinag C.Y., “An Elimination Design for drift rate effects of MEMS–Based Inertial Devices” in Microsystems, Packaging, Assembly Circuits Technology Conference, 2008. IMPACT 2008. 3rd International, 2008, pp. 153–155.
• [8] Møller M.F., “A Scaled Conjugate Gradient Algorithm For Fast Supervised Learning,” Neural Networks, vol. 6, no. 4, pp. 525–533, 1993.
• [9] Rumelhart D.E., Hinton G.E., and Williams R.J., “Learning internal representations by error propagation,” DTIC Document, 1985.

Uwagi

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