Multi-Sensors Based Inclines Measuring System

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In this paper, we propose a multi-sensor based incline measuring system. The systems include: g-sensor, e-compass sensor, and image-based incline measuring system install in a same structure, the solar cell system and wireless transmission system (zig-bee) also install in the same structure. The g-sensor and image-based incline measuring system use to measure the incline angle and the micro-vibration, and the e-compass sensor to measure the angle of rotation. The image-based incline measuring system include: a laser projector, a measuring board and a camera, and the system use the characteristic of laser, and the laser projector hanging on structure project a laser beam on the measuring board vertically. The camera captures an image to identify the coordinate of laser spot, and use the coordinate of laser spot to calculate the actual incline angle. The results of incline angle combine two different sensors to increase the accuracy of measurement result.

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1168-1172

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August 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] Y.C. Lo, The Study of Slope Monitoring System, Master thesis, St. John's University, Taiwan, (2012).

Google Scholar

[2] T.Y. Tang, Machine Vision and FPGA Based Landslides Monitoring System, Master thesis, St. John's University, Taiwan, (2011).

Google Scholar

[3] K.C. Liu , C.H. Chou, Perceptual Contrast Estimation for Color Edge Detection, in Proc. 14th International Workshop on Systems, Signals and Image Processing, 2007 and 6th EURASIP Conference focused on Speech and Image Processing, Multimedia Communications and Services, pp.86-89, June (2007).

DOI: 10.1109/iwssip.2007.4381159

Google Scholar

[4] Tan, Y. -P, Kulkarni S. -R, and Ramadge P. -J, A New Method for Camera Motion Paramater Estimeation, IEEE . International Conference on Image Processing, vol. 1, pp.406-409, (1995).

Google Scholar

[5] H. Yan, Image analysis for digital media applications, IEEE Comput. Graph. Appl., Vol. 21, No. 1, pp.18-26, Jan. (2001).

Google Scholar

[6] I.M. Creusen, R.G.J. Wijnhoven, E. Herbschleb and P.H.N. de With, Color exploitation in hog-based traffic sign detection, in Proc. Proceedings of 2010 IEEE 17th International Conference on Image Processing, pp.2669-2672 , Sept. (2010).

DOI: 10.1109/icip.2010.5651637

Google Scholar

[7] Wikipedia, YCbCr, http: /en. wikipedia. org/wiki/YCbCr.

Google Scholar

[8] Wikipedia, Accelerometer, http: /en. wikipedia. org/wiki/Accelerometer.

Google Scholar

[9] D. M. Karantonis, M. R. Narayanan, M. Mathie, N. H. Lovell, and B. G. Celler, Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring, IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 1, p.156–167, January (2006).

DOI: 10.1109/titb.2005.856864

Google Scholar

[10] C.C. Shih, The Implementation of a G-Sensor-based Pedometer, Master thesis, National Central University, Taiwan, 2010.

Google Scholar