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Sensors

  • H. B. Mitchell
Chapter

Introduction

In this chapter we consider the sensors. These are special devices which interact directly with the environment and which are ultimately the source of all the input data in a multi-sensor data fusion system [12]. The physical element which interacts with the environment is known as the sensor element and may be any device which is capable of perceiving a physical property, or environmental attribute, such as heat, light, sound, pressure, magnetism or motion. To be useful, the sensor must map the value of the property or attribute to a quantitative measurement in a consistent and predictable manner.

Keywords

Sensor Element Sensor Model Ultrasonic Sensor Local Clock Smart Sensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Boudjemaa, R., Forbes, A.B.: Parameter estimation methods for data fusion, National Physical laboratory Report No. CMSC 38–04 (2004)Google Scholar
  2. 2.
    Brajovic, V., Kanade, T.: When are the Smart Vision Sensors Smart? An example of an illumination-adaptive image sensor. Sensor Rev. 24, 156–166 (2004)CrossRefGoogle Scholar
  3. 3.
    Celebi, M.E., Hwang, S., Iyatomi, H., Schaefer, G.: Robust border detection in dermoscopy images using threshold fusion. In: Proc. IEEE Int. Conf. Image Proc., pp. 2541–2544 (2010)Google Scholar
  4. 4.
    Durrant-Whyte, H.F.: Sensor models and multisensor integration. Int. J. Robot Res. 7, 97–113 (1988b)CrossRefGoogle Scholar
  5. 5.
    Eidson, J.C., Lee, K.: Sharing a common sense of time. IEEE Instrument Meas. Mag., 26–32 (September 2003)Google Scholar
  6. 6.
    Elmenreich, W.: An introduction to sensor fusion. Institut fur Technische Informatik, Technischen Universitat Wien, Research Report 47/2001 (2002)Google Scholar
  7. 7.
    Elmenreich, W.: Sensor fusion in time-triggered systems PhD thesis, Institut fur Technische Informatik, Technischen Universitat Wien (2002)Google Scholar
  8. 8.
    Elmenreich, W., Haidinger, W., Kopetz, H.: Interface design for smart transducers. In: IEEE Instrument Meas. Tech. Conf. (IMTC), vol. 3, pp. 1643–1647 (2001)Google Scholar
  9. 9.
    Elmenreich, W., Haidinger, W., Kopetz, H., Losert, T., Obermaisser, R., Paulitsch, M., Trodhandl, C.: DSoS IST-1999-11585 Dependable systems of Systems. Initial Demonstration of Smart Sensor Case Study. A smart sensor LIF case study: autonomous mobile robot (2002)Google Scholar
  10. 10.
    Elmenreich, W., Pitzek, S.: The time-triggered sensor fusion model. In: Proc. 5th IEEE Int. Conf. Intell. Engng. Sys., Helsinki, Finland, pp. 297–300 (2001)Google Scholar
  11. 11.
    Elmenreich, W., Pitzek, S.: Smart transducers-principles, communications, and configuration. In: Proc. 7th IEEE Int. Conf. Intell. Engng. Sys., Egypt, Assuit, Luxor, pp. 510–515 (2003)Google Scholar
  12. 12.
    Fowler, K.R., Schmalzel, J.L.: Sensors: The first stage in the measurement chain. IEEE Instrument Meas. Mag., 60–65 (September 2004)Google Scholar
  13. 13.
    Konolige, K.: Improved occupancy grids for map building. Autonomous Robots 4, 351–367 (1997)CrossRefGoogle Scholar
  14. 14.
    Kopetz, H., Bauer, G.: The time-triggered architecture. Proc. IEEE 91, 112–126 (2003)CrossRefGoogle Scholar
  15. 15.
    Kopetz, H., Holzmann, M., Elmenreich, W.: A universal smart transducer interface: TTP/A. In: 3rd IEEE Int. Symp. Object-Oriented Real-Time Distributed Computing (2001)Google Scholar
  16. 16.
    Kumar, M., Garg, D.P., Zachery, R.A.: A method for the judicious fusion of inconsistent multiple sensor data. IEEE Sensors J. 7, 723–733 (2007)CrossRefGoogle Scholar
  17. 17.
    O’Sullivan, S., Collins, J.J., Mansfield, M., Eaton, M., Haskett, D.: A quantitative evaluation of sonar models and mathematical update methods for map building with mobile robots. In: 9th Int. Symposium on Artificial Life and Robotics (AROB), Japan (2004)Google Scholar
  18. 18.
    Paulitsch, M.: Fault-tolerant clock synchronization for embedded distributed multi-cluster systems. PhD thesis, Institut fur Technische Informatik, Technischen Universitat Wien (2002)Google Scholar
  19. 19.
    Todini, E.: A Bayesian technique for conditioning radar precipitation estimates to rain-gauge measurements. Hydrology Earth Sys. Sci. 5, 187–199 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Section 3424IAI Elta Electronics Ind. Ltd.AshdodIsrael

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