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Multi-Sensor Fusion

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Body Sensor Networks

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References

  1. Hernandez A, Carrault G, Mora F, Thoraval L, Passariello G, Schleich JM. Multisensor fusion for atrial and ventricular activity detection in coronary care monitoring. IEEE Transactions on Biomedical Engineering 1999; 46(10):1186–1190.

    Article  Google Scholar 

  2. Thomopoulos SCA. Sensor integration and data fusion. Journal of Robotics Systems 1990; 7:337–372.

    Article  Google Scholar 

  3. Hall DL. Handbook of multisensor data fusion. Boca Raton: CRC Press, 2001.

    Google Scholar 

  4. Esteban J, Starr A, Willetts R, Hannah P, Bryanston-Cross P. A review of data fusion models and architectures: towards engineering guidelines. Neural Computing and Applications 2005.

    Google Scholar 

  5. White FE. Data fusion lexicon. Joint Directors of Laboratories, Technical Panel for C3, Data Fusion Sub-Panel Naval Ocean Systems Center, San Diego, 1991.

    Google Scholar 

  6. Luo RC, Kay MG. Multisensor integration and fusion in intelligent systems. IEEE Transactions on Systems, Man and Cybernetics 1989; 19(5):901–931.

    Article  Google Scholar 

  7. Sadjadi FA. Selected papers on sensor and data fusion. SPIE Milestone, SPIE Optical Engineering Press, 1996.

    Google Scholar 

  8. Brooks RR, Iyengar SS. Multi-sensor fusion: fundamentals and applications with software. Upper Saddle River, New Jersey: Prentice Hall, 1998.

    Google Scholar 

  9. Luo RC, Yih C-C, Su KL. Multisensor fusion and integration: approaches, applications, and future research directions. IEEE Sensors Journal 2002; 2(2):107–119.

    Article  Google Scholar 

  10. Parhami B. Multi-sensor data fusion and reliable multi-channel computation: unifying concepts and techniques. In: Proceedings of the Twenty-Ninth Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, 1995; 1:745–749.

    Article  Google Scholar 

  11. Unser M, Eden M. Weighted averaging of a set of noisy images for maximum signal-to-noise ratio. IEEE Transactions on Acoustics, Speech, and Signal Processing 1990; 38(5):890–895.

    Article  Google Scholar 

  12. Cichocki A, Amari S-I. Adaptive blind signal and image processing: learning algorithms and applications. John Wiley, 2002.

    Google Scholar 

  13. Eriksson J, Koivunen V. Identifiability, separability, and uniqueness of linear ICA models. IEEE Signal Processing Letters 2004; 11(7):601–604.

    Article  Google Scholar 

  14. Bell AJ, Sejnowski TJ. An information maximisation approach to blind separation and blind deconvolution. Neural Computation 1995; 7(6):1004–1034.

    Article  Google Scholar 

  15. Lee T-W, Girolami M, Sejnowski TJ. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Computation 1999; 11(2):417–441.

    Article  Google Scholar 

  16. Herault J, Jutten C. Space or time adaptive signal processing by neural network models. In: Proceedings of Neural Networks for Computing, Snowbird, UT, 1986; 151:207–211.

    Google Scholar 

  17. Comon P. Independent component analysis, a new concept? Signal Processing 1994; 36(3):287–314.

    Article  MATH  Google Scholar 

  18. Hyvarinen A, Karhunen J, Oja E. Independent component analysis. John Wiley and Sons, 2001.

    Google Scholar 

  19. Boyd JP. Chebyshev and Fourier spectral methods, 1st ed. Springer-Verlag, 1989.

    Google Scholar 

  20. Hall DL, McMullen AHS. Mathematical techniques in multisensor data fusion, 2nd ed. Boston: Artech House, 2004.

    MATH  Google Scholar 

  21. Aha D, Kibler D, Albert M. Instance based learning algorithms. Machine Learning 1991; 6:37–66.

    Google Scholar 

  22. Mitchell T. Machine learning. New York: McGraw Hill, 1997.

    MATH  Google Scholar 

  23. Cover TM, Hart PE. Nearest neighbour pattern classification. IEEE Transactions on Information Theory 1967; 13(1):21–27.

    Article  MATH  Google Scholar 

  24. Parzen E. On estimation of a probability density function and mode. The Annals of Mathematical Statistics 1962; 33(3):1065–1076.

    MathSciNet  MATH  Google Scholar 

  25. MacQueen B. Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability 1967; 1:281–297.

    MathSciNet  Google Scholar 

  26. Ball GH, Hall DJ. ISODATA, an iterative method of multivariate analysis and pattern classification. In: Proceedings of the International Federation of Information Processing Societies Congress 1965.

    Google Scholar 

  27. Gowda KC, Krishna G. Agglomerative clustering using the concept of mutual nearest neighbourhood. Pattern Recognition 1978; 10(2):105–112.

    Article  MATH  Google Scholar 

  28. Dhillon I, Modha D. A data clustering algorithm on distributed memory multiprocessor. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Large-scale Parallel KDD Systems Workshop 1999; 245–260.

    Google Scholar 

  29. Friedman M, Kandel A. Introduction to pattern recognition: statistical, structural, neural and fuzzy logic approaches. London: Imperial College Press, 1999.

    Google Scholar 

  30. Bezdek JC. Pattern recognition with fuzzy objective function algorithms. New York: Plenum Press, 1981.

    MATH  Google Scholar 

  31. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees. Belmont, California: Wadsworth International Group, 1984.

    Google Scholar 

  32. Quinlan JR. C4.5: Programs for machine learning. San Mateo, California: Morgan Kaufmann Publishers, 1993.

    Google Scholar 

  33. Vapnik V. The nature of statistical learning theory. Berlin: Springer-Verlag, 1995.

    MATH  Google Scholar 

  34. Cristianini N, Shawe-Taylor J. An introduction to support vector machines. Cambridge University Press, 2000.

    Google Scholar 

  35. Cortes C, Vapnik V. Support-vector networks. Machine Learning 1995; 20(3):273–297.

    MATH  Google Scholar 

  36. Jain AK, Mao J, Mohiuddin KM. Artificial neural networks: a tutorial. Computer 1996; 29(3):31–44.

    Article  Google Scholar 

  37. Kohonen T. Self-organising maps. Springer Series in Information Sciences, Berlin: Springer-Verlag, 1995.

    Google Scholar 

  38. Carreira-Perpinan MA. Continous latent variable models for dimensionality reduction and sequential data reconstruction. University of Sheffield, PhD Thesis, 2001.

    Google Scholar 

  39. Duda RO, Hart PE. Pattern classification and scene analysis. New York: Wiley-Interscience, 1973.

    MATH  Google Scholar 

  40. Hinton GE, Revow M, Dayan P. Recognizing handwritten digits using mixtures of linear models. Advances in Neural Information Processing Systems 1995; 7:1015–1022.

    Google Scholar 

  41. Bregler C, Omoundro SM. Nonlinear image interpolation using manifold learning. Advances in Neural Information Processing Systems 1995; 7:973–980.

    Google Scholar 

  42. Kohonen T. Self-organization and associative memory, 2nd ed. Berlin: Springer-Verlag, 1987.

    Google Scholar 

  43. Bishop CM, Svensen M, Williams CKI. The generative topographic mapping. Neural Computation 1998; 10(1):215–234.

    Article  Google Scholar 

  44. Cox TF, Cox MA. Multidimensional scaling, 2nd ed. London: Chapman & Hall, 2001.

    MATH  Google Scholar 

  45. Steyvers M. Encyclopedia of cognitive science. London: Nature Publishing Group, 2002.

    Google Scholar 

  46. Roweis ST, Saul LK. Nonlinear dimensionality reduction by locally linear embedding. Science 2000; 290(5500):2323–2326.

    Article  Google Scholar 

  47. Tenenbaum JB, de Silva V, Langford JC. A global geometric framework for nonlinear dimensionality reduction. Science 2000; 290(5500):2319–2323.

    Article  Google Scholar 

  48. Blum AL, Langley P. Selection of relevant features and examples in machine learning. Artificial Intelligence 1997; 97(1–2):245–271.

    Article  MATH  MathSciNet  Google Scholar 

  49. Kohavi R, John GH. Wrappers for feature subset selection. Artificial Intelligence 1997; 97(1–2):273–324.

    Article  MATH  Google Scholar 

  50. John GH, Kohavi R, Pfleger K. Irrelevant features and the subset selection problem. In: Proceedings of the Eleventh International Conference on Machine Learning, New Brunswick, NJ, 1994; 121–129.

    Google Scholar 

  51. Koller D, Sahami M. Towards optimal feature selection. In: Proceedings of the Thirteenth International Conference on Machine Learning, Bari, Italy, 1996; 284–292.

    Google Scholar 

  52. Kira K, Rendell LA. The feature selection problem: traditional methods and a new algorithm. In: Proceedings of the Ninth National Conference on Artificial Intelligence, Cambridge, Massachusetts, 1992; 129–134.

    Google Scholar 

  53. Almuallim H, Dietterich TG. Learning with many features. In: Proceedings of the Ninth National Conference on Artificial Intelligence, Cambridge, Massachusetts, 1992; 547–552.

    Google Scholar 

  54. Dash M, Liu H. Feature selection for classification. Intelligent Data Analysis 1997; 1(3):131–156.

    Article  Google Scholar 

  55. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, San Mateo, CA, 1995; 1137–1143.

    Google Scholar 

  56. Pudil P, Novovicoca J, Kittler J. Floating search methods in feature selection. Pattern Recognition Letters 1994; 15(11):1119–125.

    Article  Google Scholar 

  57. Jain A, Zongker D. Feature selection: evaluation, application, and small sample performance. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997; 19(2):153–158.

    Article  Google Scholar 

  58. Pudil P, Novovicova J, Kittler J. Simultaneous learning of decision rules and important attributes for classification problems in image analysis. Image and Vision Computing 1994; 12:193–198.

    Article  Google Scholar 

  59. Egan JP. Signal detection theory and ROC analysis. New York: Academic Press, 1975.

    Google Scholar 

  60. Van-Trees HL. Detection estimation and modulation theory. New York: Wiley and Sons, 1971.

    MATH  Google Scholar 

  61. Scott MJJ, Niranjan M, Prager RW. Parcel: feature subset selection in variable cost domains. Department of Engineering, University of Cambridge, England, Technical Report, CUED/F-INFENG/TR, 1998.

    Google Scholar 

  62. Srinivasan A. Note on the location of optimal classifiers in n-dimensional ROC space. Oxford University Computing Laboratory, Oxford, England, Technical Report, PRG-TR-2-99, 1999.

    Google Scholar 

  63. Wilcoxon F. Individual comparisons by ranking methods. Biometrics 1945; 1:80–83.

    Article  Google Scholar 

  64. Hand DJ. Construction and assessment of classification rules. Chichester, England: John Wiley and Sons, 1997.

    MATH  Google Scholar 

  65. Coetzee F, Lawrence S, Giles CL. Bayesian classification and feature selection from finite data sets. In: Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, Stanford, CA, 2000; 89–97.

    Google Scholar 

  66. Singh M, Provan GM. Efficient learning of selective Bayesian network classifiers. In: Proceedings of the Thirteenth International Conference on Machine Learning, Bari, Italy, 1996; 453–461.

    Google Scholar 

  67. Pearl J. Probabilistic reasoning in intelligent systems: networks of plausible inference. San Mateo, CA: Morgan Kaufmann, 1988.

    Google Scholar 

  68. Dempster AP. A generalization of Bayesian inference. Journal of the Royal Statistical Society 1968; 30:205–247.

    MathSciNet  Google Scholar 

  69. Shafer G. A mathematical theory of evidence. Princeton University Press, 1976.

    Google Scholar 

  70. Shafer G. Perspectives on the theory and practice of belief functions. International Journal of Approximate Reasoning 1990; 4(5–6):323–362.

    Article  MathSciNet  MATH  Google Scholar 

  71. Shafer G. Readings in uncertain reasoning. San Mateo, California: Morgan Kaufmann, 1990.

    MATH  Google Scholar 

  72. Krohn A, Beigl M, Decker C, Kochendorfer U, Robinson P, Zimmer T. Inexpensive and automatic calibration for acceleration sensors. In: Proceedings of the Second International Symposium of Ubiquitous Computing Systems, Tokyo, Japan, 2004; Springer LNCS 3598:245–258.

    Google Scholar 

  73. Lukowicz P, Junker H, Troster G. Automatic calibration of body worn acceleration sensors. In: Proceedings of the Second International Pervasive Computing Conference, Vienna, Austria, 2004; 176–181.

    Google Scholar 

  74. Liu J, Chang K-C. Feature-based target recognition with a Bayesian network. Optical Engineering 1996; 35(3):701–707.

    Article  MathSciNet  Google Scholar 

  75. Hanley JA, McNeil BJ. The meaning and use of the area under the receiver operating characteristic (ROC) curve. Diagnostic Radiology 1982; 143:29–36.

    Google Scholar 

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Authors and Affiliations

  1. Imperial College London, UK

    Guang-Zhong Yang & Xiaopeng Hu

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  1. Guang-Zhong Yang
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  1. Institute of Biomedical Engineering and Department of Computing, Imperial College London, UK

    Guang-Zhong Yang PhD

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Yang, GZ., Hu, X. (2006). Multi-Sensor Fusion. In: Yang, GZ. (eds) Body Sensor Networks. Springer, London. https://doi.org/10.1007/1-84628-484-8_8

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  • DOI: https://doi.org/10.1007/1-84628-484-8_8

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84628-272-0

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