Advances in Modern Artificial Intelligence

Chapter
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 234)

Abstract

This chapter presents a brief overview of advances in modern artificial intelligence. It recognises that society has embraced Artificial Intelligence (AI), even if it is embedded within many of the consumer products being marketed. The reality is that society is already in the throws of digitizing its past and continues progressively moves on-line. The volume and breadth of data being processed is becoming unfathomable. This digital future heralds the dawn of virtual communities, operating a Web of Things (WoT) full of connected devices, many fitted with wireless connectivity 24/7. This pervasiveness increases the demand on researchers to provide more intelligent tools, capable of assisting humans in prosecuting this information, seamlessly, efficiently and immediately. Ultimately AI techniques have been evolving since the 1950s. This evolution began with Good Old-Fashioned Artificial Intelligence (GOFAI) using explicitly coded knowledge, heuristics and axiomatization. This digital analogy of biological systems initially failed to realise its potential, at least until the birth of personal computers. This introduced a paradigm shift towards the Fuzzy/Neural era, which furnished society with computer vision, character recognition and Evolutionary Computing (EC) (among other successes). The value engineering proponents continued to invest in automation, which spurred the growth of Machine Intelligence (MI) research, further increasing expectations for computers to do more with less human interaction. McCarthy recently agreed that it is now more appropriate to reliable AI research as Computational Intelligence (CI), because primitive methodologies have matured and science continues to witness more hybrid solutions. It is true that modern AI techniques typically employ multiple techniques and many now form hybrid systems with flexible problem solving capabilities or increased autonomy. This book contains a series of topics aimed at illustrating advances in modern AI. This book provides discussion on a number of recent innovations that include: classifiers, neural networks, fuzzy logic, Multi-Agent Systems (MASs) and several example applications.

Keywords

Artificial Intelligence  Computational Intelligence  Evolutionary Computing  Fuzzy Logic Machine Intelligence  Neural Network  

References

  1. 1.
    McCarthy, J.: Programs with common sense. In: Symposium on Mechanization of Thought Processes, National Physical Laboratory, Teddington (1958)Google Scholar
  2. 2.
    McCorduck, P.: Machines Who Think, pp. 1–375. Freeman, San Francisco (1979)Google Scholar
  3. 3.
    Minsky, M.: Society of Mind. Simon and Schuster, Pymble, Australia (1985)Google Scholar
  4. 4.
    Baard, M.: Ai founder blasts modern research. Wired News, pp. 1–2 (2003)Google Scholar
  5. 5.
    Nilsson, N.: Artificial Intelligence: A New Synthesis. Morgan Kaufmann Publishers (1998)Google Scholar
  6. 6.
    Poole, D., Mackworth, A., Goebel, R.: Computational Intelligence: A Logical Approach. Oxford University Press, New York (1998)MATHGoogle Scholar
  7. 7.
    Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. 2nd edn. Prentice Hall, Pearson Education, Inc., Upper Saddle River (2003)Google Scholar
  8. 8.
    Haugeland, J.: Artificial Intelligence: The Very Idea. MIT Press, Cambridge (1985)Google Scholar
  9. 9.
    Bourg, D.M., Seeman, G.: AI for Game Developers. O’Reilly, Media (2004)Google Scholar
  10. 10.
    Turing, A.: Intelligent machinery. In: Meltzer, D. (ed.) Machine Intelligence. vol. 5, Orginally, A National Physics Laboratory Report, pp. 3–23. Edinburgh University Press, (1948)Google Scholar
  11. 11.
    Turing, A.: Computing machinery and intelligence. In: Mind. vol. 59(236). Unpublished until 1968, 433–460 (1950)Google Scholar
  12. 12.
    Jones, M.T.: AI Application Programming. Charles River Media, Inc. Hingham (2003)Google Scholar
  13. 13.
    Ackley, H., Hinton, E., Sejnowski, J.: A learning algorithm for boltzmann machines. Cogn. Sci. 9, 147–169 (1985)Google Scholar
  14. 14.
    Hopfield, J.: Neurons with graded responses have collective computational properties like those of two-state neurons. In. Proceedings of the National Academy of Sciences (USA), vol. 81. pp. 3088–3092 (1984)Google Scholar
  15. 15.
    Rumelhart, D., Hinton, G., Williams, R.: Learning internal representations by error propagation. In: Rumelhart, D., McClelland, J. (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1. MIT Press, Cambridge (1986)Google Scholar
  16. 16.
    Carpenter, G., Grossberg, S.: Art 2: self-organization of stable category recognition codes for analog input patterns. Appl. Opt. 26(23), 4919–4930 (1987)CrossRefGoogle Scholar
  17. 17.
    Grossberg, S.: Competitive learning: from finteractive activation to adaptive resonance. Cogn. Sci. 11, 23–63 (1987)CrossRefGoogle Scholar
  18. 18.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Linsker, R.: Self-organization in a perceptual network. Computer 21(3), 105–117 (1988)CrossRefGoogle Scholar
  20. 20.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)Google Scholar
  21. 21.
    Feigenbaum, E., McCorduck, P., Nii, H.P.: The Rise of the Expert Company. Times Books, New York (1988)Google Scholar
  22. 22.
    Jackson, P.: Introduction to Expert Systems. 3rd edn. Addison-Wesley (1999)Google Scholar
  23. 23.
    Hennie, F.C.: Finite-State Models for Logical Machines. Wiley, New York (1968)MATHGoogle Scholar
  24. 24.
    Ross, T.J.: Fuzzy Logic with Engineering Application, 3rd edn. Wiley, Chichester (2010)CrossRefGoogle Scholar
  25. 25.
    Zadeh, L.A.: Fuzzy sets. Inform. Control 8(3), 338–353 (1965)MathSciNetCrossRefMATHGoogle Scholar
  26. 26.
    Grantner, J., Patyra, M.: Synthesis and analysis of fuzzy logic finite state machine models. In: Fuzzy Systems, : World Congress on Computational Intelligence, vol. 1, pp. 205–210. IEEE Press, Piscataway (1994)Google Scholar
  27. 27.
    Jennings, N., Wooldridge, M.: Software agents. IEE Review, Institut. Eng. Technol. 42(1), 17–20 (1996)Google Scholar
  28. 28.
    Wooldridge, M., Muller, J., Tambe, M.: Agent theories, architectures, and languages: a bibliography. In: Intelligent Agents II Agent Theories, Architectures, and Languages, pp. 408–31. Springer, Berlin (1996)Google Scholar
  29. 29.
    Mackworth, A.: The coevolution of AI and AAAI. AI Mag. 26, 51–52 (2005)Google Scholar
  30. 30.
    Sutton, R.S.: Learning to predict by the methods of temporal differences. Mach. Learn. 3, 9–44 (1988)Google Scholar
  31. 31.
    Watkins, C.J.C.H., Dayan, P.: Technical note: Q-learning. Mach. Learn. 8(3), 279–292 (1992)MATHGoogle Scholar
  32. 32.
    Hughes, E.: Checkers using a co-evolutionary on-line evolutionary algorithm. In: The 2005 IEEE Congress on Evolutionary Computation, 2005. vol. 2, pp. 1899–1905 (2005)Google Scholar
  33. 33.
    Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)MATHGoogle Scholar
  34. 34.
    Beyer, H.: The Theory of Evolutionary Strategies. Springer, Berlin (2001)CrossRefGoogle Scholar
  35. 35.
    Nolfi, S., Elman, J.L., Parisi, D.: Learning and evolution in neural networks. Technical report, Technical Report 9019, Center for Research in Language, University of California, San Diego (1990)Google Scholar
  36. 36.
    Holland, J.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology. Control and Artificial Intelligence. MIT Press, Cambridge (1975)Google Scholar
  37. 37.
    Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Technical Report AI2001-290, Department of Computer Sciences, The University of Texas at Austin (2002)Google Scholar
  38. 38.
    Stanley, K.O., Bryant, B.D., Miikkulainen, R.: Evolving neural network agents in the nero video game. In: Proceedings of the IEEE 2005 Symposium on Computational Intelligence and Games (CIG’05), IEEE, Piscataway (2005)Google Scholar
  39. 39.
    Stanley, K.O., Bryant, B.D., Miikkulainen, R.: Real-time neuroevolution in the nero video game. IEEE Trans. Evol. Comput. 9, 653–668 (2005)CrossRefGoogle Scholar
  40. 40.
    Elfes, A.: Why the australian manufacturing industry needs the next generation of robots. In: The Conversation, CSIRO, Canberra, pp. 1–4 (2013)Google Scholar
  41. 41.
    Johnson, G.: The advance of the robotis. Whats New Process Technol. 26(9), 4–7 (2013)Google Scholar
  42. 42.
    Hand, D.J.: Measuring Classifier Performance: A Coherent Alternative to the Area under the Roc Curve, Machine Learning, vol. 77, pp. 103–123. Springer-Velag, Berlin (2009)Google Scholar
  43. 43.
    Berman, M., Kiiveri, H., Lagerstrom, R., Ernst, A., Dunne, R., Huntington, J.: Ice: a statistical approach to identifying endmembers. IEEE Trans. Geosci. Remote Sensing 42, 2085–2095 (2004)CrossRefGoogle Scholar
  44. 44.
    Chai, S.M., Antonio, G., Lugo-Beauchamp, W.E., Cruz-Rivera, J.L., Wills, D.S.: Hyper-spectral image processing applications on the simd pixel processor for the digital battlefield. In: Computer Vision Beyond the Visible Spectrum: Methods and Applications (CVBVS ’99), pp. 130–138. IEEE Press, Piscataway (1999)Google Scholar
  45. 45.
    Tarabalka, Y., Chanussot, J., Benediktsson, J.A.: Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recogn. 43(7), 2367–2379 (2010)CrossRefMATHGoogle Scholar
  46. 46.
    Quesada-Barriuso, P., Argüello, F., Heras, D.B.: Efficient segmentation of hyperspectral images on commodity gpus. In: Graña, M., Toro, C., Posada, J., Howlett, R.J., Jain, L.C., (eds.) KES. Frontiers in Artificial Intelligence and Applications, vol. 243, pp. 2130–2139. IOS Press (2012)Google Scholar
  47. 47.
    Verstockt, S., Merci, B., Lambert, P., van de Walle, R., Sette, B.: State of the art in vision-based and smoke detection. In: Proceedings of the 14th International Conference on Automatic Fire Detection, vol. 2, pp. 285–292 (2009)Google Scholar
  48. 48.
    Toreyin, B.U., Dedeoglu, A.Y., Cetin, E.: Wavelet based real-time smoke detection in video. In: Proceedings of the 13th European Signal Processing Conference EUSIPCO, pp. 4–8 (2005)Google Scholar
  49. 49.
    Ferrari, R.J., Zhang, H., Kube, C.R.: Real-time detection of steam in video images. Pattern Recogn. 40(3), 1148–1159 (2007)CrossRefMATHGoogle Scholar
  50. 50.
    Favorskaya, M.N., Levtin, K.: Early smoke detection in outdoor space by spatio-temporal clustering using a single video camera. In: Graña, M., Toro, C., Posada, J., Howlett, R.J., Jain, L.C. (eds.): KES. Frontiers in Artificial Intelligence and Applications, vol. 243, pp. 1283–1292. IOS Press (2012)Google Scholar
  51. 51.
    McCulloch, W.S., Pitts, W.H.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943)MathSciNetCrossRefMATHGoogle Scholar
  52. 52.
    Lippmann, R.: An introduction to computing with neural nets. ASSP Magaz. IEEE 4(2), 4–22 (1987)CrossRefGoogle Scholar
  53. 53.
    Duda, R., Hart, P.E.: Pattern Classificafion and Scene Analysis. John Wiley and Sons, New York (1973)Google Scholar
  54. 54.
    Hopfield, J.J.: Neural networks and ptiysical systems with emergent collective computational abilities, pp. 2554–2558. National Academy of Science, NSF, Washinton, DC (1982)Google Scholar
  55. 55.
    Wallace, D.J.: Memory and learning in a class of neural models. In: Bunk, B., Mufter, K.H. (eds.) Workshop on Lattice Cauge Theory, Wuppertal, Plenum (1986)Google Scholar
  56. 56.
    Carpenter, G.A., Grossberg, S.: Neural dynamics of category learning and recognition: attention, memory consolidation, and amnesia. In: Davis, J., Newburgh, R., Wegman, E. (eds.) Brain Structure, Learning and Memory. AAAS Symposium Series (1986)Google Scholar
  57. 57.
    Kandel, E.R., Schwartz, J.H.: Principles of neural circuits: a model. Science 233, 625–633 (1986)Google Scholar
  58. 58.
    Sierra, C.V., Novo, J., Reyes, J.S., Penedo, M.G.: Evolved artificial neural networks for controlling topological active nets deformation and for medical image segmentation. In: Graña, M., Toro, C., Posada, J., Howlett, R.J., Jain, L.C. (eds.) KES. Frontiers in Artificial Intelligence and Applications, vol. 243, pp. 1380–1389. IOS Press (2012)Google Scholar
  59. 59.
    Ansia, F., Penedo, M., Mariño, C., Mosquera, A.: A new approach to active nets. Pattern Recogn Image Anal 2, 76–77 (1999)Google Scholar
  60. 60.
    Iwahori, Y., Shibata, K., Kawanaka, H., Funahashi, K., Woodham, R.J., Adachi, Y.: Obtaining shape from sem image using intensity modification via neural network. In: Graña, M., Toro, C., Posada, J., Howlett, R.J., Jain, L.C. (eds.) KES. Frontiers in Artificial Intelligence and Applications, vol. 243, pp. 1778–1787. IOS Press (2012)Google Scholar
  61. 61.
    Hopfield, J.J. and Tank, D.W.: “Neural” computation of decisions in optimization problems. Biol. Cybernet. 52, 141–152 (1985)Google Scholar
  62. 62.
    Lukasiewicz, J.: The logic of trivalent. Mov. Philos. 5, 169–171 (1920)Google Scholar
  63. 63.
    Sugeno, M. (ed.): Industrial applications of fuzzy control. Technology and, Engineering (1985)Google Scholar
  64. 64.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. In: IEEE Transaction of Systems, Man, and Cybernetics. vol. 15(1), pp. 116–132. IEEE Press, Piscataway (1985)Google Scholar
  65. 65.
    Mamdani, E.: Application of fuzzy algorithms for control of simple dynamic plant. Proc. Instit. Electr. Eng. 121(12), 1585–1588 (1974)CrossRefGoogle Scholar
  66. 66.
    Sivanandam, S., Sumathi, S., Deepa, S.: Introduction to fuzzy logic using MATLAB. Springer, New York, NY (2007)CrossRefMATHGoogle Scholar
  67. 67.
    Jurdziński, M.: Principles of Marine Navigation. WAM, Gdynia (2008)Google Scholar
  68. 68.
    Staker, R.: Use of bayesian belief networks in the analysis of information system network risk. Information, Decision and Control, IDC 99. Proceedings. pp. 145–150 (1999)Google Scholar
  69. 69.
    Filipowicz, W.: Fuzzy evidence reasoning and position fixing. In: Graña, M., Toro, C., Posada, J., Howlett, R.J., Jain, L.C., (eds.): KES. Frontiers in Artificial Intelligence and Applications, vol. 243, pp. 1181–1190. IOS Press (2012)Google Scholar
  70. 70.
    Denoeux, T.: Modelling vague beliefs using fuzzy valued belief structures. Fuzzy Sets and Syst. 116, 167–199 (2000)MathSciNetCrossRefMATHGoogle Scholar
  71. 71.
    Filipowicz, W.: Evidence representation and reasoning in selected applications. In: Jdrzejowicz P., Nguyen, N.T., Hoang, K. (eds.) Lecture Notes in Artificial Intelligence, pp. 251–260. Springer-Verlag, Berlin (2011)Google Scholar
  72. 72.
    Moreno, R., Graa, M., Zulueta, E.: Rgb colour gradient following colour constancy preservation. Electron. Lett. 46(13), 908–910 (2010)CrossRefGoogle Scholar
  73. 73.
    Moreno, R., D’Anjou, A.: Hyperspectral image segmentation by t-watershed and hyperspherical coordinates. In: Graa, M., Toro, C., Posada, J., Howlett, R.J., Jain, L.C. (eds.) KES. Frontiers in Artificial Intelligence and Applications, vol. 243. pp. 2114–2121. IOS Press (2012)Google Scholar
  74. 74.
    Moreno, R., D’Anjou, A.: Hyperspectral image segmentation by t-watershed and hyperspherical coordinates. In: Graña, M., Toro, C., Posada, J., Howlett, R.J., Jain, L.C. (eds.) KES. Frontiers in Artificial Intelligence and Applications. vol. 243, pp. 2114–2121. IOS Press (2012)Google Scholar
  75. 75.
    Tweedale, J., Ichalkaranje, N., Sioutis, C., Jarvis, B., Consoli, A., Phillips-Wren, G.: Innovations in multi-agent systems. J. Netw. Comput. Appl. 30(3), 1089–1115 (2006)CrossRefGoogle Scholar
  76. 76.
    Tweedale, J.W., Jain, L.C.: Advances in information processing paradigms. In: Watanabe, T., Jain, L.C. (eds.) Innovations in Intelligent Machines-2. Studies in Computational Intelligence, vol. 376, pp. 1–20. Springer, Berlin (2012)Google Scholar
  77. 77.
    Barbucha, D., Czarnowski, I., Jȩdrzejowicz, P., Ratajczak-Ropel, E., Wierzbowska, I.: Influence of the working strategy on A-team performance. Smart Inform. Knowl. Manage. 206, 83–102 (2010) (2010)Google Scholar
  78. 78.
    Wooldridge, M.: An Introduction to MultiAgent Systems, John Wiley & Sons (2002)Google Scholar
  79. 79.
    Friedman-Hill, E.: Jess in action: rule-based systems in Java. Manning Publications, Greenwich (2003)Google Scholar
  80. 80.
    Jedrzejowicz, P., Wierzbowska, I.: Impact of migration topologies on performance of teams of a-teams. In: Graña, M., Toro, C., Posada, J., Howlett, R.J., Jain, L.C. (eds.) KES. Frontiers in Artificial Intelligence and Applications, vol. 243, pp. 1161–1170. IOS Press (2012)Google Scholar
  81. 81.
    Barbucha, D.: An agent-based implementation of the multiple neighborhood search for the capacitated vehicle routing problem. In: Graña, M., Toro, C., Posada, J., Howlett, R.J., Jain, L.C., (eds.) Frontiers in Artificial Intelligence and Applications, KES vol. 243, pp. 1191–1200. IOS Press (2012)Google Scholar
  82. 82.
    Eglese, R.W.: Simulated annealing: a tool for operational research. Eur. J. Operat. Res. 46, 271–281 (1990)MathSciNetCrossRefMATHGoogle Scholar
  83. 83.
    Voudouris, C., Tsang, E.: Guided local search and its application to the traveling salesman problem. Eur. J. Oper. Res. 113, 469–499 (1999)CrossRefMATHGoogle Scholar
  84. 84.
    Gu, J., Huang, X.: Efficient local search with search space smoothing: a case study of the traveling salesman problem. IEEE Trans. Syst. Man Cybernet. 24(5), 728–735 (1994)CrossRefGoogle Scholar
  85. 85.
    Hansen, P., Mladenovic, N., Brimberg, J., and Moreno Perez, J.A.: Variable nighborhood search. In: Gendreau, M., and Potvin, J.-Y. (eds.) Handbook of Metaheuristics, International Series in Operations Research and Management Science, vol. 146, pp. 61–86. Springer, Berlin (2010)Google Scholar
  86. 86.
    Shaw, P.: Using constraint programming and local search methods to solve vehicle routing problems. In: Proceedings of Fourth International Conference on Principles and Practice of Constraint Programming CP-98. LNCS, vol. 1520, pp. 417–431 (1998)Google Scholar
  87. 87.
    Ropke, S., Pisinger, D.: An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transport. Sci. 40(4), 455–472 (2006)CrossRefGoogle Scholar
  88. 88.
    Tedin, R., Becerra, J.A., Duro, R.J., Lede, I.M.: Towards automatic estimation of the body condition score of dairy cattle using hand-held images and active shape models. In: Graña, M., Toro, C., Posada, J., Howlett, R.J., Jain, L.C. (eds.) KES. Frontiers in Artificial Intelligence and Applications, vol. 243, pp. 2150–2159. IOS Press (2012)Google Scholar
  89. 89.
    Storn, R., Price, K.V.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)MathSciNetCrossRefMATHGoogle Scholar
  90. 90.
    Caamaño, P., Tedín, R., Paz-Lopez, A., Becerra, J.A.: Jeaf: a java evolutionary algorithm framework. In: IEEE Congress on Evolutionary Computation, IEEE, pp. 1–8 (2010)Google Scholar
  91. 91.
    Guinard, D., Trifa, V., Wilde, E.: A resource oriented architecture for the web of things. In: Internet of Things (IOT), pp. 1–8 (2010)Google Scholar
  92. 92.
    Stirbu, V.: Towards a restful plug and play experience in the web of things. In: 2008 IEEE International Conference on Semantic Computing, pp. 512–517 (2008)Google Scholar
  93. 93.
    Borzemski, L., Kaminska-Chuchmala, A.: Knowledge engineering relating to spatial web performance forecasting with sequential gaussian simulation method. In: Graña, M., Toro, C., Posada, J., Howlett, R.J., Jain, L.C. (eds.): KES. Frontiers in Artificial Intelligence and Applications, vol. 243, pp. 1439–1448. IOS Press (2012)Google Scholar
  94. 94.
    Krige, D.: A statistical approach to some basic mine valuation problems on the Witwatersrand. J. Chem. Metall. Mining Soc. 52, 119–139 (1951)Google Scholar
  95. 95.
    Borzemski, L.: The experimental design for data mining to discover web performance issues in a wide area network. Cybernet. Syst. 41(1), 31–45 (2010)CrossRefMATHGoogle Scholar
  96. 96.
    Borzemski, L., Cichocki, L., Fraś, M., Kliber, M., Nowak, Z.: Mwing: A multiagent system for web site measurements. In: Nguyen, N.T. , Grzech, A., Howlett, R.J., Jain, L.C. (eds.) Agent and Multi-Agent Systems: Technologies and Applications, Lecture Notes in Computer Science, vol. 4496, pp. 278–287. Springer, Berlin (2007)Google Scholar
  97. 97.
    Ghosh, A., Tweedale, J.W., Nafalski, A., Dollard, M.: Multi-agent based system for analysing stress using the stresscafé. In: Graña, M., Toro, C., Posada, J., Howlett, R.J., Jain, L.C. (eds.) KES. Frontiers in Artificial Intelligence and Applications, vol. 243, pp. 1656–1665. IOS Press (2012)Google Scholar
  98. 98.
    Patterson, D.W.: Artificial neural networks theory and applications, Prentice Hall, International, pp. 247–264 (1996)Google Scholar
  99. 99.
    Rojas, R.: Neural Networks: A Systematic Introduction, ch. 2–6, ISBN 3-540-60505-3. Springer-Verlag, Berlin (1996)Google Scholar
  100. 100.
    Zang, Z., Zang, C.: Agent-based hybrid intelligent systems, LANI, vol. 2938, pp. 3–11. Springer-Verlag, Berlin (2004)Google Scholar
  101. 101.
    Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy Soft Comput. A computational approach to learning and machine intelligence, Mathlab Curriculum Series (1997)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Electrical and Information EngineeringUniversity of South AustraliaAdelaideAustralia
  2. 2.Faculty of Education, Science, Technology and MathematicsUniversity of CanberraBelconnenAustralia
  3. 3.Aerospace DivisionDefence Science and Technology OrganizationEdinburgh, AdelaideAustralia

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