System Requirements, Understanding, and Design Environment



The typical embedded system design starts with listing the requirements. But there will be little to go on, when the system is to be a loosely coupled network on which a number of functionalities are to be created. First, the design space has to be explored to find the practical operating limits despite the fact that not everything is clearly defined at the beginning. Applying wisdom is one approach to solve the dilemma, but a more structured development scheme is advisable.


Lateral Deviation Sequence Diagram Object Management Group Path Diagram Neural Controller 
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.


  1. 1.
    Lederman L (1993) The god particle. Houghton Mifflin, Boston, MAGoogle Scholar
  2. 2.
    Böhm B (August 1986) A spiral model of software development and enhancement. ACM SIGSOFT Softw Eng Notes 11(4):14–24Google Scholar
  3. 3.
    Buyya R Introduction to grid computing: trends, challenges, technologies, applications. The Gridbus Project, The clouds computing and distributed systems (CLOUDS) Laboratory, The University of Melbourne, Australia. Accessed 16 Oct 2010
  4. 4.
    Casimir HBG (1973) When does jam become marmalade. In: Mendoza E (ed) A random walk in science, an anthology compiled by Weber RL. Institute of Physics Publishing, London, pp 1–2Google Scholar
  5. 5.
    Randall L (2006) Warped passages. Penguin Books, LondonMATHGoogle Scholar
  6. 6.
    Isermann R (1984) Process fault detection based on modeling and estimation methods – a survey. Automatica 20(4):347–404CrossRefGoogle Scholar
  7. 7.
    van Veelen M (2007) Considerations on modeling for early detection of abnormalities in locally autonomous distributed systems. Ph. D. Thesis, Groningen University, Groningen, The NetherlandsGoogle Scholar
  8. 8.
    Nguyen D, Widrow B (1989) The truck backer–upper: an example of self–learning in neural networks. Proc IJCNN Wash DC II:357–363Google Scholar
  9. 9.
    Jenkins RE, Yuhas BP (1993) A simplified neural network solution through problem decomposition: the case of the truck backer–upper. IEEE Trans Neural Netw 4(4):718–720CrossRefGoogle Scholar
  10. 10.
    Geva S, Sitte J (1993) A cartpole experiment benchmark for trainable controllers. IEEE Control Syst 13(5):40–51CrossRefGoogle Scholar
  11. 11.
    Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan, New York, NYMATHGoogle Scholar
  12. 12.
    Keegstra H, Jansen WJ, Nijhuis JAG, Spaanenburg L, Stevens JH, Udding JT (1996) Exploiting network redundancy for lowest–cost neural network realizations. In: Proceedings ICNN’96, Washington DC, pp 951–955Google Scholar
  13. 13.
    Hornik K, Stinchcombe M, White H (September 1989) Multilayer feed forward networks are universal approximators. Neural Netw 2(5):359–366CrossRefGoogle Scholar
  14. 14.
    Narendra KS, Parthasarathy K (March 1990) Identification and control of dynamical systems using neural networks. IEEE Trans Neural Netw 1(1):4–27CrossRefGoogle Scholar
  15. 15.
    Brooks RA (1986) A robust layered control system for a mobile robot. IEEE J Robot Autom 2:14–23MathSciNetCrossRefGoogle Scholar
  16. 16.
    van der Klugt PGM (November 1997) Alarm handling at an integrated bridge. In: Proceedings 9th world congress of the association of institutes of navigation (IAIN), Amsterdam, The NetherlandsGoogle Scholar
  17. 17.
    Shepanski JF, Macy SA (June 1987) Manual training techniques of autonomous systems based on artificial neural networks. In: Proceedings of the 1st international neural network conference (ICNN87), San Diego, CA, pp 697–704Google Scholar
  18. 18.
    Mecklenburg K et al (May 1992) Neural control of autonomous vehicles. In: Proceedings of the IEEE 42th vehicular technology conference (VTC1992), vol 1, Denver, CO, pp 303–306Google Scholar
  19. 19.
    Hess RA, Modjtahedzadeh A (August 1990) A control theoretic model of driving steering behavior. IEEE Control Syst 10(5):3–8CrossRefGoogle Scholar
  20. 20.
    Franke U (May 1992) Real-time 3D road modelling for autonomous vehicle guidance. In: Johansen P, Olsen S (eds) Theory and applications of image analysis, Selected papers from 7th Scandinavian conference on image analysis, Aalborg, Denmark, 13–16 August 1991. World Scientific Publishing, Singapore, pp 277–284Google Scholar
  21. 21.
    Franke U, Fritz H, Mehring S (December 1991) Long distance driving with the Daimler-Benz autonomous vehicle VITA. In: Proceedings Prometheus workshop, Prometheus Office, Stuttgart Germany, pp 239–247Google Scholar
  22. 22.
    Neußer S, Nijhuis JAG, Spaanenburg L, Höfflinger B, Franke U, Fritz H (February 1993) Neurocontrol for lateral vehicle guidance. IEEE Micro 13(1):57–66CrossRefGoogle Scholar
  23. 23.
    Rummelhart D, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rummelhart DE, Hinton GE, McClelland JL (eds) Parallel distributed processing. MIT Press, Cambridge, MA, pp 318–362Google Scholar
  24. 24.
    Troudet T et al (July 1991) Towards practical control design using neural computation. Proc IJCNN Seattle WA II:675–681Google Scholar
  25. 25.
    Schuermann B (2000) Applications and perspectives of artificial neural networks. VDI Berichte, VDI-Verlag, Dusseldorf, Germany, 1526:1–14Google Scholar
  26. 26.
    Jansen WJ, Diepenhorst M, Nijhuis JAG, Spaanenburg L (June 1997) Assembling engineering knowledge in a modular multilayer perceptron neural network. Digest ICNN’97, Houston TX, pp 232–237Google Scholar
  27. 27.
    Auda G, Kamel M (1999) Modular neural networks: a survey. Int J Neural Syst 9(2):129–151CrossRefGoogle Scholar
  28. 28.
    ter Brugge MH, Nijhuis JAG, Spaanenburg L, Stevens JH (1999) CNN applications in toll driving. J VLSI Signal Process 23(2/3):465–477CrossRefGoogle Scholar
  29. 29.
    Nguyen CT, (2003) Method and system for converting code to executable code using neural networks implemented in a very large scale integration (VLSI) integrated circuit. US Patent 6,578,020Google Scholar
  30. 30.
    Grunditz C, Walder M, Spaanenburg L (2004) Constructing a neural system for surface inspection. Proc IJCNN Budapest Hungary III:1881–1886Google Scholar
  31. 31.
    Spaanenburg L, (March 2007) Organic computing and emergent behavior, In: van Veelen M, van den Brink T (eds) Notes of mini workshop on dependable distributed sensing. Groningen, The Netherlands, pp 17–20Google Scholar
  32. 32.
    McLuhan M, Fiore Q (1967) The medium is the massage. Penguin Books, LondonGoogle Scholar
  33. 33.
    Venema RS, Bron J, Zijlstra RM, Nijhuis JAG, Spaanenburg L (1998) Using neural networks for waste-water purification. In: Haasis H.-D, Ranze KC (eds) Computer science for environmental protection '98”, Networked structures in information technology, the environment and business, Umwelt-Informatik Aktuell 18, No. I, Metropolis Verlag, Marburg, Germany, pp 317–330Google Scholar
  34. 34.
    Shlaer S, Mellor S (1992) Object lifecycles: modeling the world in states. Yourdon Press, Upper Saddle River, NJGoogle Scholar
  35. 35.
    Moore A (6 August 2001) A unified modeling language primer. Electronic Engineering TimesGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Electrical & Information TechnologyLund UniversityLundSweden
  2. 2.Heterogeneous Computing, LLCDurhamUSA

Personalised recommendations