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Solutions to Parallel and Distributed Computing Problems: Lessons from Biological Sciences
book

Solutions to Parallel and Distributed Computing Problems: Lessons from Biological Sciences

by Albert Y. Zomaya, Fikret Ercal, Stephan Olariu
October 2000
Intermediate to advanced
288 pages
9h 22m
English
Wiley-Interscience
Content preview from Solutions to Parallel and Distributed Computing Problems: Lessons from Biological Sciences

REFERENCES

1. J. Alspector, R. Goodman, and T. X. Brown, Applications of Neural Networks to Telecommunications, Lawrence Erlbaum Assoc., 1993.

2. A. Boukerche, M. S. M. Notare, Applications of Neural Networks to Mobile and Wireless Networks, Technical report, University of North Texas, 1999.

3. T. X. Brown, Neural networks for adaptive equalization. In IEEE/INNS International Workshop on Applications of Neural Networks to Telecommunications, pp. 27–33, 1993.

4. H. Demuth and M. Beale, Neural network toolbox—For use with MatLab. In User s Guide, Version 3, pp. 7.1–7.33, 1998.

5. D. Denning, An intrusion-detection model. IEEE Trans. Software Eng., 13(2):222–232, 1987.

6. N. Funabaki and Y. Takefuji, A neural network parallel algorithm for channel assignment problems in cellular radio networks. IEEE Trans. Veh. Tech., 31(4):430–437, 1992.

7. Y. Fujimoto, N., Fakuda, and T. Akabane, Massively parallel architecture for large scale neural network simulations. IEEE Trans. Neural Networks, 3(6):876–888, 1992.

8. A Jayakumar and J. Alspector, Experimental analog neural network based decision feedback equalizer for digital mobile radio. In IEEE/INNS International Workshop on Applications of Neural Networks to Telecommunications, pp. 33–40, 1995.

9. M. R. Garey and D. S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, W.H. Freeman and Company, New York, 1979.

10. M. Junius and O. Kennemann, New methods for processing GSM radio measurement data: Applications ...

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Publisher Resources

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