<strong>Rezumat</strong> pag. 35 10.2 Direcţii <strong>de</strong> continuare a cercetării Imensul potenţial pe care un nas electronic îl are în rezolvarea în timp real şi la costuri reduse a unor probleme ce se regăsesc într-o serie <strong>de</strong> domenii cum ar fi cel alimentar, <strong>de</strong> securitate şi ajungând până la cel farmaceutic sau medical face din continuarea cercetărilor în direcţia <strong>de</strong>zvoltării unui sistem olfactiv mai performant o cerinţă imperativă. Posibilele direcţiile <strong>de</strong> cercetare <strong>de</strong> interes ştiinţific şi tehnic ce au la bază rezultate prezentate în această teză sunt: Analiza informaţiilor noi aduse prin crearea <strong>de</strong> pseudosenzori. Cunoscându-se faptul că răspunsul senzorilor <strong>de</strong> gaz <strong>de</strong> tip MOS este puternic influenţat <strong>de</strong> temperatura la care aceştia sunt încălziţi, crearea <strong>de</strong> pseudosenzori, prin încălzirea acestora la diferite temperaturi, poate constitui o soluţie privind îmbunătăţirea informaţiilor generate <strong>de</strong> chemosenzori. Implementarea unor tipuri noi <strong>de</strong> sisteme <strong>de</strong> recunoaştere inteligente. Pe lângă alte sisteme <strong>de</strong> recunoaştere cu reţele neuronale a căror biblioteci pot fi <strong>de</strong> asemenea <strong>de</strong>zvoltate, implementarea sistemelor fuzzy sau a celor hibri<strong>de</strong>: sistemele neuro-fuzzy, pot constitui o alternativă viabilă în aplicaţiile <strong>de</strong> recunoaştere a tiparelor. Aplicaţii <strong>de</strong> biometrie. Extin<strong>de</strong>rea cercetării asupra aplicaţiilor ce privesc recunoaşterea <strong>de</strong> amprente biometrice umane prin adaptarea sistemul <strong>de</strong>zvoltat în această teză la particularităţile acestui tip <strong>de</strong> aplicaţii. Extin<strong>de</strong>rea sistemului <strong>de</strong> analiză. Simţul olfactiv este in mare măsură asemănător şi strâns legat <strong>de</strong> cel gustativ, completându-se unul pe celălalt. Astfel încât, pornind <strong>de</strong> la sistemul olfactiv <strong>de</strong>zvoltat în această teză şi prin selectarea unor senzori corespunzători măsurării fazei lichi<strong>de</strong> şi a unui sistem <strong>de</strong> eşantionare a<strong>de</strong>cvat se poate obţine un sistem gustativ artificial - aşa numita limbă artificială.
Bibliografie selectivă 1. Alippi C., G. Storti‐Gajani. Simple Approximation of Sigmoidal Functions: Realistic Design of Digital Neural Networks Capable of Learning. Proc. ISCAS’91, Singapore, IEEE Press, pp. 1505‐1508, June 1991. 2. Amin H., Curtis, K.M., and Hayes–Gill, B.R.: ‘Piecewise linear approximation applied to nonlinear function of a neural network’, IEE Proc. Circuits, Devices Sys., 1997, 144, (6), pp. 313–317 3. Beiu V., J.A. Peperstraete, and R. Lauwereins. Using Threshold Gates to Implement Sigmoid Nonlinearity.Proc. ICANN’92, Elsevier Science Publishers, Amsterdam, vol. II, pp. 1447‐1450, 1992. 4. Berglund E., J. Sitte, The parameterless self‐organizing map algorithm. Neural Networks, IEEE Transactions on. vol. 17, nr 2, 2006 5. Bishop C. M., M. Svensen, and C. K. I. Williams. Gtm: The generative topographic mapping. Neural Computation, 10(1):215–235, 1998. 6. Bishop C. M., M. Svensen, C. K. I. Williams, A principled alternative to the self‐organizing map. Advances in Neural Information Processing Systems, (9), 1997. 7. Cirstea M., A. Dinu, D. Nicula: "A Practical Gui<strong>de</strong> to VHDL Design", Ed. Tehnica, Bucharest, Romania, May 2001, ISBN: 973‐31‐1539‐8. 8. Deville Y., A Neural Implementation of Complex Activation Functions for Digital VLSI Neural Networks. Microelectronic J., 24(3), 259‐262 (1993). 9. Dinu A., M. Cirstea , A Digital Neural Network FPGA Direct Hardware Implementation Algorithm Industrial Electronics, 2007. ISIE 2007. IEEE International Symposium on Volume , Issue , 4‐7 June 2007 Page(s):2307 – 2312. 10. Fra<strong>de</strong>n J., Handbook of Mo<strong>de</strong>rn Sensors. Physics, Designs and Applications, 2nd Edition, American Institute of Physics, Woodbury, New York, 1997. 11. Fritzke B., A growing neural gas network learns topologies. In G. Tesauro, D. S. Touretzky, and T. K. Leen, editors, Advances in Neural Information Processing Systems, pages 625–632. MIT Press, Cambridge MA, 1995. 12. Gardner J. W., P.N. Bartlett, “Electronic noses: Principles and Applications”, Oxford University Press: New York, 1999. 13. Gardner J.W. , P.N. Bartlett, Sensors and Actuators B‐Chemical, 1994, 18(1‐3), 211‐220; 14. Hu Yu Hen, Handbook of Neural Network Signal Processing, Crc Press, 2002. 15. Hulle M. M. V. , K. U. Leuven. Globally‐or<strong>de</strong>red topologypreserving maps achieved with a learning rule performing local weight updates only. Neural Networks for Signal Processing [1995] V. Proceedings of the 1995 IEEE Workshop, pages 95–104, Sep 1995. 16. Jurs P.C., G.A. Bakken, Computational Methods for the Analysis of Chemical Sensor Array Data from Volatile Analytes, Chem. Rev. 2000, 100, 2649‐2678. 17. Kohonen T., Self‐Organizing Maps. Springer‐Verlag, 1997. 18. Krykelis A., A Novel Massively Parallel Associative Processing Architecture for the Implementation of Artificial Neural Networks. Proc. of the Intl. Conf. on Acoustics, Speech, and Signal Processing ICASSP’91, Toronto, Canada, IEEE Press, vol. II, pp. 1057‐1060, May 1991. 19. Lavagno L., and others, A Simulink based Approach to System Level Design and Architecture Selection, research report Universita di Udine, 2004 20. Luca Mari, and others, A Simulink based Hardware / Software Co<strong>de</strong>sign Tool for Rapid Prototyping of Control Systems, research report Politecnico di Torino, 2004. 21. Mulier F., V. Cherkassky. Learning rate schedules for selforganizing maps. In Computer Vision & Image Processing.,Proceedings of the 12th IAPR International. Conference on, volume 2, pages 224–228. IEEE, 1994 22. Myers D.J., R.A. Hutchinson. Efficient Implementation of Piecewise Linear Activation Function for Digital VLSI Neural Networks. Electronics Letters, 25(24), pp. 1662‐1663, 1989 23. Oniga, A. <strong>Tisan</strong>, A. Buchman, C. Lung: Hardware Implementation of Simple Competitive Artificial Neural Networks with Neuron Parallelism. Conference on Embed<strong>de</strong>d and Ambient Systems, RCEAS 2007, Budapest, Hungary, November 22‐24, 2007, p 23‐24, ISBN 978‐963‐8431‐96‐7 24. Oniga, A. <strong>Tisan</strong>, C. Gavrincea, D. Mic, Implementari digitale ale retelelor neuronale artificiale, Symposium of Electronics and Telecommunications, ‐ Fifth Edition – Etc 2002, September, 19‐20, 2002, Timisoara, Romania, pp 43‐47. ISSN 1224‐6034 25. Oniga, A. <strong>Tisan</strong>, D. Mic, A. Buchman, A. Vida‐Ratiu, Hand Postures Recognition System Using Artificial