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Moving from co-simulation to simulation for effective smart systems design
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A cyber-physical systems architecture for industry 4.0-based manufacturing systems
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JMobile Studio
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HIFSuite: Tools for HDL code conversion and manipulation
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N. Bombieri, G. Di Guglielmo, M. Ferrari, F. Fummi, G. Pravadelli, F. Stefanni, and A. Venturelli, "HIFSuite: Tools for HDL code conversion and manipulation," EURASIP Journal on Embedded Systems, vol. 2010, no. 1, p. 436328, 2010.
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Univercm: The universal versatile computational model for heterogeneous system integration
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L. Di Guglielmo, F. Fummi, G. Pravadelli, F. Stefanni, and S. Vinco, "Univercm: the universal versatile computational model for heterogeneous system integration," IEEE Transactions on Computers, vol. 62, no. 2, pp. 225-241, 2013.
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Hardware synthesis from software-oriented UML descriptions
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M. Lora, F. Martinelli, and F. Fummi, "Hardware Synthesis from Software-oriented UML Descriptions," in Proc. of IEEE MTV, 2014, pp. 33-38.
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Lora, M.1
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Model-based engineering of widgets, user applications and servers compliant with arinc 661 specification
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Springer
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0003581143
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Symbolic model checking
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Springer
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Model-driven development using uml 2.0: Promises and pitfalls
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R. B. France, S. Ghosh, T. Dinh-Trong, and A. Solberg, "Model-driven development using uml 2.0: promises and pitfalls," Computer, vol. 39, no. 2, pp. 59-66, 2006.
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A unifying flow to ease smart systems integration
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M. Lora, S. Vinco, and F. Fummi, "A unifying flow to ease smart systems integration," in Proceedings of the IEEE International High Level Design Validation and Test Workshop (HLDVT), 2016, pp. 113-120.
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Code generation alternatives to reduce heterogeneous embedded systems to homogeneity
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F. Fummi, M. Lora, F. Stefanni, and S. Vinco, "Code Generation Alternatives to Reduce Heterogeneous Embedded Systems to Homogeneity," in Languages, Design Methods, and Tools for Electronic System Design, 2014, pp. 103-124.
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Harnessing machine learning to improve the success rate of stimuli generation
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S. Fine, A. Freund, I. Jaeger, Y. Mansour, Y. Naveh, and A. Ziv, "Harnessing machine learning to improve the success rate of stimuli generation," IEEE Transactions on Computers, vol. 55, no. 11, pp. 1344- 1355, 2006.
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Fine, S.1
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