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Model-Driven Approach for Automatic Dynamic Partially Reconfigurable IP Customization.

, , , and . IPDPS Workshops, page 407-412. IEEE Computer Society, (2012)

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On the In Vivo Recognition of Kidney Stones Using Machine Learning., , , , , , and . IEEE Access, (2024)Necessary exponential stability conditions for scalar periodic time-delay systems., , and . CCE, page 1-5. IEEE, (2014)High-level modelling and automatic generation of dynamicaly reconfigurable systems., , , and . DASIP, page 332-339. IEEE, (2011)Towards an automated classification method for ureteroscopic kidney stone images using ensemble learning., , , , , , , , and . EMBC, page 1936-1939. IEEE, (2020)Model-Driven Approach for Automatic Dynamic Partially Reconfigurable IP Customization., , , and . IPDPS Workshops, page 407-412. IEEE Computer Society, (2012)Assessing deep learning methods for the identification of kidney stones in endoscopic images., , , , , , , , , and 1 other author(s). CoRR, (2021)Critical Parameters of Integral Systems with a Class of Analytic Kernels., , and . TDS, page 248-253. International Federation of Automatic Control, (2012)Instability conditions for linear time delay systems: a Lyapunov matrix function approach., , and . Int. J. Control, 84 (10): 1601-1611 (2011)Critical frequencies and parameters for linear delay systems: A Lyapunov matrix approach., , and . Syst. Control. Lett., 62 (9): 781-790 (2013)Instability conditions for neutral type time delay systems., and . CDC, page 5286-5291. IEEE, (2008)