Obiettivo
- To efficiently design a Smart Electronic Energy Meter device which will overcome the constraints of the old Ferraris-type one, and will provide advanced services like fraud detection, multi-tariff capabilities, two alternative ways of communication for the transmission of billing and status information etc.
- To introduce and exploit novel modelling techniques based on A-HDL in mixed designs.
- To demonstrate through efficient execution of the specific experiment the improvements that could be achieved in mixed hardware designs by employing the project's emerging techniques and tools with respect to existing ones, in terms of better modelling, more reliable design procedures, reduced errors and redesign cycles and shorter time-to-market.
- To disseminate the experiences and lessons learned, through the execution of the specific experiment, in digital/analogue designs to SMEs and other interested bodies.
The aim of SEM-A-HDL is the comparison of the conventional way of implementing the Smart Electronic Energy Meter with the outcome of the Application Experiment: the same device design completed (at the behaviour level) using A-HDL based methodology. The most important goal is the exploitation of novel modelling techniques based on A-HDL (extension of VHDL for the analogue case) through modern CAD tools. The experiences accumulated during the development of the Smart Energy Meter will be valuable both for the user company for all their future mixed-signal products, as well as for all interested SMEs and Design Houses including the subcontractor. A parallel goal (supported by company resources) is the development of a Smart Electronic Energy Meter, an efficient solution that overcomes the constraints of the old Ferraris-type electricity energy-meter.
Campo scientifico (EuroSciVoc)
CORDIS classifica i progetti con EuroSciVoc, una tassonomia multilingue dei campi scientifici, attraverso un processo semi-automatico basato su tecniche NLP.
CORDIS classifica i progetti con EuroSciVoc, una tassonomia multilingue dei campi scientifici, attraverso un processo semi-automatico basato su tecniche NLP.
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ACM - Preparatory, accompanying and support measuresCoordinatore
19002 Peania
Grecia