What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Electrical engineering
His primary areas of investigation include Electronic engineering, Low-power electronics, Energy consumption, Algorithm and Power optimization.
His Electronic engineering research is multidisciplinary, incorporating perspectives in Electronic circuit and Address bus.
His Low-power electronics research is multidisciplinary, relying on both Minification, Integrated circuit design, Battery, Control engineering and Discrete event simulation.
The Algorithm study which covers Theoretical computer science that intersects with Boolean circuit and Logic synthesis.
He interconnects Bus encoding, Power budget and Computer engineering in the investigation of issues within Power optimization.
His study explores the link between Computer engineering and topics such as Digital electronics that cross with problems in Embedded system.
His most cited work include:
- Algebraic decision diagrams and their applications (533 citations)
- Asymptotic zero-transition activity encoding for address busses in low-power microprocessor-based systems (269 citations)
- Algebric Decision Diagrams and Their Applications (228 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Electronic engineering, Embedded system, CMOS, Electronic circuit and Algorithm.
His studies deal with areas such as Transistor, Electrical engineering and Low-power electronics as well as Electronic engineering.
His Algorithm study frequently draws connections to other fields, such as Automatic test pattern generation.
His Logic gate study frequently draws connections between adjacent fields such as Digital electronics.
He most often published in these fields:
- Electronic engineering (24.11%)
- Embedded system (11.88%)
- CMOS (10.46%)
What were the highlights of his more recent work (between 2015-2021)?
- Efficient energy use (8.51%)
- Artificial intelligence (7.45%)
- Distributed computing (5.50%)
In recent papers he was focusing on the following fields of study:
Enrico Macii mainly focuses on Efficient energy use, Artificial intelligence, Distributed computing, Energy and Electronic engineering.
His Efficient energy use research includes themes of Energy consumption, Building automation and Cyber-physical system.
Energy consumption connects with themes related to Battery in his study.
His work carried out in the field of Distributed computing brings together such families of science as Neuromorphic engineering, Co-simulation, Smart grid and Globally asynchronous locally synchronous.
His Energy research incorporates elements of Real-time computing and Renewable energy.
Enrico Macii has included themes like Key and Voltage in his Electronic engineering study.
Between 2015 and 2021, his most popular works were:
- Event-Driven User-Centric Middleware for Energy-Efficient Buildings and Public Spaces (38 citations)
- Building Energy Modelling and Monitoring by Integration of IoT Devices and Building Information Models (37 citations)
- Distributed Software Infrastructure for General Purpose Services in Smart Grid (36 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Electrical engineering
Enrico Macii spends much of his time researching Distributed computing, Battery, Efficient energy use, Artificial intelligence and Photovoltaic system.
His work deals with themes such as Reliability, Embedded system, Smart grid and Software deployment, which intersect with Distributed computing.
His study in Efficient energy use is interdisciplinary in nature, drawing from both Adiabatic logic, Beyond CMOS, CMOS and Cyber-physical system.
His research integrates issues of Machine learning and Scalability in his study of Artificial intelligence.
In his study, Computer engineering is inextricably linked to Bus encoding, which falls within the broad field of Algorithm.
His study looks at the intersection of Electricity generation and topics like Power optimization with Electronic engineering.
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