Acácio Manuel Raposo Amaral Editor at IgMin Research
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Biography
Acácio M. R. Amaral received the E. E. diploma, the M. S. degree and the Ph. D degree from Coimbra’s University, Portugal in 1998, 2005 and 2010, respectively.
Since 1998 has been with Coimbra’s Polytechnic, where is currently an Associate Professor in the Department of Informatics and Systems.
He is the author of five books entitled Circuit and Electronic Devices Analysis (Porto, Portugal, Publindustria, 2013 - in Portuguese), Digital Systems (Lisboa, Portugal, Edições Sílabo, 2014 - in Portuguese), Analog Electronics (Lisboa, Portugal, Edições Sílabo, 2017 - in Portuguese), Digital Electronics (Lisboa, Portugal, Edições Sílabo, 2019 - in Portuguese) and Applied Electronics (Lisboa, Portugal, Edições Sílabo, 2021 - in Portuguese).
He is the author of more than 50 papers published in technical journals and conference proceedings and co-author of a book entitled Diagnosis and Fault Tolerance of Electrical Machines, Power Electronics and Drives, (Stevenage, United Kingdom, Institution of Engineering and Technology, 2018 - in English).
His research activities include fault diagnosis, design and simulation of Power Converters, with emphasis on the consequences of aging of storage devices such as capacitors, super capacitors and batteries, as well as, the development of solutions to this problem.
Research Interest
Fault diagnosis, DC-DC Converters, Capacitors, Super capacitors, Batteries, Circuit Simulation and Machine Learning
Editor
Work Details
Professor
Polytechnic Institute of Coimbra
Department of Informatics and Systems
Portugal
0000-0001-8025-6898 Contribution by Topic Area
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Which articles are now trending?
Research Articles
- Risk of Nutritional Deficiencies and Changes in Dietary Patterns after Bariatric Surgery
- Properties of Indium Antimonide Nanocrystals as Nanoelectronic Elements
- Abrasive Wear in Some High Fe-Cr-C Alloy in Cement Powder
- Efficacy of Alternative Insecticides against Dusky Cotton Bug (Oxycarenus laetus) to Improve Yield Losses in Cotton Crops through Residue-based Bioassay
- Enhancing Material Property Predictions through Optimized KNN Imputation and Deep Neural Network Modeling
- A Review inside Innovation: AI and Additive Manufacturing for Advanced Bone Scaffold Design
