S
Sandro Rigo
Researcher at Universidade do Vale do Rio dos Sinos
Publications - 162
Citations - 1234
Sandro Rigo is an academic researcher from Universidade do Vale do Rio dos Sinos. The author has contributed to research in topics: Computer science & SystemC. The author has an hindex of 15, co-authored 146 publications receiving 918 citations. Previous affiliations of Sandro Rigo include State University of Campinas & Rutgers University.
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Journal ArticleDOI
The ArchC architecture description language and tools
TL;DR: This paper has used ArchC to synthesize both functional and cycle-based simulators for the MIPS and Intel 8051 processors, as well as functional models of architectures like SPARC V8, TMS320C62x, XScale and PowerPC.
Proceedings ArticleDOI
ArchC: a systemC-based architecture description language
TL;DR: This paper presents an architecture description language (ADL) called ArchC, which is an open-source SystemC-based language that is specialized for processor architecture description that has a storage-based co-verification mechanism that automatically checks the consistency of a refined ArchC model against a reference (functional) description.
Journal ArticleDOI
The impact of gamification on students’ learning, engagement and behavior based on their personality traits
Rodrigo Smiderle,Sandro Rigo,Leonardo Marques,Jorge Arthur Peçanha de Miranda Coelho,Patrícia Augustin Jaques +4 more
TL;DR: Investigating the effects of gamification on students’ learning, behavior, and engagement based on their personality traits in a web-based programming learning environment finds evidence that the effect ofgamification depends on the specific characteristics of users.
Journal ArticleDOI
Dropout Prediction and Reduction in Distance Education Courses with the Learning Analytics Multitrail Approach
TL;DR: A Learning Analytics system developed to deal with dropout problem in Distance Education courses on university-level education, with results from experiments carried out with courses in a Brazilian university show the dropout prediction with an average of 87% precision.
Journal ArticleDOI
Toward a Model for Personal Health Record Interoperability
Alex Roehrs,Cristiano André da Costa,Rodrigo da Rosa Righi,Sandro Rigo,Matheus Henrique Wichman +4 more
TL;DR: OmniPHR demonstrated the feasibility to provide interoperability through a standard ontology and artificial intelligence with natural language processing (NLP) with the possibility of subsidizing the creation of inferences rules about possible patient health problems or preventing future problems.