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Record W1987373466 · doi:10.1145/1086519.1086525

A curriculum for embedded system engineering

2005· article· en· W1987373466 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACM Transactions on Embedded Computing Systems · 2005
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of Waterloo
FundersUniversidade Federal do Rio Grande do Norte
KeywordsCurriculumComputer scienceCore curriculumBody of knowledgeCore KnowledgeKnowledge baseTask (project management)Software engineeringSoftwareKnowledge engineeringEngineering managementComputer engineeringKnowledge managementSystems engineeringArtificial intelligenceEngineeringOperating systemPedagogy

Abstract

fetched live from OpenAlex

The paper presents a curriculum for a 4-year undergraduate program in Embedded System Engineering (ESE). The curriculum was developed using a two-step approach. First, a body of education knowledge for Embedded System Engineering was defined. The body consists of sixteen knowledge areas. Each area is composed of several knowledge units, some designated as core and others as electives. The minimum lecture time for the core of each knowledge area is identified. The Body of Knowledge for Computer Engineering, developed by the IEEE-CS/ACM task force for Computing Curricula, was used as a reference. The education knowledge for ESE then served as the base for the development of the program curriculum. The curriculum has a strong mathematics and basic science base, an in-depth exposure to engineering science and design of systems implemented with digital hardware and software, and coverage of two prominent application areas of embedded systems. The curriculum core takes approximately 3 years of the program; the remaining part is elective.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.224
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it