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Record W4298270291 · doi:10.15332/iteckne.v9i1.2752

Metodología de diseño sobre FPGA en un curso de sistemas digitales

2014· article· es· W4298270291 on OpenAlexaff
Jose Luis Uribe Aponte, Alejandra María González Correal, Alejandro Forero Guzmán, Juan Carlos Giraldo Carvajal, Francisco Fernando Viveros Moreno

Bibliographic record

VenueITECKNE Innovación e Investigación en Ingeniería · 2014
Typearticle
Languagees
FieldSocial Sciences
TopicKnowledge Societies in the 21st Century
Canadian institutionsHewlett-Packard (Canada)
Fundersnot available
KeywordsHumanitiesPhysicsComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

Se presenta en este trabajo una metodología para el diseño de sistemas digitales sobre FPGA (Field Programmable Gate Array). Esta metodología inicia con el análisis de requerimientos del cliente y abarca hasta la implementación final en un dispositivo programable. La propuesta metodológica ha sido aplicada y probada en los cursos del área de Técnicas Digitales en la Carrera de Ingeniería Electrónica de la Pontificia Universidad Javeriana – Bogotá, de acuerdo a un modelo de aprendizaje activo basado en proyectos (PBL, Project Based Learning) para lograr en los estudiantes un mayor conocimiento técnico significativo, aumentar su nivel de motivación hacia la ingeniería e incrementar su capacidad de comunicación, trabajo en equipo y capacidad de diseño. Todo esto es fruto del trabajo de investigación en educación en ingeniería que el grupo de profesores del área de Técnicas Digitales del Departamento de Electrónica de la Universidad, ha desarrollado en los últimos cuatro años.

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.

How this classification was reachedexpand

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)0.010
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.015
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.004
Science and technology studies0.0030.005
Scholarly communication0.0010.001
Open science0.0040.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.013
GPT teacher head0.295
Teacher spread0.281 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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