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Record W2350369421

Research on the Innovative Experimental Method Based on SOPC Experimental Platform

2009· article· en· W2350369421 on OpenAlexvenueno aff

Bibliographic record

VenueMicrocomputer applications · 2009
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceElectronic design automationEmbedded systemAutomationMicroprocessorSystem on a chipComputer architectureCircuit designSystems engineeringComputer hardwareEngineering
DOInot available

Abstract

fetched live from OpenAlex

This article describes the latest SOPC(based on programmable-chip system) technology and the traditional EDA(electronic design automation) technology.Then,it discusses the problem how to do experiments by using the traditional EDA technologies and introduces the basic design method of EDA technology.And next,this paper uses a concrete testSPWM Circuit Design and Implementationto describe how to carry out SPWM circuit design under SOPC experimental platform.Compared with SOPC,this experiment can not be done under the traditional EDA technology,and a microprocessor should be added.Finally,it compares the EDA and SOPC technologies to show the advantages of SOPC.We developed innovative experiments by using SOPC platform,to make students understand and master the electronic system design through experiments,which will help improve the overall qualities of students and raises student's innovative spirit.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.004
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.041
GPT teacher head0.358
Teacher spread0.317 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2009
Admission routes1
Has abstractyes

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