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Record W2732551346 · doi:10.14569/ijacsa.2017.080640

Modeling and FPGA Implementation of a Thermal Peak Detection Unit for Complex System Design

2017· article· en· W2732551346 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

VenueInternational Journal of Advanced Computer Science and Applications · 2017
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsComputer scienceField-programmable gate arrayVHDLOverheating (electricity)MATLABRing oscillatorThermalHardware description languageEmbedded systemComputer hardwareElectronic engineeringElectrical engineering

Abstract

fetched live from OpenAlex

This paper, presents the modelization and the implementation of a thermal peak detection unit for complex system design. The modelization step starts with modeling the formula of the heat source using Simulink/Matlab tool, is the main objective of this work. Then the input temperature, the angles, the distance as well as certain frequencies, will be obtained from this formula using the GDS (gradient Direction Sensor) method based on RO (Ring Oscillator). Before the transition to the implementation in FPGA board, the use of VHDL code is necessary to describe the thermal peak detection unit, in order to verify and validate the whole module. This work offers a solution to thermally induced stress and local overheating of complex systems design which has been a major concern for the designers during the design of integrated circuit. In this paper a DE1 FPGA board cyclone V family 5CSEMA5F31C6 is used for the implementation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.678
Threshold uncertainty score0.205

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 head0.045
GPT teacher head0.325
Teacher spread0.280 · 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