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Record W2103551229 · doi:10.1109/ccece.2002.1013019

Hardware software codesign of a safety-critical embedded computer system for an automatic endoscope

2003· article· en· W2103551229 on OpenAlexaff
Gul N. Khan, Ming Jin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEmbedded Systems Design Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceEmbedded systemSoftwareInterface (matter)Hardware architectureVirtual memoryVHDLLife-critical systemEmbedded softwareFault toleranceComputer hardwareOperating systemField-programmable gate arrayMemory management

Abstract

fetched live from OpenAlex

Hardware-software codesign is presented for a safety-critical embedded computer system. The system is applied for endoscope control and navigation. The embedded system architecture provides high performance computing for real-time implementation of machine vision algorithms and fault-tolerance for patient safety. It consists of five processor cores, local memory, I/O interface and multi-port shared memory. The hardware and software system architectures are co-designed. A virtual hardware is developed to execute the application and system software tasks. The system is designed and modeled using VHDL and Eaglei toolset. We have limited system verification to co-verification of system hardware architecture and fault-tolerance strategies. Co-verification results indicate that the system performance degrades gracefully under various fault scenarios.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.030
GPT teacher head0.294
Teacher spread0.263 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2003
Admission routes1
Has abstractyes

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