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Record W2084853759 · doi:10.1142/s0218126605002374

A NEW DIGITAL SCAN CONVERSION ARCHITECTURE FOR ULTRASONIC IMAGING SYSTEMS

2005· article· en· W2084853759 on OpenAlexaff
Abdallah Kassem, Mohamad Sawan, Mounir Boukadoum

Bibliographic record

VenueJournal of Circuits Systems and Computers · 2005
Typearticle
Languageen
FieldEngineering
TopicImage Processing Techniques and Applications
Canadian institutionsUniversité du Québec à MontréalPolytechnique Montréal
Fundersnot available
KeywordsComputer scienceInterpolation (computer graphics)Raster scanField-programmable gate arrayRaster graphicsCartesian coordinate systemComputer hardwareScan lineGeographic coordinate conversionLine (geometry)Artificial intelligenceComputer graphics (images)Computer visionCoordinate systemPixelGrayscaleImage (mathematics)

Abstract

fetched live from OpenAlex

Digital scan conversion (DSC) is the process of converting received ultrasound signals, or echoes, in multi-scan lines, at varying angles (polar coordinate), to a Cartesian raster format for displaying. In this paper, we propose a new DSC technique that uses nearest-neighbor interpolation and the linear interpolation between adjacent scan lines to reduce artifacts on the far field, with smaller angular separation between the interpolated lines. A hardware implementation is described that uses only a FIFO register and a display memory. Rapid prototyping using an ARM processor with FPGA resources is achieved to validate the operation of the described system. Experimental results of the implemented design demonstrated the expected operation of the reduced complexity architecture in term of needed memory. Also, the performance of retrieved images were increased.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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 designSimulation or modeling
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

Citations8
Published2005
Admission routes1
Has abstractyes

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