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Record W2081507356 · doi:10.1155/asp.2005.1071

Perception SoC Based on an Ultrasonic Array of Sensors: Efficient DSP Core Implementation and Subsequent Experimental Results

2005· article· en· W2081507356 on OpenAlexafffund
Abdallah Kassem, Mohamad Sawan, Mounir Boukadoum, Ali Massoud Haidar

Bibliographic record

VenueEURASIP Journal on Advances in Signal Processing · 2005
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsUniversité du Québec à MontréalPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsDigital signal processingComputer scienceField-programmable gate arrayBeamformingComputer hardwareSoftware portabilityUltrasonic sensorEmbedded systemSignal processingAcousticsTelecommunications

Abstract

fetched live from OpenAlex

We are concerned with the design, implementation, and validation of a perception SoC based on an ultrasonic array of sensors. The proposed SoC is dedicated to ultrasonic echography applications. A rapid prototyping platform is used to implement and validate the new architecture of the digital signal processing (DSP) core. The proposed DSP core efficiently integrates all of the necessary ultrasonic B-mode processing modules. It includes digital beamforming, quadrature demodulation of RF signals, digital filtering, and envelope detection of the received signals. This system handles 128 scan lines and 6400 samples per scan line with a angle of view span. The design uses a minimum size lookup memory to store the initial scan information. Rapid prototyping using an ARM/FPGA combination is used to validate the operation of the described system. This system offers significant advantages of portability and a rapid time to market.

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 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: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.346
Teacher spread0.326 · 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.

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

Citations1
Published2005
Admission routes2
Has abstractyes

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