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Record W2116043386 · doi:10.1109/ijcnn.2008.4634186

Efficient hardware implementation of an image compressor for wireless capsule endoscopy applications

2008· article· en· W2116043386 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsJPEGComputer scienceDiscrete cosine transformQuantization (signal processing)Capsule endoscopyGas compressorComputer hardwareWirelessCMOSTransposeImage compressionData compressionImage processingArtificial intelligenceComputer visionElectronic engineeringImage (mathematics)EngineeringTelecommunications

Abstract

fetched live from OpenAlex

The paper presents an area- and power-efficient implementation of an image compressor for wireless capsule endoscopy application. The architecture uses a direct mapping to compute the two-dimensional Discrete Cosine Transform which eliminates the need of transpose operation and results in reduced area and low processing time. The algorithm has been modified to comply with the JPEG standard and the corresponding quantization tables have been developed and the architecture is implemented using the CMOS 0.18um technology. The processor costs less than 3.5k cells, runs at a maximum frequency of 150 MHz, and consumes 10 mW of power. The test results of several endoscopic colour images show that higher compression ratio (over 85%) can be achieved with high quality image reconstruction (over 30 dB).

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.297
Threshold uncertainty score0.421

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 science0.0010.000
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 head0.022
GPT teacher head0.340
Teacher spread0.318 · 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

Quick stats

Citations68
Published2008
Admission routes1
Has abstractyes

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