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Record W2102444517 · doi:10.1109/icccn.2008.ecp.151

Modeling and Adapting JPEG to the Energy Requirements of VSN

2008· article· en· W2102444517 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
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsJPEGComputer scienceEnergy consumptionQuantization (signal processing)Lossless JPEGDiscrete cosine transformEnergy (signal processing)Transform codingReal-time computingComputer visionArtificial intelligenceImage processingImage compressionImage (mathematics)EngineeringMathematics

Abstract

fetched live from OpenAlex

We address the problem of modeling and adapting JPEG to the energy requirements of visual sensor networks (VSN). For JPEG modeling purposes, we develop a simplified high-level energy consumption model for each stage of JPEG-like scheme, which can be used to roughly evaluate the energy dissipated by a given visual sensor. This model is based on the basic operations needed at each stage of JPEG, and it does not take into account the complexity of implementation. For JPEG adaptation, we propose to process only a reduced part of each block of 8times8 DCT coefficients of the target image, which minimizes the dissipated energy and maximizes the system lifetime, while preserving an adequate image quality at the sink.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.731
Threshold uncertainty score0.237

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 head0.039
GPT teacher head0.233
Teacher spread0.194 · 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

Citations27
Published2008
Admission routes1
Has abstractyes

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