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Record W1958677227 · doi:10.1109/ccece.2004.1345230

Models and tools for simulation of video transmission on wireless networks

2004· article· en· W1958677227 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
TopicVideo Coding and Compression Technologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceMPEG-4ScalabilityScalable Video CodingWireless networkWirelessData compressionReal-time computingCoding (social sciences)Computer networkArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

Robust transmission of video is a dominant requirement of future applications over wireless networks. MPEG-4 is an object based video encoding technique which is suitable for wireless applications due to its high compression performance, scalable video coding techniques, error-resilient capability and object-based coding functionalities. In this paper, we first model traces of MPEG-4 traffic. Based on these models, we develop tools for MPEG-4 traffic generation. These tools have an adaptive rate control-function that is capable of simulating MPEG-4's scalable video coding. These tools can be used as source traffic generator in network simulators. This enables the study of MPEG-4 transmission performance over wireless networks by using simulation. We model and generate the traffic based on the transform expand sample (TES) methodology. In our experiment, we generate MPEG-4 traffic and show the performance in terms of good matching of the characteristics of the modeled traffic.

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 score0.927
Threshold uncertainty score0.207

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 science0.0000.000
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 head0.051
GPT teacher head0.279
Teacher spread0.229 · 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