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Record W3179732084

Multimedia Application Performance on a WiMAX Network

2009· article· en· W3179732084 on OpenAlexaff
Emir Halepovic, Majid Ghaderi, Carey Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsWiMAXVoice over IPComputer scienceComputer networkPacket lossNetwork packetThe InternetMultimediaTelecommunications linkEthernetCable modemTelecommunicationsWirelessOperating system
DOInot available

Abstract

fetched live from OpenAlex

In this paper, we use experimental measurements to study the performance of multimedia applications over a commercial IEEE 802.16 WiMAX network. Voice-over-IP (VoIP) and video streaming (RealVideo) applications are tested. The WiMAX-based network solidly supports VoIP, providing adequate quality for short to medium duration calls. The voice quality degradation compared to high-speed Ethernet is moderate, despite higher packet loss and network delays. The effects of the uplink and the downlink on call quality are comparable, despite their different characteristics. On-demand video streaming performs well using UDP. Smooth playback of high-quality video/audio clips at aggregate rates exceeding 700 Kbps is achieved about 63 % of the time, with rough playback periods observed during only 7 % of the time. Our results show that WiMAX networks can adequately support currently popular multimedia Internet applications.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.224
Teacher spread0.206 · 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 designNot applicable
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

Citations0
Published2009
Admission routes1
Has abstractyes

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