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Record W2109231136 · doi:10.1109/icc.2008.18

Adaptive Rate Control Low Bit-Rate Video Transmission over Wireless Zigbee Networks

2008· article· en· W2109231136 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
TopicWireless Networks and Protocols
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceConstant bitrateVariable bitrateReal-time computingNeuRFonComputer networkWireless networkWirelessBit rateWi-Fi arrayTelecommunications

Abstract

fetched live from OpenAlex

The emerging IEEE 802.15.4 standard is designed for low data rate, low power consumption and low cost wireless personal area networks (WPANs). Video transmission over such networks is considered an issue since video traffic demands high data rates. In this paper, the TES (transform-expand-sample) methodology is used to model low rate MPEG4 video. The performance of a surveillance video application is evaluated over wireless Zigbee networks. A rate control algorithm (RC-VBR) adapted to MPEG4 variable bit rate (VBR) video coders is studied over Zigbee networks. The algorithm avoids unpredictable rate variations of the VBR coding and removes the coding delay in constant bit-rate (CBR) coders. A region of interest (ROI) encoding is added to the rate control algorithm in order to capture the important parts of the frame which is suitable for IEEE 802.15.4 (Zigbee) networks. Zigbee networks will enable a large number of applications for surveillance networks. The ns-2 simulator is used to test and validate the MPEG4 real video and modeled video over ad hoc Zigbee networks and to test the different algorithms.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.225
Teacher spread0.212 · 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

Citations14
Published2008
Admission routes1
Has abstractyes

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