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Record W2187284628 · doi:10.1109/iemcon.2015.7344448

JPEG2000 image transmission over OFDM-based Cognitive Radio network

2015· article· en· W2187284628 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
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCognitive radioOrthogonal frequency-division multiplexingComputer scienceComputer networkJPEG 2000Bandwidth (computing)Transmission (telecommunications)Channel (broadcasting)Interference (communication)Quality of serviceMultimediaElectronic engineeringTelecommunicationsWirelessImage (mathematics)Image compressionEngineeringImage processingArtificial intelligence

Abstract

fetched live from OpenAlex

Cognitive Radio (CR) is an efficient way of spectrum utilization in the way that secondary users (SU) with high bandwidth (BW) requirements such as multimedia users, can get access to the licensed frequency resources opportunistically and resolve their BW limitation. Among all the multimedia formats, JPEG2000 has many features that makes it suitable for cognitive radio networks. In this paper a channel allocation method based on JPEG2000 and orthogonal frequency division multiplexing (OFDM) is proposed, which improves the quality of the SU's received image by providing dynamic access to the available spectrum, without violating the interference requirement to the PU. Channels with better condition are used to transmit important data in JPEG2000 coded bit-stream. Therefore, reception of determinative bits is guaranteed.

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: Methods · Consensus signal: none
Teacher disagreement score0.897
Threshold uncertainty score0.675

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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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