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Record W2889636654 · doi:10.1109/icassp.2018.8462276

Digital-Analog Superposition Coding for Ofdm Channels with Application To Video Transmission

2018· article· en· W2889636654 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 institutionsUniversity of Manitoba
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingComputer scienceQuadrature amplitude modulationAnalog transmissionEncoderQuantization (signal processing)Electronic engineeringQAMVideo qualityMultiplexingCoding (social sciences)Transmission (telecommunications)Bit error rateAnalog signalAlgorithmTelecommunicationsDecoding methodsMathematicsChannel (broadcasting)Engineering

Abstract

fetched live from OpenAlex

A new approach to hybrid digital-analog (HDA) video transmission over orthogonal frequency division multiplexing (OFDM) channels is presented. The goal is to achieve the best video quality by optimal power allocation, when the OFDM sub-channels have unequal and time-varying signal-to-noise ratios (SNR). In this method, the quantization error of a video encoder is superimposed on digital quadrature amplitude modulation (QAM) symbols. A solution to the power allocation problem is presented. Experimental comparisons with layered video coding and adaptive modulation shows that the proposed HDA approach is able to achieve a better video quality most of the time, particularly whenever there is a high motion content.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.913
Threshold uncertainty score0.324

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 head0.016
GPT teacher head0.252
Teacher spread0.237 · 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