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Record W2048639857 · doi:10.1109/tce.2005.1405697

Robust data transmission using the transmitter identification sequences in ATSC DVT signals

2005· article· en· W2048639857 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

VenueIEEE Transactions on Consumer Electronics · 2005
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsUniversité LavalCommunications Research Centre Canada
Fundersnot available
KeywordsTransmitterComputer scienceRobustness (evolution)Transmission (telecommunications)Data transmissionIdentification (biology)Electronic engineeringSynchronization (alternating current)Digital radioTelecommunicationsEngineeringComputer networkChannel (broadcasting)

Abstract

fetched live from OpenAlex

Transmitter identification (TxID, or transmitter fingerprinting) technique is used to detect, diagnose and classify the operating status of radio transmitters. Due to an ever-increasing number of transmitters, the need for transmitter identification is becoming an urgent issue, since it enables the broadcast authorities and operators to identify the source of interference. As a result, transmitter identification has been recognized as an important feature in the ATSC synchronization standard for distributed transmission. A new robust data transmission technique using the transmitter identification (TxID) sequences in the digital TV (DTV) signals is proposed in this paper. The major advantage of this low data transmission system is its robustness and extremely large coverage. The principle of the proposed data transmission system is presented. The modulation technique and throughput of the data transmission system is also evaluated.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.935
Threshold uncertainty score0.904

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 science0.0020.000
Research integrity0.0000.001
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 head0.079
GPT teacher head0.312
Teacher spread0.233 · 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