MétaCan
Menu
Back to cohort
Record W2103556919 · doi:10.1109/vtcf.2006.586

Performance Analysis and Implementation of a New Position Location System Using DTV TxID Watermark

2006· article· en· W2103556919 on OpenAlexaff
Xianbin Wang, Yiyan Wu, Gilles Gagnon, Jean‐Yves Chouinard

Bibliographic record

VenueIEEE Vehicular Technology Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsUniversité LavalCommunications Research Centre Canada
Fundersnot available
KeywordsComputer scienceSynchronization (alternating current)TransmitterGlobal Positioning SystemWatermarkDigital televisionPosition (finance)Positioning systemDigital watermarkingReal-time computingDigital Video BroadcastingElectronic engineeringComputer hardwareTelecommunicationsComputer visionEngineering

Abstract

fetched live from OpenAlex

Performance of a new position location system using the transmitter identification (TxID) RF watermark in the digital TV (DTV) signals is analyzed in this paper. Compared to the Global Positioning System (GPS), DTV signals are received from transmitters at relatively short distances, while the broadcast transmitters operate at levels up to a few megawatts of effective radiated power (ERP), which makes the new position location system very robust even inside buildings. Practical receiver implementation issues including non-ideal correlation function and frequency synchronization are analyzed and discussed. New algorithms of removing the bandlimitation effect and frequency synchronization are proposed. Performances of the proposed techniques are evaluated through analysis and Monte Carlo simulations. Possible ways to improve the accuracy of the new position location system are discussed.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.007
GPT teacher head0.220
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

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 designSimulation or modeling
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

Citations1
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Vehicular Technology ConferenceSame topicIndoor and Outdoor Localization TechnologiesFrench-language works237,207