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Record W2103276862 · doi:10.1109/ccece.2002.1012961

In-building data-over-voice Internet access

2003· article· en· W2103276862 on OpenAlexaff
Daniel C. Lynch, D.E. Dodds

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsComputer scienceLine codeData transmissionThe InternetInternet accessIntegrated Services Digital NetworkComputer networkData as a serviceTelecommunicationsCoding (social sciences)BasebandTransmission (telecommunications)Service providerCDMA2000Service (business)Bandwidth (computing)Code division multiple access

Abstract

fetched live from OpenAlex

High speed Internet access is becoming an essential service in the workplace which often includes hotels. This paper investigates short-range Internet access methods suitable for hotels, hospitals, and historic office buildings where pervasive telephone service exists and there is a need for "always on" high speed Internet access. It is desirable to avoid the costly installation of new wires to provide this service. While digital subscriber line technologies have matured over the last decade, and these are more than capable of providing the required service, their cost is excessive, and when transmission distances are very short there is an opportunity to apply lower-cost data-over-voice solutions. This paper evaluates the suitability of various baseband line coding techniques for data transmission in a simultaneous voice and data environment. Through computer simulation, a number of potential line coding methods were characterized by their power spectral density and eye diagram. These characterizations were used to evaluate data transmission capacity, error performance and potential interference with voice communication. The paper shows that bipolar-return to zero (BPRZ) and bipolar-non return to zero (BPNRZ) ternary coding provide the greatest potential improvement in data transmission rate while sacrificing the least in transmission performance. Quaternary and pentary methods are shown to be poor, and 2B1Q coding (widely used in ISDN and HDSL) is not applicable for data-over-voice since low frequency data transmission is required.

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 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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.461

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 head0.055
GPT teacher head0.333
Teacher spread0.278 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2003
Admission routes1
Has abstractyes

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