MétaCan
Menu
Back to cohort
Record W2108145577 · doi:10.1109/pacrim.1993.407269

Performance evaluation on the design of a direct sequence spread spectrum system

2002· article· en· W2108145577 on OpenAlexaff
G. Sanschagrin, M. Lecours

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsLoop (graph theory)Spread spectrumPhase-shift keyingTracking errorSequence (biology)Synchronization (alternating current)Degradation (telecommunications)Direct-sequence spread spectrumComputer scienceKeyingTracking (education)Control theory (sociology)AlgorithmMathematicsTopology (electrical circuits)TelecommunicationsBit error rateArtificial intelligenceChannel (broadcasting)

Abstract

fetched live from OpenAlex

The authors evaluate the performance of a direct sequence spread spectrum system based on a differential-encoding binary phase shift keying (DE-BPSK) scheme. A non-coherent sequence tracking loop and a squaring loop are used at the receiver for sequence synchronization and carrier recovery, respectively. The performance evaluation considers the degradation related to the tracking loop and the squaring loop. It appears that degradation at low SNR (10 dB), the tracking loop becomes the main contributor to the performance degradation. In fact, the use of lead-lag sequences in the tracking loop imposes a limit on the synchronization error variance reduction for high SNRs. However, this phenomenon does not occur for the squaring loop.>

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)0.004
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.172
GPT teacher head0.307
Teacher spread0.135 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicWireless Communication Networks ResearchFrench-language works237,207