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Record W2022311993 · doi:10.1109/ccece.2013.6567763

Optimizing a matched filter in the presence of ISI and adjacent channel interference

2013· article· en· W2022311993 on OpenAlexaff
Rory Gowen, Brian L. F. Daku, D.E. Dodds

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Signal Processing Techniques
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsIntersymbol interferenceAdjacent-channel interferenceInterference (communication)Matched filterFilter (signal processing)Computer scienceRoot-raised-cosine filterElectronic engineeringChannel (broadcasting)Co-channel interferenceRaised-cosine filterTelecommunicationsBandwidth (computing)Low-pass filterEngineering

Abstract

fetched live from OpenAlex

QAM based communications systems suffer performance loss due to the practical implementation of the matched filter in the receiver. The practical matched filter causes two types of interference: Intersymbol Interference and Adjacent Channel Interference. The combined effect of the interference is minimized by applying a tapered window to the matched filter coefficients. It is shown that proper windowing improves the modulation error ratio by 0.5 to 8 dB depending upon the adjacent channel power.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
Research integrity0.0010.000
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 head0.019
GPT teacher head0.238
Teacher spread0.219 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2013
Admission routes1
Has abstractyes

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