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Record W1484494566 · doi:10.1109/iscas.2003.1205918

A high speed complex adaptive filter for an asymmetric wireless LAN using a new quantized polynomial representation

2003· article· en· W1484494566 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDigital Filter Design and Implementation
Canadian institutionsCalgary Laboratory Services
Fundersnot available
KeywordsQuantization (signal processing)Computer scienceQuadratic equationWirelessAlgorithmFinite impulse responsePolynomialRepresentation (politics)Theoretical computer scienceMathematicsTelecommunications

Abstract

fetched live from OpenAlex

This paper introduces a new number representation, the Quantized Polynomial Representation (QPR), which is used for building FIR filters where some quantization errors can be tolerated. The technique is based on the previously published Modulus Replication Residue Number System (MRRNS) but considerable savings are possible if polynomial quantization can be tolerated. The QPR can be used as a vehicle for Quadratic Residue Number System (QRNS) mapping of complex data, and the main computational architecture can be built with independent finite ring computational channels. We demonstrate this new technique on an asymmetrical Gigabit wireless LAN where the adaptive filter computes with complex arithmetic. We demonstrate area savings of up to 28% and power savings of up to 50%.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.837
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
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 head0.214
GPT teacher head0.356
Teacher spread0.143 · 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

Quick stats

Citations4
Published2003
Admission routes1
Has abstractyes

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