MétaCan
Menu
Back to cohort
Record W2097037199 · doi:10.1109/icecs.2007.4511260

Discrete-time Decimation filter Design for Multistandard RF Sub-sampling Receiver

2007· article· en· W2097037199 on OpenAlexaff
Rim Barrak, Adel Ghazel, Fadhel M. Ghannouchi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvancements in PLL and VCO Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDecimationBasebandElectronic engineeringAnti-aliasing filterReconstruction filterFilter (signal processing)Computer scienceSwitched capacitorEngineeringFilter designRoot-raised-cosine filterElectrical engineeringCapacitor

Abstract

fetched live from OpenAlex

Sampling-based downconversion architecture was proposed in (R. Barrak et al., 2007) for multistandard RF subsampling receiver. By an appropriate choice of RF and IF subsampling frequencies complete multistandard RF bands are downconverted to baseband. This paper presents a reconfigurable discrete-time switched capacitor filter at the baseband stage which serves as an anti-aliasing and a decimation filter for analogue to digital converter. The proposed decimation filter structure consists of two cascaded second order decimation filters which are adapted respectively for GSM, UMTS and IEEE-802.11g multistandard radio receiver. The overall transfer function of this structure can be changed by switching the received channel path and adjusting the clock signal frequency controlling the switched-capacitors. IIR and FIR filter switched-capacitor implementations are also investigated to evaluate designed filter complexity and performance.

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)0.000
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.035
GPT teacher head0.289
Teacher spread0.255 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

Explore more

Same topicAdvancements in PLL and VCO TechnologiesFrench-language works237,207