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Record W1536159961 · doi:10.1109/ecctd.2005.1523115

An analog-digital adaptive image-reject technique for quadrature receivers

2006· article· en· W1536159961 on OpenAlexaff
M. Hajirostam, K. Martin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsImage responseMultiplier (economics)Computer scienceElectronic engineeringAdderAdaptive quadratureQuadrature amplitude modulationAmplitudeQuadrature (astronomy)AlgorithmControl theory (sociology)TelecommunicationsBit error rateEngineeringIntermediate frequencyArtificial intelligenceRadio frequencyPhysicsOpticsCMOS

Abstract

fetched live from OpenAlex

In this paper, an analog-digital adaptive image-reject (IR) technique for quadrature receivers is presented. In these receivers, I/Q mismatches degrade the image rejection performance. To compensate the mismatches, a complex error signal is generated digitally of which the real and imaginary parts are proportional to the I/Q amplitude and phase mismatches. This error signal is applied to Digitally-Controlled Multiplier-Adder (DCMA) blocks to correct the phase and amplitude mismatches. Simulations show that the Image-rejection ratio is improved to more than 57 dB for a low-W cable modem receiver. This performance is independent of the synchronization and equalization of the receiver.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.717

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 science0.0000.000
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 head0.007
GPT teacher head0.204
Teacher spread0.197 · 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 designBench or experimental
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

Citations1
Published2006
Admission routes1
Has abstractyes

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