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Record W2133820890 · doi:10.1109/ccece.2003.1226295

Nonstationary noise cancellation in infrared wireless receivers

2004· article· en· W2133820890 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
FieldEngineering
TopicAdvanced Adaptive Filtering Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsElectronic engineeringComputer scienceBandwidth (computing)WirelessMultipath propagationNoise (video)Interference (communication)Noise floorSingle antenna interference cancellationAmbient noise levelTelecommunicationsFilter (signal processing)FadingNoise reductionAcousticsNoise measurementEngineeringPhysicsDecoding methodsArtificial intelligenceComputer vision

Abstract

fetched live from OpenAlex

Infrared is attracting much attention for indoor wireless access due to its enormous bandwidth, inherent privacy and low cost. Intensity modulated, directly detected infrared schemes do not experience multipath fading. However, ambient noise due to artificial lighting has been the major concern in infrared wireless systems in indoors. Conventionally, static or low frequency noise due to conventional light sources is removed using optical high pass filters. Nonetheless, interference from fluorescent lights equipped with electronic ballasts has periodic interference components up to 1 MHz and, cannot be filtered easily. In this paper, soft DSP filters are proposed to cancel the harmonics, ambient noise, and uncorrelated signal structures. Nonstationary noise is cancelled with an adaptive denoising filter, and a comb filter cancels interference from the electronic ballasts. Adaptive soft filters have the advantage that they can be easily updated and track the variations in noise characteristics. Simulation results show promising improvement in noise cancellation even under very low and varied SNR and noise source conditions.

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: Empirical · Consensus signal: none
Teacher disagreement score0.797
Threshold uncertainty score0.334

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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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

Citations10
Published2004
Admission routes1
Has abstractyes

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