Adaptive denoising at infrared wireless receivers
Bibliographic record
Abstract
This paper proposes an innovative approach for noise cancellation at infrared (IR) wireless receivers. Ambient noise due to artificial lighting and the sun has been a major concern in infrared systems. The background induced shot noise typically has a power from 20 to 40 dB more than the signal induced shot noise and varies with time. Due to these changing conditions, infrared wireless receivers experience high level of non-stationary noise. The objective of the work mentioned in this paper is to develop digital signal processing algorithms at the infrared wireless system to combat high power non-stationary noise. The noisy signal is decomposed using a joint time and frequency representation such as wavelets and wavelet packets, into transform domain coefficients and the lower order coefficients are removed by applying a threshold. Denoised version is obtained by reconstructing the signal with the remaining coefficients. In this paper, we evaluate different wavelet methods for denoising at an infrared wireless receiver. Simulation results indicate that if the noise is uncorrelated with the signal and the channel model is unavailable the wavelet denoising method with different wavelet analyzing functions improves the signal to noise ratio (SNR) from 4 dB to 7.8 dB.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".