Bibliographic record
Abstract
Radio frequency interference (RFI) is considered as anomalous disruptive parasite signal due to its harmful impact in wireless communication. That is why, RFI mitigation is indispensable to avoid this impact. Detecting and localizing the RFI are the first steps in RFI mitigation process. In this paper, we propose two approaches to detect and localize RFI using the supervised and unsupervised techniques of deep learning. First, our research investigates an object detection algorithm based on convolutionnal neural network as a supervised approach. This proposition is based on the object detection algorithm You Only Look Once v3 (YOLO-v3) trained on real-world data contaminated by multiples sources of RFI. Second, we propose the utilisation of Convolutionnal Autoencoder (CAE) as an unsupervised approach. Experimental results show that the RFI detection by YOLO-v3 is relatively fast and it has an excellent accurate detection rate of 94% and show that the average precision of the YOLO-V3 algorithm can achieve 89%. For CAE, the average precision achieves 78% and outperforms the supervised approach in certain cases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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.
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".