Quantitative comparison of indoor RFID channel models using bootstrap techniques
Bibliographic record
Abstract
Channel characterization is the primary phase of wireless radio frequency identification (RFID) system based tracking processes. Traditional approaches of path-loss modeling of indoor RFID channel lack of the ability to assess the consistency and reliability of the fit. In this paper, we propose two approaches to ensure the reliability of the goodness-of-fit parameters of a fit and quantify its consistency. The first approach uses a standard bootstrap validation method to measure the confidence levels of the goodness-of-fit parameters of the fit. The second approach combines a bootstrap resampling technique with a statistical significance testing method to quantify the consistency of a fit across a number of independent experiments obtained in a particular indoor environment. We present experimental analysis to show the advantages of the proposed approaches over traditional fitting method. We suggest that for proper characterization of indoor channel, it is required to consider the systematic fluctuations along with the path-loss attenuation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".