Implant Localization in Human Body Using Adaptive Least-Squares Based Algorithm
Bibliographic record
Abstract
In this study, we focus on the problem of localizing an implant or a capsule device in the human body by a mobile sensor unit using distance measurements. As a particular distance measurement technique, time of flight (TOF) based approach involving ultra wide-band signals is used, noting the important effects of the medium characteristics for different organs and tissue. We propose a least-squares based adaptive algorithm with forgetting factor to estimate the 3-D location of an implant in the human body. After discussing convergence properties of the proposed localization algorithm, we perform simulations to analyze the transient characteristics of the proposed algorithm. Different white Gaussian noises are added to emulate the TOF measurement noises and environmental disturbances, and it is observed that the proposed algorithm is robust to such noises/disturbances. The algorithm is successful in keeping the estimation error at a very low admissible level.
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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 | 0.001 |
| 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".