Bibliographic record
Abstract
Wireless capsule endoscopy systems have been shown as a gold step to develop future \nwireless biomedical multitask robotic capsules, which will be utilized in micro surgery, drug \ndelivery, biopsy and multitasks of the endoscopy. In such wireless capsule endoscopy systems, \none of the most challenging problems is accurate localization and tracking of the capsule inside \nthe human body. In this thesis, we focus on robotic biomedical capsule localization and \ntracking using range measurements via electromagetic wave and magnetic strength based \nsensors. First, a literature review of existing localization techniques with their merits and \nlimitations is presented. Then, a novel geometric environmental coefficient estimation technique \nis introduced for time of flight (TOF) and received signal strength (RSS) based range \nmeasurement. Utilizing the proposed environmental coefficient estimation technique, a 3D \nwireless biomedical capsule localization and tracking scheme is designed based on a discrete \nadaptive recursive least square algorithm with forgetting factor. The comparison between \nlocalization with novel coefficient estimation technique and localization with known coefficient \nis provided to demonstrate the proposed technique’s efficiency. Later, as an alternative \nto TOF and RSS based sensors, use of magnetic strength based sensors is considered. We \nanalyze and demonstrate the performance of the proposed techniques and designs in various \nscenarios simulated in Matlab/Simulink environment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".