An Adaptive Linearized Method for Localizing Video Endoscopic Capsule Using Weighted Centroid Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Video Capsule Endoscope (VCE) sends images of abnormalities in the gastrointestinal (GI) tract. While the physicians receive these images, they have little idea of their exact location which is needed for proper treatment. The proposed localization system consists of a 3D antenna array (with 8 receiver sensors) and one transmitter embedded inside the electronic capsule. We propose an adaptive linearized method of localization using Weighted Centroid Localization (WCL) where the position is calculated by averaging the weighted sum of the reference positions. In our proposed system, first we identify the path loss attenuation exponents using linear least square regression of the collected data (RSSI versus distance). Then the path loss model is linearized to minimize the path loss deviation which is mainly caused due to the nonhomogeneous environment of radio propagation. Then the instantaneous path loss (PL) measured by the sensors is attenuated to the above linearized model and considered as the weight of the sensors to find the location of the capsule using WCL. Finally a calibration process is applied using linear least square regression. To assess the performance, we model the path loss and implement the algorithm in Matlab for 2,530 possible positions with a resolution of 1 mm. The results show that the algorithm achieves high localization accuracy compared with other related methods when simulated using a 3D small intestine model.
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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 it