EFFICIENT MAGNETIC LOCALIZATION AND ORIENTATION TECHNIQUE FOR CAPSULE ENDOSCOPY
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To build a new wireless robotic capsule endoscope with external guidance for controllable and interactive GI tract examination, a sensing system is needed for tracking 3D location and 2D orientation of the capsule endoscope movement. An appropriate sensing method is to enclose a small permanent magnet in the capsule. The intensities of the magnetic field produced by the magnet in different spatial points can be measured by the magnetic sensors outside the patient's body. With the sensing data of magnetic sensor array, the 3D location and 2D orientation of the capsule can be calculated. Higher calculation accuracy can be obtained if more sensors and optimal algorithm are applied. In this paper, different nonlinear optimization algorithms were evaluated to solve the magnet's 5D parameters, e.g. Powell's, Downhill Simplex, DIRECT, Multilevel Coordinate Search, and Levenberg Marquardt method. We have found that Levenberg-Marquardt method provides satisfactory calculation accuracy and faster speed. Simulations were done for investigating the de-noise ability of this algorithm based on different sensor arrays. Also the real experiment shows that the results are satisfactory with high accuracy (average localization error is 5.6 mm).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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