A method for modelling and optimizing an electrical impedance tomography system
Bibliographic record
Abstract
Electrical impedance tomography (EIT) image reconstruction is an ill-posed problem requiring maximum measurement precision. Recent EIT systems claim 60 to 80 dB precision. Achieving higher values is hard in practice since measurements must be performed at relatively high frequency, on a living subject, while using components whose tolerance is usually higher than 0.1%. To circumvent this difficulty, a method for modelling the electronic circuits of an EIT system was developed in order to optimize the circuits and incorporate the model in the reconstruction algorithms. The proposed approach is based on a matrix method for solving electrical circuits and has been applied to the scan-head which contains the front-end electronic circuits of our system. The method is used to simulate the system characteristic curves which are then optimized with the Levenberg-Marquardt method to find optimal component values. A scan-head was built with the new component values and its simulated performance curves were compared with network analyser measurements. As a result of the optimization, the impedance at the operating frequency was increased to minimize the effects of variations in skin/electrode contact impedance. The transconductance and gain frequency responses were also reshaped to reduce noise sensitivity and unintended signal modulation. Integrating the model in the reconstruction algorithms should further improve overall performance of an EIT system.
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.
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".