Quantitative transmission tomography for don-destructive imaging of stored grain and biological tissue
Bibliographic record
Abstract
This thesis presents the development, testing, and verification of transmission tomography software for the applications of small-scale biological imaging and large-scale stored-grain imaging. Transmission tomography is a non-invasive technique for producing quantitative images of an object's physical properties. This is done by interrogating the object with waves and measuring the resulting response at a set of measurement positions. The properties of the received waves are analyzed, and used to calculate the properties of the object which is interrogated. First, a mathematical formulation of transmission tomography is presented and explained. The formulation is then used to build a numerical model of the physical properties which dictate wave transmission in two systems of linear algebraic equations. The algorithms that are required for building the numerical model are then explained. The rest of this thesis is devoted to a series of experiments. These experiments show the usefulness of transmission tomography in some particular applications. They also motivate the incremental development of features of the transmission tomography algorithm that was developed for this thesis. The first experiment uses acoustic data to perform two-dimensional transmission tomography. The first experiment shows that two-dimensional transmission tomography of acoustic data produces useful images of an object's wave speed. The successful two-dimensional experiments motivate the two following experiments, which use electromagnetic data to perform three-dimensional transmission tomography. In order to perform three-dimensional tomography of electromagnetic data, a modification is made to the numerical model. The successful three-dimensional transmission tomography algorithm is then applied to track the volume of grain stored in a bin. The final experiment uses acoustic data and the modified numerical model to perform three-dimensional transmission tomography to identify two distinct spherical objects which are submerged in a liquid medium.
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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 | 0.000 |
| 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".