Assessment of hardware vs software implementations for video microscopy
Bibliographic record
Abstract
Video microscopic platforms have been developed to capture cell dynamics, such as cell velocity, in real-time to quantify and usefully leverage the electro kinetic response of cells to electric fields. The first and critical task in a cell motion detection algorithm is the computationally demanding segmentation process, where a static cell boundary aids grouping the cell regions and thereby identifying the cell. We propose the use of dedicated hardware to increase the speed and efficiency of operation and the computational effort involved in the segmentation process. This dedicated hardware is compared to a software program executed on a general purpose processor to ascertain the relevance of the proposed hardware. The hardware design is verified on a FPGA to implement the cell segmentation architecture, modelled in Verilog and synthesized using QuartusII Altera Stratix device. The segmentation results presented shows relevant design statistics demonstrating equivalent cell identification performance, however, with a computational speed-up of ~5 times compared to the software based approach.
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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".