Development of a low-cost, user-customizable, high-speed camera
Bibliographic record
Abstract
High-speed imaging equipment can be an expensive investment, especially when certain applications require custom solutions. In this paper, we present a low-cost high-speed prototype camera built on a low-end Zynq-7000 System-on-Chip (SoC) platform and off-the-shelf components with the aim of removing the entry barrier into various high-speed imaging applications. The camera is standalone (does not require a host computer) and can achieve 211 frames per second (fps) at its maximum resolution of 1280x1024, and up to 2329 fps at a 256x256 resolution. With a current cost of only several hundred dollars and resource utilization of ~5%, the open-source design's modularity and customizability allows users with sufficient hardware or programming experience to modify the camera to suit their needs, potentially driving the cost lower. This can be done by utilizing the large remaining programmable logic for custom image processing algorithms, creating user interface software on the CPU, attaching extensions through the peripheral Module connections, or creating custom carrier or daughter boards. The development and design of the camera is described and a figure-of-merit is presented to provide a value assessment of some available commercial high-speed cameras against which our camera is competitive. Finally, the camera was tested to record low frequency spatial vibration and was found to be useful in investigating phenotypes associated with aging in a leading animal model, the nematode (worm) Caenorhabditis elegans.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".