Protein-based photocell for high-speed motion detection
Bibliographic record
Abstract
A high-speed motion detection system that utilizes bioelectronic photocells is described in this paper. Each individual photocell consists of a sandwich-structural device with an ITO (indium tin oxide) electrode/bR film/ITO electrode configuration. During illumination, the molecules in the thin bacteriorhodopsin (bR) film undergo a multi-state photocycle followed by a proton transport from the cytoplasmic side to the extracellular side of the cell membrane. Both the wavelength and intensity of the impinging light source influence the charge displacement and, thereby, the current flow. Experimental studies show that the bR photodetector measured by the current mode exhibits a wide dynamic range and very fast response time. The photoelectric response is approximately linear over the light power range of muW to W. The response time is related to the bR photocycle kinetics and has been measured in mus. The responsivity of the experimental photocell is ISO mV/mW at 570nm. In addition, the device exhibits a high degree of differential photosensitivity to change in incident light intensity. These photoelectric properties make bR film a viable material for designing spatio-temporal motion detection systems. Issues related to the design and fabrication of a high-resolution and high-speed motion detection system for machine vision applications are discussed
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".