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Record W2166045427 · doi:10.1109/cca.2005.1507215

Protein-based photocell for high-speed motion detection

2005· article· en· W2166045427 on OpenAlexafffund
Wei Wei Wang, George K. Knopf, A.S. Bassi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotoresistorOptoelectronicsResponsivityMaterials sciencePhotoelectric effectBacteriorhodopsinPhotodetectorDynamic rangePhotoconductivityPhotoelectric sensorOpticsResponse timeElectrodeLight intensityChemistryPhysicsComputer science

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.304
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2005
Admission routes2
Has abstractyes

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