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Record W2055927328 · doi:10.1117/12.2050571

High-performance hyperspectral imaging using virtual slit optics

2014· article· en· W2055927328 on OpenAlexaff
Bradford B. Behr, Yusuf Bismilla, Andrew T. Cenko, Brandon DesRoches, Jeffrey T. Meade, Elizabeth A. Munro, Jared Slaa, Arsen R. Hajian

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsTornado Spectral Systems (Canada)
Fundersnot available
KeywordsHyperspectral imagingImaging spectrometerCoded apertureSpectral imagingThroughputOpticsSpectrometerSpectral resolutionFull spectral imagingComputer scienceImaging technologySynthetic aperture radarRemote sensingPhysicsArtificial intelligenceTelecommunicationsGeologyDetector

Abstract

fetched live from OpenAlex

Tornado Spectral Systems (TSS) has developed High Throughput Virtual Slit (HTVS) technology that improves the performance of spectrometers by factors of several while maintaining system size. In the simplest configuration, the HTVS allows optical designers to remove the lossy slit from a spectrometer, greatly increasing throughput without a loss of resolution. This is especially useful in many standoff applications, where every photon matters. TSS has tested multiple configurations of HTVS spectral sensing and spectral imaging technology, including standoff sensing, point scan imaging, long-slit pushbroom imaging and similar configurations. The HTVS throughput-resolution advantage allows us to increase scanning speed, decrease system size, decrease aperture, decrease source intensity requirements or some combination of all four. HTVS technology expands the realm of viable spectral imaging applications. We discuss the applicability of this technology to spectral imaging and standoff sensing and present experimental results from several prototype and production spectrometers.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0010.000
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 head0.009
GPT teacher head0.221
Teacher spread0.213 · 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.

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

Citations6
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdvanced Optical Sensing TechnologiesFrench-language works237,207