Virtual spectral multiplexing for applications in in-situ imaging microscopy of transient phenomena
Bibliographic record
Abstract
Multispectral sensing is specifically designed to provide quantitative spectral information about various materials or scenes. Using spectral information, various properties of objects can be measured and analysed. Microscopy, the observing and imaging of objects at the micron- or nano-scale, is one application where multispectral sensing can be advantageous, as many fields of science and research that use microscopy would benefit from observing a specimen in multiple wavelengths. Multispectral microscopy is available, but often requires the operator of the device to switch filters which is a labor intensive process. Furthermore, the need for filter switching makes such systems particularly limiting in cases where the sample contains live species that are constantly moving or exhibit transient phenomena. Direct methods for capturing multispectral data of a live sample simultaneously can also be challenging for microscopy applications as it requires an elaborate optical systems design which uses beamsplitters and a number of detectors proportional to the number of bands sought after. Such devices can therefore be quite costly to build and difficult to maintain, particularly for microscopy. In this paper, we present the concept of virtual spectral demultiplexing imaging (VSDI) microscopy for low-cost in-situ multispectral microscopy of transient phenomena. In VSDI microscopy, the spectral response of a color detector in the microscope is characterized and virtual spectral demultiplexing is performed on the simultaneously-acquired broadband detector measurements based on the developed spectral characterization model to produce microscopic imagery at multiple wavelengths. The proposed VSDI microscope was used to observe colorful nanowire arrays at various wavelengths simultaneously to illustrate its efficacy.
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 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".