A compact THz imaging set-up at 750 microns
Bibliographic record
Abstract
Advances in infrared (IR) detector technologies over the last decade have led to compact low-cost thermal imaging systems that have become almost ubiquitous. They are now used in such market applications as automotive, security and construction. Terahertz (THz) imagers can take advantage of the state-of-the-art in the infrared domain to reduce their size and cost. Such an example is the IRXCAM-THz-384 Terahertz camera whose electronics core is based on the IRXCAM camera core and whose detector has been specifically designed and optimized for the THz. The 384 x 288 35- micron-sized pixel detectors of both cameras are uncooled microbolometers. A micro-electronics core is currently being developed for both platforms that will yield ultra-compact IR and THz cameras. While IR systems are passive and thus do not require an illumination source, the THz system does. Thus, the THz source must be included when talking about overall imaging system size and cost. There are a wide variety of THz sources, from quantum cascade lasers on the optical side of the radiation spectrum to different types of diodes on the electromagnetic micro-wave side. When considering a source for a given application, the output wavelength, output power, size, weight and cost are primary factors that must be taken into account. This paper presents a description of a compact real-time imaging system at 750 μm wavelength. An overview of the motivation for the wavelength choice is discussed, a description of the imaging components is given and finally image results are presented.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".