Terahertz imaging of large objects with high resolution
Bibliographic record
Abstract
For over 28 years, INO has been developing microbolometer arrays for the infrared and Terahertz (THz) domains. INO’s microbolometer array is the key component of INO’s broadband THz cameras. Using this detector, INO has developed active Terahertz imaging systems ranging from 250 GHz to 750 GHz. See-through THz imaging is particularly well suited for security screening of persons and non-destructive inspection of objects. Materials such as cardboard, plastic, leather and denim are transparent to THz radiation and can provide insights on objects hidden from the naked eye or from infrared cameras. In addition, Terahertz provides high resolution images and is non ionizing. In particular, the frequency range of 150 – 550 GHz is of interest for its properties of see-through imaging that enable a wide variety of potential applications. In this paper, we present images obtained around 400 GHz and 200 GHz (corresponding to wavelengths of 0.76 mm and 1.52 mm). We have chosen these two wavelengths to allow for a wide range of objects and obscuring materials to be tested. The 400 GHz wavelength allows better image resolution, while the 200 GHz provides better penetration through the materials. The THz imaging system can obtain images of objects with dimensions up to 1 meter x 0.75 meter with subcentimeter resolution. To achieve this, we use diffraction-limited imaging optics with high numerical aperture and a microbolometer array detector. For each object, multiple images are acquired that are then stitched together. Each instantaneous image can be seen in real-time during the acquisition and has the same resolution as the global reconstructed image. In the context of an application, the operator does not need to wait until the scan has been completed to identify a hidden object if the size of its features is compatible with the instantaneous field-of-view. When a more global image is required, the reconstructed image shows the features of the whole object under investigation without resolution loss. Images are acquired in two different configurations: transmission and reflection. Each imaging configuration provides different information about the features inside the object as well as its composition. In summary, this paper demonstrates the potential for our THz imaging systems by providing see-through high-resolution THz images of large objects. An analysis of the impact of wavelength and imaging configuration on the image results is also provided.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".