<title>MDSP: a modular DSP architecture for a real-time 3D laser range sensor</title>
Bibliographic record
Abstract
As electronic documents are becoming more and more popular, variety of objects, including 2D and 3D objects such as articles, books and 3D-shapes, can be easily contained in a document. Conventional systems cannot capture these objects as a 3D form. We developed a new image capturing system with 3D information for deskwork. It is an assemblage of a normal digital camera and its docking station designed for easy operation. The docking station swings the attached camera tilting step by step. Within a few steps, it covers the whole object automatically. Then each frame is combined together into one complete image with full resolution. In order to get precise 3D structure, stripe patterns are projected by a modified flash light attached on the camera. The resolution rises by means of the swing to be slipped the patterns on the object. Using the obtained 3D data we can reconstruct a correct image from a splay surface image such as a book. Experimental Results of image mosaicing and 3D reconstruction shows that the system is practical.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".