Bibliographic record
Abstract
This thesis paper demonstrates the design, fabrication, and integration of a flexible printed circuit board (FPCB) based rotational optical scanner with laser-based ranging for twodimensional finger input recognition applications. A two-dimensional (2D), position array of fingers was captured by utilizing light detection and ranging (lidar) and time-of-flight (ToF) sensors. The optical scanner and ranging sensors are implemented in two frequently touched scenarios, elevator call buttons, and automated teller machine (ATM) displays. While there are existing, vision based touchless interfaces requiring machine learning algorithms and/or advanced camera technologies, the thesis explores alternative methods to develop low-cost, easy-to-manufacture, and highly integrative sensing solutions [8]. The FPCB scanner consists of two main components; an optical reflector based on a silicon micro-mirror, and a FPCB polyamide fixture that oscillates within a magnetic field created by permanent magnets (PM) dipole. The optical scanner developed has a 15 x 15 mm aperture and can achieve 40.3° of optical angle, while being operated at a low voltage of ± 7.5 V. The measurable rotational angle of the optical scanner was determined to be 23.4° optical (combined positive and negative direction from the axis of rotation). The thesis also presents measurement limitations of the angular position of the scanner and inconsistent scan patterns at low operational frequencies due to material properties, assembly processes, and environmental conditions.
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 | 0.001 | 0.000 |
| 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".