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Record W4399828394 · doi:10.32920/26052721.v1

FPCB Micromirror Scanning ToF LiDAR for Touchless Interface

2024· preprint· en· W4399828394 on OpenAlexaff
Mehraz Haque

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsLidarInterface (matter)Remote sensingComputer scienceComputer visionGeography

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.381
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.323
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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