Automatized system of optical measurements of liquid crystal elements with improved output signal characteristics
Bibliographic record
Abstract
The article discusses the developed automated system for the research of a liquid crystal cell, which consists of hardware and software parts. Unlike previously developed devices for optical measurements of liquid crystal elements, the automated system under consideration provides signal generation accuracy within 0.5% and SFDR noise immunity of at least 80 dB. The hardware part of the system includes the development of a circuit for generating a voltage signal of a certain amplitude and frequency, the formation of signals for controlling the movement of a stepper motor and controlling the intensity of the luminous flux of four laser LEDs, the development and tracing of a printed circuit board. The software part consists in the development of an applied executive algorithm that builds a sinusoidal signal at the output of a digital-to-analog converter of a microcontroller. This example uses the STM32F746IGT6 microcontroller based on the ARM Cortex-M7 core, which has a superscalar architecture with dynamic prediction, a memory protection module, a floating-point computing unit, as well as a direct memory access controller DMA (direct memory access), which is used for accelerated data exchange between memory and peripherals. The DMA is used to quickly control the digital-to-analog converter.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".