Bibliographic record
Abstract
In a setup for a system long term stability and reliability test, sensors are used to measure physical quantities, affecting the behavior of the system, by sampling the sensor readings, convert it to digital numerical value and saving it for further detail analysis. The sensors are wired to a central location to collect and log data, due to extensive wiring requirements the setup is very difficult and sometimes even impossible to implement. This project presents an implementation of high speed wireless data acquisition system which samples sensors output at high speed (5 KHz), converts it to digital numerical form and sends wirelessly to central data gathering unit thus avoiding home run wiring from each sensor to central data gathering unit where it is logged on USB flash drive and send to PC for real time display. The implementation target was wireless link between a transmitter module, serving a maximum of 8 sensors at 5 KHz sampling rate and 16bit ADC resolution for each sensor, to data gathering unit. The implementation does fall short on specification on number of channels and sampling rate due to limitation of over the air data rate of the radio module, what we were able to achieve is 4 channels of 16 bit ADC resolution at 2 KHz sampling rate using radio module with 300 Kbps over the air data rate. Using different sensors and with different configurable settings the tests shows that the stored data at the data gathering unit and the data stored using wired data acquisition system has no difference. For future improvement radio module with over the air data rate (1.55 Mbps) which allows multiple transmitters connected wirelessly to a single data gathering unit providing more flexibility in sensor deployment. Even though the implementation falls short on some of the features but with using improved radio module and/or using some compression techniques on ADC data, before sending data wirelessly, these short comings could be overcome easily.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.028 |
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".