Web-Enabled Remote Control Laboratory Using an Embedded Ethernet Microcontroller
Bibliographic record
Abstract
In this chapter, we report on the use of the TINI (Tiny InterNet Interface) microcontroller platform, DSTINIM400, as a cost effective solution to deploy feedback control laboratory experiments online. The DSTINIM400 has a built-in 10/100 Base-T Ethernet capability and provides 24 digital inputs/outputs. A TINI runtime environment, embedded in the DSTINIM400, allows developers to interact with the microcontroller like a network terminal where Java program code is downloaded and executed via the Ethernet communication protocol. The use of Java programming environment on the TINI microcontroller yields a simple interface to many Ethernet protocols allowing programmers to intuitively define a data communication link between the TINI microcontroller and a remote graphical user interface (GUI) control panel. We utilize the DSTINIM400 to interface with a variety of laboratory experiments, execute user-selectable control algorithms, and establish Internet data communication with remote GUI control panels. We provide remote GUI control panels in the form of Java applet webpages, where sensor data is presented to remote users as a plot GUI component and control system structure and parameter values are presented as binary switches, sliders, and text boxes. Finally, safety protocols are evaluated and implemented to safeguard online laboratory experiments.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.014 |
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".