Using FPGA-based platforms for embedded control applications in Mechatronics
Bibliographic record
Abstract
This paper discusses development of embedded controllers on a reconfigurable multiprocessor system using Field Programmable Gate Array (FPGA) technology. The system is reconfigurable in hardware and software in the sense that certain components may be reused in different applications; hence allowing rapid development of embedded control systems. Concurrent real-time operation can be achieved by utilizing hardware and software modules consisting of dedicated hardware cores and real-time operating systems. For demonstration purposes we discuss development of a system consisting of a network-enabled master processor that handles two slave processors each controlling a mechatronic system. The user interface is implemented using an internet browser through the master processor which allows monitoring and supervisory control of the individual systems. A multi-threaded Real-time Operating System (RTOS) runs on each of the softcore processors which allows flexibility and modularity in software design while pre-designed hardware modules on the FPGA chip can be utilized to build computing hardware for control applications in Mechatronics. Experimental results are presented to illustrate how control applications can be developed and deployed using modular components and the hardware/software environment.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".