Real Time Sensor Data Validation Using FPGA Accelerated Artificial Neural Networks
Bibliographic record
Abstract
This study suggests an Artificial Neural Network (ANN)-based model implemented on a Field Programmable Gate Array for real-time sensor data validation.The proposed ANN model achieved 96.8% accuracy with a fast training time compared to other Machine Learnings (MLs) of about 0:00:01 sec.The Field-Programmable Gate Arrays (FPGA) design efficiently processed sensor inputs through parallel computation, ensuring high speed, low power, and stable performance.Regression analysis and confusion matrix evaluations were measured to prove the performances which confirmed the strong predictive accuracy with a regression value near 0.93297.In addition, minimum errors were achieved among all other MLs with only 3.2%.The integration of ANN + FPGA enabled reliable real-time validation for embedded sensor networks that are utilized for time-sensitive applications.Future work would be focused on optimizing ANN parameters and exploring hybrid FPGA architectures for improved performance.
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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 | 0.001 |
| 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) | 0.001 | 0.000 |
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.
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".