FPGA-Based Autonomous GPS-Disciplined Oscillatorsfor Wireless Sensor Network Nodes
Bibliographic record
Abstract
Numerous devices in distributed wireless sensor arrays require a high-accuracy timing reference. Although the GPS-disciplined oscillators have been developed for decades, the hardware design still has performance limitations. In this context, we present the hardware implementation for a GPS-disciplined oscillator with an automatic adaptive drift correction algorithm, which is implemented in a low-cost, high-speed field-programmable gate array (FPGA) device. The system design and the hardware implementation are presented to demonstrate the advantages of the proposed oscillator. To verify this oscillator in real-time applications, we tested the device in multiple environments and compared it to state-of-the-art designs. The experimental results showed that our proposed device has a low cost and high performance. This device can achieve less than 80 ns and 356 ns in 1PPS signal drift in the indoor environment test and the outdoor environment test, respectively, after 24 h of working without a GPS signal.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".