High-Accuracy Airborne Rangefinder via Deep Learning Based on Piezoelectric Micromachined Ultrasonic Cantilevers
Why this work is in the frame
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Bibliographic record
Abstract
This article presents a high-accuracy air-coupled acoustic rangefinder based on piezoelectric microcantilever beam array using continuous waves. Cantilevers are used to create a functional ultrasonic rangefinder with a range of 0-1 m. This is achieved through a design of custom arrays. This research investigates various classification techniques to identify airborne ranges using ultrasonic signals. The initial approach involves implementing individual models such as support vector machine (SVM), Gaussian Naive Bayes (GNB), logistic regression (LR), k-nearest neighbors (KNNs), and decision tree (DT). To potentially achieve better performance, the study introduces a deep learning (DL) architecture based on convolutional neural networks (CNNs) to categorize different ranges. The CNN model combines the strengths of multiple classification models, aiming for more accurate range detection. To ensure the model generalizes well to unseen data, a technique called k-fold cross-validation (CV), which provides the reliability assessment, is used. The proposed framework demonstrates a significant improvement in accuracy (100%), and area under the curve (AUC) (1.0) over other approaches.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it