High-Accuracy Airborne Rangefinder via Deep Learning Based on Piezoelectric Micromachined Ultrasonic Cantilevers
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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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".