Bibliographic record
Abstract
The purpose of this project was to build a system capable of estimating the direction of a sound source using a static array of sensors without measuring time delays. Such a system would aid in tracking a robotic vehicle over a short range and is a prototype for a radio-based tracking system. The project consisted of several parts, the first of which was the construction of an adjustable sound source, providing a constant amplitude and variable voltage. After testing many designs, a crystal earphone and a square wave tone source were used as the sound source. Next, a sound sensor consisting of a microphone and a housing to make the microphone response directional were constructed. Circuitry to convert the amplitude of the sound into a DC voltage, to be able to read by a microcontroller, was built. Several designs for directional sound sensors were tested. By rotating the sensor and sampling at different angles, the data that would be generated by a group of sensors pointing in different directions, was simulated. A static array based on the simulations, consisting of seven sensors arranged radially at 35° intervals, were used for the final design. A second-order polynomial regression was used as the basis of an algorithm to estimate the angle to the sound source. Experiments to determine the effect of the signal frequency, sampling protocols and microphone housing design on the accuracy of the angle estimates, were conducted. The best results were obtained for a frequency of 2.15 kHz. At distances of 50cm-100cm, the final array design was able to locate the direction of the sound source with an accuracy of about ±3°.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".