Using Compressive Sampling to Fill Interbatch Data Gap From Low-Cost IoT Vibration Sensor
Bibliographic record
Abstract
A low-cost wireless vibration sensor can be built using a 3-axis accelerometer, such as ADXL345, attached to a low-cost Wi-Fi microchip, such as ESP8266. In an Internet of Things (IoT) setting, a large number of such inexpensive sensor nodes can be setup with the widely used direct-read-and-send method which samples and sends individually acquired vibration data points from the sensor through the Internet to a server. In this work, we show that such a method is not effective. As the microcontroller alternates between sampling and sending the data, the micro delays of transmission will affect the sensor sampling rate and cause the data points to space unevenly, making the acquired data inaccurate. We propose that vibration should be sampled and transmitted in batches, as such data are acquired continuously without interruption and data points are more evenly spaced. However, the proposed batch-read-and-send will have interbatch gaps that need to be filled. Thus, the key contribution of this work is the novel use of compressive sampling (CS) technique to bridge those gaps. Experimental results show that the direct-read-and-send method loses more information and can only achieve a maximum sampling rate of 350 Hz with a standard uncertainty of 12.4, whereas the proposed solution can measure the vibration wirelessly and continuously up to 633 Hz. Gaps with up to 160 missing points can be filled using CS and achieve better accuracy, with a mean absolute error (MAE) of up to 0.048 and a standard uncertainty of 0.001, making the low-cost wireless vibration sensor a cost-effective solution in an IoT setting.
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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 | 0.001 | 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 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".