Fast Fourier Transform of non-periodic signals generated from a microplasma: migrating from a desktop computer to an IoT-connected smartphone
Bibliographic record
Abstract
There are many applications requiring an instrument to be brought to the sample for chemical analysis onsite (rather than bringing a sample to a lab for analysis, as is done traditionally). Ideally, for such applications, a portable chemical analysis instrument must be capable of acquiring data using a smartphone, have wireless capability and it must be able to become part of the Internet-of-Things (IoT). But do smartphones have the required processing power to execute computationally-intensive algorithms, such as a Fast Fourier Transform (FFT)? Among others, FFTs are used for filtering (e.g., de-noising) of periodic signals, thus improving Signal-to-Noise Ratio (SNR). Using non-periodic signals and Fourier-domain interpolation for resolution enhancement, it will be shown that smartphones do have the necessary power.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".