IMPEDANCE SPECTROSCOPY FOR SQUARE WAVES ON INFINEON MICROCONTROLERS
Bibliographic record
Abstract
Impedance spectroscopy is used to differentiate materials and define their properties. Impedance spectroscopy can be used in applications for lithium-ion batteries, gas, and liquid detectors, etc. The classical method of impedance spectroscopy is based on a large number of measurements of sinusoidal signals with different frequencies. IP for sinusoidal signal generation and measurement in microcontrollers is more expensive compared to square wave signals. Using the square wave signal impedance spectroscopy method on the microcontrollers require only one measurement, then several measurements on different sinusoidal signals. It reduces the number of calculations on the chip. The fewer calculations provide the ability to use this method for low-latency and low-energy products. The method of square wave impedance spectroscopy can be used in inexpensive mass-market applications. The key challenge for square wave impedance spectroscopy is to determine the correct scheme of the experimental system. The capacitance value of the container wall should be accurately measured and calculated. The method requires the constant known value of wall capacitance. For high accuracy measurements, input resistor and oscilloscope probes should have the least possible values of resistance, and the value of container wall capacitance should be the highest possible. It can be achieved by increasing the area of electrodes used on the container walls. After determining the measured curves and calculating the transition process of the system, it is possible to classify the liquids in the experimental container, with some limitations. Liquids with low values of conductivity have approximately similar transient process curves, which makes it more complicated to classify liquids by the transition curves. One way, to improve measured data is by using different integration times. Key words : impedance spectroscopy, square wave, microcontrollers.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".