Extended Kalman filter and extended sliding innovation filter in terahertz spectral acquisition
Bibliographic record
Abstract
Terahertz spectral acquisition has a fundamental limitation in implementation due to long experimental acquisition time. The long experimental acquisition time in terahertz spectral acquisition is a result of the required high integration time associated with usable dynamic range signals acquired through delay stage interferometry. This work evaluates the effectiveness of a non-linear version of the Kalman Filter, known as the extended Kalman filter (EKF), and the recently developed extended sliding innovation filter (ESIF), for increasing dynamic range in terahertz spectral acquisition. The comparison establishes that the EKF and ESIF can reduce integration time (time constant) of terahertz spectral acquisition, with EKF reducing the integration time by a factor of 23.7 for high noise signals and 1.66 for low noise signals to achieve similar dynamic ranges. The EKF developed in this work is comparable to a nominal application of wavelet denoising, conventionally used in terahertz spectral acquisitions. The implementation of this filter addresses a fundamental limitation of terahertz spectral acquisition by reducing acquisition time for usable dynamic range spectra. Incorporating this real-time post-processing technique in existing terahertz implementations to improve dynamic range will permit the application of terahertz spectral acquisition on a wide array of time sensitive systems, such as terahertz reflection imaging, and terahertz microfluidics. This is the first implementation, to our knowledge, of Kalman filtering methods on terahertz spectral acquisition.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".