First-Order Difference Energy Regularization for Enhancing Reconstruction Performance in Compressive Sensing of Foot-Gait Signals
Bibliographic record
Abstract
A new method for the regularization of the objective function for the reconstruction of the foot-gait signal from compressively sensed measurements is proposed. The method is based on using the l2 norm of the first-order difference to regularize the objective function. The state-of-the-art first-order difference sparsity promoting algorithms can introduce transient artefacts in the signal. The proposed regularization helps to reduce such artefacts. Involved optimization can be solved by using a sequential optimization procedure. The resulting algorithm is useful for enhancing the quality of reconstructed signal, especially in the situations when the CS system is applied with extremely high compression ratio. Simulation results indicate that the proposed method can offer upto 2.81dB improvement in signal-to-noise ratio, 0.02 units improvement in structural similarity measure, and a marginal increase in the computational effort.
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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".