Development of a generic signal processing structure providing array gain improvements for real time systems including 1-dimensional or 2-dimensional arrays of sensors
Bibliographic record
Abstract
This investigation aims to define an advanced signal processing structure that will allow the implementation of a wide variety of conventional, adaptive and synthetic aperture signal processing schemes in 1-dimensional (1-D) and 2-dimensional (2-D) real time systems and will exploit processing concept similarities among radar, sonar and medical tomography imaging systems. The long term objective of this project is the re-definition of the current signal processing approach in 1-D and 2-D real time systems by introducing advanced signal processing schemes to account for the effects that cause performance degradation due to the impact of partially correlated noise sources. Preliminary real data results of the advanced signal processing structure implemented in a line array system demonstrate that adaptive and synthetic aperture processing schemes achieve robust performance and provide improvements in array gain for signals embedded in partially correlated noise fields. The performance improvements, however, of the adaptive and synthetic aperture processing schemes are effective only for specific applications. This restriction has been the basis of our generic approach that a synergism between the conventional and advanced processing schemes is required for effective practical use of signal processing developments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".