Bibliographic record
Abstract
In this thesis, modified Kronecker-based compressive sensing (CS) 1-D and 2-D recovery techniques with random and deterministic measurement matrices are investigated to improve signal quality despite resource restricted acquisition. For regular recovery of individual segments of the compressed signal, the measurement and sparsifying matrices are required. While the regular Kronecker-based CS recovery technique uses expanded Kronecker measurement and basis matrices to achieve one-time recovery of a collection of compressively acquired segmented signals, in the proposed modified Kronecker-based CS recovery, a new basis matrix, which is an expanded and dense version of the original basis matrix, is used. The reduction of mutual coherence between the expanded Kronecker measurement and the expanded basis matrix leads to improvement in the recovery of the signal. Deterministic sensing further improves the recovery and preserves the structure of the acquired signal in the compressed domain which can be exploited for compressed domain signal processing algorithms. Khedr, Mr. Zachary Baird who helped me in all possible ways. I would also like to thank NSERC & Carleton University for supporting the work and last but not the least my parents for their unwavering support. 6.4 Feature-based template matching from compressed high resolution images. (A) Original image compressed at CR = 50%. (B) Template generated from uncompressed image. (C) Outlier removed match point identification using SURF algorithm. (D) Bounding box indicating the position of the template in the compressed image. . . . . . . . . . . .
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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".