Using an adaptive UPA scheme with a channel-aware OFDM technique for wireless transmission of JPEG2000 images
Bibliographic record
Abstract
In this paper, an adaptive scheme is developed for an Unequal Power Allocation (UPA) algorithm in a way to allow variable size grouping of the coding passes to enhance the optimization process at different Signal to Noise Ratio (SNR) values. The optimization algorithm exploits the hierarchical structure of the JPEG2000 images and uses a distortion model along with the channel state information (CSI) for allocating optimal values of power on each coding pass to minimize the end-to-end distortion. Orthogonal Frequency Division Multiplexing (OFDM) technique is incorporated within the transmitter to enable transmission of the JPEG2000 images over block fading frequency selective channels. In this paper, we propose a channel assignment strategy, within our OFDM transmitter, which passes the more important coding passes over the subcarriers with higher channel gain. Simulation results indicate an improvement of up to 2 dB in the decoded image quality and potentiality of energy conservation when the adaptive UPA scheme along with the channel assignment strategy are used.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".