Next-generation UOWC enabling high-speed and secure RGB image transmission using IRSM-OCDMA with PSO-based image enhancement
Bibliographic record
Abstract
Underwater Optical Wireless Communication systems face severe signal attenuation, scattering, and turbulence, which significantly degrade image transmission quality and limit the communication range. To address these challenges, this paper proposes a secure and high-capacity RGB image transmission framework based on Optical Code Division Multiple Access (OCDMA) using Identity Row Shift Matrix (IRSM) codes. The IRSM-OCDMA scheme enhances data confidentiality by assigning unique orthogonal codes to each user while supporting simultaneous multiuser transmission with an aggregate rate of up to 30 Gbps. System performance is analyzed across five water types: Pure Seawater (PS), Clear Ocean, Coastal Ocean, Harbour I, and Harbour II (HR II), covering a broad range of attenuation coefficients. Image quality is quantitatively evaluated using standard metrics including Root Mean Square Error, Signal-to-Noise Ratio, Peak Signal-to-Noise Ratio, Structural Similarity Index Measure, and Correlation Coefficient. Two distinct post-processing methods are applied: median filtering for impulsive noise reduction and a Particle Swarm Optimization-based correction algorithm that adaptively restores image features under underwater channel conditions. Simulation results show a maximum transmission distance of 27 m in PS and 4 m in turbid HR II water, demonstrating the effectiveness of the proposed framework. The combination of IRSM coding with adaptive post-processing offers a robust solution for secure, high-quality image transmission in Internet of Underwater Things applications.
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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 | 0.000 |
| 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 | 0.001 |
| 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".