VLC-based Data Transfer and Energy Harvesting Mobile System
Bibliographic record
Abstract
This paper explores a low-cost portable visible light communication (VLC) system to support the increasing needs of lightweight mobile applications. VLC grows rapidly in the past decade for many applications (e.g., indoor data transmission, human sensing, and visual MIMO) due to its RF interference immunity and inherent high security. However, most existing VLC systems heavily rely on fixed infrastructures with less adaptability to emerging lightweight mobile applications. This work proposes Light Storage, a portable VLC system takes the advantage of commercial smartphone flashlights as the transmitter and a solar panel equipped with both data reception and energy harvesting modules as the receiver. Light Storage can achieve concurrent data transmission and energy harvesting from the visible light signals. It develops multi-level light intensity data modulation to increase data throughput and integrates the noise reduction functionality to allow portability under various lighting conditions. The system supports synchronization together with adaptive error correction to overcome both the linear and non-linear signal offsets caused by the low time-control ability from the commercial smartphones. Finally, the energy harvesting capability in Light Storage provides sufficient energy support for efficient short range communication. Light Storage is validated in both indoor and outdoor environments and can achieve over 98% data decoding accuracy, demonstrating the potential as an important alternative to support low-cost and portable short range communication.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".