Reconstructing the Plenoptic function from wireless multimedia sensor networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The development of low-cost image/video sensors has enabled wireless multimedia sensor networks (WMSN). Equipped with the ability to collect images/video streams from surrounding environments, many interesting applications will emerge through the use of WMSN. One of the key targets of a WMSN application is reconstructing the Plenoptic function by using collected images. However, due to the characteristics of wireless channels, this task is challenging. In this paper, a reliable transport protocol for WMSN is presented. This protocol utilizes JPEG stream semantics to distinguish the importance of different parts within the stream, and schedules the transmission based on the importance of the information. In order to investigate the performance of the proposed protocol, a novel image mosaicking algorithm is used as a sample WMSN application. The experimental results show that the proposed protocol and application can offer snapshots of the interested environment with larger field-of-view (FOV) and with little delay. The quality of the view can be improved gradually with the arrival of the data representing the higher frequency part of the images.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.001 | 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 it