LED location beacon system based on processing of digital images
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
This paper is in the field of vehicle positioning technology for the intelligent transportation systems. The ideas of an innovative light-emitting diode (LED)-based location beacon system are developed and verified. The system developed is a combination of several latest technologies which include a CMOS vision sensor, high brightness LED, and digital image processing techniques. It belongs to a new kind of simplex communication link. A digital camera is used to capture images contained in the LED beacon signal. The captured digital images are processed by the algorithms developed and a location code is extracted. The location code can be used for calibration of a vehicle positioning system which may consist of a GPS, inertial navigation system (INS) and other sensors. The issues examined include the structure of the transmitter and the receiver, the signaling method, the transmission protocol of the LED panel, the relationship between the camera capturing rate and the LED pattern update rate, the digital camera exposure technology, and the efficiency of the image processing algorithms. Experiments using a prototype transmitter and a receiver were performed. The experimental results provide a good demonstration of the viability of the ideas and methodologies developed.
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 | 0.001 |
| 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 it