Transmitter Identification Using Embedded Pseudo Random Sequences
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
A transmitter identification system for DTV distributed transmission network using embedded pseudo random sequences is investigated. Different orthogonal pseudo random sequences and their suitability for transmitter identification are discussed. Code generators are developed to study the auto-correlation and cross-correlation properties of the Kasami sequences. To speed up the identification process, the embedded pseudo random sequence is preferred to be time-synchronized with the DTV frame structure. Therefore, the length of the identification code has to be truncated before it is fitted into each field of the ATSC DTV signal. The impact of truncation noise and in-band DTV interference on transmitter identification is also investigated. It is shown that the auto-correlation and cross-correlation properties are only slightly affected by truncation. It is also found that the dominant interference to the transmitter identification is the in-band DTV signal. The signal to truncation noise ratio and signal to DTV interference ratio in the correlation output are derived, and verified via simulation. It is further recognized that in-band DTV interference can only be mitigated by increasing the code length or by time-domain averaging technique to smoothen out the in-band interference.
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 | 0.000 |
| 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