Bibliographic record
Abstract
In this paper, a new Markov-jump-system (MJS)-based secure chaotic communication technique is proposed. An MJS evolves by switching from one state evolution model to another according to a finite state Markov chain. The transmitter in the proposed communication system is an MJS consisting of multiple transmission maps, that is, the transmitter switches from one chaotic map to another during the transmission of data. This switching feature makes it difficult to identify and follow the transmission without knowing the transmitter parameters, i.e., to eavesdrop, thereby increasing the security offered by the inherently secure chaotic communication system. If the chaotic maps used at the transmitter, and the corresponding Markov transition probability matrix of the MJS are known to the (authorized) receiver, then a multiple model estimator can be used to track the MJS transmitter. In this paper, the use of the interacting multiple model (IMM) estimator is proposed as part of the receiver to follow the switching transmitter. The effectiveness of the IMM-estimator-based receiver to follow the switching transmitter is evaluated by means of simulations. A new modulation technique that uses the MJS transmitter is also introduced. Further, it is shown that the same receiver framework, when used as a receiver for chaotic parameter modulation, provides significant performance improvement in terms of bit-error rate compared to a receiver that uses extended Kalman filter. In addition, the seemingly more complex IMM-estimator-based receiver is shown to significantly reduce the computational complexity per transmitted bit, thus resulting in increased data rate.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".