Secure communication using chaotic systems and Markovian jump systems
Bibliographic record
Abstract
In this paper, a secure communication system using chaotic systems and Markovian jump systems (MJS) is proposed. MJS evolve by switching from one model to another according to a finite state Markov chain. In the proposed system the chaotic transmitter jumps from one chaotic map (model) to another while transmitting. This feature makes it difficult for someone to track the transmitter, i.e., to eavesdrop, without knowing the exact map parameters. It is assumed that the Markov chain transition matrix is known. The Interacting Multiple Model (IMM) estimator is used at the intended receiver to track the transmitter state. It is also shown that the proposed receiver framework can be used to eavesdrop an unknown chaotic transmitter with limited success. It is shown further that the IMM based receiver structure improves the chaotic parameter modulation receiver based on the extended Kalman filter.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".