Secure communication using a chaos based signal encryption scheme
Bibliographic record
Abstract
The large-scale proliferation of wireless communications both inside and outside the home-office environment has led to an increased demand for effective and cheap encryption schemes. Now a new chaos based signal encryption scheme is proposed to transmit digital information signals by using the conventional synchronization of chaos and digital encryption approaches. In this scheme, either a chaotic or hyperchaotic system is used to generate a digital key after thresholding a chaotic signal. This signal along with the information digital signal is used to generate the encrypted signal. Then the encrypted signal is masked by one of the chaotic signals of the transmitter and is transmitted through the channel to the receiver as well as used to drive the transmitter chaotic system using the concept of self-modulation. At the receiver end, a suitable feedback loop is constructed for unmasking and then the decryption rule is used to recover the information signal. By suitable combinations of the chaotic signals, the effect of additional nonlinear-keys has also been considered. The effect of typical perturbing factors, like channel noise and parameter mismatch, are included and their corresponding performance analysis is discussed. By considering an appropriate circuit configuration, simulation results are presented.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".