Chaotic Systems Based Real-Time Implementation of Visual Cryptography Using LabVIEW
Bibliographic record
Abstract
In recent years, chaotic systems have begun to take a substantial place in literature due to increasing importance of secure communication. Chaotic synchronization which has emerged as a necessity for secure communication can now be performed with many different methods. This study purposes the Master-Slave synchronization via active controller and real time simulation of five different chaotic systems such as Lorenz, Sprott, Rucklidge, Moore-Spiegel, Rössler. Master slave synchronization was performed because of synchronization realized between the same type of chaotic systems with different initial parameters and also because of the systems were expected to behave similarly as a result of synchronization. Active control method was used to amplify the difference signal between master and slave systems which have different initial parameters and to return back synchronization information to the slave system. The real time simulation and synchronization of the master and slave systems performed successfully in LabVIEW environment. Furthermore, for the real time implementation, analogue outputs of NI-DAQ card used and real time results also were observed on an oscilloscope and secure communication application using sinusoidal signal and an image encryption application achieved successfully.
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.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".