Name
Abstract | ||
|---|---|---|
According to the state conversion idea of Markov process, we propose a new chaotic sequence quantization algorithm. The Markov quantization algorithm (MQA) we proposed solves the problem of poor sequence balance which is caused by the asymmetry of probability distribution on both sides of the threshold in binary quantization algorithm (BQA). Both the theoretical and simulation study results show that the balance degree, the autocorrelation and cross-correlation function of the sequence generated by MQA can converge to the best state after several iterations. In addition, from the simulation results of runs test and linear complexity we can conclude that the sequence has good randomness and linear complexity, so it has a strong anti-crack ability. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICSPCC.2017.8242573 | 2017 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, COMMUNICATIONS AND COMPUTING (ICSPCC) |
Keywords | DocType | Citations |
Markov, Quantization, Randomness, Anti-crack | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhongxin Bai | 1 | 2 | 1.87 |
| Xiaomin Zhang | 2 | 0 | 0.34 |
| Kai Zheng | 3 | 0 | 0.34 |