Correlation and spectral properties of chaotic signals generated by a piecewise-linear map with multiple segments

Costa, Rafael Alves da; Loiola, Murilo Bellezoni; Eisencraft, Marcio

doi:10.1016/j.sigpro.2016.10.025

Cited by 12 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We use the multi-segment piecewise linear mapping [25,47] to generate the desired chaos sequence k. It is an iteration procedure that can be programmatically expressed as follows:Initialize the first element of the sequence bold-italick0 with a random value, known as “seed” in computer science;If kl>1/2, repeat the computation kl:=kl−|2ξbold-italickl| with ξ∈[0,1] until the constraint is satisfied, otherwise, go to the next step;If kl<−1/2, repeat the computation kl:=kl+|2ξbold-italickl| until the constraint is satisfied, otherwise, go to the next step;Compute the next element bold-italickl+1: kl+1={2(N−ξ)bold-italickl+(N−0.5)…”

Section: Message Signal Modulationmentioning

confidence: 99%

“…As the simulation results shown in Figure 1b, adopting chaos sequence significantly increases the difficulties of the parameter retrieving. Up till now, a series of ingenious chaos maps have been proposed (e.g., logistic [20], tent [21], cubic [22], Chebyshev [23], Bernoulli [24], multi-segment piecewise linear maps [25]), considerably improving communication security.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Demodulation of Chaos Phase Modulation Spread Spectrum Signals Using Machine Learning Methods and Its Evaluation for Underwater Acoustic Communication

Marzani

Yang

2018

Sensors

View full text Add to dashboard Cite

The chaos phase modulation sequences consist of complex sequences with a constant envelope, which has recently been used for direct-sequence spread spectrum underwater acoustic communication. It is considered an ideal spreading code for its benefits in terms of large code resource quantity, nice correlation characteristics and high security. However, demodulating this underwater communication signal is a challenging job due to complex underwater environments. This paper addresses this problem as a target classification task and conceives a machine learning-based demodulation scheme. The proposed solution is implemented and optimized on a multi-core center processing unit (CPU) platform, then evaluated with replay simulation datasets. In the experiments, time variation, multi-path effect, propagation loss and random noise were considered as distortions. According to the results, compared to the reference algorithms, our method has greater reliability with better temporal efficiency performance.

show abstract

Section: Message Signal Modulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Demodulation of Chaos Phase Modulation Spread Spectrum Signals Using Machine Learning Methods and Its Evaluation for Underwater Acoustic Communication

Marzani

Yang

2018

Sensors

View full text Add to dashboard Cite

show abstract

“…No presente texto, revisitam-se alguns resultados analíticos relacionadosà DEP desses sinais [24][25][26][27]. A ideia básicaé empregar para sinais caóticos o arcabouço utilizado usualmente para tratar sinais aleatórios, seguindo a visão da reconhecida obra de A. Lasota and M. MacKey, Probabilistic properties of deterministic systems [28].…”

unclassified

Caos em tempo discreto: introdução, exemplos e representação estatística

Eisencraft¹

2018

Learn. Nonlin. Mod.

Self Cite

View full text Add to dashboard Cite

Resumo-No presente texto revisitam-se alguns aspectos de sinais caóticos gerados por sistemas de tempo discreto ou mapas. Em particular, destaca-se o emprego de técnicas comumente utilizadas para sinais aleatórios na sua caracterização. Para tanto, inicia-se com uma breve revisão das principais definições e propriedades dos sinais caóticos de tempo discreto, exemplificando-se também diversos mapas capazes de gerá-los. A seguir, as técnicas e conceitos apresentados são utilizados na dedução das características temporais e espectrais de sinais gerados por alguns mapas lineares por partes. Por fim, sugerem-se temas de pesquisa futuros. Palavras-chave-Sistemas dinâmicos, processamento de sinais, sistemas não lineares, teoria do caos, comunicações.

show abstract