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This work proposes a generalization of the family of chaotic maps without fixed points, proposed by Jafari et al.; 2016 and termed the Vertigo maps. The original map family is parameterized by four control parameters, which can be used to scale the function used as a seed and control its domain. Several theoretical results are provided regarding the existence of the fixed points, the periodic cycles, and the Lyapunov exponents of the maps. Furthermore, two map examples are provided based on the logistic and tent seed functions, which are then studied using a series of numerical tools, like phase portraits, bifurcation diagrams, and Lyapunov exponent diagrams. Finally, an application to a Pseudo-Random Bit Generator is considered. The generator utilizes an exponential-based hash function in combination with the remainder operator.

Simple polynomial unimodal maps which show robust chaos, that is, a unique chaotic attractor and no periodic windows in their bifurcation diagrams, are constructed. Their invariant distributions and Lyapunov exponents are examined.

We examine and assess deterministic chaos as an observable. First, we present the development of model ecological systems. We illustrate how to apply the Kolmogorov theorem to obtain limits on the parameters in the system, which assure the existence of either stable equilibrium point or stable limit cycle behavior in the phase space of two-dimensional (2D) dynamical systems. We also illustrate the method of deriving conditions using the linear stability analysis. We apply these procedures on some basic existing model ecological systems. Then, we propose four model ecological systems to study the dynamical chaos (chaos and intermittent chaos) and cycles. Dynamics of two predation and two competition models have been explored. The predation models have been designed by linking two predator–prey communities, which differ from one another in one essential way: the predator in the first is specialist and that in the second is generalist. The two competition models pertain to two distinct competition processes: interference and exploitative competition. The first competition model was designed by linking two predator–prey communities through inter-specific competition. The other competition model assumes that a cycling predator–prey community is successfully invaded by a predator with linear functional response and coexists with the community as a result of differences in the functional responses of the two predators. The main criterion behind the selection of these two model systems for the present study was that they represent diversity of ecological interactions in the real world in a manner which preserves mathematical tractability. For investigating the dynamic behavior of the model systems, the following tools are used: (i) calculation of the basin boundary structures, (ii) performing two-dimensional parameter scans using two of the parameters in the system as base variables, (iii) drawing the bifurcation diagrams, and (iv) performing time series analysis and drawing the phase space diagrams. The results of numerical simulation are used to distinguish between chaotic and cyclic behaviors of the systems.

The conclusion that we obtain from the first two model systems (predation models) is that it would be difficult to capture chaos in the wild because ecological systems appear to change their attractors in response to changes in the system parameters quite frequently. The detection of chaos in the real data does not seem to be a possibility as what is present in ecological systems is not robust chaos but short-term recurrent chaos. The first competition model (interference competition) shares this conclusion with those of predation ones. The model with exploitative competition suggests that deterministic chaos may be robust in certain systems, but it would not be observed as the constituent populations frequently execute excursions to extinction-sized densities. Thus, no matter how good the data characteristics and analysis techniques are, dynamical chaos may continue to elude ecologists. On the other hand, the models suggest that the observation of cyclical dynamics in nature is the most likely outcome.

Chaotic systems are often considered to be a basis for various cryptographic methods due to their properties, which correspond to cryptographic properties like confusion, diffusion and algorithm (attack) complexity. In these kinds of applications, chaos robustness is desired. It can be defined by the absence of periodic windows and coexisting attractors in some neighborhoods of the parameter space. On the other hand, when used as a basis for neuromorphic modeling, chaos robustness is to be avoided, and the edge-of-chaos regime is needed. This paper analyses the robustness and edge-of-chaos for Chua’s systems, comprising either a piecewise linear or a smooth function nonlinearity, using a novel figure of merit based on correlation coefficient and Lyapunov exponent. Calculation complexity, which is important when a chaotic system is implemented, is evaluated for double and decimal data types, where needed calculation time varies by a factor of about 1500, depending on the nonlinearity function and the data type. On the other hand, different data types result in different number precision, which has some practical advantages and drawbacks presented in the paper.

When chaotic systems are used for speech encryption, their chaotic performance largely determines the security of speech encryption. However, traditional chaotic systems have problems such as parameter discontinuity, easy occurrence of chaos degradation, low complexity, and the existence of periodic windows in chaotic intervals. In real applications, chaotic mappings may fall into periodic windows, which is extremely unfavorable for security. In this paper, a new chaotic mapping 2D-LMSM is proposed by improving the chaotic logistic and sine mappings, and applied to speech encryption. Performance evaluation shows that this map can effectively generate robust chaotic signals in a wide parameter range. The 2D-LMSM achieves better robustness and desired chaotic properties than several existing two-dimensional chaotic maps. We propose a novel speech encryption algorithm using this map. First, it performs Fast Fourier Transform (FFT) on the input speech signal to obtain real and imaginary values, which are encrypted by one-time scrambling encryption and XOR diffusion encryption with pseudorandom numbers generated by chaos; then, it performs secondary scrambling encryption by Discrete Wavelet Transform (DWT) and 2D-LMSM; finally, it obtains encrypted speech data by Discrete Wavelet Inverse Transform (IDWT) and Fast Fourier Inverse Transform (IFFT). Experimental results show that this algorithm has good encryption and decryption performances and ensures system security.