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Chaotic systems are suitable for image encryption owing to their numerous intrinsic characteristics. However, chaotic maps and algorithmic structures employed in many existing chaos-based image encryption algorithms exhibit various shortcomings. To overcome these, in this study, we first construct a two-dimensional logistic tent modular map (2D-LTMM) and then develop a new colour image encryption algorithm (CIEA) using the 2D-LTMM, which is referred to as the LTMM-CIEA. Compared with the existing chaotic maps used for image encryption, the 2D-LTMM has a fairly wide and continuous chaotic range and more uniformly distributed trajectories. The LTMM-CIEA employs cross-plane permutation and non-sequential diffusion to obtain the diffusion and confusion properties. The cross-plane permutation concurrently shuffles the row and column positions of pixels within the three colour planes, and the non-sequential diffusion method processes the pixels in a secret and random order. The main contributions of this study are the construction of the 2D-LTMM to overcome the shortcomings of existing chaotic maps and the development of the LTMM-CIEA to concurrently encrypt the three colour planes of images. Simulation experiments and security evaluations show that the 2D-LTMM outperforms recently developed chaotic maps, and the LTMM-CIEA outperforms several state-of-the-art image encryption algorithms in terms of security. [ABSTRACT FROM AUTHOR]

The celebrated Lotka–Volterra (LV) model is vastly used to describe the simple competition between prey and predator populations. The stochastic LV model, for its turn, considers the competition among two similar prey and two similar predator populations, with each group being described by identical mathematical equations. However, each of these groups differs in stochastic fluctuations, which are interpreted as small variations in each species’ specimens (Genetic Diversity and Phenotypic Expression Diversity) and environmental conditions. The relative statistical variation presented is inversely proportional to the square root of the amount of specimens in each population. The model begins with two prey and two predator groups and, after a transient time, is reduced to one element of each group, returning to the classic LV system. That is, the model ends in asymmetric states despite starting from an initial symmetric condition without population excess. Spontaneous population symmetry breaking without population excess was obtained using the stochastic method. For its turn, the deterministic method could be used to analyze such breaking by forcing a perturbative fluctuation or adding little excess to one population, but the stochastic method can simulate such break in symmetry naturally. [ABSTRACT FROM AUTHOR]

Deterministic dynamic models for coupled resident and invader populations are considered with the purpose of finding quantities that are effective at predicting when the invasive population will become established asymptotically. A key feature of the models considered is the stage-structure, meaning that the populations are described by vectors of discrete developmental stage- or age-classes. The vector structure permits exotic transient behaviour—phenomena not encountered in scalar models. Analysis using a linear Lyapunov function demonstrates that for the class of population models considered, a large so-called population inertia is indicative of successful invasion. Population inertia is an indicator of transient growth or decline. Furthermore, for the class of models considered, we find that the so-called invasion exponent, an existing index used in models for invasion, is not always a reliable comparative indicator of successful invasion. We highlight these findings through numerical examples and a biological interpretation of why this might be the case is discussed. [ABSTRACT FROM AUTHOR]

This paper presents a novel hardware architecture for adaptive systems whose exact specification is unknown. The architecture is suitable for linear and nonlinear systems whose inputs are real or complex signals (variables), and that have an acceptable representation as low order polynomials in these variables. The implementation is based on using an a priori selected subset of Walsh spectral coefficients. The proposed architecture can acquire its target functionality and adapt itself to changing environments even if the number of variables, their order and precision are unknown in advance. This is in contrast to conventional multiply-and-accumulate (MAC) based architectures where this information must be determined before the design and implementation of the system. In this context (of systems whose functionality is unknown), the delay and the implementation cost of the proposed architecture is significantly lower than MAC-based solutions. [ABSTRACT FROM AUTHOR]

This note proposes a robust nonlinear observer for systems with Lipschitz nonlinearity. The proposed nonlinear observer, whose linear part adopts the linear LTR observer design technique, has two important advantages over previous designs. First, the new observer does not impose the small-Lipschitz-constant condition on the system nonlinearity, nor other structural conditions on the system dynamics as in the existing observer designs. Second, it is robust in the sense that its state estimation error decays to almost zero even in the face of large external disturbances. [ABSTRACT FROM AUTHOR]

This article is about fault detection and isolation (FDI) methods by the use of neural networks. The system to supervise is modelised by using a particular structure of neural networks where the hidden layer presents some additive neurons which are only connected to the output neuron and one of the input neurons. These supplementary neurons permit to perform an estimation of each input of the network and the comparison of this estimation with the actual one permits, by using a statistical test, to detect and locate the presence of fault on one input. The proposed method is tested on a simulation example. [Copyright &y& Elsevier]