Secure Complex Systems: A Dynamic Model in the Synchronization

Chaotic systems are one of the most significant systems of the technological period because their qualities must be updated on a regular basis in order for the speed of security and information transfer to rise, as well as the system’s stability. The purpose of this research is to look at the special features of the nine-dimensional, difficult, and highly nonlinear hyperchaotic model, with a particular focus on synchronization. Furthermore, several criteria for such models have been examined; Hamiltonian, synchronizing, Lyapunov expansions, and stability are some of the terms used. The geometrical requirements, which play an important part in the analysis of dynamic systems, are also included in this research due to their importance. The synchronization and control of complicated networks’ most nonlinear control is important to use and is based on two major techniques. The linearization approach and the Lyapunov stability theory are the foundation for attaining system synchronization in these two ways.

Neuron dynamics and synchronous transition of symmetrical electrically coupled Sherman

Based on the study of the synchronization of two electric synapse-coupled Sherman neuron systems, this paper analyzes the rich discharge behavior of Sherman neurons through the peak-to-peak interval bifurcation diagram, which determines the parameter values for the study of the electrical synapse coupling Sherman neuron system synchronization. By using the synchronization difference and the correlation coefficient value, this paper analyzes the synchronous transition process of the two electrical synapse-coupled Sherman neuron systems with the change of coupling intensity and studies the bifurcation behavior of neurons in the two electrical synapse-coupled Sherman neuron systems. The experimental results show the transition process of two electrical synapse-coupled Sherman neurons from nonsynchronized, peak-independent cluster synchronization to fully synchronized. In addition, we study the synchronization process of the ring-connected electrical synapse-coupled Sherman neuron system. The experimental results show that the two electrical synapse-coupled Sherman neuron systems show a similar synchronous transition process.

Dreaming of Perfect Data: Characterizing Noise in Archaeo-Geophysical Measurements

For the interpretation of archaeological geophysical data as archaeological features, it is essential that the recorded anomalies can be clearly delineated and analyzed, and therefore, care has been taken to obtain the best possible data. However, as with all measurements, data are degraded by unwanted components, or noise. This review clarifies the terminology, discusses the four major sources of noise (instrument, use of instrument, external, soil), and demonstrates how it can be characterized using geostatistical and wavenumber methods. It is important to recognize that even with improved instruments, some noise sources, like soil noise, may persist and that degraded data may be the result of unexpected sources, for example, global positioning system synchronization problems. Suggestions for the evaluation and recording of noise levels are provided to allow estimation of the limit of detection for archaeological geophysical anomalies.