Over the past 15 years, many feature extraction methods have been used and developed for
the recognition of plant species. These methods have mostly been performed using …

Self-organization of complex morphological patterns from local interactions is a fascinating
phenomenon in many natural and artificial systems. In the artificial world, typical examples of …

Texture recognition is one of the most important tasks in computer vision and, despite the
recent success of learning-based approaches, there is still need for model-based solutions …

We utilise the 0–1 test to automatically classify elementary cellular automata. The
quantitative results of the 0–1 test reveal a number of advantages over Wolfram's qualitative …

We propose a descriptor based on binary patterns extracted from network-automata time-
evolution patterns (TEP) aiming to characterize networks. More, in particular, we explore …

We propose a novel, lossless compression algorithm, based on the 2D Discrete Fast Fourier
Transform, to approximate the Algorithmic (Kolmogorov) Complexity of Elementary Cellular …

The emergent dynamics in spacetime diagrams of cellular automata (CAs) is often
organised by means of a number of behavioural classes. Whilst classification of elementary …

In recent years, deep learning, which is widely used in machine learning and computer
vision, offers many new solutions, especially for agricultural problems. In this study, an …

For the supersonic flow field with large density fluctuation, an effective method to obtain the
beam path is solving ray equation. Generally, when the refractive index distribution is …

A novel cellular learning automaton traffic flow model is proposed to solve the problem that
the probability of randomization in the NaSch model is not consistent with the actual traffic …