Abstract
This paper advocates for the integration of system identification in graduate-level control system courses using accessible theoretical tools. Emphasising real-world applications, particularly in Remotely Operated Vehicle (ROV), the study proposes ROV as educational platforms for teaching control principles. As a concrete example, the paper presents a graduation course project focusing on designing a depth control system for an ROV, where students derive the model from experimental data. This practical application not only enhances the students skills in system identification but also prepares them for challenges in controlling complex systems in both academic and industrial settings.

Abstract
Knowledge models often use hierarchical structures, which help break down complex data into manageable components. This enables better understanding and aids in reasoning and decision-making. Hierarchical structures are effective in organizing, managing, and processing complex information. Traditional Self-Organizing Maps are typically flat, two-dimensional grids for visualizing and grouping data. They can be shaped into hierarchical structures, offering benefits such as improved data representation, scalability, enhanced grouping and visualization, and hierarchical feature extraction while preserving data topology. This paper introduces a self-organizing hierarchical map with an appropriate topology and a suitable learning mechanism for retaining information in an organized way. In this conceptual model, information is selectively absorbed in each layer. These characteristics make the Hierarchical Self-organising Maps a powerful non-linear classifier. Simulations are conducted to test and evaluate the performance of this neural structure as a classifier. © 2024 IEEE.

Abstract
The step response of first-order systems is vital in control systems and electronics. Understanding this behaviour is key but often challenging. This article uses Arduino with PWM to teach the step response in RC circuits, since Arduino enables real-time data acquisition and visualisation, connecting theory to practice. The research seeks to illustrate the step response of an RC circuit using Arduino, deepen knowledge of first-order systems, and offer a technique for collecting experimental data. All of this, since combining practical experiments with theoretical concepts boosts student involvement and understanding of dynamic systems. The work includes theoretical foundations, experimental procedures, and a brief discussion on the educational value of these activities.

Abstract
The resting brain has been extensively investigated for low frequency synchrony between brain regions, namely Functional Connectivity. However the other main stream of the brain connectivity analysis that seeks causal interactions between brain regions, Effective Connectivity, has been still little explored. Inherent complexity of brain activities in resting-state, as observed in Blood Oxygenation-Level Dependant fluctuations, calls for exploratory methods for characterizing these causal networks [1]. To determine the structure of the network that causes this dynamics, it is developed a method of identification based on least squares, which assumes knowledge of the signals of brain activity in different regions. As there is no access to functional Magnetic Resonance Imaging, data it is developed a model to obtain the Blood Oxygenation Level Dependent signals and it is implemented a reverse hemo-dynamic function. To assess the performance of the created model Monte Carlo simulations have been used. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

Abstract
This article addresses the problem of wheelchair path planning. In particular, to minimize the length of the trajectory within an environment containing a variable number of obstacles. The positions and quantities of these obstacles are pre-determined. To tackle this challenge, we present a methodology that integrates optimisation techniques and heuristic algorithms to find trajectories both optimal and collision-free. The effectiveness of this methodology is illustrated through a practical example, demonstrating how it successfully generates a collision-free trajectory, even when a large number of obstacles is present in the workspace. In the future, we intend to continue investigating the same problem, taking into account energy consumption as well as time minimisation. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.