Abstract
Remote control devices are commonly used for interaction with multimedia equipment and applications (e.g., smart TVs, gaming, etc.). To improve conventional keypad-based technologies, haptic feedback and user input capabilities are being developed for enhancing the UX and providing advanced functionalities in remote control devices. Although the sensation provided by haptic feedback is similar to mechanical push buttons, the former offers much greater flexibility, due to the possibility of dynamically choosing different mechanical effects and associating different functions to each of them. However, selecting the best haptic feedback effects among the wide variety that is currently enabled by recent technologies, remains a challenge for design engineers aiming to optimise the UX. Rich interaction further requires text input capability, which greatly influences the UX. This work is a contribution towards UX evaluation of remote control devices with haptic feedback and text input. A user evaluation study of a wide variety of haptic feedback effects and text input methods is presented, considering different technologies and different number of actuators on a device. The user preferences, given by subjective evaluation scores, demonstrate that haptic feedback has undoubtedly a positive impact on the UX. Moreover, it is also shown that different levels of UX are obtained, according to the technological characteristics of the haptic actuators and how many of them are used on the device.

Abstract

Abstract
A significant number of search and optimisation techniques whose principles seek inspiration from nature and biology phenomena have been proposed in the last decades. These methods have been successfully applied to solve a wide range of engineering problems. This is also the case of greenhouse environment control, which has been incorporating this type of techniques into its design. This paper addresses evolutionary and bio-inspired methods in the context of greenhouse environment control. Algorithm principles for reference techniques are reviewed, namely: simulated annealing, genetic algorithm, differential evolution and particle swarm optimisation. The last three techniques are considered using single and multiple objective formulations. A review of these algorithms within greenhouse environment control applications is presented, considering single and multiple objective problems, as well as their current trends.

Abstract
The design of Multi-Input Multi-Output nonlinear control systems for a quadrotor can be a difficult task. Nature inspired optimization techniques can greatly improve the design of non-linear control systems. Two recently proposed hunting-based swarm intelligence inspired techniques are the Grey Wolf Optimizer (GWO) and the Ant Lion Optimizer (ALO). This paper proposes the use of both GWO and ALO techniques to design a Sliding Mode Control (SMC) flight system for tracking improvement of altitude and attitude in a quadrotor dynamic model. SMC is a nonlinear technique which requires that its strictly coupled parameters related to continuous and discontinuous components be correctly adjusted for proper operation. This requires minimizing the tracking error while keeping the chattering effect and control signal magnitude within suitable limits. The performance achieved with both GWO and ALO, considering realistic disturbed flight scenarios are presented and compared to the classical Particle Swarm Optimization (PSO) algorithm. Simulated results are presented showing that GWO and ALO outperformed PSO in terms of precise tracking, for ideal and disturbed conditions. It is shown that the higher stochastic nature of these hunting-based algorithms provided more confidence in local optima avoidance, suggesting feasibility of getting a more precise tracking for practical use.

Abstract
The objective is to investigate the tools and methodologies used in Higher Education (HE) of Electrical and Computer Engineering (ECE), identifying the curricular units that used them. The analysis was based on the perception of the course coordinators. Triangulation between multiple methodologies and different methods of analysis was used, combining lexical analysis, analysis of keywords and content analysis. Course coordinators were interviewed at three universities in Portugal and Brazil. In the content analysis, two categories emerged: the coordinators' perception of non-traditional methodologies, and the perception of software tools as didactic resources. The initiatives using non-traditional teaching processes and methodologies were reduced and punctual, with individual actions by teachers and not of educational standards discussed and institutionalized. The precariousness of laboratory infrastructure and software licenses was a common report.

Abstract
Recently Shannon’s Entropy has been incorporated in nature inspired metaheuristics with good results. Depending on the problem, the Grey Wolf Optimization (GWO) algorithm may suffer from premature convergence. Here, an Entropy Grey Wolf Optimization (E-GWO) technique is proposed with the overall aim to improve the original GWO performance. The entropy is used to track the GWO swarm diversity, comparing the distance values between the Alpha in relation to the Beta and Delta wolves. The aim of the E-GWO variant is to improve convergence and prevent stagnation in local optima, since ideally restarting the swarm agents will prevent this from happening. Simulation results are presented showing that E-GWO restarting mechanism can achieve better results than the original GWO algorithm for some benchmark functions. © 2020, Springer Nature Switzerland AG.