Abstract

Abstract
Responsive design allows software developers to build a Web page that can dynamically adapt to the size of the devices. This development philosophy enables the rendering of Web pages in a fast and optimized way, ensuring a good user experience on mobile devices, tablet and desktop. In the scope of this study, we intend to explore the main advantages and limitations associated with responsive Web design. We adopted a quantitative approach based on a questionnaire filled by 181 professionals in the industry that allowed us to identify the reasons that lead software developers to the adoption of the responsive design and also address the limitations felt by them. The results obtained indicate that offering a good user experience and increasing accessibility stands out as being the most important advantages. On the other hand, the main limitations include the compatibility with older Web browsers, the higher loading time and the difficulties in optimizing user experience. Finally, it was found that the perception of the advantages and limitations of responsive design is distinct for professionals with more professional experience in the field and for freelancer developers. © 2017, Fernando Almeida and José Monteiro.

Abstract
This paper presents a teaching/learning experiment on the use of MITAppI2 as a friendly tool in Automation courses. The goal was to assess if the up-to-date mobile applications can act as promoters in learning automation topics. The experiment took place in two Portuguese universities. The results achieved point towards a successful use of these tools in university classes. © Springer International Publishing Switzerland 2017.

Abstract
An unsupervised method for convolutional neural network (CNN) architecture design is proposed. The method relies on a variable neighborhood search-based approach for finding CNN architectures and hyperparameter values that improve classification performance. For this purpose, t-Distributed Stochastic Neighbor Embedding (t-SNE) is applied to effectively represent the solution space in 2D. Then, k-Means clustering divides this representation space having in account the relative distance between neighbors. The algorithm is tested in the CIFAR-10 image dataset. The obtained solution improves the CNN validation loss by over 15% and the respective accuracy by 5%. Moreover, the network shows higher predictive power and robustness, validating our method for the optimization of CNN design.

Abstract
It is commonly accepted that one of the most important factors for assuring the high performance of an electrical network is the surveillance and the respective preventive maintenance. From a long time ago that TSOs and DSOs incorporate in their maintenance plans the surveillance of the grid, where is included the aerial power lines inspection. Those inspections started by human patrol, including structure climbing when needed and later were substituted by helicopters with powerful sensors and specialised technicians. More recently the Unmanned Aerial Vehicles (UAV) technology has been used, taking advantage of its numerous advantages. This paper addresses the problem of improving the real-time perception capabilities of UAVs for endowing them with capabilities for safe and robust autonomous and semi-autonomous operations. It presents a new vision based power line detection algorithm denoted by PLineD, able to improve the detection robustness even in the presence of image with background noise. The algorithm is tested in real outdoor images of a dataset with multiple backgrounds and weather conditions. The experimental results demonstrate that the proposed approach is effective and able to implemented in real-time image processing pipeline.

Abstract