Abstract
The constant search for methods that allow the production processes improvement is a driving force for the development and integration of current technological solutions in systems which are, currently, still purely human based. It is in this context that the company "Factoryplay" comes forward with the challenge to upgrade its current sewing stations by adding a set of mechanization and automation solutions. This article documents the steps carried out to provide the current solution with the required technical attributes. In this paper, the instrumentation and actuation devised solutions, as well as the method employed to design an embedded PI controller, will be presented. The PI controller allows the closed-loop control of the station movement speed as a function of the sewing machine speed. The practical results obtained, regarding the dynamic response of the sewing station, are in line with the simulated ones.

Abstract
Current students and technologies demand using new learning/teaching techniques. The potentialities of using mobile devices such as smartphones for teaching/learning purposes are huge. However, in some teaching areas its use is still residual. The use of mobile applications in the context of teaching PLC programming techniques is addressed in this work. The MIT App-Inventor II is deployed to develop mobile applications for learning purposes. An android based application entitled Time-Counts is proposed here, developed to support the teaching/learning process of both Timers and Counters. Results regarding its use by students are presented.

Abstract
Due to strong improvements and developments achieved in the last decade, it is clear that applied research using remote sensing technology such as unmanned aerial vehicles (UAVs) can provide a flexible, efficient, non-destructive, and non-invasive means of acquiring geoscientific data, especially aerial imagery. Simultaneously, there has been an exponential increase in the development of sensors and instruments that can be installed in UAV platforms. By combining the aforementioned factors, unmanned aerial system (UAS) setups composed of UAVs, sensors, and ground control stations, have been increasingly used for remote sensing applications, with growing potential and abilities. This paper's overall goal is to identify advantages and challenges related to the use of UAVs for aerial imagery acquisition in forestry and coastal environments for preservation/prevention contexts. Moreover, the importance of monitoring these environments over time will be demonstrated. To achieve these goals, two case studies using UASs were conducted. The first focuses on phytosanitary problem detection and monitoring of chestnut tree health (Padrela region, Valpacos, Portugal). The acquired high-resolution imagery allowed for the identification of tree canopy cover decline by means of multi-temporal analysis. The second case study enabled the rigorous and non-evasive registry process of topographic changes that occurred in the sandspit of Cabedelo (Douro estuary, Porto, Portugal) in different time periods. The obtained results allow us to conclude that the UAS constitutes a low-cost, rigorous, and fairly autonomous form of remote sensing technology, capable of covering large geographical areas and acquiring high precision data to aid decision support systems in forestry preservation and coastal monitoring applications. Its swift evolution makes it a potential big player in remote sensing technologies today and in the near future.

Abstract
In agricultural applications hyperspectral imaging is used in cases where differences in spectral reflectance of the examined objects are small. However, the large amount of data generated by hyperspectral sensors requires advance processing methods. Machine learning approaches may play an important role in this task. They are known for decades, but they need high volume of data to compute accurate results. Until recently, the availability of hyperspectral data was a big drawback. It was first used in satellites, later in manned aircrafts and data availability from those platforms was limited because of logistics complexity and high price. Nowadays, hyperspectral sensors are available for unmanned aerial vehicles, which enabled to reach a high volume of data, thus overcoming these issues. This way, the aim of this paper is to present the status of the usage of machine learning approaches in the hyperspectral data processing, with a focus on agriculture applications. Nevertheless, there are not many studies available applying machine learning approach to hyperspectral data for agricultural applications. This apparent limitation was in fact the inspiration for making this survey. Preliminary results using UAV-based data are presented, showing the suitability of machine learning techniques in remote sensed data. © 2018 Association for Computing Machinery.

Abstract
Advances in Unmanned Aerial Systems (UAS) allowed them to become both flexible and cost-effective. When combined with computer vision data processing techniques they are a good way to obtain high-resolution imagery and 3D information. As such, UAS can be advantageous both for agriculture and forestry areas, where the need for data acquisition at specific times and within a specific time frame is crucial, enabling the extraction of several measurements from different crop types. In this study a low-cost UAS was used to survey an area mainly composed by chestnut trees (Castanea sativa Mill.). Flights were performed at different heights (ranging from 30 to 120 m), in single and double grid flight patterns, and photogrammetric processing was then applied. The obtained information consists of orthophoto mosaics and digital elevation models which enable the measurement of individual tree’s parameters such as tree crown diameter and tree height. Results demonstrate that despite its lower spatial resolution, data from single grid flights carried out at higher heights provided more reliable results than data acquired at lower flight heights. Higher number of images acquired in double grid flights also improved the results. Overall, the obtained results are encouraging, presenting a R2 higher than 0.9 and an overall root mean square error of 44 cm. © 2018 Association for Computing Machinery.

Abstract
The cost-effectiveness of unmanned aerial systems (UAS) makes them suitable platforms to survey cultural heritage sites. Developments in photogrammetry provide methods capable to generate accurate 3D models out of 2D aerial images. Considering the involved technologies, the purpose of this paper is to document the Chapel of Espiríto Santo: a very relevant monument for Vila Real (Portugal) that is currently located at the campus of the University of Trás-os-Montes and Alto Douro. The UAS-based aerial imagery survey approach is presented along with photogrammetric process to build chapel’s 3D model. Moreover, two photogrammetric software were compared – Pix4Dmapper Pro and Agisoft Photoscan – in terms of modelling accuracy and functionalities ease of use. © 2018 Association for Computing Machinery.