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
Carcass data were collected from 24 kids (average live weight of 12.5 +/- 5.5 kg; range 4.5 to 22.4 kg) of Jarmelista Portuguese native breed, to evaluate bioelectrical impedance analysis (BIA) as a technique for prediction of light kid carcass and muscle chemical composition. Resistance (Rs, Omega) and reactance (Xc, Omega), were measured in the cold carcasses with a single frequency bioelectrical impedance analyzer and, together with impedance (Z, Omega), two electrical volume measurements (Vol(A) and Vol(B), cm(2)/Omega), carcass cold weight (CCW), carcass compactness and several carcass linear measurements were fitted as independent variables to predict carcass composition by stepwise regression analysis. The amount of variation explained by Vol(A) and Vol(B) only reached a significant level (P < 0.01 and P < 0.05, respectively) for muscle weight, moisture, protein and fat-free soft tissue content, even so with low accuracy, with VolA providing the best results (0.326 <= R-2 <= 0.366). Quite differently, individual BIA parameters (Rs, Xc and Z) explained a very large amount of variation in dissectible carcass fat weight (0.814 <= R-2 <= 0.862; P < 0.01). These individual BIA parameters also explained a large amount of variation in subcutaneous and intermuscular fat weights (respectively 0.749 <= R-2 <= 0.793 and 0.718 <= R-2 <= 0.760; P < 0.01), and in muscle chemical fat weight (0.663 <= R-2 <= 0.684; P < 0.01). Still significant but much lower was the variation in muscle, moisture, protein and fat-free soft tissue weights (0.344 <= R-2 <= 0.393; P < 0.01) explained by BIA parameters. Still, the best models for estimation of muscle, moisture, protein and fat-free soft tissue weights included Rs in addition to CCW, and accounted for 97.1% to 99.8% (P < 0.01) of the variation observed, with CCW by itself accounting for 97.0% to 99.6% (P < 0.01) of that variation. Resistance was the only independent variable selected for the best model predicting subcutaneous fat weight. It was also selected for the best models predicting carcass fat weight (combined with carcass length, CL; R-2 = 0.943; P < 0.01) and intermuscular fat weight (combined with CCW; R-2 = 0.945; P < 0.01). The best model predicting muscle chemical fat weight combined CCW and Z, explaining 85.6% (P < 0.01) of the variation observed. These results indicate BIA as a useful tool for prediction of light kids' carcass composition.

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
Vineyard parcels delimitation is a preliminary but important task to support zoning activities, which can be burdensome and time-consuming when manually performed. In spite of being desirable to overcome such issue, the implementation of a semi-/fully automatic delimitation approach can meet serious development challenges when dealing with vineyards like the ones that prevail in Douro Region (north-east of Portugal), mainly due to the great diversity of parcel/row formats and several factors that can hamper detection as, for example, interrupted rows and inter-row vegetation. Thereby, with the aim of addressing vineyard parcels detection and delimitation in Douro Region, a preliminary method based on segmentation and morphological operations upon high-resolution aerial imagery is proposed. This method was tested in a data set collected from vineyards located at the University of Trás-os-Montes and Alto Douro(Vila Real, Portugal). The presence of some of the previously mentioned challenging conditions - namely interrupted rows and inter-row grassing - in a few parcels contributed to lower the overall detection accuracy, pointing out the need for future improvements. Notwithstanding, encouraging preliminary results were achieved. © 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.