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
The benefits of blockchain go beyond its applicability in finance. Electronic Voting Systems (EVS) are considered as a way to achieve a more effective act of voting. EVS are expected to be verifiable and tamper resistant. The blockchain partially fulfills this requirements of EVS by being an immutable, verifiable and distributed record of transactions. The adoption of EVS has been hampered mainly by cultural and political issues rather than technological ones. The authors believe that blockchain is the technology that, due to the overall attention it has been receiving, is capable of fostering the adoption of EVS. In the current work we compare blockchain-based EVS, identifying their strengths and shortcomings. © Springer Nature Switzerland AG 2019.

Abstract

Abstract

Abstract
Measuring human gait is important in medicine to obtain outcome parameter for therapy, for instance in Parkinson's disease. Recently, small inertial sensors became available which allow for the registration of limb-position outside of the limited space of gait laboratories. The computation of gait parameters based on such recordings has been the subject of many scientific papers. We want to add to this knowledge by presenting a 4-segment leg model which is based on inverse kinematic and Kalman filtering of data from inertial sensors. To evaluate the model, data from four leg segments (shanks and thighs) were recorded synchronously with accelerometers and gyroscopes and a 3D motion capture system while subjects (n = 12) walked at three different velocities on a treadmill. Angular position of leg segments was computed from accelerometers and gyroscopes by Kalman filtering and compared to data from the motion capture system. The four-segment leg model takes the stance foot as a pivotal point and computes the position of the remaining segments as a kinematic chain (inverse kinematics). Second, we evaluated the contribution of pelvic movements to the model and evaluated a five segment model (shanks, thighs and pelvis) against ground-truth data from the motion capture system and the path of the treadmill. Results: We found the precision of the Kalman filtered angular position is in the range of 2-6 degrees (RMS error). The 4-segment leg model computed stride length and length of gait path with a constant undershoot of 3% for slow and 7% for fast gait. The integration of a 5th segment (pelvis) into the model increased its precision. The advantages of this model and ideas for further improvements are discussed.

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.