Abstract
In this paper a method to control and teach industrial robots in real-time by human demonstration is presented. It aims to decrease down-times for reconfigurations and programming costs, particularly in a mass customisation manufacturing scenario. This system uses high-intensity visual markers that make it sufficiently robust for industrial environments not requiring special lighting. An automated camera calibration method was implemented which enables any non-skilled user a quick and easy configuration of the system. The teaching of the robot is achieved by recording the detected points and replaying them to the manipulator, therefore this is a "teaching-by-showing" method. The final program that the robot is uploaded with is a smooth trajectory calculated using all the recorded points, which are analised and grouped into different motion segments according to position and speed.

Abstract
One important role of Enterprise Architecture aims at modeling enterprise artifacts and their relationships, ranging from the high-level concepts to physical ones such as communication networks and enterprise premises. As it is well known, these artifacts evolve over time, as well as their relationships. The dynamic nature of such artifacts has been a difficulty not only in modeling but also in keeping enterprise blueprints updated. This paper presents our approach to handle blueprints of the Enterprise Architecture, based on several years and projects in large organizations, both in the financial and telecommunication industry. We started by considering "projects" as the changing elements of Enterprise artifacts and achieve a scenario where blueprints are automatically generated and updated, and a time bar allows traveling from the past (AS-WAS), to the present (AS-IS) and to the future scenarios (TO-BE). The paper also presents an overview of the underlying model, the applied methodology and the blueprints that we found to be a valuable instrument amongst elements of different communities: Project Management, IT Governance and IT Architecture. In spite that the cases studies are targeted to the IT domain, the lessons are valid for other architectural areas. © 2009 Springer Berlin Heidelberg.

Abstract
Target Controlled Infusion (TCI) systems are based in drug pharmacokinetic and pharmacodynamic models implemented in an algorithm to drive an infusion pump. Infusion control algorithms have been designed, implemented and validated for several anesthetic drugs, devices and controllers. The maintenance phase in these algorithms is represented by an equation that compensates the loss of drug from the central compartment and maintains the set target concentration. The goal of the current study was to improve existing TCI software with a new method for the maintenance phase. We compared and analyzed two different methods to find the more efficient method for the maintenance phase in an open-loop control TCI system. ©2009 IEEE.

Abstract
Target Controlled Infusion (TCI) systems are based in drug pharmacokinetic and pharmacodynamic models implemented in an algorithm to drive an infusion pump. Infusion control algorithms have been designed, implemented and validated for several anesthetic drugs, devices and controllers. The maintenance phase in these algorithms is represented by an equation that compensates the loss of drug from the central compartment and maintains the set target concentration. The goal of the current study was to improve existing TCI software with a new method for the maintenance phase. We compared and analyzed two different methods to find the more efficient method for the maintenance phase in an open-loop control TCI system.

Abstract
Most of industrial robots are still programmed using the typical teaching process, through the use of the robot teach pendant. In this paper is proposed an accelerometer-based system to control an industrial robot using two low-cost and small 3-axis wireless accelerometers. These accelerometers are attached to the human arms, capturing its behavior (gestures and postures). An Artificial Neural Network (ANN) trained with a back-propagation algorithm was used to recognize arm gestures and postures, which then will be used as input in the control of the robot. The aim is that the robot starts the movement almost at the same time as the user starts to perform a gesture or posture (low response time). The results show that the system allows the control of an industrial robot in an intuitive way. However, the achieved recognition rate of gestures and postures (92%) should be improved in future, keeping the compromise with the system response time (160 milliseconds). Finally, the results of some tests performed with an industrial robot are presented and discussed.

Abstract