Abstract
The inspiration for this paper comes from a successful experiment conducted with students in the "Mobile Robots" course in the fifth year of the integrated Master's program in the Department of Electrical and Computer Engineering, Faculty of Engineering, University of Porto (FEUP), Porto, Portugal. One of the topics in this Mobile Robots course is "Localization of Mobile Robots using the Extended Kalman Filter in a LEGO NXT," which gives the students the opportunity to study the concepts of localization. This experiment comes within the framework of teaching localization concepts in mobile robotics and focuses primarily on explaining the Kalman filter concept. It involves a specific tool developed by the authors and based on LEGO NXT technology. The work presented here could be a helpful guide for teaching concepts related to localization in mobile robotics to ensure adequate understanding of the concept and of the use of the extended Kalman filter (EKF). The LegoFeup robot described here was built using a LEGO Mindstorms NXT and tested both in simulation and in real scenarios. Based on the results obtained, the authors concluded that the developed tool is effective in motivating students. The implementation of the tool, the structure of the Mobile Robots course, and the criteria for student assessment are described in this paper.

Abstract
The Portuguese coastline, like many other worldwide coastlines, is often submitted to several types of extreme events resulting in erosion, thus, acquisition of high quality field measurements has become a common concern. The nearshore survey systems have been traditionally based on in situ measurements or in the use of satellite or aircraft mounted remote sensing systems. As an alternative, video-monitoring systems proved to be an economic and efficient way to collect useful and continuous data, and to document extreme events. In this context, is under development the project MoZCo (Advanced Methodologies and Techniques Development for Coastal Zone Monitoring), which intends to develop and implement monitoring techniques for the coastal zone based on a low cost video monitoring system. The pilot study area is Ofir beach (north of Portugal), a critical coastal area. In the beginning of this project (2010) a monitoring video station was developed, collecting snapshots and 10 minutes videos every hour. In order to process the data, several video image processing algorithms were implemented in Matlab (R), allowing achieve the main video-monitoring system products, such as, the shoreline detection. An algorithm based on image processing techniques was developed, using the HSV color space, the idea is to select a study and a sample area, containing pixels associated with dry and wet regions, over which a thresholding and some morphological operators are applied. After comparing the results with manual digitalization, promising results were achieved despite the method's simplicity, which is in continuous development in order to optimize the results.

Abstract
This paper describes and compares several approaches applied to compute the inverse kinematics of a 10 degrees of freedom hyper-redundant robot. The proposed approaches are based on an exhaustive method and several error optimization algorithms. The algorithms' performance was evaluated based on two criteria: computing time and final actuator positioning error. The mentioned hyper-redundant robot was projected to be used in biomedical applications. © 2012 IEEE.

Abstract
The present work describes and compares several approaches applied to compute the inverse kinematics of a ten degrees of freedom hyper-redundant robot. The proposed approaches are based on an exhaustive method and several error-optimization algorithms. The algorithms' performance was evaluated based on two criteria: computational processing time and final actuator positioning error. The results obtained show that for a small number of modules (less or equal to four), the exhaustive method provides the best problem solution: acceptable computational processing time as well as minimum error. However, for larger number of modules, the error-optimization approach has far better performance regarding the error to processing time ratio. The mentioned hyper-redundant robot was projected to be used in biomedical applications.

Abstract
Nowadays location systems are used within a large variety of applications. The application of these systems within indoor environments is already provided by several solutions. However, the need for high accuracy within these environments to pursue such purpose implies the use of specific infrastructures designed towards it. Our project tries to meet the requirements for a simple, low-cost, and scalable location system through different approaches. The main idea of it is to re-construct topological maps of indoor spaces through location estimation, i.e. using off-the-shelf technologies. We try to perform location estimations and then re-create the indoor maps as topological maps as a means of reducing the precision requirements other systems have, and develop a scalable and highly applicable system using sensors featuring mobile devices.

Abstract
Nowadays location systems are used within a large variety of applications. The application of these systems within indoor environments is already provided by several solutions. However, the need for high accuracy within these environments to pursue such a purpose implies the use of specific infrastructures designed towards it. Our project tries to meet the requirements for a simple, low-cost, and scalable location system through different approaches. The main idea of it is to re-construct topological maps of indoor spaces through location estimation, and to serve as a means of reducing the precision requirements other systems may have to develop a scalable and highly applicable solution. © 2012 Authors.