Abstract
Several monitoring approaches have been used to understand the physical, chemical, and biological processes associated with coastal sewage discharges. However, these efforts have not improved the understanding of the interaction of effluent plume/coastal ocean processes. Autonomous underwater vehicles (AUVs) have already been shown to be very useful for performing high-resolution surveys of small features such as outfall plumes. Some of the advantages of these platforms include easier field logistics, low cost per deployment, good spatial coverage, sampling over repeated sections, and the ability to perform feature based or adaptive sampling. Once the data have been collected in the field, it is necessary to extrapolate from monitoring samples to unsampled locations. Geostatistics has been successfully used to obtain information; for example, regarding the spatial distribution of soil properties. Besides giving estimated values at unsampled locations, it provides a measure of the accuracy of the estimate, which is a significant advantage over traditional methods used to assess pollution. In this work, geostatistics is used to model and map the spatial distribution of temperature measurements gathered by an AUV in a sea ouffall monitoring campaign, with the aim of distinguishing the effluent plume from the receiving waters and characterizing its spatial variability in the vicinity of the discharge. The results demonstrate that this methodology can provide good estimates of the dispersion of effluent, and it is therefore very valuable in assessing the environmental impact and managing sea outfalls.

Abstract

Abstract
This paper proposes a novel architecture for real-time 3D ball trajectory estimation with a monocular camera in Middle Size League scenario. Our proposed system consists on detecting possible multiple ball candidates in the image, that are filtered in a multi-target data association layer. Validated ball candidates have their 3D trajectory estimated by Maximum Likelihood method (MLM) followed by a recursive refinement obtained with an Extended Kalman Filter (EKF). Our approach was validated in real RoboCup scenario, evaluated recurring to ground truth information obtained by alternative methods allowing overall performance and quality assessment. © 2012 Springer-Verlag Berlin Heidelberg.

Abstract
Nowadays,it is far more common to see mobile robotics working in the industrial sphere due to the mandatory need to achieve a new level of productivity and increase profits by reducing production costs. Management scheduling and task scheduling are crucial for companies that incessantly seek to improve their processes, increase their efficiency, reduce their production time and capitalize on their infrastructure by increasing and improving production. However, when faced with the constant decrease in production cycles, management algorithms can no longer solely focus on the mere management of the resources available, they must attempt to optimize every interaction between them, to achieve maximinn efficiency for each production resource. In this paper we focus on the presentation of the new competition called Robot Factory, its environment and its main objectives, paying special attention to the scheduling algorithm developed for this specific case study. The findings from the simulation approach have allowed us to conclude that mobile robotic path planning and the scheduling of the associated tasks represent a complex problem that has a strong impact on the efficiency of the entire production process.

Abstract
Teaching robotics to students at the beginning of their studies has become a huge challenge. Simulation environments can be an effective solution to that challenge where students can interact with simulated robots and have the first contact with robotic constraints. From our previous experience with simulation environments it was possible to observe that students with lower background knowledge in robotics where able to deal with a limited number of constraints, implement a simulated robotic platform and study several sensors. The question is: after this first phase what should be the best approach? Should the student start developing their own hardware? Hardware development is a very important part of an engineer's education but it can also be a difficult phase that could lead to discouragement and loss of motivation in some students. Considering the previous constraints and first year engineering students' high abandonment rate it is important to develop teaching strategies to deal with this problem in a feasible way. The solution that we propose is the integration of a low-cost standard robotic platform WowWee Rovio as an intermediate solution between the simulation phase and the stage where the students can develop their own robots. This approach will allow the students to keep working in robotic areas such as: cooperative behaviour, perception, navigation and data fusion. The propose approach proved to be a motivation step not only for the students but also for the teachers. Students and teachers were able to reach an agreement between the level of demand imposed by the teachers and satisfaction/motivation of the students.

Abstract
Currently, due to the widespread use of computers and the internet, students are trading libraries for the World Wide Web and laboratories with simulation programs. In most courses, simulators are made available to students and can be used to prove theoretical results or to test a developing hardware/product. Although this is an interesting solution: low cost, easy and fast way to perform some courses work, it has indeed major disadvantages. As everything is currently being done with/in a computer, the students are loosing the "feel" of the real values of the magnitudes. For instance, in engineering studies, and mainly in the first years, students need to learn electronics, algorithmic, mathematics and physics. All of these areas can use numerical analysis software, simulation software or spreadsheets and in the majority of the cases data used is either simulated or random numbers, but real data could be used instead. For example, if a course uses numerical analysis software and needs a dataset, the students can learn to manipulate arrays. Also, when using the spreadsheets to build graphics, instead of using a random table, students could use a real dataset based, for instance, in the room temperature and its variation across the day. In this work we present a framework which uses a simple interface allowing it to be used by different courses where the computers are the teaching/learning process in order to give a more realistic feeling to students by using real data. A framework is proposed based on a set of low cost sensors for different physical magnitudes, e. g. temperature, light, wind speed, which are connected to a central server that the students have access with an Ethernet protocol or are connected directly to the student computer/laptop. These sensors use the communication ports available such as: serial ports, parallel ports, Ethernet or Universal Serial Bus (USB). Since a central server is used, the students are encouraged to use sensor values results in their different courses and consequently in different types of software such as: numerical analysis tools, spreadsheets or simply inside any programming language when a dataset is needed. In order to do this, small pieces of hardware were developed containing at least one sensor using different types of computer communication. As long as the sensors are attached in a server connected to the internet, these tools can also be shared between different schools. This allows sensors that aren't available in a determined school to be used by getting the values from other places that are sharing them. Another remark is that students in the more advanced years and (theoretically) more know how, can use the courses that have some affinities with electronic development to build new sensor pieces and expand the framework further. The final solution provided is very interesting, low cost, simple to develop, allowing flexibility of resources by using the same materials in several courses bringing real world data into the students computer works.