Abstract
The metabolic disorder which entails the absent or reduced control of blood glucose in the body by means of insulin dependence (Type 1) or intolerance (Type 2) affected more than 366 million people in 2011. This represents an increase of 28% new cases in one year. Diabetes Mellitus has become the most common chronic diseases in nearly all countries, and continues to increase in numbers and significance, as economic development and urbanization lead to changing lifestyles characterized by reduced physical activity, and increased obesity. Recent advances in wireless sensor networking technology have led to the development of low cost, low power, multifunctional sensor nodes that enable environment sensing together with data processing. Instrumented with a variety of sensors, such as temperature, humidity, volatile compound detection, bio implanted sensors; the development of such networks requires testing for transmission distance and human body interference. As Bluetooth Low-Energy (BLE) operates in the free 2.4GHz ISM band, the same band that Wi-Fi signals operate, tests regarding interference, robustness and coexistence must be made in order to ensure Quality of Service (QoS) and therefore achieve medical diagnostic equipment status. This paper presents a BLE prototype and compares the results obtained in terms of radiated power over distance with and without physical barriers. © 2013 The Science and Information Organization.

Abstract
The objective of this work is to develop a highly robust 3D part localization and recognition algorithm. This research work is driven by the needs specified by enterprises with small production series that seek for full robotic automation in their production line, which processes a wide range of products and cannot use dedicated identification devices due to technological processes. With the correct classification of the part, the robot will be able to autonomously select the correct program to execute. For this purpose, the Perfect Match algorithm, which is known by its computational efficiency, high precision and robustness, was adapted for object recognition achieving a 99.7% of classification rate. The expected practical implication of this work is contributing to the integration of industrial robots in highly dynamic and specialized lines, reducing the companies' dependency on skilled operators.

Abstract
Robotic autonomous driving is a very complex task that tries to replicate the human behavior when performing such task. This paper presents a high-level overview of an architecture applicable for small-scale vehicles in autonomous driving competitions. A special emphasis is given on the sensory and navigation sub-systems since they are the most prominent intelligent decision layers. The former is almost entirely based on computer vision, processing the raw image content from two ordinary and inexpensive "web-cams", conveying further relevant information to the later, on a real-time basis. The proposed architecture was implemented with minimal interventions to an Ackermann-like vehicle which was originally designed for recreational purposes, serving as a "proof of concept" of the developed system. Results show that a low-cost, scalable and modular system can be easily integrated on regular small-scale vehicles obtaining exciting results at a minimal cost.

Abstract
The target searching problem is a situation where a formation of multi-robot systems is set to search for a target and converge towards it when it is found. This problem lies in the fact that the target is initially absent and the formation must search for it in the environment. During the target search, false targets may appear dragging the formation towards it. Therefore, in order to avoid the formation following a false target, this paper presents a new methodology using the Takagi-Sugeno type fuzzy automaton (TS-TFA) in the area of formation control to solve the target searching problem. The TS fuzzy system is used to change the formation through the modifications in the states of the automaton. This change does not only switch the rules and therefore the state of each robot, but also the controllers and cost functions. This approach amplifies the versatility of the formation of mobile robots in the target searching problem. In this paper, the TS-TFA is presented and its implications in the formation are explained. Simulations and results with real robot are presented where it can be noticed that the formation is broken to maximize the perception range based on each robot's observation of a possible target. Finally this work is concluded in the last section.

Abstract
The main objective of this project was to evaluate the sensing system for person detection in the scope of its integration in a mobile robotic platform for Ambient Assisted Living. Two sensors were considered, a camera and a depth sensor. For the camera (2D), 3 different algorithms were implemented: face detection, tracking and recognition. For the depth sensor the whole body detection was tested using skeleton tracking. The results showed that the face detection and recognition algorithms had a very small range, and the face tracking demonstrated to reach a higher distance. However, the latter exhibited poor results when confronted with illumination variations. The skeleton tracking algorithm provided good results, being capable of tracking relatively close to the sensor and up to 3 meters distance. Thus, the 2D face recognition can be used for short distances to identify the person, while the 3D skeleton tracking can be appropriated for distant tracking of the person. This work showed that the integration of these sensing systems, in a robotic platform, can make a robust robot capable of human interaction in home environments. © 2013 IEEE.

Abstract
Maximizing the performance of cooperative perception of a tracked target by a team of mobile robots while maintaining the team's formation is the core problem addressed in this work. We propose a solution by integrating the controller and the estimator modules in a formation control loop. The controller module is a distributed non-linear model predictive controller and the estimator module is based on a particle filter for cooperative target tracking. A formal description of the integration followed by simulation and real robot results on two different teams of homogeneous robots are presented. The results highlight how our method successfully enables a team of homogeneous robots to minimize the total uncertainty of the tracked target's cooperative estimate while complying with the performance criteria such as keeping a pre-set distance between the team-mates and/or the target and obstacle avoidance.