Abstract
In this paper, some considerations about the stateof- the-art and the current trends in the design and mechanisms of robotic hands are reported and discussed. Several robotic hands with multiple degrees-of-freedom have been developed over the last two decades, mainly through the use of traditional means of action (e.g., electric actuators). This paper surveys the related work accomplished by the scientific community so far, focusing on the problems engendered by these often conflicting requirements, and the work that has been done in this area. Although there are many robotic hands projects, only a few have been addressing alternative technologies such as the use of Shape Memory Alloy.

Abstract
Ensuring a mobile ad-hoc network (MANET) in real and complex environments is an arduous task since the strength of the connection between two robots can rapidly change over time or even disappear. An extension of the Particle Swarm Optimization to multi-robot applications was recently proposed and denoted as Robotic Darwinian PSO (RDPSO). This paper contributes with a further extension of the RDPSO, by integrating a fault-tolerant distributed search in order to prevent communication network splits. To that end, each robot performs packet forwarding according to a paradigm of multi-hop communication, thus maintaining a maximum range or minimum signal quality between its "best" neighbors. This results in a sum of virtual forces for each robot to ensure a multi-connected MANET over time. Experimental results with 15 physical robots show that a more fault-tolerant strategy clearly influences the time needed to converge to the final solution but is less susceptible to robot failures.

Abstract
The Robotic Darwinian Particle Swarm Optimization (RDPSO) recently introduced in the literature has the ability to dynamically partition the whole population of robots based on simple "punish-reward" rules. Although this evolutionary algorithm enables the reduction of the amount of required information exchange among robots, a further analysis on the communication complexity of the RDPSO needs to be carried out so as to evaluate its scalability. This paper analyses the architecture of the RDPSO communication system, thus describing the dynamics of the communication data packet structure shared between teammates. Moreover, a set of simple communication rules is also proposed in order to reduce the communication overhead within swarms of robots. Experimental results with teams of 15 real robots show that the proposed methodology reduces the communication overhead, thus improving the scalability and applicability of the RDPSO algorithm.

Abstract

Abstract
Currently, we are facing increasingly environmental issues affecting our cities and consequently the health of living beings. A practical example it's the pollution, the excess of different odors and gases. Thus, our project is to create a sensor integrated in a smart grid that can measure gas pollution. This new sensor has the ability of detecting and classification of different odors that reflect the air quality in a given space.

Abstract
Motion sensing plays an important role in the study of human movements, motivated by a wide range of applications in different fields, such as sports, health care, daily activity, action recognition for surveillance, assisted living and the entertainment industry. In this paper, we describe how to classify a set of human movements comprising daily activities using a wearable motion capture suit, denoted as FatoXtract. A probabilistic integration of different classifiers recently proposed is employed herein, considering several spatiotemporal features, in order to classify daily activities. The classification model relies on the computed confidence belief from base classifiers, combining multiple likelihoods from three different classifiers, namely Na < ve Bayes, artificial neural networks and support vector machines, into a single form, by assigning weights from an uncertainty measure to counterbalance the posterior probability. In order to attain an improved performance on the overall classification accuracy, multiple features in time domain (e.g., velocity) and frequency domain (e.g., fast Fourier transform), combined with geometrical features (joint rotations), were considered. A dataset from five daily activities performed by six participants was acquired using FatoXtract. The dataset provided in this work was designed to be extremely challenging since there are high intra-class variations, the duration of the action clips varies dramatically, and some of the actions are quite similar (e.g., brushing teeth and waving, or walking and step). Reported results, in terms of both precision and recall, remained around 85 %, showing that the proposed framework is able to successfully classify different human activities.