Abstract
The research work presented in this paper focuses on the development of a 3D object localization and recognition system to be used in robotics conveyor coating lines. These requirements were specified together with enterprises with small production series seeking a full robotic automation of their production line that is characterized by a wide range of products in simultaneous manufacturing. Their production process (for example heat or coating/painting treatments) limits the use of conventional identification systems attached to the object in hand. Furthermore, the mechanical structure of the conveyor introduces geometric inaccuracy in the object positioning. With the correct classification and localization of the object, the robot will be able to autonomously select the right program to execute and to perform coordinate system corrections. A cascade system performed with Support Vector Machine and the Perfect Match (point cloud geometric template matching) algorithms was developed for this purpose achieving 99.5% of accuracy. The entire recognition and pose estimation procedure is performed in a maximum time range of 3 s with standard off the shelf hardware. It is expected that this work contributes to the integration of industrial robots in highly dynamic and specialized production lines.

Abstract

Abstract
This work presents a methodology for selecting the most relevant measurements for real-time power system monitoring. A genetic algorithm is employed to find the meter plan, composed of relevant, real-time measurements and pseudo-measurements that present the best compromise between investment costs and state estimation performance. This is achieved by minimizing both the number of real-time measurements in the power network and the degradation of the estimated states. Performance measures based on the Information Theory are investigated. Simulation results illustrate the performance of the proposed method.

Abstract
The use of remote sensing for obtaining evapotranspiration (ET) from natural and agricultural surfaces is already widely used. For irrigated agriculture the two basic approaches are: (1) the solution of the energy balance equation, using remotely sensed surface temperatures and reflectances to estimate variables and components of this equation, and (2) the crop coefficient and reference evapotranspiration (ETo) approach where the crop coefficient is obtained through canopy reflectance measurements. For the latter, theoretical and field studies have shown that satellite reflectance-derived vegetation indices (VIs) are closely relate to carbon and moisture fluxes and, when combined with ground data or appropriately calibrated models, they can produce valuable estimates of crop transpiration and related processes at the canopy or ecosystem scale (D'Urso and Calera, 2006). In this study the crop coefficient approach was used. For estimation of actual transpiration of irrigated and very high tree-density hedgerow orchards grown in Alentejo the procedure correlates (a) the basal crop transpiration coefficients (K-cb = T-max/ETo) of the unstressed full irrigated treatment to the normalized difference vegetation index (NDVI) values assessed from Landsat5 TM and Landsat7 ETM+ (r(2) = 0.86) and (b) the crop stress coefficient (K-s = T-a/ KcbETo), obtained from the ratio of the sustained deficit irrigation (T-a) and fully irrigated (T-max) daily transpiration rates, to a plant stress indicator, in the case, the basal leaf water potential (r(2)= 0.85). Daily tree transpiration rates on both treatments were obtained from sap flow measurements. The unstressed crop status of the full irrigation treatment was warranted from the high Willmott index of agreement (IA = 0.88) obtained with transpiration values simulated with the Penman-Monteith "big leaf" model (Willmott, 1982). In this algorithm, a specific model of bulk daily canopy conductance (G(c)) for unstressed olive canopies was used (Orgaz et al., 2007). From the resulting relationship equations, known field values of leaf basal water potential and satellite-derived NDVI's suffice to get estimates of Ks and Kcb, respectively and from them derive and map the actual olive tree transpiration (T-a = K-cb K-s ETo) rates. The study is under way, and thus further validation applications are planned prior to using the approach for mapping olive transpiration orchards of different tree density and scale areas.

Abstract
Mobile Ad hoc Networks have attracted much attention in the last years, since they allow the coordination and cooperation between agents belonging to a multi-robot system. However, initially deploying autonomously a wireless sensor robot network in a real environment has not taken the proper attention. Moreover, maintaining the connectivity between agents in real and complex environments is an arduous task since the strength of the connection between two nodes (i.e., robots) can change rapidly in time or even disappear. This paper compares two autonomous and realistic marsupial strategies for initial deployment in unknown scenarios, in the context of swarm exploration: Random and Extended Spiral of Theodorus. These are based on a hierarchical approach, in which exploring agents, named scouts, are autonomously deployed through explicit cooperation with supporting agents, denoted as rangers. Experimental results with a team of heterogeneous robots are conducted using both real and virtual robots. Results show the effectiveness of the methods, using a performance metric based on dispersion. Conclusions drawn in this work pave the way for a whole series of possible new approaches.

Abstract
Biometric systems are vulnerable to different kinds of attacks. Particularly, the systems based on iris are vulnerable to direct attacks consisting on the presentation of a fake iris to the sensor trying to access the system as it was from a legitimate user. The analysis of some countermeasures against this type of attacking scheme is the problem addressed in the present paper. Several state-of-the-art methods were implemented and included in a feature selection framework so as to determine the best cardinality and the best subset that conducts to the highest classification rate. Three different classifiers were used: Discriminant analysis, K nearest neighbours and Support Vector Machines. The implemented methods were tested in existing databases for iris liveness purposes (Biosec and Clarkson) and in a new fake database which was constructed for evaluation of iris liveness detection methods in the mobile scenario. The results suggest that this new database is more challenging than the others. Therefore, improvements are required in this line of research to achieve good performance in real world mobile applications.