Abstract
The article presents the results of the study and comparison of innovation of Portuguese and Polish companies. For this research, the perspective is taken from innovation based in intangible factors, trying to measure, above all, the organizational culture of companies on innovation. The results state that Portuguese firms are less innovative than Polish ones. At the same time, in both countries manufacturing companies are more innovative than companies in the construction sector. In the case of Polish companies it was demonstrated that there was a relationship between the size and age of the innovation. The research results will be used to assess the culture of enterprises' innovation in other countries.

Abstract
In recent years, tools in perceptual coding of high-quality audio have been tailored to capture highly detailed information regarding signal components so that they gained an intrinsic ability to represent audio parametrically. In a recent paper, we described a first validation model to such an approach applied to parametric coding of wideband speech. In this paper we describe specific advances to such an approach that improve coding efficiency and signal quality. A special focus is devoted to the fact that persistent transmission to the decoder of phase information is avoided, to the synthesis of both impulse-like and noise-based plosives using short-term windows, to improved ways of spectral envelope modelling, and to the fact that direct synthesis in the time-domain of the periodic content of speech is allowed in order to cope with fast F0 changes. A few examples of signal coding and transformation illustrate the impact of those improvements.

Abstract
This paper presents the last developments towards vision-based target tracking by an AUV. The main concepts behind the visual relative localization are provided and the results from a statistical analysis for the relative localization algorithm are presented. The purpose of this analysis is to ensure properness of data used to feed controllers that are responsible for governing the AUV motion. A new set of controllers enabling the AUV to track a visual target is given. Experimental data from obtained from tests in tank are presented, validating both the visual relative localization and control of the AUV.

Abstract

Abstract
Particle filters (PFs) are computationally intensive sequential Monte Carlo estimation methods with applications in the field of mobile robotics for performing tasks such as tracking, simultaneous localization and mapping (SLAM) and navigation, by dealing with the uncertainties and/or noise generated by the sensors as well as with the intrinsic uncertainties of the environment. However, the application of PFs with an important number of particles has traditionally been difficult to implement in real-time applications due to the huge number of operations they require. This work presents a hardware implementation on FPGA (field programmable gate arrays) of a PF applied to SLAM which aims to accelerate the execution time of the PF algorithm with moderate resource. The presented system is evaluated for different sensors including a low cost Neato XV-11 laser scanner sensor. First the system is validated by post processing data provided by a realistic simulation of a differential robot, equipped with a hacked Neato XV-11 laser scanner, that navigates in the Robot@Factory competition maze. The robot was simulated using SimTwo, which is a realistic simulation software that can support several types of robots. The simulator provides the robot ground truth, odometry and the laser scanner data. Then the proposed solution is further validated on standard laser scanner sensors in complex environments. The results achieved from this study confirmed the possible use of low cost laser scanner for different robotics applications which benefits in several aspects due to its cost and the increased speed provided by the SLAM algorithm running on FPGA.

Abstract
This paper presents a new dynamic and stochastic decision supporting model for distributed generation investment planning (DGIP). The model is formulated as a mixed integer linear programming (MILP) optimization problem that simultaneously minimizes emission, operation and maintenance, as well as reliability costs. One of the salient features of the model is that it is based on a two-period planning horizon: a short-term planning period that requires robust decisions to be made and a medium to long-term one involving exploratory or flexible investment decisions. Each period has multiple decision stages. The operational variability introduced by intermittent generation sources and electricity demand are accounted for via probabilistic methods. To ensure computational tractability, the associated operational states are reduced via a clustering technique. Moreover, uncertainties related to emission price, demand growth and the unpredictability of intermittent generation sources are taken into account stochastically. A real-life distribution network system is used as a case study, and the results of our analyses generally show the efficacy of the proposed model.