Abstract
In this work, we analyze a remote optical sensor system composed of two Fiber Bragg Gratings (FBGs) and one Long Period Grading (LPG) capable of simultaneously sensing the temperature and the refractive index, separated by 50 km from the optical source and the interrogation unit. Since the active components of the system and the sensor head are separated over such a large distance, it is necessary to consider Raman amplification o strengthen the optical signal. We present both experimental measurements and the results of numerical simulations, which describe the signal evolution and predict the measurement results for a remote sensor based on a LPG. The simulation codes are also used to study a hybrid sensor composed of two FBGs with a LPG. We show that the power ratio between the two central wavelengths of the FBG has a linear relation with the change of refractive index of the sensored medium.

Abstract
The transmission of multimedia content between mobile devices is increasingly an area of exploration. The evolution of mobile devices and networks that support them, provide potential to create more sophisticated and innovating services. The fact that the Android platform does not provide the SIP protocol in its architecture, is a limiting factor for the development of new streaming applications. Throughout this paper the Android platform is presented, and more specifically the streaming protocols supported by the current streaming Media Framework. Based on the current Android Media Framework, this paper presents a possible architecture for the integration of the SIP protocol. The integration of this architecture is to surpass the limitations of the current Android platform and promote an improved performance in the current SIP applications, which is reflected in a lower power consumption of the device. © 2011 IEEE.

Abstract
Several meta-learning approaches have been developed for the problem of algorithm selection. In this context, it is of central importance to collect a sufficient number of datasets to be used as meta-examples in order to provide reliable results. Recently, some proposals to generate datasets have addressed this issue with successful results. These proposals include datasetoids, which is a simple manipulation method to obtain new datasets from existing ones. However, the increase in the number of datasets raises another issue: in order to generate meta-examples for training, it is necessary to estimate the performance of the algorithms on the datasets. This typically requires running all candidate algorithms on all datasets, which is computationally very expensive. One approach to address this problem is the use of an active learning approach to meta-learning, termed active meta-learning. In this paper we investigate the combined use of an active meta-learning approach based on an uncertainty score and datasetoids. Based on our results, we conclude that the accuracy of our method is very good results with as little as 10% to 20% of the meta-examples labeled.

Abstract
A single-mode nonadiabatic tapered optical fiber (NATOF) sensor was inserted into a fiber loop mirror (FLM) enabling us to tune its sensitivity towards refractive index (RI). The NATOF was fabricated by the heat pulling method, utilizing a CO laser. The adjustment of the polarization controllers (PCs) inserted in loop allowed us to excite different cladding modes in the interferometric taper resulting in different optical paths for the clockwise and the counterclockwise beams. By variation of the PCs' settings, the sensitivity of the sensor for RI in the range from 1.3380 to 1.3510 could be tuned from 876.24 to 1233.07 nm/RIU. Experimental results show that the sensitivity to the external RI increased with the order of the cladding mode.

Abstract
This paper presents a biased random-key genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. Active schedules are constructed using a priority-rule heuristic in which the priorities of the activities are defined by the genetic algorithm. A forward-backward improvement procedure is applied to all solutions. The chromosomes supplied by the genetic algorithm are adjusted to reflect the solutions obtained by the improvement procedure. The heuristic is tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

Abstract
Optical coherence tomography is nowadays an established imaging technique in Ophthalmology, with a key role on early detection of macular diseases, benefiting from the tremendous evolution in principles and technological developments of the last 20 years. In this paper the most important physical principles behind time-domain, spectral-domain and Fourier-domain OCT will be presented, along with examples of applications in different imaging fields, emphasizing the limitations of current systems, their performance parameters, as well as the challenges for the future within this field of development.