Abstract
This study provides a review of all-optical flip-flops (AOFFs) technologies, and their possible experimental implementation solutions, for a variety of applications in optical communication networks. A description of the state-of-the-art experimental implementations and validation testing of the technologies used in different AOFFs schemes is made, presenting to the interested reader an overview of the up to date AOFFs design schemes. Some of the research results presented in this study were performed under the EU NoE EURO-FOS project consortium. This study also provides researchers working on this topic with interesting trends that are worth considering in their own research studies.

Abstract
Sampling techniques play a key role in achieving efficient network measurements by reducing the amount of traffic processed while trying to maintain the accuracy of network statistical behavior estimation. Despite the evolution of current techniques regarding the correctness of network parameters estimation, the overhead associated with the volume of data involved in the sampling process is still considerable. In this context, this paper proposes a new technique for multiadaptive traffic sampling based on linear prediction, which allows to reduce significantly the traffic under analysis, keeping the representativeness of samples in capturing network behavior. A proof-of-concept, evaluating this technique for real traffic traces representing distinct traffic profiles, demonstrates the effectiveness of the proposal, outperforming classic techniques both in accuracy and data volumes processed. © 2012 Springer-Verlag.

Abstract
The evacuation of complex buildings is a challenge under any circumstances. Fire drills are a way of training and validating evacuation plans. However, sometimes these plans are not taken seriously by their participants. It is also difficult to have the financial and time resources required. In this scenario, serious games can be used as a tool for training, planning and evaluating emergency plans. In this paper a prototype of a serious games evacuation simulator is presented. To make the environment as realistic as possible, 3D models were made using Blender and loaded onto Unity3D, a popular game engine. This framework provided us with the appropriate simulation environment. Some experiences were made and results show that this tool has potential for practitioners and planners to use it for training building occupants.

Abstract
In this paper, two hybrid multimode/single mode fiber Fabry-Perot (FP) cavities were compared. The cavities fabricated by chemical etching are presented as high temperature and strain sensors. In order to produce this FP cavity a single mode fiber was spliced to a graded index multimode fiber with 62.5 mu m core diameter. The Fabry-Perot cavities were tested as a high temperature sensor in the range between room temperature and 700 C and as strain sensors. A reversible shift of the interferometric peaks with temperature allowed to estimate a sensitivity of 0.75 +/- 0.03 pm/degrees C and 0.98 +/- 0.04 pm/degrees C for the sensor A and B respectively. For strain measurement sensor A demonstrated a sensitivity of 1.85 +/- 0.07 pm/mu and sensor B showed a sensitivity of 3.14 +/- 0.05 pm/mu. The sensors demonstrated the feasibility of low cost fiber optic sensors for high temperature and strain.

Abstract
Metalearning is a subfield of machine learning with special propensity for dynamic and complex environments, from which it is difficult to extract predictable knowledge. The field of study of this work is the electricity market, which due to the restructuring that recently took place, became an especially complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. This paper presents the development of a metalearner, applied to the decision support of electricity markets' negotiation entities. The proposed metalearner takes advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets' participating players. Using the outputs of each different strategy as inputs, the metalearner creates its own output, considering each strategy with a different weight, depending on its individual quality of performance. The results of the proposed method are studied and analyzed using MASCEM - a multi-agent electricity market simulator that models market players and simulates their operation in the market. This simulator provides the chance to test the metalearner in scenarios based on real electricity markets' data.

Abstract
Traffic sampling techniques are crucial and extensively used to assist network management tasks. Nevertheless, combining accurate network parameters' estimation and flexible lightweight measurements is an open challenge. In this context, this paper proposes a self-adaptive sampling technique, based on linear prediction, which allows to reduce significantly the measurement overhead, while assuring that sampled traffic reflects the statistical characteristics of the global traffic under analysis. The technique is multiadaptive as several parameters are considered in the dynamic configuration of the traffic selection process. The devised test scenarios aim at exploring the proposed sampling technique ability to join accurate network estimates to reduced overhead, using throughput as reference parameter. The evaluation results, obtained resorting to real traffic traces representing wired and wireless aggregated traffic scenarios and actual network services, prove that the simplicity, flexibility and self-adaptability of this technique can be successfully explored to improve network measurements efficiency over distinct traffic conditions. For optimization purposes, this paper also includes a study of the impact of varying the order of prediction, i.e., of considering different degrees of past memory in the self-adaptive estimation mechanism. The significance of the obtained results is demonstrated through statistical benchmarking.