Abstract
Signal interference and packet collisions are typically viewed as negative factors that hinder wireless communication networks. When security is the primary concern, signal interference may actually be very helpful. Starting with a stochastic network model, we are able to show that packet collisions caused by jamming nodes can indeed be used effectively to attain new levels of secrecy in multiterminal wireless environments. To this effect, we propose a practical jamming protocol that uses the well-known request-to-send/clear-to-send (RTS/CTS) handshake of the IEEE 802.11 standard as a signaling scheme. Various jammer selection strategies are investigated depending on the position of source, destination, and jamming nodes. The goal is to cause asmuch interference as possible to eavesdroppers that are located in unknown positions, while limiting the interference observed by the legitimate receiver. To evaluate the performance of each strategy, we introduce and compute a measure for the secure throughput. Our results show that jamming can increase the levels of secrecy significantly albeit at a substantial cost in terms of energy efficiency.

Abstract
Manufacturing systems are dynamic, non-linear and often chaotic environments, subjected to the occurrence of unexpected disturbances that provoke deviations from the initial plans and degrades the system's performance. Traditionally, disturbance management is performed in a centralised manner, using a fail-fix approach and considering only one type of disturbance, the machine failure. However, the new generation of intelligent manufacturing systems should be able to treat emergencies much quicker and in an effective way, to minimise its negative impact on the production performance. This paper addresses this challenge by introducing a holonic disturbance management architecture based on the ADACOR foundations. The disturbance handling functions are performed in a distributed manner, introducing a predictive dimension to the re-scheduling problem and considering the major types of shop floor disturbances. An experimental implementation was performed and the obtained results showed the applicability of the proposed approach.

Abstract

Abstract
This paper addresses the problematic of operating isolated networks with large penetration of intermittent renewable power sources, as well as the benefits that electric vehicles might bring to these systems. A small island's network was used as case study and a 100% renewable dispatch for a valley period, only with hydro and wind generation, was tested in a dynamic simulation platform developed in Eurostag. Two distinct wind speed disturbances were simulated and, for both, the impact in the network's frequency was evaluated considering two different situations: electric vehicles only in charging mode and electric vehicles participating in primary frequency control. It was assumed the existence of 575 electric vehicles in the island. The impact of having electric vehicles performing primary frequency control in the expected batteries state of charge was also evaluated. © 2011 IEEE.

Abstract
In this work, a laser sensor is described that uses the multipath interference produced inside a ring cavity to measure the power loss induced by a moving taper intensity sensor. The laser is created due to the virtual distributed mirror formed by the Rayleigh scattering produced in a dispersion compensating fiber when pumped by a Raman laser. Two laser peaks were formed, one of them is obtained by the Raman gain (1555 nm) inside the ring and the second is created by the combination of the Raman gain and the Rayleigh scattering (1565 nm). A taper sensor is used as displacement sensor and when the losses is applied in the taper the second laser peak is reduced and the first peak is maintained constant and can be used as reference level.

Abstract
Research on time series forecasting is mostly focused on point predictions - models are obtained to estimate the expected value of the target variable for a certain point in future. However, for several relevant applications this type of forecasts has limited utility (e.g. costumer wallet value estimation, wind and electricity power production, control of water quality, etc.). For these domains it is frequently more important to be able to forecast a range of plausible future values of the target variable. A typical example is wind power production, where it is of high relevance to predict the future wind variability in order to ensure that supply and demand are balanced. This type of predictions will allow timely actions to be taken in order to cope with the expected values of the target variable on a certain future time horizon. In this paper we study this type of predictions - the prediction of a range of expected values for a future time interval. We describe some possible approaches to this task and propose an alternative procedure that our extensive experiments on both artificial and real world domains show to have clear advantages. Copyright 2011 ACM.