Abstract
We analyze the role of jamming as a means to increase the security of wireless systems. Specifically, we characterize the impact of cooperative/friendly jamming on the secrecy outage probability of a quasi-static wiretap fading channel. We introduce jamming coverage and jamming efficiency as security metrics, and evaluate the performance of three different jamming strategies that rely on various levels of channel state information. The analysis provides insight for the design of optimal jamming configurations and indicates that one jammer is not enough to maximize both metrics simultaneously.

Abstract
The sustainable conservation of mountain semi-natural meadows depends on the knowledge of their vegetation dynamics and management practices. Time series of vegetation indices (VI) derived from high temporal resolution satellite images can be a useful tool to the sustainable management of semi-natural meadows ecosystem and grazing activities. In this study satellite VI from the Moderate Resolution Imaging Spectroradiometer (MODIS) are evaluated against in situ measurements of VIs and plant height in the semi-natural mountain meadows of Northeast Portugal. In two testes sites, we evaluated the performance of Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from MODIS and field spectroradiometer sensor in characterizing semi-natural meadows phenology and plant height. The Savitzky-Golay filter was used for smoothing each VI time series, as well as to extract a number of NDVI and EVI metrics by computing derivatives. There was weak to reasonable agreement between VIs-metrics from MODIS and ground based derived phenology. The NDVI had a great sensitivity to crop growth changes during start of growth season, whereas the EVI exhibited more sensitivity at the pick of the maximum green biomass. The relationship between vegetation height and both VI from MODIS or field spectroradiometer, fit a non-linear model with similar pattern function for each test site. Regression analysis revealed that 67% of the in-season plant height variability could be explained by MODIS(EVI). These results suggest a great sensibility of MODIS(EVI) to detect the phenology and plant height of semi-natural meadows, even in situations of high plant height.

Abstract

Abstract
This paper describes the participation of FEUP, from the University of Porto, in the TREC 2010 Blog Track. FEUP participated in the baseline blog distillation task with work focused on the use of link features available in the TREC Blogs08 collection. The approach presented in this paper uses the link information available in most individual posts to amplify each post's score. Blog scores, and subsequent ranks, are obtained by combining individual post scores. We boost post scores using the in-degree of each post and the h-index of each blog. This results in an improvement of P@10, over our baseline, for the in-degree and the h-index runs. When compared to the in- degree, the h-index run results in higher performance values for each of the applied evaluation metrics.

Abstract
The segmentation stage is a key aspect of an object-based image analysis system. However, the segmentation quality is usually difficult to evaluate for satellite images. The Synthetic Image TEsting Framework (SITEF) is a tool to evaluate and compare image segmentation results. This paper presents an example of the use of SITEF for the evaluation of a segmentation algorithm, using a SPOT HRG satellite image with 6 vegetation land cover classes identified in an agricultural area. The segmentation results were evaluated under various perspectives, including the parcel size and shape, the land cover types, and the parameters used in the segmentation algorithm.

Abstract
The ubiquity of communication devices such as smartphones has led to the emergence of context-aware services that are able to respond to specific user activities or contexts. These services allow communication providers to develop new, added-value services for a wide range of applications such as social networking, elderly care and near-emergency early warning systems. At the core of these services is the ability to detect specific physical settings or the context a user is in, using either internal or external sensors. For example, using built-in accelerometers, it is possible to determine whether a user is walking or running at a specific time of day. By correlating this knowledge with GPS data, it is possible to provide specific information services to users with similar daily routines. This article presents a survey of the techniques for extracting this activity information from raw accelerometer data. The techniques that can be implemented in mobile devices range from classical signal processing techniques such as FFT to contemporary string-based methods. We present experimental results to compare and evaluate the accuracy of the various techniques using real data sets collected from daily activities.