Abstract-Cognitive Radio (CR) is one of the most promising techniques for optimizing the spectrum usage. However, the large amount of data of spectral information that must be processed to identify and assign spectral resources increases the channel assignment times, therefore worsening the quality of service for the devices using the spectrum. Compressive Sensing (CS) is a digital processing technique that allows the reconstruction of sparse or compressible signals using fewer samples than those required traditionally. This paper presents a model that addresses the Spectral Sensing problem in Cognitive Radio using Compressive Sensing as an effective way of decreasing the number of samples required in the sensing process. This model is based on Compressive Spectral Imaging (CSI) architectures where a centralized spectrum manager selects what power data must be delivered by the different wireless devices using binary patterns, and builds a multispectral data cube image with the geographical and spectral data power information. The results show that this multispectral data cube can be built with only a 50% of the samples generated by the devices and, therefore reducing the data traffic dramatically.

Internet grows larger year by year and makes users to be confronted with large quantities of data that they cannot fully comprehend. The ongoing transition from Web 2.0 to the Semantic Web makes the development of intelligent services with the ability to discern, classify and simplify web information of vital importance. In this paper we present a new model for web-history organizing in order to improve the user action over the Internet. Based on this model we proposed an application, delivered as a Google Chrome browser extension, which organizes the web-history into semantic clusters, providing the user with an easy-to-follow hierarchal structure. The paper covers the main algorithms in the field, offering a comprehensive critical analysis, such as document vectorization, relational clustering, fuzzy and genetic variations and the item-set-based approach. Our work consists of adapting these algorithms to support an ever-increasing set of input data. The result is a hybrid variation that rapidly offers an acceptable solution, which is optimized in time, a quality preserved during the extensive web explorations a user may undergo. A variety of test results is presented in the end, with under-stress behavior and a selection of user experience.

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