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Learning-based sensing policies for multi-band flexible spectrum use, in particular cognitive radios operating in non-stationary radio environments are proposed. The proposed policies stem from the stochastic non-stationary restless multi-armed bandit formulation of opportunistic spectrum access. The non-stationary radio environment assumed in this paper is an appropriate model for a realistic cognitive radio systems, where the obtainable data rates depend on many unknown time-varying factors. These are e.g. mobility, fading and primary user activity. The developed policies are index policies, where the index of a frequency band depends on the discounted average reward of the band and a recency-based exploration bonus. The exploration bonus encourages sensing frequency bands that have not been explored for a long time. However, there is a maximum number of time instances when any band can remain unexplored. These index policies are computationally simple making them attractive for mobile cognitive radios. In our simulation examples, we demonstrate that the proposed policies can often provide higher cumulative data rate than other existing state-of-the-art policies.

There are two aspects in functional magnetic resonance imaging (fMRI) data that make them awkward to analyse with traditional multivariate methods - high order and high dimension. The first of these refers to the tensorial nature of observations as array-valued elements instead of vectors. Although this can be circumvented by vectorizing the array, doing so simultaneously loses all the structural information in the original observations. The second aspect refers to the high dimensionality along each dimension making the concept of dimension reduction a valuable tool in the processing of fMRI data. Different methods of tensor dimension reduction are currently gaining popUlarity in literature, and in this paper we apply two recently proposed methods of tensorial independent component analysis to simulated task-based fMRI data. Additionally, as a preprocessing step we introduce a novel extension of PCA for tensors. The simulations show that when extracting a sufficiently large number of principal components, the tensor methods find the task signals very reliably, something the standard temporal independent component analysis (tICA) fails in. peerReviewed

The Compute-and-Forward relaying strategy achieves high computation rates by decoding linear combinations of transmitted messages at intermediate relays. However, if the involved relays independently choose which combinations of the messages to decode, there is no guarantee that the overall system of linear equations is solvable at the destination. In this article it is shown that, for a Gaussian fading channel model with two transmitters and two relays, always choosing the combination that maximizes the computation rate often leads to a case where the original messages cannot be recovered. It is further shown that by limiting the relays to select from carefully designed sets of equations, a solvable system can be guaranteed while maintaining high computation rates. The proposed method has a constant computational complexity and requires no information exchange between the relays., 5 figures, 5 pages, submitted

The Internet of Things (IoT) technologies interconnect wide ranges of network devices irrespective of their resource capabilities and local networks. The device constraints and the dynamic link creations make it challenging to use pre-shared keys for every secure end-to-end (E2E) communication scenario in IoT. Variants of Host Identity Protocol (HIP) are adopted for constructing dynamic and secure E2E connections among the heterogenous network devices with imbalanced resource profiles and less or no previous knowledge about each other. We propose a collaborative HIP solution with an efficient key establishment component for the high constrained devices in IoT, which delegates the expensive cryptographic operations to the resource rich devices in the local networks. Finally, we demonstrate the applicability of the key establishment in collaborative HIP solution for the constrained IoT devices rather than the existing HIP variants, by providing performance and security analysis.

We present an approach to diversify entity search by utilizing semantics present and inferred from the initial entity search results. Our approach makes use of ontologies and independent component analysis of the entity descriptions to reveal direct and latent semantic connections between the entities present in the initial search results. The semantic connections are then used to sample a set of diverse entities. We empirically demonstrate the performance of our approach through retrieval experiments that use a real-world dataset composed from four entity databases. The results indicate that our approach significantly improves both diversity and effectiveness of entity search.

Recent work in the Smart Grid field has concentrated on the utilities' requirements, whereas user research on energy awareness and conservation has only been done at a relatively small scale. We attempt to bridge this gap by analyzing the requirements for a grid-wide real time energy monitoring system and combine them with requirements derived from state of the art user feedback applications, both based on the results of joint projects on the subject. We also present example applications that enable a real time system to better advise end users on their habits and administrative users to monitor the state of whole system.

Structural health monitoring aims to detect damages in man-made engineering structures by monitoring changes in their vibration response. Unsupervised learning algorithms can be used to obtain a model of the undamaged condition and detect which new samples of the structure are not in agreement with it. However, in real structures with a sensor network configuration, the number of candidate features usually becomes large. Therefore, complexity increases and it is necessary to perform feature selection and/or dimensionality reduction to achieve good detection accuracy. In this paper, we propose to exploit the three-way structure of data and apply a true multi-way data analysis algorithm: Parallel Factor Analysis. A simple model is obtained and used to train novelty detectors. The methods are tested both with real and simulated structural data to assess that the three-way analysis can be successfully used in structural health monitoring. Furthermore, the usefulness of the approach for feature selection is also analyzed.