Optimal pilot placement for frequency offset estimation and data detection in burst transmission systems

In this letter, we address the problem of pilot design for Carrier Frequency Offset (CFO) and data detection in digital burst transmission systems. We consider a quasi-static flat-fading channel. We find that placing half of the pilot symbols at the beginning of the burst and the other half at the end of the burst is optimal for both CFO estimation and data detection. Our findings are based on the Cram´er-Rao bound and on empirical evaluations of the bit error rate for different pilot designs. The equal-preamble-postamble pilot design is shown to provide a significant gain in performance over the conventional preambleonly pilot design

Hardy is (almost) everywhere: nonlocality without inequalities for almost all entangled multipartite states

We show that all $n$-qubit entangled states, with the exception of tensor products of single-qubit and bipartite maximally-entangled states, admit Hardy-type proofs of non-locality without inequalities or probabilities. More precisely, we show that for all such states, there are local, one-qubit observables such that the resulting probability tables are logically contextual in the sense of Abramsky and Brandenburger, this being the general form of the Hardy-type property. Moreover, our proof is constructive: given a state, we show how to produce the witnessing local observables. In fact, we give an algorithm to do this. Although the algorithm is reasonably straightforward, its proof of correctness is non-trivial. A further striking feature is that we show that $n+2$ local observables suffice to witness the logical contextuality of any $n$-qubit state: two each for two for the parties, and one each for the remaining $n-2$ parties.Comment: 23 pages. Submitted for publicatio

On Recommendation of Learning Objects using Felder-Silverman Learning Style Model

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation

Published incidents and their proportions of human error

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Purpose - The information security field experiences a continuous stream of information security incidents and breaches, which are publicised by the media, public bodies and regulators. Despite the need for information security practices being recognised and in existence for some time the underlying general information security affecting tasks and causes of these incidents and breaches are not consistently understood, particularly with regard to human error. Methodology - This paper analyses recent published incidents and breaches to establish the proportions of human error, and where possible subsequently utilises the HEART human reliability analysis technique, which is established within the safety field. Findings - This analysis provides an understanding of the proportions of incidents and breaches that relate to human error as well as the common types of tasks that result in these incidents and breaches through adoption of methods applied within the safety field. Originality - This research provides original contribution to knowledge through the analysis of recent public sector information security incidents and breaches in order to understand the proportions that relate to human erro

Spin Polarization Dependence of Carrier Effective Mass in Semiconductor Structures: Spintronic Effective Mass

We introduce the concept of a spintronic effective mass for spin-polarized carriers in semiconductor structures, which arises from the strong spin-polarization dependence of the renormalized effective mass in an interacting spin-polarized electron system. The majority-spin many-body effective mass renormalization differs by more than a factor of 2 at rs=5 between the unpolarized and the fully polarized two-dimensional system, whereas the polarization dependence (~15%) is more modest in three dimensions around metallic densities (rs~5). The spin-polarization dependence of the carrier effective mass is of significance in various spintronic applications.Comment: Final versio