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

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

Neutrinos in Large Extra Dimensions and Short-Baseline νe\nu_e Appearance

We show that, in the presence of bulk masses, sterile neutrinos propagating in large extra dimensions (LED) can induce electron-neutrino appearance effects. This is in contrast to what happens in the standard LED scenario and hence LED models with explicit bulk masses have the potential to address the MiniBooNE and LSND appearance results, as well as the reactor and Gallium anomalies. A special feature in our scenario is that the mixing of the first KK modes to active neutrinos can be suppressed, making the contribution of heavier sterile neutrinos to oscillations relatively more important. We study the implications of this neutrino mass generation mechanism for current and future neutrino oscillation experiments, and show that the Short-Baseline Neutrino Program at Fermilab will be able to efficiently probe such a scenario. In addition, this framework leads to massive Dirac neutrinos and thus precludes any signal in neutrinoless double beta decay experiments.Comment: 15 pages, 11 figure

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

Removing measurements from quantum walks

Quantum walks are very useful tools in designing quantum algorithms. Amplitude amplification is a key technique to increase the success probability of a quantum-walk-based algorithm, and it is quadratically faster than classical probabilistic amplification. However, amplitude amplification only applies to quantum walks with one-shot hitting time, where no measurements except a final one are performed, and not to quantum walks with concurrent hitting time, where measurements happen or absorbing boundaries exist at each step. In this paper, we propose a procedure to modify quantum walks with concurrent hitting time by removing measurements from them. This procedure enables us to use amplitude amplification to design algorithms based on the modified quantum walks which are faster than those based on the original walks with a concurrent hitting time and more robust than those based on the corresponding walks with a one-shot hitting time. © 2013 American Physical Society

Optimal universal programmable detectors for unambiguous discrimination

We discuss the problem of designing unambiguous programmable discriminators for any n unknown quantum states in an m-dimensional Hilbert space. The discriminator is a fixed measurement that has two kinds of input registers: the program registers and the data register. The quantum state in the data register is what users want to identify, which is confirmed to be among the n states in program registers. The task of the discriminator is to tell the users which state stored in the program registers is equivalent to that in the data register. First, we give a necessary and sufficient condition for judging an unambiguous programmable discriminator. Then, if $m=n$, we present an optimal unambiguous programmable discriminator for them, in the sense of maximizing the worst-case probability of success. Finally, we propose a universal unambiguous programmable discriminator for arbitrary n quantum states.Comment: 7 page

A general software defect-proneness prediction framework

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.BACKGROUND - Predicting defect-prone software components is an economically important activity and so has received a good deal of attention. However, making sense of the many, and sometimes seemingly inconsistent, results is difficult. OBJECTIVE - We propose and evaluate a general framework for software defect prediction that supports 1) unbiased and 2) comprehensive comparison between competing prediction systems. METHOD - The framework is comprised of 1) scheme evaluation and 2) defect prediction components. The scheme evaluation analyzes the prediction performance of competing learning schemes for given historical data sets. The defect predictor builds models according to the evaluated learning scheme and predicts software defects with new data according to the constructed model. In order to demonstrate the performance of the proposed framework, we use both simulation and publicly available software defect data sets. RESULTS - The results show that we should choose different learning schemes for different data sets (i.e., no scheme dominates), that small details in conducting how evaluations are conducted can completely reverse findings, and last, that our proposed framework is more effective and less prone to bias than previous approaches. CONCLUSIONS - Failure to properly or fully evaluate a learning scheme can be misleading; however, these problems may be overcome by our proposed framework.National Natural Science Foundation of Chin

Observation of Zeeman effect in topological surface state with distinct material dependence

The helical Dirac fermions on the surface of topological insulators host novel relativistic quantum phenomena in solids. Manipulating spins of topological surface state (TSS) represents an essential step towards exploring the theoretically predicted exotic states related to time reversal symmetry (TRS) breaking via magnetism or magnetic field. Understanding Zeeman effect of TSS and determining its g-factor are pivotal for such manipulations in the latter form of TRS breaking. Here, we report those direct experimental observations in Bi2Se3 and Sb2Te2Se by spectroscopic imaging scanning tunneling microscopy. The Zeeman shifting of zero mode Landau level is identified unambiguously by judiciously excluding the extrinsic influences associated with the non-linearity in the TSS band dispersion and the spatially varying potential. The g-factors of TSS in Bi2Se3 and Sb2Te2Se are determined to be 18 and -6, respectively. This remarkable material dependence opens a new route to control the spins in the TSS.Comment: main text: 17 pages, 4 figures; supplementary: 15 pages, 7 figure