Generalized Selection Combining in Cognitive MIMO Relay Networks

We propose transmit antenna selection with receive generalized selection combining (TAS/GSC) in dual-hop cognitive decode-and-forward (DF) relay networks for reliability enhancement and interference relaxation. In this paradigm, a single antenna which maximizes the receive signal-to-noise ratio (SNR) is selected at the secondary transmitter and a subset of receive antennas with the highest SNRs are combined at the secondary receiver. To demonstrate the impact of multiple primary users on the cognitive relay network, we derive new closed-form expressions for the exact and asymptotic outage probability with TAS/GSC in the secondary network. Several important design insights are reached. We corroborate that the full diversity gain is achieved, which is entirely determined by the total number of antennas in the secondary network. The negative impact of the primary network on the secondary network is reflected in the SNR gain.

Transmit Antenna Selection for Interference Management in Cognitive Relay Networks

We propose transmit antenna selection (TAS) in decode-and-forward (DF) relaying as an effective approach to reduce the interference in underlay spectrum sharing networks with multiple primary users (PUs) and multiple antennas at the secondary users (SUs). We compare two distinct protocols: 1) TAS with receiver maximal-ratio combining (TAS/MRC) and 2) TAS with receiver selection combining (TAS/SC). For each protocol, we derive new closed-form expressions for the exact and asymptotic outage probability with independent Nakagami-m fading in the primary and secondary networks. Our results are valid for two scenarios related to the maximum SU transmit power, i.e., P, and the peak PU interference temperature, i.e., Q. When P is proportional to Q, our results confirm that TAS/MRC and TAS/SC relaying achieve the same full diversity gain. As such, the signal-to-noise ratio (SNR) advantage of TAS/MRC relaying relative to TAS/SC relaying is characterized as a simple ratio of their respective SNR gains. When P is independent of Q, we find that an outage floor is obtained in the large P regime where the SU transmit power is constrained by a fixed value of Q. This outage floor is accurately characterized by our exact and asymptotic results.

Secured Cooperative Cognitive Radio Networks with Relay Selection

In this paper, we propose physical layer security for cooperative cognitive radio networks (CCRNs) with relay selection in the presence of multiple primary users and multiple eavesdroppers. To be specific, we propose three relay selection schemes, namely, opportunistic relay selection (ORS), suboptimal relay selection (SoRS), and partial relay selection (PRS) for secured CCRNs, which are based on the availability of channel state information (CSI) at the receivers. For each approach, we derive exact and asymptotic expressions for the secrecy outage probability. Results show that under the assumption of perfect CSI, ORS outperforms both SoRS and PRS.

Interference Investigation for Cognitive Spectrum Sharing Networks with Reactive DF Relay Selection

Cognitive radio (CR) with spectrum-sharing has been envisioned as emerging technology for the next generation of mobile and wireless networks by allowing the unlicensed customers simultaneously utilize the licensed radio frequency spectrums. However, the CR has faced some practical challenges due to its deduced system performance as compared to non spectrum-sharing counterpart. In this paper, we therefore consider the potential of incorporating the cooperative communications into CR by introducing the concept of reactive multiple decode-and-forward (DF) relays. In particular, we derive new results for exact and asymptotic expressions for the performance of cognitive relay networks with K-th best relay selection. Our novel results have exhibited the significance of using relay networks to enhance the system performance of CR.

Bandwidth selection by cross-validation for forecasting long memory financial time series

The paper addresses the issue of choice of bandwidth in the application of semiparametric estimation of the long memory parameter in a univariate time series process. The focus is on the properties of forecasts from the long memory model. A variety of cross-validation methods based on out of sample forecasting properties are proposed. These procedures are used for the choice of bandwidth and subsequent model selection. Simulation evidence is presented that demonstrates the advantage of the proposed new methodology.

The role of personality and self-efficacy in the selection and retention of successful nursing students:a longitudinal study

Aim: This paper is a report of a study to examine the role of personality and self-efficacy in predicting academic performance and attrition in nursing students.

Background: Despite a considerable amount of research investigating attrition in nursing students and new nurses, concerns remain. This particular issue highlights the need for a more effective selection process whereby those selected are more likely to complete their preregistration programme successfully, and remain employed as Registered Nurses.

Method: A longitudinal design was adopted. A questionnaire, which included measures of personality and occupational and academic self-efficacy, was administered to 384 students early in the first year of the study. At the end of the programme, final marks and attrition rates were obtained from university records for a total of 350 students. The data were collected from 1999 to 2002.

Findings: Individuals who scored higher on a psychoticism scale were more likely to withdraw from the programme. Occupational self-efficacy was revealed to be a statistically significant predictor of final mark obtained, in that those with higher self-efficacy beliefs were more likely to achieve better final marks. Extraversion was also shown to negatively predict academic performance in that those with higher extraversion scores were more likely to achieve lower marks.

Conclusion: More research is needed to explore the attributes of successful nursing students and the potential contribution of psychological profiling to a more effective selection process.

Automotive waste heat recovery: Working fluid selection and related boundary conditions

This paper presents the rational for the selection of fluids for use in a model based study of sub and supercritical Waste Heat Recovery (WHR) Organic Rankine Cycle (ORC). The study focuses on multiple vehicle heat sources and the potential of WHR ORC’s for its conversion into useful work. The work presented on fluid selection is generally applicable to any waste heat recovery system, either stationary or mobile and, with careful consideration, is also applicable to single heat sources. The fluid selection process presented reduces the number of potential fluids from over one hundred to a group of under twenty fluids for further refinement in a model based WHR ORC performance study. The selection process uses engineering judgement, legislation and, where applicable, health and safety as fluid selection or de-selection criteria. This paper also investigates and discusses the properties of specific ORC fluids with regard to their impact on the theoretical potential for delivering efficient WHR ORC work output. The paper concludes by looking at potential temperature and pressure WHR ORC limits with regard to fluid properties thereby assisting with the generation of WHR ORC simulation boundary conditions.

Optimal wafer site selection using forward selection component analysis

Reducing wafer metrology continues to be a major target in semiconductor manufacturing efficiency initiatives due to it being a high cost, non-value added operation that impacts on cycle-time and throughput. However, metrology cannot be eliminated completely given the important role it plays in process monitoring and advanced process control. To achieve the required manufacturing precision, measurements are typically taken at multiple sites across a wafer. The selection of these sites is usually based on a priori knowledge of wafer failure patterns and spatial variability with additional sites added over time in response to process issues. As a result, it is often the case that in mature processes significant redundancy can exist in wafer measurement plans. This paper proposes a novel methodology based on Forward Selection Component Analysis (FSCA) for analyzing historical metrology data in order to determine the minimum set of wafer sites needed for process monitoring. The paper also introduces a virtual metrology (VM) based approach for reconstructing the complete wafer profile from the optimal sites identified by FSCA. The proposed methodology is tested and validated on a wafer manufacturing metrology dataset. © 2012 IEEE.

Adaptive uncertain information fusion to enhance plan selection in BDI agent systems

Correctly modelling and reasoning with uncertain information from heterogeneous sources in large-scale systems is critical when the reliability is unknown and we still want to derive adequate conclusions. To this end, context-dependent merging strategies have been proposed in the literature. In this paper we investigate how one such context-dependent merging strategy (originally defined for possibility theory), called largely partially maximal consistent subsets (LPMCS), can be adapted to Dempster-Shafer (DS) theory. We identify those measures for the degree of uncertainty and internal conflict that are available in DS theory and show how they can be used for guiding LPMCS merging. A simplified real-world power distribution scenario illustrates our framework. We also briefly discuss how our approach can be incorporated into a multi-agent programming language, thus leading to better plan selection and decision making.