Secure Multiuser Multiple Amplify-and-Forward Relay Networks in Presence of Multiple Eavesdroppers

In this paper, we study the information-theoretical security of a downlink multiuser cooperative relaying network with multiple intermediate amplify-and-forward (AF) relays, where there exist multiple eavesdroppers which can overhear the message. To prevent the wiretap and strength the network security, we select one best relay and user pair, so that the selected user can receive the message from the base station assisted by the selected relay. The relay and user selection is performed by maximizing the ratio of the received signal-to-noise ratio (SNR) at the user to the eavesdroppers, which is based on both the main and eavesdropper links. For the considered system, we derive the closed-form expression of the secrecy outage probability, and provide the asymptotic expression in high main-to-eavesdropper ratio (MER) region. From the asymptotic analysis, we can find that the system diversity order is equivalent to the number of relays regardless of the number of users and eavesdroppers.

Secure Multiuser Communications in Multiple Amplify-and-Forward Relay Networks

This paper proposes relay selection in order to increase the physical layer security in multiuser cooperative relay networks with multiple amplify-and-forward (AF) relays, in the presence of multiple eavesdroppers. To strengthen the network security against eavesdropping attack, we present three criteria to select the best relay and user pair. Specifically, criterion I and II study the received signal-to-noise ratio (SNR) at the receivers, and perform the selection by maximizing the SNR ratio of the user to the eavesdroppers. To this end, criterion I relies on both the main and eavesdropper links, while criterion II relies on the main links only. Criterion III is the standard max-min selection criterion,
which maximizes the minimum of the dual-hop channel gains of main links. For the three selection criteria, we examine the system secrecy performance by deriving the analytical expressions for the secrecy outage probability. We also derive the asymptotic analysis for the secrecy outage probability with high main-to eavesdropper ratio (MER). From the asymptotic analysis, an interesting observation is reached: for each criterion, the system diversity order is equivalent to the number of relays regardless of the number of users and eavesdroppers.

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.

MSC-clustering and forward stepwise regression for virtual metrology in highly correlated input spaces

Increasingly semiconductor manufacturers are exploring opportunities for virtual metrology (VM) enabled process monitoring and control as a means of reducing non-value added metrology and achieving ever more demanding wafer fabrication tolerances. However, developing robust, reliable and interpretable VM models can be very challenging due to the highly correlated input space often associated with the underpinning data sets. A particularly pertinent example is etch rate prediction of plasma etch processes from multichannel optical emission spectroscopy data. This paper proposes a novel input-clustering based forward stepwise regression methodology for VM model building in such highly correlated input spaces. Max Separation Clustering (MSC) is employed as a pre-processing step to identify a reduced srt of well-conditioned, representative variables that can then be used as inputs to state-of-the-art model building techniques such as Forward Selection Regression (FSR), Ridge regression, LASSO and Forward Selection Ridge Regression (FCRR). The methodology is validated on a benchmark semiconductor plasma etch dataset and the results obtained are compared with those achieved when the state-of-art approaches are applied directly to the data without the MSC pre-processing step. Significant performance improvements are observed when MSC is combined with FSR (13%) and FSRR (8.5%), but not with Ridge Regression (-1%) or LASSO (-32%). The optimal VM results are obtained using the MSC-FSR and MSC-FSRR generated models. © 2012 IEEE.

Cognitive amplify-and-forward relay networks with beamforming under primary user power constraint over Nakagami-m fading channels

In this paper, we analyze the performance of cognitive amplify-and-forward (AF) relay networks with beamforming under the peak interference power constraint of the primary user (PU). We focus on the scenario that beamforming is applied at the multi-antenna secondary transmitter and receiver. Also, the secondary relay network operates in channel state information-assisted AF mode, and the signals undergo independent Nakagami-m fading. In particular, closed-form expressions for the outage probability and symbol error rate (SER) of the considered network over Nakagami-m fading are presented. More importantly, asymptotic closed-form expressions for the outage probability and SER are derived. These tractable closed-form expressions for the network performance readily enable us to evaluate and examine the impact of network parameters on the system performance. Specifically, the impact of the number of antennas, the fading severity parameters, the channel mean powers, and the peak interference power is addressed. The asymptotic analysis manifests that the peak interference power constraint imposed on the secondary relay network has no effect on the diversity gain. However, the coding gain is affected by the fading parameters of the links from the primary receiver to the secondary relay network

Autism and ABA: Best Practice in America and the Way Forward for Europe.

According to recent estimates, 1 in each 68 new-borns will be diagnosed with an Autism Spectrum Disorder (ASD) in the USA (Centers for Disease Control and Prevention, 2014), while 1 in every 29 children will be diagnosed with ASD in the UK (Dillenburger, Jordan, McKerr, & Keenan, 2015). Individuals diagnosed with ASD share a set of characteristics at varying levels of severity: impairments in social communication skills and presence of restricted interests and repetitive behaviours (American Psychiatric Association, 2013).Notwithstanding these figures, little effort has been placed in European countries’ policies for reaching an early diagnosis. This has a detrimental effect on future prognosis for children with ASD, since research has clearly shown that when evidence-based interventions are accessed early in life, they can lead to great improvements on the overall functioning of children with ASD, including significant gains in social communication and reduction of inappropriate behaviours (Dawson, Rogers, Munson, Smith, Winter, Greenson, Donaldson, & Varley, 2009).Additionally, when looking at the services available for children with ASD and their families in Europe, it seems that not much improvement has been made in the last decades. Traditional eclectic approaches and a wealth of non-scientific methods seem to be available and often recommended by public bodies, while state-funded evidence-based interventions are not offered as part of the education or health system. Given that there is a wealth of evidence on the effectiveness of interventions based on the science of ABA, it seems that specific action is required to correct the situation, respecting children’s right to effective treatment and inclusion.In the present paper, these issues are fully discussed and recommendations for best practice are offered.

Forward and backward least angle regression for nonlinear system identification

A forward and backward least angle regression (LAR) algorithm is proposed to construct the nonlinear autoregressive model with exogenous inputs (NARX) that is widely used to describe a large class of nonlinear dynamic systems. The main objective of this paper is to improve model sparsity and generalization performance of the original forward LAR algorithm. This is achieved by introducing a replacement scheme using an additional backward LAR stage. The backward stage replaces insignificant model terms selected by forward LAR with more significant ones, leading to an improved model in terms of the model compactness and performance. A numerical example to construct four types of NARX models, namely polynomials, radial basis function (RBF) networks, neuro fuzzy and wavelet networks, is presented to illustrate the effectiveness of the proposed technique in comparison with some popular methods.

A novel forward gene selection algorithm for microarray data

This paper investigates the gene selection problem for microarray data with small samples and variant correlation. Most existing algorithms usually require expensive computational effort, especially under thousands of gene conditions. The main objective of this paper is to effectively select the most informative genes from microarray data, while making the computational expenses affordable. This is achieved by proposing a novel forward gene selection algorithm (FGSA). To overcome the small samples' problem, the augmented data technique is firstly employed to produce an augmented data set. Taking inspiration from other gene selection methods, the L2-norm penalty is then introduced into the recently proposed fast regression algorithm to achieve the group selection ability. Finally, by defining a proper regression context, the proposed method can be fast implemented in the software, which significantly reduces computational burden. Both computational complexity analysis and simulation results confirm the effectiveness of the proposed algorithm in comparison with other approaches

Dual-Hop Cognitive Amplify-and-Forward Relaying Networks over η− μ Fading Channels

This paper presents a thorough performance analysis of dual-hop cognitive amplify-and-forward (AF) relaying networks under spectrum-sharing mechanism over independent non-identically distributed (i.n.i.d.) 􀀀 fading channels. In order to guarantee the quality-of-service (QoS) of primary networks, both maximum tolerable peak interference power Q at the primary users (PUs) and maximum allowable transmit power P at secondary users (SUs) are considered to constrain transmit power at the cognitive transmitters. For integer-valued fading parameters, a closed-form lower bound for the outage probability (OP) of the considered networks is obtained. Moreover, assuming arbitrary-valued fading parameters, the lower bound in integral form for the OP is derived. In order to obtain further insights on the OP performance, asymptotic expressions for the OP at high SNRs are derived, from which the diversity/coding gains and the diversity-multiplexing gain tradeoff (DMT) of the secondary network can be readily deduced. It is shown that the diversity gain and also the DMT are solely determined by the fading parameters of the secondary network whereas the primary network only affects the coding gain. The derived results include several others available in previously published works as special cases, such as those for Nakagami-m fading channels. In addition, performance evaluation results have been obtained by Monte Carlo computer simulations which have verified the accuracy of the theoretical analysis.