Channel estimation and symbol detection for block transmission using data-dependent superimposed training

We address the problem of frequency-selective channel estimation and symbol detection using superimposed training. The superimposed training consists of the sum of a known sequence and a data-dependent sequence that is unknown to the receiver. The data-dependent sequence cancels the effects of the unknown data on channel estimation. The performance of the proposed approach is shown to significantly outperform existing methods based on superimposed training (ST)

Design and Application of a Service Outsourcing Cloud for the Insurance Industry

With the development and maturity of cloud computing technology, many cloud-based solutions for specific industry applications are also rapidly emerging. This study designed and implemented a Service Outsourcing Cloud for the Insurance Industry (SOC-II) for China's huge market demand, especially for Business Process Outsourcing (BPO) companies serving the insurance industry. Firstly, this research presents the cloud computing ecosystem, conducts SOC-II needs analysis, and then proposes the system architecture and logical architecture of SOC-II. Secondly, this paper introduces an image processing case in a SOC-II production operation system, and gives the operating mode and management mode of SOC-II. Thirdly, we summarize the main features of SOC-II and the new changes that SOC-II brings to the insurance industry. Finally, the article discusses the challenges of cloud computing

Analysis of the Least Squares Approach to Broadband Beamspace Beamforming

In this paper, we present a comprehensive comparison of different structures for broadband beamforming. We focus on both the tapped delay line (TDL) and the least squares (LS), beamspace approaches. Through simulations we confirm the superiority of the beamspace method (i.e., less complex and better frequency invariance). However, its anti-jamming ability is reduced due to non-orthogonal beams. We show how to mitigate this via a reduced rank approximation of the autocorrelation matrix

Mobile Robot Path Planners with Memory for Mobility Diversity Algorithms

Mobile robots (MRs) using wireless communications often experience small-scale fading so that the wireless channel gain can be low. If the channel gain is poor (due to fading), the robot can move (a small distance) to another location to improve the channel gain and so compensate for fading. Techniques using this principle are called mobility diversity algorithms (MDAs). MDAs intelligently explore a number of points to find a location with high channel gain while using little mechanical energy during the exploration. Until now, the location of these points has been predetermined. In this paper, we show how we can adapt their positions by using channel predictors. Our results show that MDAs, which adapt the location of those points, can in fact outperform (in terms of the channel gain obtained and mechanical energy used) the MDAs that use predetermined locations for those points. These results will significantly improve the performance of the MDAs and consequently allow MRs to mitigate poor wireless channel conditions in an energy-efficient manner

A Secure Optimum Distributed Detection Scheme in Under-Attack Wireless Sensor Networks

We address the problem of centralized detection of a binary event in the presence of fraction falsifiable sensor nodes (SNs) (i.e., controlled by an attacker) for a bandwidth constrained under-attack spatially uncorrelated distributed wireless sensor network (WSN). The SNs send their one-bit test statistics over orthogonal channels to the fusion center (FC), which linearly combines them to reach to a final decision. Adopting the modified deflection coefficient as an alternative function to be optimized, we first derive in a closed-form the FC optimal weights combining. But as these optimal weights require a-priori knowledge that cannot be attained in practice, this optimal weighted linear FC rule is not implementable. We also derive in a closed-form the expressions for the attacker “flipping probability” (defined in paper) and the minimum fraction of compromised SNs that makes the FC incapable of detecting. Next, based on the insights gained from these expressions, we propose a novel and non-complex reliability-based strategy to identify the compromised SNs and then adapt the weights combining proportional to their assigned reliability metric. In this way, the FC identifies the compromised SNs and decreases their weights in order to reduce their contributions towards its final decision. Finally, simulation results illustrate that the proposed strategy significantly outperforms (in terms of FC’s detection capability) the existing compromised SNs identification and mitigation schemes

Distributed Optimal Quantization and Power Allocation for Sensor Detection Via Consensus

We address the optimal transmit power allocation problem (from the sensor nodes (SNs) to the fusion center (FC)) for the decentralized detection of an unknown deterministic spatially uncorrelated signal which is being observed by a distributed wireless sensor network. We propose a novel fully distributed algorithm, in order to calculate the optimal transmit power allocation for each sensor node (SN) and the optimal number of quantization bits for the test statistic in order to match the channel capacity. The SNs send their quantized information over orthogonal uncorrelated channels to the FC which linearly combines them and makes a final decision. What makes this scheme attractive is that the SNs share with their neighbours just their individual transmit powers at the current states. As a result, the SN processing complexity is further reduced

Sparse Reconstruction of Time-Frequency Representation using the Fractional Fourier Transform

This paper describes a novel method to approximate instantaneous frequency of non-stationary signals through an application of fractional Fourier transform (FRFT). FRFT enables us to build a compact and accurate chirp dictionary for each windowed signal, thus the proposed approach offers improved computational efficiency, and good performance when compared with chirp atom method

Modelling and performance evaluation of non-uniform two-tier cellular networks through Stienen model

In this paper we introduce Stienen's model for analysing the performance of a non-uniform two-tier networks. The topology of the network consists of a set of macro base stations (MBSs) uniformly deployed, and a set of femtocell access points (FAPs) deployed only outside exclusion areas (discs) surrounding the MBSs. The MBSs serve users within the innermost areas of each macrocell, while the femtocells are restricted to serve users located in the outermost areas towards the edge of the macrocells. Results show that the edge user performance in terms of coverage is highly increased by the addition of femtocells. Moreover, the coverage in the macrocell tier can be also increased in comparison with a macrocell-only network if the number of femtocells deployed is judiciously selected. Furthermore, a well balanced network can be achieved, where the same performance is expected throughout the entire area

Quantized fusion rules for energy-based distributed detection in wireless sensor networks

We consider the problem of soft decision fusion in a bandwidth-constrained wireless sensor network (WSN). The WSN is tasked with the detection of an intruder transmitting an unknown signal over a fading channel. A binary hypothesis testing is performed using the soft decision of the sensor nodes (SNs). Using the likelihood ratio test, the optimal soft fusion rule at the fusion center (FC) has been shown to be the weighted distance from the soft decision mean under the null hypothesis. But as the optimal rule requires a-priori knowledge that is difficult to attain in practice, suboptimal fusion rules are proposed that are realizable in practice. We show how the effect of quantizing the test statistic can be mitigated by increasing the number of SN samples, i.e., bandwidth can be traded off against increased latency. The optimal power and bit allocation for the WSN is also derived. Simulation results show that SNs with good channels are allocated more bits, while SNs with poor channels are censored

Cumulative live birth rates following blastocyst- versus cleavage-stage embryo transfer in the first complete cycle of IVF : a population-based retrospective cohort study

Acknowledgements: We thank the Human Fertilisation and Embryological Authority for permission to analyse their database, extracting the requested information and assisting with our queries in an efficient manner. We acknowledge the data management support of the Grampian Data Safe Haven (DaSH) and the associated financial support of NHS Research Scotland, through NHS Grampian investment in the Grampian DaSH. For more information, visit the DaSH website http://www.abdn.ac.uk/iahs/facilities/grampian-data-safe-haven.php. Funding: N.J.C. received a Wolfson Foundation Intercalated Degree Research Fellowship funded by the Wolfson Foundation, through the Royal College of Physicians. This work was supported by a Chief Scientist Office postdoctoral training fellowship in health services research and health of the public research (ref PDF/12/06). The views expressed here are those of the authors and not necessarily those of the Chief Scientist Office. The funders had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; or decision to submit the manuscript for publication.Peer reviewedPostprin