In this letter, we investigate the physical layer security in multi-user multi-relay networks, where each relay is not merely a traditional helper, but at the same time, can become a potential eavesdropper. We first propose an efficient low-complexity user and relay selection scheme to significantly reduce the amount of channel estimation as well as the amount of potential links for comparison. For the proposed scheme, we derive the closed-form expression for the lower bound of ergodic secrecy rate (ESR) to evaluate the system secrecy performance. Simulation results are provided to verify the validity of our expressions and demonstrate how the ESR scales with the number of users and relays.

This paper studies the problem of recovering an arbitrarily distributed sparse matrix from its one-bit (1-bit) compressive measurements. We propose a matrix sketching based binary method iterative hard thresholding (MSBIHT) algorithm by combining the two dimensional version of BIHT (2DBIHT) and the matrix sketching method, to solve the sparse matrix recovery problem in matrix form. In contrast to traditional one-dimensional BIHT (BIHT), the proposed algorithm can reduce computational complexity. Besides, the MSBIHT can also improve the recovery performance comparing to the 2DBIHT method. A brief theoretical analysis and numerical experiments show the proposed algorithm outperforms traditional ones.

In a previous study, we proposed a new method based on copula theory to evaluate the detection performance of distributed-processing multistatic radar systems, in which the dependence of local decisions was modeled by a Gaussian copula with linear dependence and no tail dependence. However, we also noted that one main limitation of the study was the lack of investigations on the tail-dependence and nonlinear dependence among local detectors' inputs whose densities have long tails and are often used to model clutter and wanted signals in high-resolution radars. In this work, we attempt to overcome this shortcoming by extending the application of the proposed method to several types of multivariate copula-based dependence models to clarify the effects of tail-dependence and different dependence models on the system detection performance in detail. Our careful analysis provides two interesting and important clarifications: first, the detection performance degrades significantly with tail dependence; and second, this degradation mainly originates from the upper tail dependence, while the lower tail and nonlinear dependence unexpectedly improve the system performance.

A novel dual mode bandpass filter (BPF) with improved spurious response is presented in this letter. To obtain low insertion loss, the coupling structure using the dual mode resonator and the feeding scheme using coplanar-waveguide (CPW) are constructed on the two sides of a dielectric substrate. A defected ground structure (DGS) is designed on the ground plane of the CPW to achieve the goal of spurious suppression of the filter. The filter has been investigated numerically and experimentally. Measured results show a good agreement with the simulated analysis.

In this paper we study general complex eigenvalue problems in engineering fields. The eigenvalue problems can be transformed into the associated problems for solving large systems of nonlinear ordinary differential equations (dynamic equations) by optimization techniques. The known waveform relaxation method in circuit simulation can then be successfully applied to compute the resulting dynamic equations. The approach reported here, which is implemented on a message passing multi-processor system, can determine all eigenvalues and their eigenvectors for general complex matrices without any restriction on the matrices. The numerical results are provided to confirm the theoretical analysis.

For many sensor network applications such as military, homeland security, it is necessary for users (sinks) to access sensor networks while they are moving. However, sink mobility brings new challenges to secure routing in large-scale sensor networks. Mobile sinks have to constantly propagate their current location to all nodes, and these nodes need to exchange messages with each other so that the sensor network can establish and maintain a secure multi-hop path between a source node and a mobile sink. This causes significant computation and communication overhead for sensor nodes. Previous studies on sink mobility have mainly focused on efficiency and effectiveness of data dissemination without security consideration. In this paper, we propose a secure and energy-efficient data dissemination protocol -- Secure COodination-based Data dissEmination (SCODE) -- for mobile sinks in sensor networks. We take advantages of coordination networks (grid structure) based on Geographical Adaptive Fidelity (GAF) protocol to construct a secure and efficient routing path between sources and sinks. Our security analysis demonstrates that the proposed protocol can defend against common attacks in sensor network routing such as replay attacks, selective forwarding attacks, sinkhole and wormhole, Sybil attacks, HELLO flood attacks. Our performance evaluation both in mathematical analysis and simulation shows that the SCODE significantly reduces communication overhead and energy consumption while the latency is similar compared with the existing routing protocols, and it always delivers more than 90 percentage of packets successfully.

With the development of global navigation satellite systems (GNSS), the interference among global navigation satellite systems, known as the radio frequency compatibility problem, has become a matter of great concern to system providers and user communities. The acceptable compatibility threshold should be determined in the radio frequency compatibility assessment process. However, there is no common standard for the acceptable threshold in the radio frequency compatibility assessment. This paper firstly introduces the comprehensive radio frequency compatibility methodology combining the spectral separation coefficient (SSC) and code tracking spectral sensitivity coefficient (CT_SSC). Then, a method for determination of the acceptable compatibility threshold is proposed. The proposed method considers the receiver processing phase including acquisition, code and carrier tracking and data demodulation. Simulations accounting for the interference effects are carried out at each time step and every place on earth. The simulations mainly consider the signals of GPS, Galileo and BeiDou Navigation Satellite System (BDS) in the L1 band. Results show that all of the sole systems are compatible with other GNSS systems with respect to a special receiver configuration used in the simulations.

Nonprimitive non-narrow-sense BCH codes have been studied by many scholars. In this paper, we utilize nonprimitive non-narrow-sense BCH codes to construct a family of asymmetric quantum codes and two families of quantum convolutional codes. Most quantum codes constructed in this paper are different from the ones in the literature. Moreover, some quantum codes constructed in this paper have good parameters compared with the ones in the literature.

Considering that different people are different in their linguistic preference and in order to determine the consensus state when using Computing with Words (CWW) for supporting consensus decision making, this paper first proposes an interval composite scale based 2-tuple linguistic model, which realizes the process of translation from word to interval numerical and the process of retranslation from interval numerical to word. Second, this paper proposes an interval composite scale based personalized individual semantics model (ICS-PISM), which can provide different linguistic representation models for different decision-makers. Finally, this paper proposes a consensus decision making model with ICS-PISM, which includes a semantic translation and retranslation phase during decision process and determines the consensus state of the whole decision process. These models proposed take into full consideration that human language contains vague expressions and usually real-world preferences are uncertain, and provide efficient computation models to support consensus decision making.

In tutoring systems, students are more likely to utilize hints to assist their decisions about difficult or confusing problems. In the meanwhile, students with weaker knowledge mastery tend to choose more hints than others with stronger knowledge mastery. Hints are important assistances to help students deal with questions. Students can learn from hints and enhance their knowledge about questions. In this paper we firstly use hints alone to build a model named Hints-Model to predict student performance. In addition, matrix factorization (MF) has been prevalent in educational fields to predict student performance, which is derived from their success in collaborative filtering (CF) for recommender systems (RS). While there is another factorization method named non-negative matrix factorization (NMF) which has been developed over one decade, and has additional non-negative constrains on the factorization matrices. Considering the sparseness of the original matrix and the efficiency, we can utilize an element-based matrix factorization called regularized single-element-based NMF (RSNMF). We compared the results of different factorization methods to their combination with Hints-Model. From the experiment results on two datasets, we can find the combination of RSNMF with Hints-Model has achieved significant improvement and obtains the best result. We have also compared the Hints-Model with the pioneer approach performance factor analysis (PFA), and the outcomes show that the former method exceeds the later one.

In this paper, we present the sexual dimorphism analysis in 3D human face and perform gender classification based on the result of sexual dimorphism analysis. Four types of features are extracted from a 3D human-face image. By using statistical methods, the existence of sexual dimorphism is demonstrated in 3D human face based on these features. The contributions of each feature to sexual dimorphism are quantified according to a novel criterion. The best gender classification rate is 94% by using SVMs and Matcher Weighting fusion method. This research adds to the knowledge of 3D faces in sexual dimorphism and affords a foundation that could be used to distinguish between male and female in 3D faces.

A highly integrated monolithic Multi-Service Transport Platform (MSTP) ASIC MSEOSX8-6 incorporating more than 26M transistors has been fabricated with 0.18 µm CMOS technology. The chip is a powerful monolithic MSTP ASIC that supports RPR applications and serves as a generic building block for MSTP network. To accelerate the chip design, we devise a novel methodology called Embedded Reduced Self-Tester (ERST), which integrates the reduced self-tester structure into the chip to shorten the duration of dynamic simulation. Moreover, we divide the design into 12 smaller Hierarchical Layout Blocks (HLB) to enable parallel layout. Resultantly, the whole design has been completed in 5 months, which saves at least 80% of the design cycle in all.

In this study, we propose a method to reduce the mutual coupling between two J-shaped folded monopole antennas (JFMAs), which cover the IEEE 802.11 b/g (2400-2484 MHz) band. First, the change in mutual coupling with the spacing between the two antenna elements is investigated by considering two feeding models, and the effects of changes in the coupling on the antenna efficiency are studied. Subsequently, we try the method to reduce mutual coupling, the method involves the use of a bridge line that links the two antennas. The mutual coupling can be significantly reduced and the total antenna efficiency can be improved by linking two shorting strips with the bridge line. In a past study, we had found that in the case of L-shaped folded monopole antennas (LFMAs), the mutual coupling and antenna efficiency vary with the linking location on the bridge line. Moreover, we compare the characteristics of the LFMA and JFMA and show that the JFMA is effective when miniaturized.

In this letter, two types of blind adaptive frequency offset estimator for multicarrier code division multiple access systems are proposed that do not need any specific training sequence. It can not only accurately estimate the frequency offset, but also improve the revision capability and convergence rate. Several computer simulations confirm the effectiveness of the blind estimate approaches.

This paper focuses on power control in relay-enhanced multicell networks with universal frequency reuse for maximizing the overall system throughput, subject to interference and noise impairments, and individual power constraints at both BSs and RSs. With a high signal-to-interference-plus-noise ratio (SINR) approximation, an energy efficiency based power allocation algorithm is proposed to achieve the maximum sum throughput with the least power consumption. Moreover, an iterative quasi-distributed power allocation algorithm is also presented, which is suitable for any SINR regime. Numerical results indicate that the proposed algorithms approach the optimal power allocation and the system performance can be significantly improved in terms of network throughput and energy efficiency.

In many parallel programs, run-time data redistribution is usually required to enhance data locality and reduce remote memory access on the distributed memory multicomputers. Research on data redistribution algorithms has recently matured. The time required to generate data sets and processor sets is much lesser than before. Therefore, packing/unpacking has become a relatively high cost in redistribution. In this paper, we present methods to perform BLOCK-CYCLIC(s) to BLOCK-CYCLIC(t) redistribution, using MPI user-defined datatypes. This method reduces the required memory buffers and avoids unnecessary movement of data. Theoretical models are presented to determine the best method for redistribution. The methods were implemented on an IBM SP2 parallel machine to evaluate the performance of the proposed methods. The experimental results indicate that this approach can clearly improve the redistribution in most cases.

Being a new feature of next generation of wireless networks, Mobile Multi-hop Relay (MMR) is proposed for the purpose of coverage extension and throughput enhancement in LTE-Advanced, IEEE 802.16 j/m. Besides, with the help of relay, the system energy consumption could be well saved. In this paper, an energy saving scheduling scheme is proposed for OFDMA based two-hop relay systems. The novel scheme adjusts the modulation and coding (MC) mode and allocates the transmit power dynamically according to the resource intensity. It can also guarantee the Quality of Service (QoS) of different services by setting the scheduling priority. The simulation results show that the novel scheduling scheme can reduce energy consumption up to 76.27% compared to the conventional scheduling scheme, and achieve higher throughput while guaranteeing QoS.

A lot of work has been conducted on time series classification and similarity search over the past decades. However, the classification of a time series with high accuracy is still insufficient in applications such as ubiquitous or sensor systems. In this paper, a novel textual approximation of a time series, called TAX, is proposed to achieve high accuracy time series classification. l-TAX, an extended version of TAX that shows promising classification accuracy over TAX and other existing methods, is also proposed. We also provide a comprehensive comparison between TAX and l-TAX, and discuss the benefits of both methods. Both TAX and l-TAX transform a time series into a textual structure using existing document retrieval methods and bioinformatics algorithms. In TAX, a time series is represented as a document like structure, whereas l-TAX used a sequence of textual symbols. This paper provides a comprehensive overview of the textual approximation and techniques used by TAX and l-TAX

In this Letter, we propose a least mean square (LMS) with adaptive step-size (AS) algorithm for adaptive blind carrier frequency offset (CFO) estimation in the orthogonal frequency division multiplexing system. In conjunction with the closed-loop estimate structure, the proposed algorithm eliminates the inter-carrier interference caused by time varying CFO. To improve the convergence performance of the fixed step-size LMS estimator, the regular AS LMS algorithm offers faster convergence speed and more accuracy to the CFO estimate. Several computer simulation examples are presented for illustrating the effectiveness of the proposed algorithm.