In this paper, we propose a low-complexity widely-linear minimum mean square error (WL-MMSE) signal detection based on the Chebyshev polynomials accelerated symmetric successive over relaxation (SSORcheb) algorithm for uplink (UL) over-loaded large-scale multiple-input multiple-output (MIMO) systems. The technique of utilizing Chebyshev acceleration not only speeds up the convergence rate significantly, and maximizes the data throughput, but also reduces the cost. By utilizing the random matrix theory, we present good estimates for the Chebyshev acceleration parameters of the proposed signal detection in real large-scale MIMO systems. Simulation results demonstrate that the new WL-SSORcheb-MMSE detection not only outperforms the recently proposed linear iterative detection, and the optimal polynomial expansion (PE) WL-MMSE detection, but also achieves a performance close to the exact WL-MMSE detection. Additionally, the proposed detection offers superior sum rate and bit error rate (BER) performance compared to the precision MMSE detection with substantially fewer arithmetic operations in a short coherence time. Therefore, the proposed detection can satisfy the high-density and high-mobility requirements of some of the emerging wireless networks, such as, the high-mobility Internet of Things (IoT) networks.

The smart grid is expected to be the next generation electricity grid. It is beneficial for communication systems to improve energy efficiency and reduce carbon emissions. In this paper, we propose a distributed game theoretical framework for decode-and-forward (DF) cooperative relay networks with smart grid. A relay selection and power allocation strategy based on the buyer-seller game is proposed that processes the statistic channel-state information (CSI) available. The user is modeled as a buyer who selects the optimal relay and determines the optimal amount of power to be bought from the relay by the maximum utility criterion. The relay powered by the smart grid is modeled as a seller who determines the price of the power to achieve the maximum profit with its own cost. The equilibrium conditions of the game between the two sides are analyzed. The simulation results verify the existence of a Nash equilibrium point and illustrate that the proposed strategy may guarantee the utility of the source, the relay and the network and increase the energy efficiency.

In this paper, we focus on two-layer wireless sensor networks (WSNs) that consist of sensor-concentrator and inter-concentrator networks. In order to collect as much data as possible from a wide area, improving of network capacity is essential because data collection applications often require to gather data within a limited period, i.e., acceptable collection delay. Therefore, we propose a two-stage scheduling method for inter-concentrator networks. The proposed method first strictly schedules time slots of links with heavy interference and congestion by exploiting the combination metric of interference and traffic demand. After that, it simply schedules time slots of the remaining sinks to mitigate complexity. Simulation-based evaluations show our proposal offers much larger capacity than conventional scheduling algorithms. In particular, our proposal improves up to 70% capacity compared with the conventional methods in situations where the proportion of one- and two-hop links is small.

The combination of GPS measurements with the dynamic model via a Kalman filter or an extended Kalman filter, also known as GPS based reduced dynamic orbit determination (RDOD) techniques, have been widely used for accurate and real time navigation of satellites in low earth orbit (LEO). In previous studies, the GPS measurement noise variance is empirically taken as a constant, which is not reasonable because of insufficient prior information or dynamic environment. An improper estimate of the measurement noise may lead to poor performance or even divergence of the filter. In this letter, an adaptive extended Kalman filter (AEKF)-based approach using GPS dual-frequency pseudo-range measurements is presented, where the GPS pseudo-range measurement noise variance is adaptively estimated by the Carrier to Noise Ratio (C/N0) from the tracking loop of GPS receiver. The simulation results show that the proposed AEKF approach can achieve apparent improvements of the position accuracy and almost brings no extra computational burdens for satellite borne processor.

In this letter, robust sparse signal recovery is considered in the presence of heavy-tailed impulsive noise. Two Bayesian approaches are developed where a Bayesian framework is constructed by utilizing the Laplace distribution to model the noise. By rewriting the noise-fitting term as a reweighted quadratic function which is optimized in the sparse signal space, the Type I Maximum A Posteriori (MAP) approach is proposed. Next, by exploiting the hierarchical structure of the sparse prior and the likelihood function, we develop the Type II Evidence Maximization approach optimized in the hyperparameter space. The numerical results verify the effectiveness of the proposed methods in the presence of impulsive noise.

Let G be a graph such that each edge has its list of available colors, and assume that each list is a subset of the common set consisting of k colors. Suppose that we are given two list edge-colorings f0 and fr of G, and asked whether there exists a sequence of list edge-colorings of G between f0 and fr such that each list edge-coloring can be obtained from the previous one by changing a color assignment of exactly one edge. This problem is known to be PSPACE-complete for every integer k ≥ 6 and planar graphs of maximum degree three, but any computational hardness was unknown for the non-list variant in which every edge has the same list of k colors. In this paper, we first improve the known result by proving that, for every integer k ≥ 4, the problem remains PSPACE-complete even for planar graphs of bounded bandwidth and maximum degree three. Since the problem is known to be solvable in polynomial time if k ≤ 3, our result gives a sharp analysis of the complexity status with respect to the number k of colors. We then give the first computational hardness result for the non-list variant: for every integer k ≥ 5, the non-list variant is PSPACE-complete even for planar graphs of bandwidth quadratic in k and maximum degree k.

In this paper, we propose a received signal interpolation method for enhancing the performance of multiple signal classification (MUSIC) algorithm. In general, the performance of the conventional MUSIC algorithm is very sensitive to signal-to-noise ratio (SNR) of the received signal. When array elements receive the signals with nonuniform SNR values, the resolution performance is degraded compared to elements receiving the signals with uniform SNR values. Hence, we propose a signal calibration technique for improving the resolution of the algorithm. First, based on original signals, rough direction of arrival (DOA) estimation is conducted. In this stage, using frequency-domain received signals, SNR values of each antenna element in the array are estimated. Then, a deteriorated element that has a relatively lower SNR value than those of the other elements is selected by our proposed scheme. Next, the received signal of the selected element is spatially interpolated based on the signals received from the neighboring elements and the DOA information extracted from the rough estimation. Finally, fine DOA estimation is performed again with the calibrated signal. Simulation results show that the angular resolution of the proposed method is better than that of the conventional MUSIC algorithm. Also, we apply the proposed scheme to actual data measured in the testing ground, and it gives us more enhanced DOA estimation result.

With an annual growth of billions of sensor-based devices, it is an urgent need to do stream mining for the massive data streams produced by these devices. Cloud computing is a competitive choice for this, with powerful computational capabilities. However, it sacrifices real-time feature and energy efficiency. Application-specific integrated circuit (ASIC) is with high performance and efficiency, which is not cost-effective for diverse applications. The general-purpose microcontroller is of low performance. Therefore, it is a challenge to do stream mining on these low-cost devices with scalability and efficiency. In this paper, we introduce an FPGA-based scalable and parameterized architecture for stream mining.Particularly, Dynamic Time Warping (DTW) based k-Nearest Neighbor (kNN) is adopted in the architecture. Two processing element (PE) rings for DTW and kNN are designed to achieve parameterization and scalability with high performance. We implement the proposed architecture on an FPGA and perform a comprehensive performance evaluation. The experimental results indicate thatcompared to the multi-core CPU-based implementation, our approach demonstrates over one order of magnitude on speedup and three orders of magnitude on energy-efficiency.

In recent years, Fischlin and Fleischhacker showed the impossibility of proving the security of specific types of FS-type signatures, the signatures constructed by the Fiat-Shamir transformation, via a single-instance reduction in the non-programmable random oracle model (NPROM, for short). In this paper, we pose a question whether or not the impossibility of proving the security of any FS-type signature can be shown in the NPROM. For this question, we show that each FS-type signature cannot be proven to be secure via a key-preserving reduction in the NPROM from the security against the impersonation of the underlying identification scheme under the passive attack, as long as the identification scheme is secure against the impersonation under the active attack. We also show the security incompatibility between the security of some FS-type signatures in the NPROM via a single-instance key-preserving reduction and the underlying cryptographic assumptions. By applying this result to the Schnorr signature, one can prove the incompatibility between the security of the Schnorr signature in this situation and the discrete logarithm assumption, whereas Fischlin and Fleischhacker showed that such an incompatibility cannot be proven via a non-key-preserving reduction.

In the plane wave scattering from a periodic grating high order diffracted plane waves disappear at a low grazing angle limit of incidence. In this paper the scattering of a beam wave by the end-face of an ordered waveguide system composed of identical cores of equal space is treated by the perturbation method and the scattered field is analytically derived. The possibility that high order diffracted beam waves remain at a low grazing angle limit of incidence is shown.

In cellular network environments, where users are not evenly distributed across cells, overloaded base stations handling many users have difficulties in providing effective and fair services with their limited resources. Additionally, users at the cell edge may suffer from the potential problems resulting from low signal-to-interference ratio owing to the incessant interference from adjacent cells. In this paper, we propose a relay-assisted load balancing scheme to resolve these traffic imbalance. The proposed scheme can improve the performance of the overall network by utilizing relay stations to divert heavy traffic to other cells, and by adopting a partial frequency-reuse scheme to mitigate inter-cell interference. Each user and relay station calculates its own utility influence in the neighboring candidates for reassociation and decides whether to stay or move to another cell presenting the maximum total network utility increment. Simulation results show that the proposed scheme improves the overall network fairness to users by improving the performance of cell boundary users without degrading the total network throughput. We achieve a system performance gain of 16 ∼ 35% when compared with conventional schemes, while ensuring fairness among users.

In this paper, a spectrum efficient spatial polarized quadrature amplitude modulation (SPQM) scheme for physical layer security in dual-polarized satellite systems is proposed, which uses the carrier's polarization state, amplitude, phase and the polarization characteristics of the transmitting beams as information bearing parameters, which can improve the transmission efficiency and enhance the transmission security at the same time. As we know, the depolarization effect is the main drawback that affects the symbol error rate performance when polarization states are used to carry information. To solve the problem, we exploit an additional degree of freedom, time, in the proposed scheme, which means that two components of the polarized signal are transmitted in turn in two symbol periods, thus they can be recovered without mutual interference. Furthermore, orthogonal polarizations of the transmitting beam are used as spatial modulation for further increasing the throughput. In addition, in order to improve the transmission security, two transmitting beams are designed to transmit the two components of the polarized signal respectively. In this way, a secure transmission link is formed from the transmitter to the receiver to prevent eavesdropping. Finally, superiorities of SPQM are validated by the theoretical analysis and simulation results in dual-polarized satellite systems.

This paper newly proposes a fast computation technique on the method of image Green's function for p-characteristic calculations, when a plane wave with the transverse wavenumber p is incident on a periodic rough surface having perfect conductivity. In the computation of p-characteristics, based on a spectral domain periodicity of the periodic image Green's function, the image integral equation for a given incidence p maintains the same form for other particular incidences except for the excitation term. By means of a quadrature method, such image integral equations lead to matrix equations. Once the first given matrix equation is performed by a solution procedure as calculations of its matrix elements and its inverse matrix, the other matrix equations for other particular incidences no longer need such a solution procedure. Thus, the total CPU time for the computation of p-characteristics is largely reduced in complex shaped surface cases, huge roughness cases or large period cases.

This paper proposes a novel method to estimate non-integer shift of images based on least squares approximation in the phase region. Conventional methods based on Phase Only Correlation (POC) take correlation between an image and its shifted image, and then estimate the non-integer shift by fitting the model equation. The problem when estimating using POC is that the estimated peak of the fitted model equation may not match the true peak of the POC function. This causes error in non-integer shift estimation. By calculating the phase difference directly in the phase region, the proposed method allows the estimation of sub-pixel shift through least squares approximation. Also by utilizing the characteristics of natural images, the proposed method limits adoption range for least squares approximation. By these improvements, the proposed method achieves high accuracy, and we validate through some examples.

This paper describes a parametric representation of ultra-wideband radar signatures and its physical interpretation. Under the scattering theory of electromagnetic waves, a transfer function of radar scattering is factorized into three elementary parts and a radar signature with three parameters is derived. To use these parameters for radar target classification and identification, the relation between them and the response waveform is analytically revealed and numerically checked. The result indicates that distortion of the response waveform is sensitive to these parameters, and thus they can be expected to be used as features for radar target classification and identification.

In this work we utilize feature transformations that are common in supervised learning without having prior supervision, with the goal to improve Dirichlet process Gaussian mixture model (DPGMM) based acoustic unit discovery. The motivation of using such transformations is to create feature vectors that are more suitable for clustering. The need of labels for these methods makes it difficult to use them in a zero resource setting. To overcome this issue we utilize a first iteration of DPGMM clustering to generate frame based class labels for the target data. The labels serve as basis for learning linear discriminant analysis (LDA), maximum likelihood linear transform (MLLT) and feature-space maximum likelihood linear regression (fMLLR) based feature transformations. The novelty of our approach is the way how we use a traditional acoustic model training pipeline for supervised learning to estimate feature transformations in a zero resource scenario. We show that the learned transformations greatly support the DPGMM sampler in finding better clusters, according to the performance of the DPGMM posteriorgrams on the ABX sound class discriminability task. We also introduce a method for combining posteriorgram outputs of multiple clusterings and demonstrate that such combinations can further improve sound class discriminability.

This paper presents a new GPU-based rasterization algorithm for Boolean operations that handles arbitary closed polygons. We construct an efficient data structure for interoperation of CPU and GPU and propose a fast GPU-based contour extraction method to ensure the performance of our algorithm. We then design a novel traversing strategy to achieve an error-free calculation of intersection point for correct Boolean operations. We finally give a detail evaluation and the results show that our algorithm has a higher performance than exsiting algorithms on processing polygons with large amount of vertices.

Dynamic textures are sequences of images of moving scenes that exhibit certain stationarity properties in time. Hidden Markov model (HMM) is a statistical model, which has been used to model the dynamic texture. However, the texture is a region property. The traditional HMM models the property of a single pixel along the time, and does not consider the dependence of the spatial adjacent pixels of the dynamic texture. In this paper, the multivariate hidden Markov model (MHMM) is proposed to characterize and classify the dynamic textures. Specifically, the spatial adjacent pixels are modeled with multivariate hidden Markov model, in which the hidden states of those pixels are modeled with the multivariate Markov chain, and the intensity values of those pixels are modeled as the observation variables. Then the model parameters are used to describe the dynamic texture and the classification is based on the maximum likelihood criterion. The experiments on two benchmark datasets demonstrate the effectiveness of the introduced method.

This paper reports the development of a landmine visualization system based on complex-valued self-organizing map (CSOM) by employing one-dimensional (1-D) array of taper-walled tapered slot antennas (TSAs). Previously we constructed a high-density two-dimensional array system to observe and classify complex-amplitude texture of scattered wave. The system has superiority in its adaptive distinction ability between landmines and other clutters. However, it used so many (144) antenna elements with many mechanical radio-frequency (RF) switches and cables that it has difficulty in its maintenance and also requires long measurement time. The 1-D array system proposed here uses only 12 antennas and adopts electronic RF switches, resulting in easy maintenance and 1/4 measurement time. Though we observe stripe noise specific to this 1-D system, we succeed in visualization with effective solutions.

In this paper, we present a new three-way split formula for binary polynomial multiplication (PM) with five recursive multiplications. The scheme is based on a recently proposed multievaluation and interpolation approach using field extension. The proposed PM formula achieves the smallest space complexity. Moreover, it has about 40% reduced time complexity compared to best known results. In addition, using developed techniques for PM formulas, we propose a three-way split formula for Toeplitz matrix vector product with five recursive products which has a considerably improved complexity compared to previous known one.