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.

With the Semantic Web data standards defined, more applications demand inference engines in providing support for intelligent processing of the Semantic Web data. Rule-based inference engines or rule-based reasoners are used in many domains, such as in clinical support, and e-commerce recommender system development. This article reviews and compares key features of three freely-available rule-based reasoners: Jena inference engine, Euler YAP Engine, and BaseVISor. A performance evaluation study was conducted to assess the scalability and efficiency of these systems using data and rule sets adapted from the Berlin SPARQL Benchmark. We describe our methodology in assessing rule-based reasoners based on the benchmark. The study result shows the efficiency of the systems in performing reasoning tasks over different data sizes and rules involving various rule properties. The review and comparison results can provide a basis for users in choosing appropriate rule-based inference engines to match their application requirements.

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.

Regular expression matching is essential in network and big-data applications; however, it still has a serious performance bottleneck. The state-of-the-art schemes use a multi-pattern exact string-matching algorithm as a filtering module placed before a heavy regular expression engine. We design a new approximate string-matching filter using multiple q-grams; this filter not only achieves better space compactness, but it also has higher throughput than the existing filters.

Threats to a society and its social infrastructure are inevitable and endanger human life and welfare. Resilience is a core concept to cope with such threats in strengthening risk management. A resilient system adapts to an incident in a timely manner before it would result in a failure. This paper discusses the secondary use of personal data as a key element in such conditions and the relevant process mining in order to reduce IT risk on safety. It realizes completeness for such a proof on data breach in an acceptable manner to mitigate the usability problem of soundness for resilience. Acceptable soundness is still required and realized in our scheme for a fundamental privacy-enhancing trust infrastructure. Our proposal achieves an IT baseline protection and properly treats personal data on security as Ground Truth for deriving acceptable statements on data breach. An important role plays reliable broadcast by means of the block chain. This approaches a personal IT risk management with privacy-enhancing cryptographic mechanisms and Open Data without trust as belief in a single-point-of-failure. Instead it strengthens communities of trust.

The radio wave shadowing by a two-dimensional human body is examined numerically as the scattering problem by using the Method of Moments (MoM) in order to verify the equivalent human body diameter. Three human body models are examined: (1) a circular cylinder, (2) an elliptical cylinder, and (3) an elliptical cylinder with two circular cylinders are examined. The scattered fields yields by the circular cylinder are compared with measured data. Since the angle of the model to an incident wave affects scattered fields in models other than a circular cylinder, the models of an elliptical cylinder and an elliptical cylinder with two circular cylinders are converted into a circular cylinder of equivalent diameter. The frequency characteristics for the models are calculated by using the equivalent diameter.

This paper deals with the scattering of a cylindrical wave by a perfectly conductive periodic surface. This problem is equivalent to finding the Green's function G(x,z|xs,zs), where (x,z) and (xs,zs) are the observation and radiation source positions above the periodic surface, respectively. It is widely known that the Green's function satisfies the reciprocity: G(x,z|xs,zs)=G(xs,zs|x,z), where G(xs,zs|x,z) is named the reversal Green's function in this paper. So far, there is no numerical method to synthesize the Green's function with the reciprocal property in the grating theory. By combining the shadow theory, the reciprocity theorem for scattering factors and the average filter introduced previously, this paper gives a new numerical method to synthesize the Green's function with reciprocal property. The reciprocity means that any properties of the Green's function can be obtained from the reversal Green's function. Taking this fact, this paper obtains several new formulae on the radiation and scattering from the reversal Green's function, such as a spectral representation of the Green's function, an asymptotic expression of the Green's function in the far region, the angular distribution of radiation power, the total power of radiation and the relative error of power balance. These formulae are simple and easy to use. Numerical examples are given for a very rough periodic surface. Several properties of the radiation and scattering are calculated for a transverse magnetic (TM) case and illustrated in figures.

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.

Among various indoor positioning technologies, impulse-radio UWB is a promising technique to provide indoor positioning and tracking services with high precision. Because UWB regulations turned to imposing restrictions on UWB low band, UWB high band becomes attractive for enabling simple and low cost implementation. However, UWB high band endures much larger propagation loss than UWB low band. In this paper, we propose two separated methods to compensate the deficiency of high band in propagation. With the first method, we bundle several IR-UWB modules to increase the average transmission power, while an adaptive detection threshold is introduced at the receiver to raise receiving sensitivity with the second method. We respectively implement each of these two proposed methods and evaluate their performance through measurements in laboratory. The results show that each of them achieves about 7dB gains in signal power. Furthermore, positioning performance of these two proposed methods are evaluated and compared through field measurements in an indoor sports land.

ZHFE, proposed by Porras et al. at PQCrypto'14, is one of the very few existing multivariate encryption schemes and a very promising candidate for post-quantum cryptosystems. The only one drawback is its slow key generation. At PQCrypto'16, Baena et al. proposed an algorithm to construct the private ZHFE keys, which is much faster than the original algorithm, but still inefficient for practical parameters. Recently, Zhang and Tan proposed another private key generation algorithm, which is very fast but not necessarily able to generate all the private ZHFE keys. In this paper we propose a new efficient algorithm for the private key generation and estimate the number of possible keys generated by all existing private key generation algorithms for the ZHFE scheme. Our algorithm generates as many private ZHFE keys as the original and Baena et al.'s ones and reduces the complexity from O(n2ω+1) by Baena et al. to O(nω+3), where n is the number of variables and ω is a linear algebra constant. Moreover, we also analyze when the decryption of the ZHFE scheme does not work.

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.

The stream cipher Sprout with a short internal state was proposed in FSE 2015. Although the construction guaranteed resistance to generic Time Memory Data Tradeoff attacks, there were some weaknesses in the design and the cipher was completely broken. In this paper we propose a family of stream ciphers LILLE in which the size of the internal state is half the size of the secret key. Our main goal is to develop robust lightweight stream cipher. To achieve it, our cipher based on the two-key Even Mansour construction and thus its security against key/state recovery attacks reduces to a well analyzed problem. We also prove that like Sprout, the construction is resistant to generic Time Memory Data Tradeoff attacks. Unlike Sprout, the construction of the cipher guarantees that there are no weak key-IV pairs which produce a keystream sequence with short period or which make the algebraic structure of the cipher weaker and easy to cryptanalyze. The reference implementations of all members of the LILLE family with standard cell libraries based on the STM 90nm and 65nm processes were also found to be smaller than Grain v1 while security of LILLE family depend on reliable problem in the symmetric cryptography.

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.

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 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.

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.

A new method is proposed for the construction of pairing-friendly elliptic curves. For any fixed embedding degree, it can transform the problem to solving equation systems instead of exhaustive searching, thus it's more targeted and efficient. Via this method, we obtain various families including complete families, complete families with variable discriminant and sparse families. Specifically, we generate a complete family with important application prospects which has never been given before as far as we know.

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.

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.