Three-dimensional (3-D) scattering center models use a finite number of point scatterers to efficiently represent complex radar target signature. Using the CLEAN algorithm, 3-D scattering center model is extracted from the inverse synthetic aperture radar (ISAR) image, which is generated based on the shooting and bouncing ray (SBR) technique. The conventional CLEAN extracts the strongest peak iteratively based on the assumption that the scattering centers are isolated. In a realistic target, however, both interference from the closely spaced points and additive noise distort the extraction process. This paper proposes a matched filter-based CLEAN algorithm to improve accuracy efficiently. Using the matched filtering of which impulse response is the known point spread function (PSF), a point most correlated with the PSF is extracted. Thus, the proposed method optimally enhances the accuracy in the presence of massive distortions. Numerical simulations using canonical and realistic targets demonstrate that the extraction accuracy is improved without loss of time-efficiency compared with the existing CLEAN algorithms.

Wireless Sensor Networks (WSNs) are randomly deployed in a hostile environment and left unattended. These networks are composed of small auto mouse sensor devices which can monitor target information and send it to the Base Station (BS) for action. The sensor nodes can easily be compromised by an adversary and the compromised nodes can be used to inject false vote or false report attacks. To counter these two kinds of attacks, the Probabilistic Voting-based Filtering Scheme (PVFS) was proposed by Li and Wu, which consists of three phases; 1) Key Initialization and assignment, 2) Report generation, and 3) En-route filtering. This scheme can be a successful countermeasure against these attacks, however, when one or more nodes are compromised, the re-distribution of keys is not handled. Therefore, after a sensor node or Cluster Head (CH) is compromised, the detection power and effectiveness of PVFS is reduced. This also results in adverse effects on the sensor network's lifetime. In this paper, we propose a Fuzzy Rule-based Key Redistribution Method (FRKM) to address the limitations of the PVFS. The experimental results confirm the effectiveness of the proposed method by improving the detection power by up to 13.75% when the key-redistribution period is not fixed. Moreover, the proposed method achieves an energy improvement of up to 9.2% over PVFS.

Graph sampling is an effective method to sample a representative subgraph from a large-scale network. Recently, researches have proven that several classical sampling methods are able to produce graph samples but do not well match the distribution of the graph properties in the original graph. On the other hand, the validation of these sampling methods and the scale of a good graph sample have not been examined on weighted graphs. In this paper, we propose the weighted graph sampling problem. We consider the proper size of a good graph sample, propose novel methods to verify the effectiveness of sampling and test several algorithms on real datasets. Most notably, we get new practical results, shedding a new insight on weighted graph sampling. We find weighted random walk performs best compared with other algorithms and a graph sample of 20% is enough for weighted graph sampling.

This paper proposes a scheme for automatic generation of mixed-integer programming problems for scheduling with multiple resources based on colored timed Petri nets. Our method reads Petri net data modeled by users, extracts the precedence and conflict relations among transitions, information on the available resources, and finally generates a mixed integer linear programming for exactly solving the target scheduling problem. The mathematical programing problems generated by our tool can be easily inputted to well-known optimizers. The results of this research can extend the usability of optimizers since our tool requires just simple rules of Petri nets but not deep mathematical knowledge.

In this paper, we consider wireless powered sensor networks. In these networks, the energy access point (EAP) transmits the energy packets to the sensor nodes and then, the sensor nodes send their sensing data to the information access point (IAP) by exploiting the harvested energy. Because the sensor nodes have a limited information queue (data storage) and energy queue (battery), energy packet/data packet scheduling is important. Accordingly, to reduce the total energy required to support the associated sensor network and simultaneously avoid sensing data loss, the energy packet/data packet transmission periods are jointly optimized. Furthermore, analyses identify the optimal location of EAP which will yield energy-efficient wireless powered sensor networks. Through the computer simulations, the performance of the proposed packet scheduling and deployment policy is demonstrated.

As an effective method, exponential discriminant analysis (EDA) has been proposed and widely used to solve the so-called small-sample-size (SSS) problem. In this paper, a simple and effective generalization of EDA is presented and named as GEDA. In GEDA, a general exponential function, where the base of exponential function is larger than the Euler number, is used. Due to the property of general exponential function, the distance between samples belonging to different classes is larger than that of EDA, and then the discrimination property is largely emphasized. The experiment results on the Extended Yale and CMU-PIE face databases show that, GEDA gets more advantageous recognition performance compared to EDA.

Surveillance with multiple cameras and microphones is promising to trace activities of suspicious persons for security purposes. When these sensors are connected to the Internet, they might also jeopardize innocent people's privacy because, as a result of human error, signals from sensors might allow eavesdropping by malicious persons. This paper presents a proposal for exploiting super-resolution to address this problem. Super-resolution is a signal processing technique by which a high-resolution version of a signal can be reproduced from a low-resolution version of the same signal source. Because of this property, an intelligible speech signal is reconstructed from multiple sensor signals, each of which is completely unintelligible because of its sufficiently low sampling rate. A method based on Bayesian linear regression is proposed in comparison with one based on maximum likelihood. Computer simulations using a simple sinusoidal input demonstrate that the methods restore the original signal from those which are actually measured. Moreover, results show that the method based on Bayesian linear regression is more robust than maximum likelihood under various microphone configurations in noisy environments and that this advantage is remarkable when the number of microphones enrolled in the process is as small as the minimum required. Finally, listening tests using speech signals confirmed that mean opinion score (MOS) of the reconstructed signal reach 3, while those of the original signal captured at each single microphone are almost 1.

The study of universal detector for fingerprinting code is strongly dependent on the design of scoring function. The optimal detector is known as MAP detector that calculates an optimal correlation score for a given single user's codeword. However, the knowledge about the number of colluders and their collusion strategy are inevitable. In this paper, we propose a new scoring function that equalizes the bias between symbols of codeword, which is called bias equalizer. We further investigate an efficient scoring function based on the bias equalizer under the relaxed marking assumption such that white Gaussian noise is added to a pirated codeword. The performance is compared with the MAP detector as well as some state-of-the-art scoring functions.

Multilinear maps have lots of cryptographic applications including multipartite key exchange and indistinguishability obfuscations. Since the concept of multilinear map was suggested, three kinds of candidate multilinear maps are constructed. However, the security of multilinear maps suffers from various attacks. In this paper, we overview suggested multilinear maps and cryptanalysis of them in diverse cases.

This paper proposes an oscillation model for analyzing the dynamics of activity propagation across social media networks. In order to analyze such dynamics, we generally need to model asymmetric interactions between nodes. In matrix-based network models, asymmetric interaction is frequently modeled by a directed graph expressed as an asymmetric matrix. Unfortunately, the dynamics of an asymmetric matrix-based model is difficult to analyze. This paper, first of all, discusses a symmetric matrix-based model that can describe some types of link asymmetry, and then proposes an oscillation model on networks. Next, the proposed oscillation model is generalized to arbitrary link asymmetry. We describe the outlines of four important research topics derived from the proposed oscillation model. First, we show that the oscillation energy of each node gives a generalized notion of node centrality. Second, we introduce a framework that uses resonance to estimate the natural frequency of networks. Natural frequency is important information for recognizing network structure. Third, by generalizing the oscillation model on directed networks, we create a dynamical model that can describe flaming on social media networks. Finally, we show the fundamental equation of oscillation on networks, which provides an important breakthrough for generalizing the spectral graph theory applicable to directed graphs.

Wireless video sensor networks address problems, such as low power consumption of sensor nodes, low computing capacity of nodes, and unstable channel bandwidth. To transmit video of distributed video coding in wireless video sensor networks, we propose an efficient scalable distributed video coding scheme. In this scheme, the scalable Wyner-Ziv frame is based on transmission of different wavelet information, while the Key frame is based on transmission of different residual information. A successive refinement of side information for the Wyner-Ziv and Key frames are proposed in this scheme. Test results show that both the Wyner-Ziv and Key frames have four layers in quality and bit-rate scalable, but no increase in complexity of the encoder.

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 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.

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.

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.

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.

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.

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.

Knowledge graphs (KG) play a crucial role in many modern applications. However, constructing a KG from natural language text is challenging due to the complex structure of the text. Recently, many approaches have been proposed to transform natural language text to triples to obtain KGs. Such approaches have not yet provided efficient results for mapping extracted elements of triples, especially the predicate, to their equivalent elements in a KG. Predicate mapping is essential because it can reduce the heterogeneity of the data and increase the searchability over a KG. In this article, we propose T2KG, an automatic KG creation framework for natural language text, to more effectively map natural language text to predicates. In our framework, a hybrid combination of a rule-based approach and a similarity-based approach is presented for mapping a predicate to its corresponding predicate in a KG. Based on experimental results, the hybrid approach can identify more similar predicate pairs than a baseline method in the predicate mapping task. An experiment on KG creation is also conducted to investigate the performance of the T2KG. The experimental results show that the T2KG also outperforms the baseline in KG creation. Although KG creation is conducted in open domains, in which prior knowledge is not provided, the T2KG still achieves an F1 score of approximately 50% when generating triples in the KG creation task. In addition, an empirical study on knowledge population using various text sources is conducted, and the results indicate the T2KG could be used to obtain knowledge that is not currently available from DBpedia.

In Cognitive Radio Networks (CRNs), spectrum sensing is performed by secondary (unlicensed) users to utilize transmission opportunities, so-called white spaces or spectrum holes, in the primary (licensed) frequency bands. Secondary users (SUs) perform sensing upon arrival to find an idle channel for transmission as well as during transmission to avoid interfering with primary users (PUs). In practice, spectrum sensing is not perfect and sensing errors including false alarms and misdetections are inevitable. In this paper, we develop a continuous-time Markov chain model to study the effect of false alarms and misdetections of SUs on several performance measures including the collision rate between PUs and SUs, the throughput of SUs and the SU delay in a CRN. Numerical results indicate that sensing errors can have a high impact on the performance measures.