Key predistribution schemes (KPSs) have played an important role in security of wireless sensor networks (WSNs). Due to comprehensive and simple structures, various types of combinatorial designs are used to construct KPSs. In general, compared to random KPSs, combinatorial KPSs have higher local connectivity but lower resilience against a node capture attack. In this paper, we apply two methods based on hash chains on KPSs based on transversal designs (TDs) to improve the resilience and the expressions for the metrics of the resulting schemes are derived.

Deterministic ID-based signatures are digital signatures where secret keys are probabilistically generated by a key generation center while the signatures are generated deterministically. Although the deterministic ID-based signatures are useful for both systematic and cryptographic applications, to the best of our knowledge, there is no scheme with a tight reduction proof. Loosely speaking, since the security is downgraded through dependence on the number of queries by an adversary, a tighter reduction for the security of a scheme is desirable, and this reduction must be as close to the difficulty of its underlying hard problem as possible. In this work, we discuss mathematical features for a tight reduction of deterministic ID-based signatures, and show that the scheme by Selvi et al. (IWSEC 2011) is tightly secure by our new proof framework under a selective security model where a target identity is designated in advance. Our proof technique is versatile, and hence a reduction cost becomes tighter than the original proof even under an adaptive security model. We furthermore improve the scheme by Herranz (The Comp. Jour., 2006) to prove tight security in the same manner as described above. We furthermore construct an aggregate signature scheme with partial aggregation, which is a key application of deterministic ID-based signatures, from the improved scheme.

Tunstall code is known as an optimal variable-to-fixed length (VF) lossless source code under the criterion of average coding rate, which is defined as the codeword length divided by the average phrase length. In this paper we define the average coding rate of a VF code as the expectation of the pointwise coding rate defined by the codeword length divided by the phrase length. We call this type of average coding rate the average pointwise coding rate. In this paper, a new VF code is proposed. An incremental parsing tree construction algorithm like the one that builds Tunstall parsing tree is presented. It is proved that this code is optimal under the criterion of the average pointwise coding rate, and that the average pointwise coding rate of this code converges asymptotically to the entropy of the stationary memoryless source emitting the data to be encoded. Moreover, it is proved that the proposed code attains better worst-case coding rate than Tunstall code.

The remaining issues in optical transmission technology are the degradation of optical signal to noise power ratio due to amplifier noise and the distortions due to optical nonlinear effects in a fiber. Therefore in addition to the Shannon limit, practical channel capacity is believed to be restricted by the nonlinear Shannon limit. The nonlinear Shannon limit has been derived under the assumption that the received signal points on the constellation map deviated by optical amplifier noise and nonlinear interference noise are symmetrically distributed around the ideal signal point and the sum of the noises are regarded as white Gaussian noise. The nonlinear Shannon limit is considered as a kind of theoretical limitation. However it is doubtful that its derivation process and applicable range have been understood well. In this paper, some fundamental papers on the nonlinear Shannon limit are reviewed to better understanding its meaning and applicable range.

In this paper, we theoretically analyse the influence of intersymbol interference (ISI) and continuous wave interference (CWI) on the bit error rate (BER) performance of the spread spectrum (SS) system using a real-valued Huffman sequence under the additive white Gaussian noise (AWGN) environment. The aperiodic correlation function of the Huffman sequence has zero sidelobes except the shift-end values at the left and right ends of shift. The system can give the unified communication and ranging system because the output of a matched filter (MF) is the ideal impulse by generating transmitted signal of the bit duration T=NTc, N=2n, n=1,2,… from the sequence of length M=2kN+1, k=0,1,…, where Tc is the chip duration and N is the spreading factor. As a result, the BER performance of the system is improved with decrease in the absolute value of the shift-end value, and is not influenced by ISI if the shift-end value is almost zero-value. In addition, the BER performance of the system of the bit duration T=NTc with CWI is improved with increase in the sequence length M=2kN+1, and the system can decrease the influence of CWI.

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.

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.

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.

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.

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.

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.

360 videos have recently become a popular virtual reality content type. However, a good quality metric for 360 videos is still an open issue. In this work, our goal is to identify appropriate objective quality metrics for 360 video communications. Especially, fourteen objective quality measures at different processing phases are considered. Also, a subjective test is conducted in this study. The relationship between objective quality and subjective quality is investigated. It is found that most of the PSNR-related quality measures are well correlated with subjective quality. However, for evaluating video quality across different contents, a content-based quality metric is needed.

Multi-camera videos with abundant information and high flexibility are useful in a wide range of applications, such as surveillance systems, web lectures, news broadcasting, concerts and sports viewing. Viewers can enjoy an enhanced viewing experience by choosing their own viewpoint through viewing interfaces. However, some viewers may feel annoyed by the need for continual manual viewpoint selection, especially when the number of selectable viewpoints is relatively large. In order to solve this issue, we propose an automatic viewpoint navigation method designed especially for sports. This method focuses on a viewer's personal preference for viewpoint selection, instead of common and professional editing rules. We assume that different trajectory distributions of viewing objects cause a difference in the viewpoint selection according to personal preference. We learn the relationship between the viewer's personal viewpoint-selection tendency and the spatio-temporal game context represented by the objects trajectories. We compare three methods based on Gaussian mixture model, SVM with a general histogram and SVM with a bag-of-words to seek the best learning scheme for this relationship. The performance of the proposed methods are evaluated by assessing the degree of similarity between the selected viewpoints and the viewers' edited records.

Block compressed sensing (CS) with optimal permutation is a promising method to improve sampling efficiency in CS-based image compression. However, the existing optimal permutation scheme brings a large amount of extra data to encode the permutation information because it needs to know the permutation information to accomplish signal reconstruction. When the extra data is taken into consideration, the improvement in sampling efficiency of this method is limited. In order to solve this problem, a new optimal permutation strategy for block CS (BCS) is proposed. Based on the proposed permutation strategy, an improved optimal permutation based BCS method called BCS-NOP (BCS with new optimal permutation) is proposed in this paper. Simulation results show that the proposed approach reduces the amount of extra data to encode the permutation information significantly and thereby improves the sampling efficiency compared with the existing optimal permutation based BCS approach.

Word sense disambiguation (WSD) is to identify the right sense of ambiguous words via mining their context information. Previous studies show that classifier combination is an effective approach to enhance the performance of WSD. In this paper, we systematically review state-of-the-art methods for classifier combination based WSD, including probability-based and voting-based approaches. Furthermore, a new classifier combination based WSD, namely the probability weighted voting method with dynamic self-adaptation, is proposed in this paper. Compared with existing approaches, the new method can take into consideration both the differences of classifiers and ambiguous instances. Exhaustive experiments are performed on a real-world dataset, the results show the superiority of our method over state-of-the-art methods.

Encoded lookup tables used in white-box cryptography are known to be vulnerable to power analysis due to the imbalanced encoding. This means that the countermeasures against white-box attacks can not even defend against gray-box attacks. For this reason, those who want to defend against power analysis through the white-box cryptographic implementation need to find other ways. In this paper, we propose a method to defend power analysis without resolving the problematic encoding problem. Compared with the existing white-box cryptography techniques, the proposed method has twice the size of the lookup table and nearly the same amount of computation.

Object detection has been a hot topic of image processing, computer vision and pattern recognition. In recent years, training a model from labeled images using machine learning technique becomes popular. However, the relationship between training samples is usually ignored by existing approaches. To address this problem, a novel approach is proposed, which trains Siamese convolutional neural network on feature pairs and finely tunes the network driven by a small amount of training samples. Since the proposed method considers not only the discriminative information between objects and background, but also the relationship between intraclass features, it outperforms the state-of-arts on real images.