Boolean functions and vectorial Boolean functions are the most important components of stream ciphers. Their cryptographic properties are crucial to the security of the underlying ciphers. And how to construct such functions with good cryptographic properties is a nice problem that worth to be investigated. In this paper, using two small nonlinear functions with t-1 resiliency, we provide a method on constructing t-resilient n variables Boolean functions with strictly almost optimal nonlinearity >2n-1-2n/2 and optimal algebraic degree n-t-1. Based on the method, we give another construction so that a large class of resilient vectorial Boolean functions can be obtained. It is shown that the vectorial Boolean functions also have strictly almost optimal nonlinearity and optimal algebraic degree.

This paper addresses the association problem of tracking closely spaced targets in group or formation. In the Labeled Multi-Bernoulli Filter (LMB), the weight of a hypothesis is directly affected by the distance between prediction and measurement. This may generate false associations when dealing with the closely spaced multiple targets. Thus we consider utilizing structure information among the group or formation. Since, the relative position relation of the targets in group or formation varies slightly within a short time, the targets are considered as nodes of a topological structure. Then the position relation among the targets is modeled as a hypergraph. The hypergraph matching method is used to resolve the association matrix. At last, with the structure prior information introduced, the new joint cost matrix is re-derived to generate hypotheses, and the filtering recursion is implemented in a Gaussian mixture way. The simulation results show that the proposed algorithm can effectively deal with group targets and is superior to the LMB filter in tracking precision and accuracy.

Using linear programming (LP) decoding based on alternating direction method of multipliers (ADMM) for low-density parity-check (LDPC) codes shows lower complexity than the original LP decoding. However, the development of the ADMM-LP decoding algorithm could still be limited by the computational complexity of Euclidean projections onto parity check polytope. In this paper, we proposed a bisection method iterative algorithm (BMIA) for projection onto parity check polytope avoiding sorting operation and the complexity is linear. In addition, the convergence of the proposed algorithm is more than three times as fast as the existing algorithm, which can even be 10 times in the case of high input dimension.

Due to the rapid development of small satellite technology and the advantages of LEO satellite with low delay and low propagation loss as compared with the traditional GEO satellite, the broadband LEO constellation satellite communication system has gradually become one of the most important hot spots in the field of satellite communications. Many countries and satellite communication companies in the world are formulating the project of broadband satellite communication system. The broadband satellite communication system is different from the traditional satellite communication system. The former requires a higher transmission rate. In the case of high-speed transmission, if the low elevation constellation is adopted, the satellite beam will be too much, which will increase the complexity of the satellite. It is difficult to realize the low-cost satellite. By comparing the complexity of satellite realization under different elevation angles to meet the requirement of terminal speed through link computation, this paper puts forward the conception of building broadband LEO constellation satellite communication system with high elevation angle. The constraint relation between satellite orbit altitude and user edge communication elevation angle is proposed by theoretical Eq. deduction. And the simulation is carried out for the satellite orbit altitude and edge communication elevation angle.

The security of Unmanned Aerial Vehicle (UAV) swarms is threatened by the deployment of anti-UAV systems under complicated environments such as battlefield. Specifically, the faults caused by anti-UAV systems exhibit sparse and compressible characteristics. In this paper, in order to improve the survivability of UAV swarms under complicated environments, we propose a novel multi-abnormality self-detecting and faults location method, which is based on compressed sensing (CS) and takes account of the communication characteristics of UAV swarms. The method can locate the faults when UAV swarms are suffering physical damages or signal attacks. Simulations confirm that the proposed method performs well in terms of abnormalities detecting and faults location when the faults quantity is less than 17% of the quantity of UAVs.

Unmanned aerial vehicle (UAV) communication has drawn rising interest recently with the distinctive gains brought by its inherent mobility. In this paper, we investigate the throughput maximization problem in UAV-enabled uplink communication, where multiple ground nodes communicate with a UAV while a group of ground jammers send jamming signals to jam the communications between UAV and the ground nodes. In contrast to the previous works that only considering UAV's transmit power allocation and two-dimension (2D) trajectory design, the ground nodes' transmit power allocation and scheduling along with the UAV's three-dimensional (3D) trajectory design are jointly optimized. The formulated throughput maximization problem is a mixed-integer non-convex programme that hard to be solved in general. Thus, we propose an iterative algorithm to make the problem trackable by applying the block coordinate descent and successive convex optimization techniques. Simulation results show that our proposed algorithm outperforms the benchmark methods that improving the throughput of the system significantly.

The distinctive characteristics of unmanned aerial vehicle networks (UAVNs), including highly dynamic network topology, high mobility, and open-air wireless environments, may make UAVNs vulnerable to attacks and threats. Due to the special security requirements, researching in the high reliability of the power and communication network in drone monitoring system become special important. The reliability of the communication network and power in the drone monitoring system has been studied. In order to assess the reliability of the system power supply in the drone emergency monitoring system, the accelerated life tests under constant stress were presented based on the exponential distribution. Through a comparative analysis of lots of factors, the temperature was chosen as the constant accelerated stress parameter. With regard to the data statistical analysis, the type-I censoring sample method was put forward. The mathematical model of the drone monitoring power supply was established and the average life expectancy curve was obtained under different temperatures through the analysis of experimental data. The results demonstrated that the mathematical model and the average life expectancy curve were fit for the actual very well. With overall consideration of the communication network topology structure and network capacity the improved EED-SDP method was put forward in drone monitoring. It is concluded that reliability analysis of power and communication network in drone monitoring system is remarkably important to improve the reliability of drone monitoring system.

In this paper, we consider a wide family of λ-quasi-twisted (λ-QT) codes of index 2 and provide a bound on the minimum Hamming distance. Moreover, we give a sufficient condition for dual containing with respect to Hermitian inner product of these involved codes. As an application, some good stabilizer quantum codes over small finite fields F2 or F3 are obtained from the class of λ-QT codes.

In order to achieve an antenna with robustness to metal for closed space wireless communications, two types of the folded monopole antenna with different input impedance have been studied. In this study, we propose the folded monopole antenna, which can switch the input impedance by a simple method. Both simulated and measured results show that the proposed antenna can improve robustness to the proximity of the metal.

As a research hotspot and difficulty in the field of computer vision, pedestrian detection has been widely used in intelligent driving and traffic monitoring. The popular detection method at present uses region proposal network (RPN) to generate candidate regions, and then classifies the regions. But the RPN produces many erroneous candidate areas, causing region proposals for false positives to increase. This letter uses improved residual attention network to capture the visual attention map of images, then normalized to get the attention score map. The attention score map is used to guide the RPN network to generate more precise candidate regions containing potential target objects. The region proposals, confidence scores, and features generated by the RPN are used to train a cascaded boosted forest classifier to obtain the final results. The experimental results show that our proposed approach achieves highly competitive results on the Caltech and ETH datasets.

Positioning information plays a significant role in multi-unmanned aerial vehicles (UAVs) applications. Traditionally, the positioning information is widely provided by Global Navigation Satellite System (GNSS) due to its good performance and global coverage. However, owing to complicated flight environment or signal blockage, jamming and unintentional interference, the UAVs may fail to locate themselves by using GNSS alone. As a new method to resolve these problems, cooperative positioning, by incorporating peer-to-peer range measurements and assisted information, has attracted more and more attentions due to its ability to enhance the accuracy and availability of positioning. However, achieving good performance of cooperative positioning of multi-UAVs is challenging as their mobility, arbitrary nonlinear state-evolution, measurement models and limited computation and communication resources. In this paper, we present a factor graph (FG) representation and message passing methodology to solve cooperative positioning problem among UAVs in 3-dimensional environment where GNSS cannot provide services. Moreover, to deal with the nonlinear state-evolution and measurement models while decreasing the computation complexity and communication cost, we develop a distributed algorithm for dynamic and hybrid UAVs by means of Spherical-Radial Cubature Rules (CR) method with belief propagation (BP) and variational message passing (VMP) methods (CRBP-VMP) on the FG. The proposed CRBP deals with the highly non-linear state-evolution models and non-Gaussian distributions, the VMP method is employed for ranging message, gets the simpler message representation and can reduce communication cost in the joint estimation problem. Simulation results demonstrate that the higher positioning accuracy, the better convergence as well as low computational complexity and communication cost of the proposed CRBP-VMP algorithm, which can be achieved compared with sum-product algorithm over a wireless network (SPAWN) and traditional Cubature Kalman Filters (CKF) method.

Fault prediction aims to identify whether a software module is defect-prone or not according to metrics that are mined from software projects. These metric values, also known as features, may involve irrelevance and redundancy, which hurt the performance of fault prediction models. In order to filter out irrelevant and redundant features, a Hybrid Feature Selection (abbreviated as HFS) method for software fault prediction is proposed. The proposed HFS method consists of two major stages. First, HFS groups features with hierarchical agglomerative clustering; second, HFS selects the most valuable features from each cluster to remove irrelevant and redundant ones based on two wrapper based strategies. The empirical evaluation was conducted on 11 widely-studied NASA projects, using three different classifiers with four performance metrics (precision, recall, F-measure, and AUC). Comparison with six filter-based feature selection methods demonstrates that HFS achieves higher average F-measure and AUC values. Compared with two classic wrapper feature selection methods, HFS can obtain a competitive prediction performance in terms of average AUC while significantly reducing the computation cost of the wrapper process.

We propose two secret sharing schemes realizing general access structures, which are based on unauthorized subsets. In the proposed schemes, shares are generated by Tassa's (k,n)-hierarchical threshold scheme instead of Shamir's (k,n)-threshold scheme. Consequently, the proposed schemes can reduce the number of shares distributed to each participant.

In this paper, we investigate the maximum induced matching problem (MaxIM) on C5-free d-regular graphs. The previously known best approximation ratio for MaxIM on C5-free d-regular graphs is $left(rac{3d}{4}-rac{1}{8}+rac{3}{16d-8} ight)$. In this paper, we design a $left(rac{2d}{3}+rac{1}{3} ight)$-approximation algorithm, whose approximation ratio is strictly smaller/better than the previous one when d≥6.

The Shortest Vector Problem (SVP) is one of the most important lattice problems in computer science and cryptography. The LLL lattice basis reduction algorithm runs in polynomial time and can compute an LLL-reduced basis that provably contains an approximate solution to the SVP. On the other hand, the LLL algorithm in practice tends to solve low-dimensional exact SVPs with high probability, i.e., >99.9%. Filling this theoretical-practical gap would lead to an understanding of the computational hardness of the SVP. In this paper, we try to fill the gap in 3,4 and 5 dimensions and obtain two results. First, we prove that given a 3,4 or 5-dimensional LLL-reduced basis, the shortest vector is one of the basis vectors or it is a limited integer linear combination of the basis vectors. In particular, we construct explicit representations of the shortest vector by using the LLL-reduced basis. Our analysis yields a necessary and sufficient condition for checking whether the output of the LLL algorithm contains the shortest vector or not. Second, we estimate the failure probability that a 3-dimensional random LLL-reduced basis does not contain the shortest vector. The upper bound seems rather tight by comparison with a Monte Carlo simulation.

In previous studies, we determined that workloads often contain many input-output (IO) concentrations. Such concentrations are aggregations of IO accesses. They appear in narrow regions of a storage volume and continue for durations of up to about an hour. These narrow regions occupy a small percentage of the logical unit number capacity, include most IO accesses, and appear at unpredictable logical block addresses. We investigated these workloads by focusing on page-level regularity and found that they often include few regularities. This means that simple caching may not reduce the response time for these workloads sufficiently because the cache migration algorithm uses page-level regularity. We previously developed an on-the-fly automated storage tiering (OTF-AST) system consisting of an SSD and an HDD. The migration algorithm identifies IO concentrations with moderately long durations and migrates them from the HDD to the SSD. This means that there is little or no reduction in the response time when the workload includes few such concentrations. We have now developed a hybrid storage system consisting of a cache drive with an SSD and HDD and a multi-tier SSD that uses OTF-AST, called “OTF-AST with caching.” The OTF-AST scheme handles the IO accesses that produce moderately long duration IO concentrations while the caching scheme handles the remaining IO accesses. Experiments showed that the average response time for our system was 45% that of Facebook FlashCache on a Microsoft Research Cambridge workload.

Secure two-party comparison plays a crucial role in many privacy-preserving applications, such as privacy-preserving data mining and machine learning. In particular, the available comparison protocols with the appropriate input/output configuration have a significant impact on the performance of these applications. In this paper, we firstly describe a taxonomy of secure two-party comparison protocols which allows us to describe the different configurations used for these protocols in a systematic manner. This taxonomy leads to a total of 216 types of comparison protocols. We then describe conversions among these types. While these conversions are based on known techniques and have explicitly or implicitly been considered previously, we show that a combination of these conversion techniques can be used to convert a perhaps less-known two-party comparison protocol by Nergiz et al. (IEEE SocialCom 2010) into a very efficient protocol in a configuration where the two parties hold shares of the values being compared, and obtain a share of the comparison result. This setting is often used in multi-party computation protocols, and hence in many privacy-preserving applications as well. We furthermore implement the protocol and measure its performance. Our measurement suggests that the protocol outperforms the previously proposed protocols for this input/output configuration, when off-line pre-computation is not permitted.

Boolean functions used in the filter model of stream ciphers should have balancedness, large nonlinearity, optimal algebraic immunity and high algebraic degree. Besides, one more criterion called strict avalanche criterion (SAC) can be also considered. During the last fifteen years, much work has been done to construct balanced Boolean functions with optimal algebraic immunity. However, none of them has the SAC property. In this paper, we first present a construction of balanced Boolean functions with SAC property by a slight modification of a known method for constructing Boolean functions with SAC property and consider the cryptographic properties of the constructed functions. Then we propose an infinite class of balanced functions with optimal algebraic immunity and SAC property in odd number of variables. This is the first time that such kind of functions have been constructed. The algebraic degree and nonlinearity of the functions in this class are also determined.

The electromagnetic interference (EMI) generated by power electronic converters is largely influenced by parasitic inductances and capacitances of the converter. One of the most popular EMI simulation methods that can take account of the parasitic parameters is the three-dimensional electromagnetic simulation by finite element method (FEM). A noise-source model should be given in the frequency domain in comprehensive FEM simulations. However, the internal impedance of the noise source is static in the frequency domain, whereas the transient switching of a power semiconductor changes its internal resistance in the time domain. In this paper, we propose the use of a voltage-source noise model and a current-source noise model to simulate EMI noise with the two components of voltage-dependent noise and current-dependent noise in the frequency domain. In order to simulate voltage-dependent EMI noise, we model the power semiconductor that is turning on by a voltage source, whose internal impedance is low. The voltage-source noise is proportional to the amplitude of the voltage. In order to simulate current-dependent EMI noise, we model the power semiconductor that is turning off by a current source, whose internal impedance is large. The current-source noise is proportional to the amplitude of the current. The measured and simulated conducted EMI agreed very well.