In order to resist the malware sandbox evasion behavior, improve the efficiency of malware analysis, a code-evolution-based sandbox evasion technique for detecting the malware behavior is proposed. The approach can effectively accomplish the detection and identification of malware by first extracting the static and dynamic features of malware software and then differentiating the variations of such features during code evolution using sandbox evasion techniques. With the proposed algorithm, 240 malware samples with sandbox-bypassing behaviors can be uncovered successfully from 7 malware families. Compared with the JOE analysis system, the proposed algorithm improves the accuracy by 12.5% and reduces the false positive to 1%, which validates the proposed correctness and effectiveness.
Under Single Measurement Vector (SMV) and low Signal-to-Noise Ratio (SNR) conditions, the sparse reconstruction method can improve the estimation accuracy of Time Of Arrival (TOA). However, the existing reconstruction algorithms have some mistakes and missing in the selection of sparse support set elements, which leads to limited estimation accuracy. In order to solve this problem, this paper proposes an algorithm based on sparse reconstruction Loop Matching Pursuit (LMP), which improves the estimation accuracy of the direct path. The algorithm first establishes a sparse representation model of channel impulse response. Then, under the premise of having obtained initial support set, the elements in the support set are removed cyclically. In addition, according to the maximum value of the current residual within the product, the remaining elements are used to match and add the new elements until the residual product is the same. Finally, the estimate of the TOA is obtained using the relationship between the time delay value and the sparse support set. The simulation results show that the proposed algorithm has higher estimation accuracy than the traditional sparse reconstruction time delay estimation algorithm. At the same time, based on the USRP platform, the effectiveness of the proposed algorithm is verified by the actual signal.
In cloudlet enhanced Fiber-Wireless (FiWi) access network, there is a problem that the traditional energy saving mechanism does not match the offload traffic. An offload collaboration sleep mechanism with load transfer is proposed. By analyzing the load of the optical network unit and combining the transmission delay of the multi-hop in the wireless domain and the sending time of the report frame of the target optical network unit, the proposed mechanism can determine the sleeping and the destination optical network unit to complete load transfer. Then, the optical network unit jointly considers the arrival time of the returned data of the edge severs and the sending time of the control frame in the wireless domain to select the optimal sleep duration and reduce the controlling overhead. Simulation results show that the proposed mechanism can effectively reduce the network energy consumption while ensuring the delay performance of offload traffic.
For the problem of the finite word length effect of prototype filters in hardware implementation of the filter bank system, this paper studies how to improve the performance of roundoff noise caused by signal quantization for the FIR prototype filter, that is, to reduce the roundoff noise gain. An FIR filter optimization structure is proposed. By analyzing the source of roundoff noise, a polynomial parameterization method is used to derive the roundoff noise gain expression. The simulation example shows that the amplitude-frequency and phase-frequency response of the proposed structure filter are basically consistent with the ideal state under different constraint of word length. Compared with the existing algorithms, the proposed structure has a smaller roundoff noise gain.
Magnetic Anomaly Detection (MAD) is a widely used passive target detection method. Its applications include surface warship target monitoring, underwater moving targets, and land target detection and identification. It is of great significance to research on the reliability detection method of weak magnetic anomaly signals based on geomagnetic background. This paper proposes a single sensor detection method based on the fractal characteristics of target magnetic anomaly signal based on the study of the differences in geomagnetic background and fractal characteristics of magnetic anomaly signals and conducts actual field test verification. The experimental results show that the method can accurately distinguish the geomagnetic background interference and magnetic anomaly signals, and can detect the weak magnetic anomaly signals in the geomagnetic background noise.
With the development of network information system, virus propagation and immunization strategy become one of the hot topics in the field of network security. In this paper, a new virus with hybrid attacking is introduced, which can attack network in two modes. One is to attack and infect the network nodes directly, and the another is to hide itself in the nodes by hiding its viral characteristic. According to its characteristics, this type of virus is defined as " Two-go and One-live” and the corresponding virus propagation model is established. Moreover, the stability of the system is studied by solving the equilibrium points and analyzing the basic reproduction number R0. Numerical simulations are presented to verify effectiveness and stability of the novel model.
Keystone transform is an effective broadband array signal pre-processing method, but it has a main problem of array data missing. In order to solve this problem, an enhanced Keystone transform algorithm, which combines the autoregression model with traditional Keystone transform, is proposed in this paper for sonar broadband adaptive beamforming. After phase alignment of broadband array signal using traditional Keystone transform, autoregression models for each frequency are constructed to compensate the missing array data. Then, a robust adaptive beamforming approach is utilized to obtain the target bearing results. The results of simulation studies indicate that the proposed broadband adaptive beamforming algorithm based on enhanced Keystone transform outperforms the beamforming algorithms based on traditional Keystone transform, steered minimum variance and frequency focusing.
Incoherent scatter spectrum plays an important role in studying the physical parameters of the ionosphere. The conventional theoretical model of incoherent scatter spectrum for derivation and calculation is extremely complicated and the model of the autocorrelation function can not be obtained . In this paper, the simplified model of ionospheric incoherent scatter spectrum is re-derived and the corresponding autocorrelation function is proposed. In the procedure of traditional incoherent scattering radar signal processing, the autocorrelation function is imbalance at different delays. This is mainly because the range resolution of zero-lag is very low, which affects the estimated performance of ionospheric scatter spectrum. Focus on this problem, a method based on data fitting is proposed to estimate the autocorrelation at zero-lag. Considering the computational complexity, a fast implementation method by polynomial functions is proposed to approach the autocorrelation function. Finally, experimental results on real echo data demonstrate the correctness and efficiency of the proposed method, which is of great significance for ionospheric detection.
In Near-Field (NF) applications of Ultra-High-Frequency Radio Frequency IDentification (UHF RFID) systems, due to the structural characteristics of the microstrip tag, the traditional inter-coil mutual impedance expression has a large error in the estimation of the mutual coupling effect such as the frequency shift of the prediction system, and the accuracy is not enough. Firstly, based on the transformer model, the mutual impedance expressions of the NF dense tags are derived from the perspective of radio energy transmission. Then, the electrical parameter values are obtained indirectly by establishing the electromagnetic simulation model combining with the NF inductance coupling tag. Finally, the derivation formula is verified and UHF RFID NF frequency shift is studied from the perspective of environmental factors that affect the mutual impedance between the two tags. The test results show that the derived mutual impedance expression is applied to the frequency offset calculation with error range in 1.6 MHz～7.3 MHz when the tags’ spacing is less than 30 mm. The results provide a reference for studying the mutual coupling effect between UHF RFID NF tags based on the mutual impedance between tags.
To solve the low performance problem of the existing Modulated Wideband Converter (MWC)-based sub-Nyquist sampling recovery algorithm, this paper proposes a support recovery algorithm based on the kernel space of sampling value and a random compression rank-reduction idea. Combining them, a high-performance sampling recovery algorithm is achieved. Firstly random compression transforms are used to convert the sampling equation into several new multiple-measurement-vector problems, without changing the sparsity of the unknown matrix. Then the orthogonal relationship between the kernel space of sampling value and the support vectors of sampling matrix is utilized to obtain joint sparse support set of the unknown. The final recovery is performed by the pseudo inversion. The proposed method is analyzed and verified by theory and experiment. Numerical experiments show that, compared with the traditional recovery algorithm, the proposal can improve the recovery success rate, and reduce the channel number required for high-probability recovery. Furthermore, in general, the recovery performance improves with the rise of compression times.
The existing Ring Oscillator (RO) Physical Unclonable Function (ROPUF) design has low reliability and uniqueness, resulting in poor application security. A statistical model for ROPUF is proposed, the factors of reliability and uniqueness are quantitatively analyzed, it is found that the larger delay difference can improve the reliability, and the lower process difference between RO units can improve the uniqueness. According to the conclusion of the model, a dynamic RO unit is designed based on the mesh topological structure. In combination with the frequency distribution characteristics of the RO array, a new frequency sorting algorithm is designed to increase the delay difference and reduce the process variation of the RO unit, thereby improving the reliability and uniqueness of ROPUF. The results show that compared with other improved ROPUF designs, the reliability and uniqueness of the proposed design has significant advantages, which can reach 99.642% and 49.1%, and temperature changes affect minimally them. It is verified by security analysis that the proposed design has strong anti-modeling attack capabilities.
This paper investigates the design of hybrid analog and digital precoder and combiner for multi-user millimeter wave MIMO systems. Considering the problem of signal interference between multiple users due to diffuse scattering of signal propagation, a robust hybrid precoding algorithm based on Successive Interference Cancellation (SIC) is proposed. By deducing the orthogonal decomposition formula of the channel matrix to eliminate the interference from the known users’ signals, the multi-user links optimization problem with nonconvex constraints can be decompose into multiple single-user link optimization problems. The phase extraction algorithm is then used to search each user’s optimal transmission link one by one, and the multi-user hybrid precoding matrix is obtained in combination with Minimum Mean Square Error (MMSE) criterion. Simulation results show that the proposed algorithm has significant performance advantages compared with the existing hybrid precoding algorithms under severe interference conditions.
A low power and cost BeiDou-reflectometry used to retrieve Significiant Wave Height (SWH) and wind is designed and implemented. To improve the retrieval accuracy, a correction method based on the power function of the elevation angle sinusoidal and a delay correlation for the rapid change of wind speed is proposed. Moreover, combined observation of multi-satellite signals and single-side filtering for the observable are performed to improve further the retrieval accuracy. The experiment results of observating SWH and wind speed using reflected BeiDou signals show that designed and developed system could implement long-term and stable observation; the retrieval accuracies of SWH and wind speed retrieved by propsoed retrieval models and improvement methods of the retreival accuracy are 0.13 m and 1.28 m/s which are 0.13 m and 0.78 m/s higher than the methods proposed by Soulat et al.
In order to achieve more suitable night vision fusion images for human perception, a novel night-vision image fusion algorithm is proposed based on intensity transformation and two-scale decomposition. Firstly, the pixel value from the infrared image is used as the exponential factor to achieve intensity transformation of the visible image, so that the task of infrared-visible image fusion can be transformed into the merging of homogeneous images. Secondly, the enhanced result and the original visible image are decomposed into base and detail layers through a simple average filter. Thirdly, the detail layers are fused by the visual weight maps. Finally, the fused image is reconstructed by synthesizing these results. The fused image is more suitable for the visual perception, because the proposed method presents the result in the visual spectrum band. Experimental results show that the proposed method outperforms obviously the other five methods. In addition, the computation time of the proposed method is less than 0.2 s, which meet the real-time requirements. In the fused result, the details of the background are clear while the objects with high temperature variance are highlighted as well.
Multiband fusion imaging can effectively improve the range resolution of Inverse Synthetic Aperture Radar (ISAR) imaging. The traditional Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) spectral estimation signal fusion algorithm uses only the complex measured data without using their conjugate data. This paper proposes to modify the unitary ESPRIT method, which is based on synthesizing complex observation data and its conjugate data, to achieve unitary ESPRIT based multiband fusion ISAR imaging. The unitary ESPRIT method makes full use of the information of complex observations, which is more beneficial to multiband frequency spectrum estimation and ISAR imaging. Furthermore, for the correction of Migration Through Resolution Cell (MTRC) of scatterers in multiband fusion, the traditional processing flow is adjusted and optimized. The migration through range cell correction and the migration through Doppler cell correction are performed before and after the multiband fusion respectively, which avoids the influence of the fast time frequency - slow time coupling in the echo and the phase compensation on the spectrum fusion processing, thereby a better multiband fusion ISAR image is obtained. Simulation and real data experimental results show that the proposed methods can not only get high quality ISAR images, but also have good antinoise performance and higher computational efficiency.
For the fact that current gridless Direction Of Arrival (DOA) estimation methods with two-dimensional array suffer from unsatisfactory performance, a novel girdless DOA estimation method is proposed in this paper. For two-dimensional array, the atomic L0-norm is proved to be the solution of a Semi-Definite Programming (SDP) problem, whose cost function is the rank of a Hermitian matrix, which is constructed by finite order of Bessel functions of the first kind. According to low rank matrix recovery theorems, the cost function of the SDP problem is replaced by the log-det function, and the SDP problem is solved by Majorization-Minimization (MM) method. At last, the gridless DOA estimation is achieved by Vandermonde decomposition method of semidefinite Toeplitz matrix built by the solutions of above SDP problem. Sample covariance matrix is used to form the initial optimization problem in MM method, which can reduce the iterations. Simulation results show that, compared with on-grid MUSIC and other gridless methods, the proposed method has better Root-Mean-Square Error (RMSE) performance and identifiability to adjacent sources; When snapshots are enough and Signal-Noise-Ratio (SNR) is high, proper choice of the order of Bessel functions of the first kind can achieve approximate RMSE performance as that of higher order ones, and can reduce the running time.
Virtualization is a new technology that can effectively solve the low resource utilization and service inflexibility problem in the current Wireless Sensor Network (WSN). For the resource competition problem in virtualized WSN, a multi-task resource allocation strategy based on Stackelberg game is proposed. According to the different Quality of Service (QoS) requirements of the business carried by Virtual Sensor Network Request (VSNR), the importance of multiple VSNRs is quantified. Then, the optimal price of WSN and the optimal resource requirements of VSNRs are obtained by using distributed iteration method. Finally, the resource corresponding to multiple VSNRs is acquired according to optimal price and optimal resource allocation determined by Nash equilibrium. The simulation results show that the proposed strategy can not only meet the diversified needs of users, but also improve the resource utilization of nodes and links.
Considering that it is difficult to balance efficiency and resource utilization of Service Chain (SC) mapping problem in Software Defined Network (SDN)/Network Function Virtualization (NFV) environment, this paper proposes a collaborative mapping method for SC based on matching game. Firstly, it defines a SC mapping model named MUSCM to maximize the utility of network resources. Secondly, it divides the SC mapping problem into Virtual Network Function (VNF) deployment and connection parts. As for the VNF deployment part, an algorithm is designed to collaborate the selection of the SC and the service node based on many-to-one matching game, improving the mapping efficiency of SC and utilization of physical resource effectively. On the basis of it, an algorithm is designed based on segment routing strategy to accomplish the traffic steering between VNF instances to finish the VNF connection part, reducing the link transmission delay effectively. The experiment result shows that, compared with the classical algorithm, this algorithm ensures the mapping request received rate, and at the same time, it reduces the average transmission delay of the service chain and improves the physical resources utilization of the system effectively.
To improve accuracy and reliability of the traditional turbine-vital capacity meter, a novel four-line turbine-detection method is presented for the high precision and high reliability Chronic Obstructive Pulmonary Disease (COPD) monitoring system. On the hardware, a four-line breath signal acquisition circuit is designed following the four-line turbine-type detection method, which improves the resolution of the optical path through reasonable components arrangement. On the software, a linear regression algorithm is used to obtain early screening and diagnostic indicators such as Forced Vital Capacity (FVC), Peak Expiratory Flow (PEF) and so on. The standard Fluke air flow analyzer is used for data calibration, compared with the traditional medical turbine-type lung function meter: FVC average relative error is reduced from 1.98% to 1.47% and PEF average relative error is reduced from 2.04% to 1.02%. It is showed that the expiratory parameters of the four-line turbine-type COPD monitoring system is more accurate and reliable than that of the traditional COPD system which is suitable for early screening and accurate diagnosis of COPD. Combined with pulse oxygen saturation, End-tidal CO2, it can be used to achieve the medical care for COPD and play an important role to early detect and control of disease for moderate or severe COPD patients.
The deep learning model based on the residual network and the spectrogram are used to recognize infant crying in this paper. The corpus has balanced proportion of infant crying and non-crying samples. Finally, through the 5-fold cross validation, compared with three models of Support Vector Machine (SVM), Convolutional Neural Network (CNN) and the cochleagram residual network based on Gammatone filters (GT-Resnet), the spectrogram based residual network gets the best F1-score of 0.9965 and satisfies requirements of real time. This paper proves that the spectrogram could react acoustics features intuitively and comprehensively in the recognition of infant crying. The residual network based on spectrogram is a good solution to infant crying recognition problem.
Due to the probabilistic failure of the optical fiber of the underlying network in the virtual environment, traditional full protection configures one protection path at least which leads to high resource redundancy and low acceptance rate of the virtual network. In this paper, a Security Awareness-based Diverse Virtual Network Mapping (SA-DVNM) strategy is proposed to provide security guarantee in the event of failures. In SA-DVNM, the physical node weight formula is designed by considering the hops between nodes and the bandwidth of adjacent links, besides, a path-balanced link mapping mechanism is proposed to minimize the overloaded link. For improving the acceptability of virtual network, SA-DVNM strategy designs a resource allocation mechanism that allows path cut when a single path is unavailable for low security. Considering the difference of time delay to ensure the security of delay-sensitive services, a multipath routing spectrum allocation method based on delay difference is designed to optimize the routing and spectrum allocation for SA-DVNM strategy. The simulation results show that the proposed SA-DVNM strategy can improve the spectrum utilization and virtual optical network acceptance rate in the probabilistic fault environment, and reduce the bandwidth blocking probability.
For the Inverse Synthetic Aperture Radar (ISAR) imaging, the ISAR image obtained by the Range-Doppler (RD) or time-frequency analysis methods can not display the target's real shape due to its azimuth relating to the target Doppler frequency, thus the cross-range scaling is required for ISAR image. In this paper, a fast cross-range scaling method for ISAR is proposed to estimate the Rotational Angular Velocity (RAV). Firstly, the proposed method utilizes efficient Pseudo Polar Fast Fourier Transform (PPFFT) to transform the rotational motion of two ISAR images from two different instant time into translation in the polar angle direction. Then, a new cost function called integrated correction is defined to obtain the RAV coarse estimation. Finally, the optimal RAV can be estimated using the Bisection method to realize the cross-range scaling. Compared with the available algorithms, the proposed method avoids the problems of precision loss and high computational complexity caused by interpolation operation. The results of computer simulation and real data experiments are provided to demonstrate the validity of the proposed method.
Considering the possible security problems of directly extending steganographic schemes for gray-scale images to color images, an adaptive distortion-updated steganography method is put forward based on the Modification Strategy for Color Components (CCMS). First, the correlation between color components and RGB channels is analyzed, and the principle of distortion cost modification is proposed. Moreover, the optimal modification mode is conducted to maintain the statistical correlation of adjacent components. Finally, color image steganography schemes called CCMS are proposed. The experimental results show that the proposed HILL-CCMS and WOW-CCMS make great improvement over HILL and WOW methods under 5 embedding rates in resisting state-of-the-art color steganalytic methods such as CRM and SCCRM.
In order to achieve service data isolation in advanced metering Infrastructure for water, electricity, gas, and heat Meters and improve the stability and coverage of local data collection network, a network virtualization scheme of Advanced Metering Infrastructure (AMI) is proposed. In this scheme, the end-to-end isolated service data collection channels are constructed utilizing virtual Access Point Name (APN) and Software Defined Network (SDN) slice technology. The micro-power wireless and low-voltage power line carriers are used to constructed a real-time and reliable local dual mode virtual network. Furthermore, the networking algorithm based on global link-state and hierarchical iterative algorithm are proposed. The simulation and experiments show the packet loss rate and transmission delay of collected data are decreased utilizing the proposed scheme, and business support capability is improved. Moreover, the service data isolation is implemented in AMI for water, electricity, gas, and heat Meters and multiplexing ability of communication network infrastructure is improved.
To solve problem of the high delay caused by the change of physical network topology under the 5G access network C-RAN architecture, this paper proposes a scheme about dynamic deployment of Service Function Chain (SFC) in access network based on Partial Observation Markov Decision Process (POMDP). In this scheme, the system observes changes of the underlying physical network topology through the heartbeat packet observation mechanism. Due to the observation errors, it is impossible to obtain all the real topological conditions. Therefore, by the partial awareness and stochastic learning of POMDP, the system dynamically adjust the deployment of the SFC in the slice of the access network when topology changes, so as to optimize the delay. Finally, point-based hybrid heuristic value iteration algorithm is used to find SFC deployment strategy. The simulation results show that this model can support to optimize the deployment of SFC in the access network side and improve the access network’s throughput and resource utilization.
The efficiency of Service Function Chain (SFC) depends closely on where functions are deployed and how to select paths for data transmission. For the problem of SFC deployment in a resource-constrained network, this paper proposes an optimization algorithm for SFC deployment based on the Longest Effective Function Sequence (LEFS). To optimize function deployment and bandwidth requirement jointly, the upper bound of path length is set and relay nodes are searched incrementally on the basis of LEFS until the service request is satisfied. Simulation results show that, the proposed algorithm can balance network resource and optimize the function deploymen rate and bandwidth utilization. Compared with other algorithms, the utilization of network resource decreases 10%, so that more service requests can be supported. What is more, the algorithm has a lower computation complexity and can response to service requests quickly.
In order to improve the robustness of MLAPG algorithm, a person re-identification algorithm, called Equid-MLAPG algorithm is proposed, which is based on the equidistance measurement learning strategy. Due to the imbalanced distribution of positive and negative sample pairs in the mapping space, sample spacing hyper-parameter of MLAPG algorithm is more affected by the distance of negative sample pairs. Therefore, Equid-MLAPG algorithm tends to map the positive sample pair to be a point in the transform space. That is, the distance of a positive sample pair in the transform space is mapped to be zero, resulting in no intersection in the distribution of positive and negative sample pairs in the transform space when algorithm convergences. Experiments show that the Equid-MLAPG algorithm can achieve better experimental results on commonly used person re-identification datasets with better recognition rate and wide applicability.
The general method for inversion of Digital Surface Model (DSM) in forest region has great errors due to the inestimable waves’ penetration depth. For this problem, an approach to inversion of high-precision DSM is proposed. First, the phases of high and low scattering phase centers of the waves in forest are obtained by maximizing the phase separation of the coherence optimization. Then, the normal height variation models of the high and low scattering centers with extinction factors are constructed. According to the models, the least penetration depth of the waves in forest is acquired. Eventually, by implementing the interferometric technique on the phase of high scattering phase center, a coarse DSM is retrieved, and a high-precision DSM is developed by compensating the least penetration depth to the coarse one. The validation of the method is investigated by simulated datasets of PolSARpro under different tree species and different forest heights and by airborne real datasets. It shows that the proposed method can improve the accuracy on the inversion of DSM effectively in forest region.
Device-free passive localization is a key issue of the intruder detection, environmental monitoring, and intelligent transportation. The existing device-free passive localization method can obtain the multidimensional measurement information by channel state information, but the existing scheme can not fully exploit the frequency diversity on multiple channels to improve the localization performance. This paper proposes a Compressive Sensing (CS) based multi-target device-free passive localization algorithm using multidimensional measurement information. It takes advantage of the frequency diversity of multidimensional measurement information to improve the accuracy and robustness of localization results under the CS framework. The dictionary is built according to the saddle surface model, and the multi-target device-free passive localization problem is modeled as a joint sparse recovery problem based on multiple measurement vectors. The target location vector is estimated based on the multiple sparse Bayesian learning algorithm. Simulation results indicate that the proposed algorithm can make full use of the multidimensional measurement information to improve the localization performance.
A multi-parameter convolutional neural network method is proposed for gesture recognition based on Frequency Modulated Continuous Wave (FMCW) radar. A multidimensional parameter dataset is constructed for gestures by performing time-frequency analysis of the radar signal to estimate the distance, Doppler and angle parameters of the gesture target. To realize feature extraction and classification accurately, an end-to-end structured Range-Doppler-Angle of Time (RDA-T) multi-dimensional parameter convolutional neural network scheme is further proposed using multi-branch network structure and high-dimensional feature fusion. The experimental results reveal that using the combined gestures information of distance, Doppler and angle for multi-parameter learning, the proposed scheme resolves the problem of low information quantity of single-dimensional gesture recognition methods, and its accuracy outperforms the single-dimensional methods in terms of gesture recognition by 5%～8%.
Due to the limitation of individual controller’s processing capacity in large-scale complex Software Defined Networks (SDN), an efficient online algorithm for load balancing among controllers based on efficiency range is proposed to improve load balancing among controllers and reduce the propagation delay between a controller and the switch. In the initial static network, the initial set of controllers is selected by a greedy algorithm, then M improved Minimum Spanning Trees (MST) rooted at the initial set of controllers are constructed, so initial M subnets with load balancing are determined. With the dynamic changes of load, for the purpose of making the controller work within efficiency range at any time, several switches in different subnets are reassigned by Breadth First Search (BFS). The initial set of controllers is updated for minimizing propagation delay in the algorithms’ last step. The algorithm is based on the connectivity of intra-domain and inter-domain. Simulation results show that the proposed algorithms not only guarantee the load balancing among controllers, but also guarantee the lower propagation delay. As to compare to PSA algorithm, optimized K-Means algorithm, etc., it can make Network Load Balancing Index (NLBI) averagely increase by 40.65%.
To address track-to-track association problem of radar and Electronic Support Measurements (ESM) in the presence of sensor biases and different targets reported by different sensors, an anti-bias track-to-track association algorithm based on track vectors detection is proposed according to the statistical characteristics of Gaussian random vectors. The state estimation decomposition equation is firstly derived in the Modified Polar Coordinates (MPC). The track vectors are obtained by the real state cancellation method. Second, In order to eliminate most non-homologous target tracks, the rough association is performed according to the features of the azimuthal rate and Inverse-Time-to-Go (ITG). Finally, the track-to-track association of radar and ESM is extracted based on track vectors chi-square distribution. The effectiveness of the proposed algorithm are verified by Monte Carlo simulation experiments in the presence of sensor biases, targets densities and detection probabilities.
The existing virtual network reconfiguration algorithms do not consider the fragment resources generated in the physical network, which results in the improvement of the performance of the online virtual network embedding algorithms is not obvious. To solve this problem, a definition of network resource fragmentation is given, and a Fragment-Aware Secure Virtual Network Reconfiguration (FA-SVNR) algorithm is proposed. In the process of reconfiguration, the virtual node set to be migrated is selected by considering the fragmentation of nodes in the physical network periodically, and the best virtual node migration scheme is selected by considering the reduction of the fragmentation of the physical network and the reduction of the embedding cost of the virtual network. Simulation results show that the proposed algorithm has the higher acceptance ratio and revenue to cost ratio compared with the existing virtual network reconfiguration algorithm, especially in the metric of revenue to cost ratio.
China is a flood disaster-prone country, where floods occur frequently every year, from July to August. Therefore, rapid disaster detection and assessment of floods affected areas is of great significance. GF-3 SAR satellite data has obvious advantages of all-day, all-weather imaging characteristics in flood disaster reduction applications because of its active observation technology. For the purpose of rapid water detection in flooding area, a rapid detection method of flood area based on GF-3 single-polarized SAR data is proposed, including SAR preprocessing, flood extraction based on Markov random fields, shadow false alarm removal. Its detecting accuracy is evaluated with manual detection result. The test results show that this method can realize the rapid and accurate extraction of waters in flood disaster area.
The traditional feature-based image matching method has many problems such as many redundant points and low matching accuracy, which can hardly meet the real-time and robustness requirements. In this regard, a fast scene matching method based on Scale Invariant Feature Transform (SIFT) is proposed. In the feature detection phase, FAST (Features from Accelerated Segment Test) is used to detect characteristics in multi-scale, after then, combining with Difference Of Gauss (DOG) operators to filter characteristics again. From this, the feature search process is simplified. In feature matching phase, the affine transformation model is used to simulate the transformation relation and establish the geometric constraint, to overcome the mismatching because of ignoring the geometric information. The experimental results show that the proposed method is superior to the SIFT in efficiency and precision, also has good robustness to light, blur and scale transformation, achieves scene matching better.
Circular polarizer is a key component in feed systems with circular polarization in radio astronomy telescope and satellite communication antennas. Conventional polarizers are capable of operating over a maximum bandwidth of 40% with an axial ratio value of 0.75 dB, which is unable to meet the growing demand for wide band applications. In this paper, the design of the wide band quad ridges waveguide polarizer is introduced, and the relationship between the phase constants of two orthogonal principal modes is analyzed. The broadband phase shift characteristics are achieved by employing different horizontal and vertical ridges dimensions. Based on this method, a C-Band polarizer is designed, which operates at 3.625～7.025 GHz, 64% bandwidth. The effects of main parameters on the polarizer performances are studied. A prototype of the polarizer is developed. The measurements of the prototype show that return losses are less than –21 dB for two orthogonal polarizations and the phase difference is 90°±3.8°, the corresponding axial ratio is less than 0.6 dB. Measured and simulated results show good agreements, thus validating the analysis and design methods.
Honeypot technology is a network trap in cyber defense. It can attract and deceive attackers and record their attack behavior, so as to study the target and attack means of the adversary and protect real service resources. However, because of the static configuration and the fixed deployment in traditional honeypots, it is as easy as a pie for intruders to identify and escape those traps, which makes them meaningless. Therefore, how to improve the dynamic characteristic and the camouflage performance of honeypot becomes a key problem in the field of honeypot. In this paper, the recent research achievements in honeypot are summarized. Firstly, the development history of honeypot in four stages is summed up. Subsequently, by focusing on the key honeypot mechanism, the analysis on process, deployment, counter-recognition and game theory are carried out. Finally, the achievements of honeypot in different aspects are characterized and the development trends of honeypot technology is depicted.
For the problems of the Composite Binary Offset Carrier (CBOC) signal pseudo code period and combination code sequence are difficult to estimate in a non-cooperative context, two blind methods are proposed based on power spectrum reprocessing and Radial Basis Function (RBF) neural networks. it can get the CBOC pseudo code period through two power spectrum calculations. Firstly, the received one pseudo code period is overlapped segmentation based on the estimated pseudo code period. Secondly, the learning coefficient is optimized selection and each segment of date vector as an input signal to the RBF neural networks to supervised adjustment. Finally, through the continuous input signal, it can restore the original combination code sequence according to the convergent weight vectors. Simulation results show that the pseudo code period can be estimated using the secondary power spectrum under low Signal-to-Noise (SNR). Compared with the Back Propagation (BP) neural networks and the Sanger neural networks, the proposed RBF neural networks improve the SNR by 1 dB and 3 dB respectively and the number of data groups required is less through RBF neural networks under the same condition.
To solve the problem of spatial parameter estimation of multi-frequency hopping signals, the sparsity in spatial domain of frequency hopping signals is used to realize the Direction Of Arrival (DOA) estimation based on Sparse Bayesian Learning (SBL). First, the spatial discrete grid is constructed and the offset between the actual DOA and the grid points is modeled into it. The data model of the uniform linear array with multiple frequency hopping signals is established. Then the posterior probability distribution of the sparse signal matrix is obtained by the SBL theory, and the line sparsity of the signal matrix and the offset is controlled by the hyperparameters. Finally, The expectation maximization algorithm is used to iterate the hyper parameters, and the maximum posteriori estimation of the signal matrix is obtained to complete the DOA estimation. Theoretical analysis and simulation experiments show that this method has good estimation performance and can adapt to less snapshots.
Accurately estimating rotor rotation frequency of Unmanned Aerial Vehicle (UAV) is of great significance for UAV detection and recognition. For the UAV target echo model of LFMCW (Linear Frequency Modulated Continuous Wave) radar, this paper proposes an auto-correlation and cepstrum to estimate the rotor rotation frequency of UAV, which derives the mapping relationship between the rotor rotation frequency of UAV and the periodic delay in the radar echo cepstrum output, and more effectively estimates the rotor frequency of multi-rotor UAV by weighted equilibrium, making up for the shortages of traditional methods. The effectiveness of the method is verified by simulation and real scene experiments.
To solve the problem of the loss in the motion features during the transmission of deep convolution neural networks and the overfitting of the network model, a cross layer fusion model and a multi-model voting action recognition method are proposed. In the preprocessing stage, the motion information in a video is gathered by the rank pooling method to form approximate dynamic images. Two basic models are presented. One model with two horizontally flipping layers is called " non-fusion model”, and then a fusion structure of the second layer and the fifth layer is added to form a new model named " cross layer fusion model”. The two basic models of " non-fusion model” and " cross layer fusion model” are trained respectively on three different data partitions. The positive and negative sequences of each video are used to generate two approximate dynamic images. So many different classifiers can be obtained by training the two proposed models using different training approximate dynamic images. In testing, the final classification results can be obtained by averaging the results of all these classifiers. Compared with the dynamic image network model, the recognition rate of the non-fusion model and the cross layer fusion model is greatly improved on the UCF101 dataset. The multi-model voting method can effectively alleviate the overfitting of the model, increase the robustness of the algorithm and get better average performance.
Inspired by the idea of multi-antenna interferometric processing in Interferometric Inverse Synthetic Aperture Radar (InISAR), by utilizing an L-shaped three-antenna imaging model, a Three-Dimensional (3-D) interferometric imaging and micro-motion feature extraction method for rotating space targets is proposed. Based on the integration of micro-Doppler (m-D) effect theory and multi-antenna interferometry processing technology, the m-D curves corresponding to different scatterers are obtained on the time-frequency plane and separated via Viterbi algorithm effectively, and then the projected coordinates of scatterers along the direction of baselines are reconstructed by interferometric processing. The height information of scatterers is solved by ellipse fitting, and 3-D imaging for the rotating space target is realized. Meanwhile, some 3-D micro-motion features are exactly extracted during imaging. Simulation results validate the effectiveness and the robustness of the method.
When evaluating the enhancement quality of a whole image set, the existing average score criterion will vary inconsistently with different image sets and produce a large evaluation quality fluctuation. Therefore, this paper proposes a consistency enhancement quality assessment criterion in confidence interval for any image set. By setting application parameters and using confidence interval to screen data, the proposed criterion compares the quality score difference before and after enhancing each image, and evaluates the consistency of image quality enhancement, and then calculates the effective value of consistency enhancement quality scores. Among many image enhancement algorithms, the proposed criterion can select the high-reliability enhancement algorithm for a specific application. The experimental results show that the proposed criterion has good subjective and objective consistency and outperforms the existing average score criterion, which provides an evaluation criterion for those image enhancement algorithms applied to any image set.
The Digital Video Broadcasting-Common Scrambling Algorithm (DVB-CSA) is a hybrid symmetric cipher. It is made up of the block cipher encryption and the stream cipher encryption. DVB-CSA is often used to protect MPEG-2 signal streams. This paper focuses on impossible differential cryptanalysis of the block cipher in DVB-CSA called CSA-BC. By exploiting the details of the S-box, a 22-round impossible differential is constructed, which is two rounds more than the previous best result. Furthermore, a 25-round impossible differential attack on CSA-BC is presented, which can recover 24 bit key. For the attack, the data complexity, the computational complexity and the memory complexity are 253.3 chosen plaintexts, 232.5 encryptions and 224 units, respectively. For impossible differential cryptanalysis of CSA-BC, the previous best result can attack 21-round CSA-BC and recover 16 bit key. In terms of the round number and the recovered key, the result significantly improves the previous best result.
The signal source position can only be estimated by passive monitoring of the signal in terms of that the signal monitored by the spectrum monitoring system can not be controlled and there is no prior knowledge. To address this issue, based on Received Signal Strength Indication Difference (RSSID) and using Kalman filtering, a location algorithm is proposed to improve its localization accuracy. The proposed algorithm transforms the RSSID between two base stations into the ratio of the distance from the location of the signal source to the two base stations, and the distances to constructs the matrix of location equations is obtained according to the ratio, and then the least square method to find the signal source position is obtained. The simulation results show that the proposed algorithm has better performance than the classical RSSI localization algorithm, reducing the impact of environmental factors on the positioning accuracy, and better meet the positioning service needing fewer parameters. This algorithm can be effectively applied to the spectrum monitoring system. In addition, Kalman algorithm can effectively improve the system's positioning accuracy, and achieve the expected positioning effect.
In recent years, searchable encryption technology and fine-grained access control attribute encryption is widely used in cloud storage environment. Considering that the existing searchable attribute-based encryption schemes have some flaws: It only support single-keyword search without attribute revocation. The single-keyword search may result in the waste of computing and broadband resources due to the partial retrieval from search results. A verifiable multi-keyword search encryption scheme that supports revocation of attributes is proposed. The scheme allows users to detect the correctness of cloud server search results while supporting the revocation of user attributes in a fine-grained access control structure without updating the key or re-encrypting the ciphertext during revocation stage. The aforementioned scheme is proved by the deterministic linearity hypothesis, and the relevant analysis results indicate that it can resist the attacks of keyword selection and the privacy of keywords in the random oracle model with high computational efficiency and storage effectiveness.
The obvious orbit curvature of Medium Earth Orbit (MEO) results in severe two-dimensional space variance in the received signals. Thus, the focusing of MEO SAR data is still a problem to be solved. Fourth-order polynomial is used to model the range history. Also, an azimuth two-step resampling method is proposed to address the azimuth variance. The azimuth resampling in the time domain can adjust the azimuth chirp rate to be the same, then CS/RMA algorithm can be used to handle the space variance of the RCM. The second-step azimuth resampling can correct the left space variance of the Doppler parameters, including range-azimuth coupled space variance of the azimuth chirp rate, and the higher-order focusing parameters. The proposed method can well address the azimuth space variance of the whole scene, make the conventional frequency-domain focusing algorithms applicable to large scene focusing. Finally, the comparison results obtained by the proposed method and the reference method, validate the effectiveness of the proposed method.
Firefly Algorithm (FA) may suffer from the defect of low convergence accuracy depending on the complexity of the optimization problem. To overcome the drawback, a novel learning strategy named Orthogonal Opposition Based Learning (OOBL) is proposed and integrated into FA. In OOBL, first, the opposite is calculated by the centroid opposition, making full use of the population search experience and avoiding depending on the system of coordinates. Second, the orthogonal opposite candidate solutions are constructed by orthogonal experiment design, combining the useful information from the individual and its opposite. The proposed algorithm is tested on the standard benchmark suite and compared with some recently introduced FA variants. The experimental results verify the effectiveness of OOBL and show the outstanding convergence accuracy of the proposed algorithm on most of the test functions.
Heavy computational burden, or complex training procedure and poor universality caused by the manual setting of the fixed thresholds are the main issues associated with most of the noise image quality evaluation algorithms using domain transformation or machine learning. As an attempt for solution, an improved spatial noisy image quality evaluation algorithm based on the masking effect is presented. Firstly, according to the layer-layer progressive rule based on Hosaka principle, an image is divided into sub-blocks with different sizes that match the frequency distribution of its content, and a masking weight is assigned to each sub-block correspondingly. Then noise in the image is detected through the pixel gradient information extraction, via a two-step strategy. Following that, the preliminary evaluation value is obtained by using the masking weights to weighting the noise pollution index of all the sub-blocks. Finally, the correction and normalization are carried out to generate the whole image quality evaluation parameter——i.e. Modified No-Reference Peak Signal to Noise Ratio (MNRPSNR). Such an algorithm is tested on LIVE and TID2008 image quality assessment database, covering a variety of noise types. The results indicate that compared with the current mainstream evaluation algorithms, it has strong competitiveness, and also has the significant effects in improving the traditional algorithm. Moreover, the high degree of consistency to the human subjective feelings and the applicability to multiple noise types are well demonstrated.
A Novel Matrix Mapping (NMM) method is proposed for the synthesis of sparse rectangular arrays with multiple constraints. Firstly, the sizes of element coordinate matrices are resized to improve the Degree Of Freedom (DOF) of elements by taking account of both placeable number and distributable range of elements. Then, a selection matrix is established to determine which elements should be turned off when the coordinate matrices should be thinned. By establishing two different mapping functions, a NMM method is presented to overcome the drawbacks of existing methods in terms of flexibility and effectiveness. Finally, comparison experiments are conducted to verify the effectiveness of the proposed method. The numerical validation points out that the proposed method outperforms the existing methods in the design of sparse rectangular arrays.
For Network Function Virtualization (NFV) environment, the existing placement methods can not guarantee the mapping cost while optimizing the network delay, a service function chaining optimal placement algorithm is proposed based on the IQGA-Viterbi learning algorithm. In the training process of Hidden Markov Model (HMM) parameters, the traditional Baum-Welch algorithm is easy to fall into the local optimum, so the quantum genetic algorithm is proposed which can better optimize the model parameters. In each iteration, the improved algorithm maintains the diversity of feasible solutions and expands the scope of the spatial search by replicating the best fitness population with equal proportion, thus improving the accuracy of the model parameters. In the process of solving Hidden Markov chain, to overcome the problem that can not be directly observed for hidden sequences, Viterbi algorithm can solve the implicit sequences exactly and solve the problem of optimal service paths in the directed graph. Experimental results show that the network delay and mapping costs are lower compared with the existing algorithms. In addition, the acceptance ratio of requests is raised.
As a competitive Non-Orthogonal Multiple Access (NOMA) technique, Sparse Code Multiple Access (SCMA) improves efficiently the system spectral efficiency by combining the high dimensional modulation and sparse spread spectrum. To address the existing issues of SCMA codebook design, in this paper, an optimization design method for SCMA codebooks is proposed for both Rayleigh fading and Gaussian channels. In the method, by rotating the base constellation and the mother constellation, the minimum Euclidean distance between the projection points of the mother constellation on each dimension, and between the constellation points on the constellations corresponding to each user in the total constellation on a single resource block is maximized in order to improve the performance of the SCMA codebooks over Gaussian channels; On the basis of it, by rotating the constellation of multiple users superimposed on each resource block, the corresponding minimum product distance and the Signal Space Diversity (SSD) order of the users’ constellations are optimized; At last, an additional diversity gain is achieved by using Q-coordinate interleaving technology to improve further the performance over the Rayleigh fading channels. Simulation results show that the performance of the proposed SCMA codebooks outperforms that of the HUAWEI’ SCMA codebooks and Low Density Signature Multiple Access (LDS-MA) in both the Gaussian channels and the Rayleigh fading channels.
For qualitative and quantitative complex evaluation problem of electromagnetic environment. This paper proposes a novel electromagnetic environment complex evaluation algorithm based on fast S-transform and time-frequency space model, which can count time-complex, frequency-complex and energy-complex simultaneously. At the same time, the computation methods and concept of qualitative and quantitative evaluation degree are introduced in this paper. To overcome the limitations of the traditional, F-norm and root-mean-square are selected as two important evaluation indicators, which have the advantage in accurate evaluation. Simulation results show that the proposed method is accurate and effective to reflect the intensity degree of electromagnetic interference; Meanwhile, the interference experiment of bus card confirms the correctness of the time-frequency space model. The experimental test results verify the correctness of the evaluators mentioned in this paper.
Single beacon location algorithm based on additive noise model can not accurately represent the actual characteristics of distance measurement, leading to a problem of model mismatch. A two step location algorithm considering the multiplicative noise characteristics is presented, which combines least squares algorithm and nonlinear fading filter. A range error model in the background of multiplicative noise is established baed on the analysis of the effective sound velocity error. The nonlinear fading filtering algorithm with single fading factor under multiplicative noise background is improved by introducing the attenuation factor which increases the track continuity. Using the least squares based pre-location process to solve the problem that the improved algorithm is sensitive to the initial value. The simulation and experimental data show that the location precision of the proposed algorithm is obviously better than the extended Kalman filtering algorithm under the additive noise background.
To reduce the beamforming training cost and network delay, make the best of Beacon and S-CAP sub-period in the existing Terahertz Wireless Personal Access Network (TWPAN) directional MAC protocols, an Adaptive Directional MAC (AD-MAC) protocol for TWPAN is proposed. AD-MAC adaptively uses the entire network cooperative beam training in a static scenario, and makes network nodes quickly respond to beam training frames based on historical information in a dynamic scenario. The reverse listening strategy is used to reduce the collision probability of same sector nodes. The control frame and data frame are transmitted simultaneously in the Beacon and S-CAP slot using time-slot reuse. Theoretical analysis verifies the effectiveness of AD-MAC. Also, simulation results show that, comparing with ENLBT-MAC, AD-MAC reduces about 21.84% of beamforming training cost and 22.70% of the average network delay in static scene, and reduces about 18.7% of beamforming training cost and 13.07% of the average network delay in dynamic scene.
Considering the limits of fuzzy comprehensive evaluation on quality of early warning radar intelligence in actual training, a method of quality evaluation on radar intelligence based on the theory of asymmetric proximity and multilevel fuzzy comprehensive evaluation is proposed. Through the analysis of the producing, transmission, using environmental factors of early warning radar intelligence, the evaluating metric of quality evaluation on radar intelligence integrated for six classes, that are timely, accuracy, completeness, continuity, objectiveness and so on, and then factor set, weight set, and comment set are established, and the quality of the radar intelligence based on the asymmetric proximity with the fuzzy comprehensive evaluation is carried out. This researching methods and results not only can take comprehensive evaluations of a certain quality of radar intelligence, help for finding out the factors to determine the quality of the radar intelligence, and also can fight for providing certain reference to solve complex environment of radar intelligence of operational effectiveness evaluation problem.
This paper presents an approach of combining the existing enhanced inter-cell interference coordination technology and the downlink joint transmission scheme of coordinated multi-point transmission technology to solve the problem of serious cross-layer interference in 5G ultra-dense heterogeneous network. With using tools from stochastic geometry theory, the expressions such as the outage probability, spectrum efficiency and network average ergodic capacity of two-layer ultra-dense heterogeneous network are derived. Simulation results show that the joint interference coordination scheme proposed in this paper not only reduces the number of cooperative users compared with the traditional coordinated multi-point transmission technology, but also reduces the outage probability of users by 15% in the network at 0 dB. Compared with the enhanced inter-cell interference coordination technology, when the bias value is 10 dB, the user spectrum efficiency in the extended area is improved to 35% and the average traversal capacity of the entire network is increased by 3.4%.
In view of the correction for tropospheric delay is limited by the shortage of sounding data, which leads to the problem that the low correction efficiency, this paper proposes a model named as Sa+GPT2w, combining Saastamoinen model and GPT2w model. In this paper, the real-time correction for Zenith Tropospheric Delay (ZTD) over china is realized by using the high-precision meteorological values provided by the GPT2w model, and the results are verified by the measured data. Taking the ZTD in 2015-2017 of International GNSS Service(IGS) as a reference, the accuracy of the Sa+GPT2w model (bias: 1.661 cm, RMS: 4.711 cm) rises by 50.5%, 41.9% and 37.1%, respectively, relative to the Sa+EGNOS, Sa+UNB3m and the Hop+GPT2w models. Moreover, using the ZTD from Global Geodetic Observing System (GGOS) in 2017 as a standard, the Sa+GPT2w model (bias: 1.551 cm, RMS: 4.859 cm) improves the accuracy by 49.5%, 38.5% and 46.8% relative to other three models, respectively. Finally, this paper analyzes the temporal and spatial distribution characteristics of the bias and RMS of the above three models. The results provide a significant reference for the effectiveness of correction for ZTD by using different meteorological models in the research of navigation and atmospheric refraction over China.
Blind separation performance bound of Paired Carrier Multiple Access (PCMA) mixed signal is a measure of the separability of mixed signals and the performance of the separation algorithm. For the PCMA mixed signal, the spatial mapping of the modulation signal bits and symbols is constructed from the transmit signal model. The maximum likelihood criterion is used to derive the lower bound expression of separation performance independent of the separation algorithm. Numerical results agree well with the Viterbi simulation results under ideal conditions, which verify the rationality of the derived performance boundaries.
For the problem of high precision frequency measurement of dynamic signals with high fundamental frequency and small frequency change value in electronic measurement, a method of differential frequency measurement is introduced. A novel dynamic adjustable multi-stage frequency-difference circuit structure is proposed. The fast differential frequency measurement system based on FPGA is used to design the Fast Fourier Transform (FFT) algorithm on the FPGA to realize the data processing function of the system. The simulation and experimental results show that the structure of the multi-stage differential frequency circuit can be designed with high precision frequency, and the result can be obtained when the spectrum analysis is carried out. The system can realize the fast FFT operation. Compared with the MATLAB software platform, the system has obvious advantages in the efficiency of data processing. The structure of the FFT model can be dynamically adjusted to meet the requirements of FFT operation of different scale points, and the system performance index can meet the requirements of data acquisition system.
Recommendation systems can help people make decisions conveniently. However, few studies considere the effect of removing irrelevant noise users and retaining a small number of core users to make recommendations. A new method of core user extraction is proposed based on trust relationship and interest similarity. First all users trust and interest similarity between pairs are calculated and sorted, then according to the frequency and position weight users travel in the nearest neighbor in the list of two kinds of strategies for the selection of candidate core collection of users. Finally, according to the user’s ability the core users are sieved out. Experimental results show that the core user recommendation effectiveness, and the core of user 20% can reach more than recommended accuracy of 90%, and through the use of core user recommendation the negative effects can be resisted caused by the attacks on the recommendation system.
Oriented to the high-rapid development of Internet applications, new challenges are encountered by the conventional Routing and Spectrum Assignment (RSA). A new direction for the blocking rate reduction and the Quality of Experience (QoE) assurance is provided to the Elastic Optical Network (EON) integrated by Degraded Service (DS) technology. Due to the inefficiency of spectrum resources and the revenue decline caused by DS, a Mixed Integer Linear Programming (MILP) model is proposed with a joint objective that minimizes both spectrum consumption and the priorities and DS frequency of online services. And a dynamic RSA algorithm based on differentiated DS and adaptive modulation is proposed, which considers service-priority differentiation, the adaptive modulation and DS technology. Meanwhile, DS loss function and DS window selection strategy are designed to differentiate service levels, and ideal spectrum location and resource are assigned for the impending blocked services. And the network revenue function considering the relationship between spectrum and revenue balance is designed to achieve efficient utilization of spectrum resources, reduce the impact of degradation, and enhance network revenue. The simulation results verify the advantages of the proposed algorithm in terms of blocking rate, network profit, etc.
The convolutive blind source separation can be effectively solved in frequency domain, but blind source separation in frequency domain must solve the problem of ranking ambiguity. A frequency-domain blind source separation sorting algorithm is proposed based on regional growth correction. First, the convolutional mixed signal short-time Fourier transform is used to establish an instantaneous model at each frequency point in the frequency domain for independent component analysis. Based on this, the correlation of the power ratio of the separated signal is used to sort all frequency points one by one replacement. Second, according to the threshold, the sorted result is divided into several small areas. Finally. regional replacement and merging is performed according to the regional growth method, and the correct separation signal is finally obtained. Regional growth correction minimizes the mis-proliferation of frequency sorting and improves separation results. The speech blind source separation experiments are performed in the simulated and real environments respectively. The results show the effectiveness of the proposed algorithm.
In view of the problem that the Cardinalized Probability Hypothesis Density (CPHD) probability hypothesis density filtering algorithm based on the Pairwise Markov Chains (PMC) model (PMC-CPHD) is not suitable for implementation, the PMC-CPHD algorithm is modified into a polynomial form to facilitate implementation, and the Gauss Mixture (GM) implementation of the improved algorithm is given. The experimental results show that the given GM implementation realizes multitarget tracking effectively, and improves the stability of the target number estimation compared with the GM implementation of the probability hypothesis density filtering algorithm based on the PMC model (PMC-PHD).
In wireless relay networks, random transmission delays among relay nodes will lead to substantial performance degradation, for which delay-tolerant Distributed Linear Convolutive Space-Time Code (DLC-STC) is proposed. However, its diversity gain on fast fading Rayleigh channels is not clear. This paper analyzes the diversity gain of the DLC-STC on fast fading Rayleigh channels. It is shown that the DLC-STC can achieve full asynchronous cooperative diversity order with Maximum Likelihood (ML) receivers on fast fading Rayleigh channels, although it is originally proposed for slow fading channels. The numerical results verify the theoretical analysis and show that MMSE-DFE receivers, can collect the same diversity order as ML receivers on fast fading Rayleigh channels.
To solve the problems of low resource utilization rate, high energy consumption and poor user service quality in the existing virtualized Cloud Radio Access Network (C-RAN), a virtual resource allocation mechanism based on energy consumption and delay is proposed. According to the network and traffic characteristics of the virtualized C-RAN, considering the resource constraints and proportional fairness, an energy consumption and delay optimization model is established. Furthermore, a heuristic algorithm is used to allocate resources for different types of virtual C-RAN and user virtual base stations to complete resource global optimization configuration. Simulation results show that the proposed resource allocation mechanism can effectively save energy by 62.99% and reduce the latency by 32.32% while improving the network resource utilization.
Firewall policy is defined as access control rules in Software Definition Network (SDN), and distributing these ACL (Access Control List) rules across the networks, it can improve the quality of service. In order to reduce the number of rules placed in the network, the Heuristic Algorithm of Rules Allocation (HARA) of rule multiplexing and merging is proposed in this paper. Considering TCAM storage space of commodity switches and connected link traffic load of endpoint switches, a mixed integer linear programming model which minimize the number of rules placed in the network is established, and the algorithm solves the rules placement problem of multiple routing unicast sessions of different throughputs. Compared with the nonRM-CP algorithms, simulations show that HARA can save 18% TCAM at most and reduce the bandwidth utilization rate of 13.1% at average.
In order to solve the unreasonable virtual resource allocation caused by the uncertainty of service and delay of information feedback in wireless virtualized networks, an online adaptive virtual resource allocation algorithm proposed based on Auto Regressive Moving Average (ARMA) prediction. Firstly, a cost of virtual networks minimization is studied by jointly allocating the time-frequency resources and buffer space, while guaranteeing the overflow probability of each virtual network. Secondly, considering the different demand of virtual networks to different resources, a resource dynamic scheduling mechanism designed with multiple time scales, in which the reservation strategy of buffer space is realized based on the ARMA’s prediction information in slow time scale and the virtual networks are sorted according to the overflow probability derived by the large deviation principle and dynamically schedules the time-frequency resources in fast time scale, so as to meet the service demand. Simulation results show that the algorithm can effectively reduce the bit loss rate and improve the utilization of physical resources.
Deep learning based ship detection method has a strict demand for the quantity and quality of the SAR image. It would take a lot of manpower and financial resources to collect the large volume of the image and make the corresponding label. In this paper, based on the existing SAR Ship Detection Dataset (SSDD), the problem of insufficient utilization of the dataset is solved. The algorithm is based on Generative Adversarial Network (GAN) and Online Hard Examples Mining (OHEM). The spatial transformation network is used to transform the feature map to generate the feature map of the ship samples with different sizes and rotation angles. This can improve the adaptability of the detector. OHEM is used to discover and make full use of the difficult sample in the process of backward propagation. The limit of positive and negative proportion of sample in the detection algorithm is removed, and the utilization ratio of the sample is improved. Experiments on the SSDD dataset prove that the above two improvements improve the performance of the detection algorithm by 1.3% and 1.0% respectively, and the combination of the two increases by 2.1%. The above two methods do not rely on the specific detection algorithm, only increase the time in training, and do not increase the amount of calculation in the test. It has very strong generality and practicability.
At present, microwave radiometers suffer from serious Radio Frequency Interference (RFI), especially in low frequency. In this paper, a radio frequency detection algorithm is proposed for L-band phased array radiometer, which is used to measure the sea surface salinity and soil moisture. First, the L-band phased array radiometer is introduced in briefly. Secondly, the radio frequency detection algorithm is introduced in details, which consists of the raw RFI flag, the RFI first moving–averaged window flag, the RFI second moving–averaged window flag and the expanded RFI flag. Finally, the experimental data obtained by the L-band phased array radiometer is dealt with the proposed RFI detection algorithm. The results indicate that the proposed detection RFI algorithm can effectively detect the RFI contaminated abnormal data, and exhibits a good detected ability.
Hardware Trojan horse detection has become a hot research topic in the field of chip security. Most existing detection algorithms are oriented to ASIC circuits and FPGA circuits, and rely on golden chips that are not infected with hardware Trojan horses, which are difficult to adapt to the coarse-grained reconfigurable array consisting of large-scale reconfigurable cells. Therefore, aiming at the structural characteristics of Coarse-grained reconfigurable cryptographic logical arrays, a hardware Trojan horse detection algorithm based on partitioned and multiple variants logic fingerprints is proposed. The algorithm divides the circuit into multiple regions, adopts the logical fingerprint feature as the identifier of the region, and realizes the hardware Trojan detection and diagnosis without golden chip by comparing the multiple variant logic fingerprints of the regions in both dimensions of space and time. Experimental results show that the proposed detection algorithm has high detection success rate and low misjudgment rate for hardware Trojan detection.
The abnormal pixels in hyperspectral images are often have the characteristics of low probability and scattered outside the background data cloud. How to automatically detect these abnormal pixels is an important research direction in hyperspectral imagery processing. Classical hyperspectral anomaly detection methods are usually based on statistical perspective. The RXD algorithm which is widely used can give the anomalies distribution directly through the second order statistical feature of the image, but the disadvantage is that it does not take into account the higher order statistics of the image. Anomaly detection algorithm based on Independent Component Analysis (ICA) considers the sensitivity of higher order statistics to outliers, but it needs iteration process to extract abnormal components first. And then the extracted components is used for anomaly detection. A method based on cokurtosis tensor for anomaly detection is proposed in this paper. This method does not need to extract anomaly components first. It can directly detect the observed pixels and give the distribution of abnormal pixels. Experiments results on both simulated and real data show that it can detect abnormal pixels while suppressing the background information better. Therefore, it can reduce false alarm rate and improve detection accuracy.
A improved methods is proposed for compensating the distortion created by mismatches in Time-Interleaved Analog-to-Digital Converters (TI ADCs). The error compensation of offset and gain is realized by error parameters, and the error compensation of sampling time is realized by the simplified Lagrange interpolation algorithm. The compensation method is implemented in FPGA with the low complexity of fixed-point algorithm, and the online calibration of multi-channel ADC sampling data is implemented in the TIADC hardware platform. The experimental results show that the proposed method improves the Spurious-Free Dynamic Range (SFDR) of sampling data up to 51 dB in the simulation environment, and optimizes the SFDR up to 45 dB in the process of hardware implementation. Under the premise of maintaining the error estimation precision and compensation effect, this method not only reduces the computational complexity of the algorithm, but also the compensation structure is not limited by the number of TIADC channels.
The security issue of wireless transmission becomes a significant bottleneck in the development of Internet of Things (IoT). The limited computing capability and hardware configuration of IoT terminals and eavesdroppers equipped with massive Multiple-Input Multiple-Output (MIMO) bring new challenges to physical layer security technology. To solve this problem, a lightweight noise injection scheme is proposed that can combat massive MIMO eavesdropper. Firstly, the proposed noise injection scheme is introduced, along with the corresponding secrecy analysis. Then, the close-formed expression of the throughput is derived based on the proposed scheme. Furthermore, the slot allocation coefficient and power allocation coefficient are optimized. The analytical and simulation results show that the proposed noise injection scheme can achieve the security of private information transmission by designing of the IoT system parameters.
In this paper, Two novel Artificial Magnetic Conductor (AMC) structures, based on circular loop patch and substrate, are designed to realize 180° reflection phase difference in a wide frequency band. These two AMCs’ reflection phase property is applied to redirect the scattering fields of a radar target to reduce its Radar Cross Section (RCS). This method of RCS reduction can be realized by covering with a chessboard surface composed of two proposed AMC structures, so the RCS reduction in a wide frequency band can be achieved as well. Compared with the same-sized metallic surface, this proposed chessboard surface can reduce RCS drastically from 8 to 20 GHz under normally incident waves, and the RCS also can be reduced under obliquely incident waves. Meanwhile, this surface also can be used as antenna. By precisely designing feed network, the metasurface antenna can be designed. This antenna also has a low profile. The simulated impedance matching frequency band is from 9.08 to 10.30 GHz. Excellent agreement is obtained between simulation and measurement for metasurface antenna and chessboard surface. Such method gives a method for integrated design of antenna and metasurface, so the RCS reduction can be achieved, at the same time the radiation properties can be maintained.
As an efficient anti-interference technique, Luby Transform (LT) codes are applied to cognitive radio systems for reliable data transmission of secondary users. Encoding and decoding are critical issue for the anti-interference performance of LT codes. To improve the reliability and speed of data transmission, a novel encoding and decoding method Combined Poisson Robust Soliton Distribution-Hierarchical, (CPRSD-H) for LT codes is proposed to apply to cognitive radio systems. In the process of encoding, the encoder first produces encoded symbols and generator matrix based on CPRSD, and then uses column vectors corresponding to degree –1 and –2 in the generator matrix to carry dual information: the relationship between the degree –1 and –2 encoded symbols and their connected input symbols; and part of the original data. Contrarily, in the decoding process, the decoder first uses the Belief Propagation (BP) algorithm to decode by the first information, and then correct some unrecovered bits by the second information. Simulation results show that the proposed method CPRSD-H and application to cognitive radio systems can significantly reduce the Bit Error Rate (BER) of LT codes, the goodput performance of secondary users and the encoding and decoding speed of LT codes.
Proxy re-encryption plays an important role for encrypted data sharing and so on in cloud computing. Currently, almost all of the constructions of identity-based proxy re-encryption over lattice are in the random oracle model. According to this problem, an efficient identity-based proxy re-encryption is constructed over lattice in the standard model, where the identity string is just mapped to one vector and getting a shorter secret key for users. The proposed scheme has the properties of bidirectional, multi-use, moreover, it is semantic secure against adaptive chosen identity and chosen plaintext attack based on Learning With Errors(LWE) problems in the standard mode.
A new Joint Blind Source Separation (J-BSS) algorithm is proposed based on joint diagonalization of fourth-order cumulant tensors. This algorithm constructs first a set of fourth-order tensors by computing the fourth-order cross cumulant of the multiset signals. Then, based on the Jacobian successive rotation strategy, the highly nonlinear optimization problem of joint tensor diagonalization is transformed into a series of simple sub-optimization problems, each admitting a closed form solution. The multiset mixing matrices are hence updated via alternating iterations, which diagonalize jointly the data tensors. Simulation results show that the proposed algorithm has nice convergence pattern and higher accuracy than existing BSS and J-BSS algorithms of similar type. In addition, the algorithm works well in a real-world application to fetal ECG separation.
For the full-duplex two-way relay network, a two-way relay transmission scheme that is robust to the relay residual self-interference signal is proposed. Firstly, the residual self-interference signal of the relay is analyzed, the infinite self-interfering signal is modeled as an equivalent multipath signal, and the cyclic prefix of OFDM is used to combat the equivalent multipath phenomenon to reduce the residual self-interference signal impact. Based on the equivalent multipath scheme, the paper aims at maximizing the SINR of the system, and deduces the optimal amplification factor solving method of the relay in bidirectional full-duplex relay transmission. Finally, the simulation verifies the correctness of the optimal amplification factor of relay, and the effectiveness of the proposed two-way relay transmission scheme is verified through simulation.
Monthly Journal Founded in 1979
The Source Journal of EI Compendex The Source Journal of ESCI Database
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ISSN 1009-5896 CN 11-4494/TN
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