In order to suppress effectively the interference signal and improve further the performance of radar system, a joint transmitting subarray partition and beamforming design method based on two-dimensional phased-MIMO radar is proposed. Firstly, the transmitting array of MIMO radar system is equally partitioned into a number of non-overlapping subarrays and the transmit power of each antenna is equal, so as to guarantee that the transmit signal has constant modulus characteristic. Then, the optimization model for subarray structure of transmitting array, transmit beamformer weight vectors and receive beamformer weight vector is established by maximizing the output signal-to-interference-plus-noise ratio of the receive beamformer under certain constraint conditions. Simulation results demonstrate the correctness and effectiveness of the proposed method.

Wideband distributed coherent radar technology can effectively improve the target measurement accuracy and recognition performance, and has important research value. For the existing radar equipment generally does not have the ability to stretch the positive and negative frequency modulation rate wideband LFM signal simultaneously, that is the problem that the delay phase value of wideband signal transmit coherent can not be obtained by wideband stretch method in receive coherent synthesis phase. This article uses the delay difference between the unit radar and the target, equivalents the unit radar transmitting wideband LFM signal to the target signal, performs cross-correlation processing on the received signal to obtain the value of transmit coherent parameter. Through modeling and simulation receiving coherent and transmitting coherent synthesis processing are realized, and the two input single output coherent detection test of the aircraft target is carried out, and the ideal test results are obtained. The method has the advantages of high estimation accuracy, small calculation amount and good real-time performance, can be applied to distributed coherent radar engineering implementation.

The BDS-2 (BeiDou-2 System) officially provided services to the Asia-Pacific region in 2012. The GEO-3 satellite has been retired and replaced by the GEO-7 satellite. Studying the signal quality characteristics of the satellite during the pro-retirement period can not only analyze the satellite payload status of the BDS-2, but also provide important reference value for other signal characteristics of the pro-retirement satellite. At the same time, it has important enlightenment and reference significance for the signal quality controlling and optimizing of the GEO satellite load of the BDS-3. Using the multi-method monitoring data of the 40 m large-diameter antenna of the Hao-ping Radio Observatory(HRO), the power spectrum, the ground receiving power and S-Curve Bias (SCB) of the GEO-3 satellite B1 civil signal are analyzed. The long-term trend of these characteristics is given, and the corresponding statistical results are given. Relevant suggestions for satellite payload signal quality optimization are proposed.

For the DOA estimation problem of low-elevation target of VHF radar, a new multi-frame phase feature enhancement based method is proposed, which solves effectively the phase feature ambiguity of direct signal, and thus improves the accuracy of DOA estimation. By learning the complex mapping relationship between the phase distribution of the multi-frame data and ideal phase distribution of the direct signal, the fuzzy phase information is enhanced and is used to reconstruct a new data matrix with original amplitude information. The DOA is estimated by conventional methods using new data matrix, which effectively improves the DOA estimation accuracy of the low-elevation target. The effectiveness of proposed method is validated by computer simulation experiments and real data, and it shows higher accuracy compared with physics-driven methods including MUSIC method and state-of-the-art data-driven method including feature reversal and Support Vector Regression (SVR).

The Yellow River is an important water resource in China. Using radar remote sensing to monitor the runoff of the Yellow River can conveniently reflect the changing trend of drought and flood, which has important practical significance. At present, Radar Altimeter (RA) commonly is used to construct a water depth-runoff model in runoff inversion. This method ignores the influence of river surface change on runoff fluctuation and has certain limitations. A Multi-source Radar Remote Sensing Runoff Calculation Model (MRRS-RCM) is proposed. In this study, RA technology and Synthetic Aperture Radar (SAR) technology are used to construct MRRS-RCM model on the basis of the Manning’s equation to realize runoff inversion. Three stations are selected for experiments in the lower reaches of the Yellow River. The results show that the Relative Root Mean Square Error (RRMSE) of MRRS-RCM runoff inversion reaches 13.969%, which is better than the accuracy requirement of traditional runoff monitoring of 15%～20%.

In the passive location of moving target, the closed-form solution can reach Cramér-Rao Lower Bound (CRLB) under the low noise level, but these algorithms often can not adapt to the large measurement noise condition. For this problem, this paper proposes a passive positioning algorithm based on the Semi-Definite Relaxation (SDR) using Time Difference Of Arrival (TDOA) and Frequency Difference Of Arrival (FDOA). Firstly, this method constructs the pseudo-linear equation of the typical closed-form solution. Secondly, the idea of Stochastic Robust Least Squares (SRLS) and the nonlinear relationship between the target parameters and the additional variables are used to transform the localization problem into the least squares problem with quadratic equality. Using Semi-Definite Programming (SDP) technique, constrained least squares problem is then converted into the SDP problem, which is finally solved by the optimization toolbox. The proposed method does not require an initial priori information and simulations show the effectiveness of the proposed method.

Accurate prediction of low-frequency sky-wave has significance for the lower ionosphere detection and remote navigation timing. The characteristics of sky-wave propagation time delay in the Earth-ionosphere waveguide are studied in this paper based on the traditional wave-hop theory and FDTD method. Time delay variations of 100 kHz one-hop sky waves are given under homogeneous/exponentially graded isotropic ionosphere waveguide models. The great-circle distance between the transmitter and the receiver is within 200 km. Together with a sky- and ground-wave separation technique in the time domain, the narrow-band Loran-C signals are employed in two methods. Compared to the results of wave-hop theory, the method in this paper has higher calculation accuracy by considering the influence of irregular earth and inhomogeneous distribution of ionospheric day-night parameters at the same time.

Parameter estimation is very important for radar to detect and recognize targets. In this paper, a high speed multi-target parameter estimation method for Frequency Agility-Orthogonal Frequency Division Multiplexing(FA-OFDM) radar based on Expectation Maximization(EM) algorithm is proposed. Firstly, a promising idea is to combine narrowband Orthogonal Frequency Division Multiplexing (OFDM) signals and frequency agility, multiple subcarriers that frequency hopping randomly are simultaneously transmitted within each pulse width. Then, all echoes of a single pulse are compressed and sparsely reconstructed to achieve 1-demension high range resolution. Subsequently, the high resolution range of multiple targets at each pulse time are obtained to constitute the observation data, and Gauss mixture model is established. EM algorithm is applied to estimate the parameters of the model and the range and velocity of multiple targets. Also, multiple time-range lines are fitted at the same time, and the slope of the line corresponds to the velocity of the target, as well as, the vertical intercept of the line corresponds to the initial range of the target, separately. Finally, the influence of the Signal-to-Noise Ratio (SNR) on detection probability and the target velocity on relative error of estimation are analyzed, respectively. Simulations are provided to verify the effectiveness of the proposal.

Considering the problem of multi-group maneuvering target tracking, a fast tracking method based on Interactive Multiple Maneuvering Gaussian Mixture Probability Hypothesis Density (IMM-GM-PHD) algorithm is proposed. Firstly, based on the completion of the IMM-GM-PHD algorithm prediction process, the density detection mechanism is added, and the correlation domain is used to select effective measurement for all predicted Gaussian components, and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is combined to detect whether a new formation target appears. Secondly, based on the completion of the state update of the IMM-GM-PHD algorithm, the update of the model probability is completed by updating the composition of the Gaussian component. Finally, in the process of state estimation optimization, combined with the characteristics of formation targets, the similarity discrimination technique is added, and the Jensen-Shannon (JS) divergence is used to measure the similarity between Gaussian components, and the Gaussian components without similar components are eliminated, and the estimation results are further optimized. The simulation results show that the proposed algorithm can track multi-group maneuvering targets quickly and effectively, and has better tracking performance.

The non-coherent integration detectors for coherent radar systems can promote the detection rate of the radar and meet the required real-time processing, however, these detectors are not Constant False Alarm Rate (CFAR) with respect to the reference cell number, the accumulated pulse number, the clutter speckle covariance matrix, and the shape parameter of the sea clutter model. Based on block-whitening method to whiten the sea clutter, a Pre-Whitening Cell-Averaging CFAR (PWCA-CFAR) detector and a Pre-Whitening Cell-Median CFAR (PWCM-CFAR) detector are proposed where the detection thresholds matching the reference cell number, accumulated pulse number and shape parameter are used. The experiment results show that the PWCM-CFAR detector attains better detection performance than the PWCA-CFAR detector when there exist abnormal cells.

The monolithic signal processing circuit system for Light Detection And Ranging (LiDAR) measurement has significant practical values in terms of improving LiDAR measurement accuracy and data rate, shortening measurement time, and reducing equipment size and power consumption. As the environment interface problem is less considered, the appropriate input interface model must be established to break through the technology difficulty to associate circuit system with photodetectors, die chip, package, transmission line, test board and so on in the operating frequency range. By the combination of theoretical analysis and model simulation, the real working environment of circuit systemfor LiDAR signal processing can be simulated reasonably. Furthermore, based on CMOS technology, the signal processing circuit chip is tested with different photodetector parasitic capacitances. The well agreements between simulation and the testing results validate the feasibility of the input interface model.

Integrated Circuits (ICs) are suffering severer threats caused by Hardware Trojans (HTs), some of which hide in routine operations by coercing firmware or hardware. Along with conventional side-channel detection not always getting golden-chip, HTs become more difficult to detect. An improved Support Vector Machine (SVM) machine learning frameworks for this is proposed using system-level side-channel analysis. Cross validation experimental results on Field Programmable Gate Array (FPGA) show that in the condition of golden-chip, supervised SVM achieves 85.8% test accuracy in average. After grouping, outlier-removing and normalization, it rises by 4%. Even if golden-chip is out of hand, semi-supervised SVM has accuracy to judge HTs existence, averaging in 52.9%-79.5% under different test modes. Comparing with existing researches, this work verifies the efficiency of SVM for HT detection in instruction level, and points out the relationship between diversified learning conditions with detection performance.

Acoustic navigation technology is widely used for autonomous navigation of underwater mobile platforms. The commonly used acoustic navigation models are mostly circle intersection models. The model is simple in structure and convenient in calculation, but it does not consider the influence of platform motion. It is a static model. Under the condition of platform motion, the time when the platform receives each underwater acoustic beacon (underwater star station node) and the spatial position of the platform are different, that is, there is a difference in time and space, which will cause model mismatch and affect navigation accuracy. Aiming at the above problems, this paper deduces the formula of model mismatch error caused by platform  motion, quantitatively analyzes the influence of motion on navigation accuracy and the spatial distribution of error, and focuses on the influence of navigation speed and heading angle on navigation accuracy. The simulation verification is carried out. The results show that the mismatch error of the acoustic navigation circle intersection model exists only when considering the influence of the platform motion, and it is related to the spatial position of the platform in the array. The error space characteristics are approximate concentric elliptic distribution; the model mismatch error is related to the navigation parameters of the platform. The model mismatch error is sensitive to the speed change. As the navigation speed increases, the approximate linear trend increases and the impact is serious. The heading angle has little influence on the global precision variation range, which mainly affects the space of the model mismatch error. The distribution is embodied as a kind of “rotation” with the heading angle, and the direction of the ellipse is aligned with the direction of motion of the platform.

Use of FM radio of additional information channel as a carrier lays a solid foundation for time pass for FM radio. Based on the research of FM radio additional channels, a new design method of time-sharing spread spectrum code is proposed and the content of spread spectrum code is designed in detail. It not only conforms to the requirements of the channel conditions, but also is helpful for accurate tracking capture at the receiving end, and for the time information transmission and timing functions. The measured results show the feasibility and accuracy of the method.

Electromagnetic vortices are introduced into wireless communication to improve spectral efficiency and anti-interference capability. In this paper, the basic principle and characteristics of Orbital Angular Momentum (OAM) and electromagnetic eddy are introduced firstly. The principle of generating Orbital Angular Momentum from supersurface is given, and the methods and research status of generating orbital angular momentum based on supersurface are summarized. The transmission performance, receiving and detecting method, multiplexing and demultiplexing performance of orbital angular momentum are summarized. Finally, the key problems to be solved in the future application of wireless communication orbital angular momentum are discussed.

Considering the shortcomings on the Bit Error Rate (BER) and the compression ratio of the existing asymmetric Distributed Source Coding (DSC) schemes, a scheme named Distributed Source Coding Using Improved Side Information (DSCUISI) is proposed. At the sender, the source sequence is sampled and divided into a sampled and an un-sampled sub-sequences. The un-sampled sub-sequence is compressed by arithmetic coder while the syndrome of the sampled sub-sequence is calculated. The receiver exploits the correlation between the side information and the un-sampled sub-sequence to estimate the sampled symbols, so that the correlation between the estimated sequence and the original sampled sub-sequence is improved. Finally, the syndromes and the estimated sequence are used to recover the sampled sub-sequence. Experiment results show that the DSCUISI can reach high compression ratio, when the correlation among neighboring symbols is strong. The BER of the reconstructed sequence can be kept low when the correlation between sources are weak. It is an efficient, practical DSC scheme and is easy to be implemented.

Many researches demonstrate that cell-edge users are more susceptible to pilot contamination than the cell-center users in massive MIMO systems. Therefore, this paper proposes a dynamic pilot allocation scheme called Joint User Grouping and Alliance Game (JUG-AG) to mitigate pilot contamination. According to the user signal strength, the users are divided into two groups, namely A and B. Users with weak strength of received Base Stations (BSs) signals are recorded as group A, and the remaining users are group B. The users of group A use mutually orthogonal pilots, and the users of group B reuse the remaining orthogonal pilots by means of alliance game. In the alliance game for the users of group B, users are divided into several disjoint user sub-alliances, users belonging to different sub-alliances are allocated different orthogonal pilot sequences, and users in the same sub-alliance reuse the same pilot sequence. Compared with the existing pilot allocation schemes, the proposed JUG-AG scheme is more flexible and can be used for scenarios that all users are randomly distributed. Moreover, the algorithm can obtain the overall optimal solution through cyclic searching. The simulation results demonstrate that the JUG-AG scheme can effectively reduce the average Root Mean Square Error (RMSE) of user signal detection in the uplink and improve the average service rate of users.

Optimal node recovery is an effective measure to control cascading failure of interdependent networks. In view of the fact that the previous recovery model does not consider the node load, this paper analyzes first the cascading failure process including dependent failure and overload failure, and constructs the recovery model of interdependent network under load. Then, considering the structure and dynamic properties of the mutual boundary nodes, a Preferential Recovery method based on Capacity and Connectivity Link (PRCCL) is proposed. Experiment results show that in scale-free independent networks, the recovery effect of PRCCL is better than benchmark methods, the recovery time is shorter, and the recovered networks have higher average degree and robustness. In the independent network composed of Power grid and Internet network, the recovery effect of PRCCL method is also better than the benchmark methods. The advantages of PRCCL are proportional to the recovery ratio, load control parameters and inversely proportional to the tolerance coefficient. The experimental results verify the validity of the PRCCL method, which has scientific guidance value for the recovery of interdependent networks in reality.

The existing similar entity search method has poor adaptability to the length of the observed sequence, and the data storage overhead in the search process is too large, and the accuracy of the search result is insufficient. To this end, an efficient search method is proposed for the IoT Entity Search with Similarity Adaptive Estimation (SAEES). Firstly, in order to reduce the storage overhead of the entity observation sequence, a lightweight method of segmentation representation of the observation sequence is designed to perform a lightweight segmentation compression representation of the original observation sequence of the entity collected by the sensor. Then, in order to achieve an accurate estimation of the similarity of entities with different observation sequence lengths, an adaptive estimation method for observation sequence similarity is proposed. Finally, by exploiting the designed efficient similar entity search matching method, the exact search matching of the entity is completed according to the estimated entity similarity. The simulation results show that the proposed method can greatly improve the efficiency of similar entity search.

Survivable virtual optical network mapping is an important technology to improve the optical network response to disaster failures. In order to solve the problem of bandwidth capacity loss caused by multi-area faults resulted from disasters in Elastic Optical Networks (EONs), a multi-area disaster fault model of survivable virtual network based on risk assessment is established, and a Disaster Fault Model based Ant Colony Optimization for Virtual Network Mapping (DFM-ACO-VNM) algorithm is proposed in the paper. An optical node ranking mapping criterion based on node resources and global potential failure probability of adjacent links in EONs is designed. Then, a heuristic information formula is designed to realize cooperative mapping of virtual nodes and virtual links with minimum bandwidth capacity loss under multi-area faults. The simulation results show that the proposed algorithm can decrease the bandwidth capacity loss, reduce the bandwidth blocking probability and improve the spectrum utilization in multi-area faults.

In order to solve the problem of inter-core crosstalk in Space Division Multiplexing Elastic Optical Network (SDM-EON), which leads to the decline of service transmission quality and the increase of blocking probability, a routing, fiber core and spectrum allocation method for reducing inter-core crosstalk through sparse configuration spectrum converter at nodes is proposed in the paper. This method configures the spectrum converter according to the node’s centrality sparseness in SDM-EON. During service routing, a weighting method for optical path selection considering both optical path load and node spectrum conversion capability is designed.to reduce crosstalk. In the core spectrum allocation stage, a method of fiber core grouping and spectrum partition allocation is utilized. Finally, spectrum conversion is used to reduce traffic crosstalk and improve bandwidth blocking probability for services with high crosstalk. The simulation results show that the proposed algorithm can effectively improve the spectrum utilization and reduce the bandwidth blocking probability caused by fibers inter-core crosstalk.

In cloudlet enhanced Fiber-Wireless (FiWi) network, there is a problem that energy consumption and communication overhead of offloading are too large. An Energy Saving mechanism with Adaptive Offloading Compression (ESAOC) is proposed. According to the different types of service attributes and the maximum tolerant delay, combined with the load changes of the optical network unit and the traffic of the wireless mesh network, the ratio of the offloading compression of service is dynamically adjusted to reduce the communication overhead of the offloading by the average arrival rate of the offloaded data of different priorities obtained by means of statistical methods and combined with the delay of compression of each node. At the same time, a queuing model is established to analyze the delay of the offloading service in the MEC server and cooperatively schedule the relay node in wireless mesh network, thereby performing the schedule of collaborative sleeping on the optical network units and the terminal devices to maximize the duration of sleeping and improving the energy efficiency of system. The results show that the proposed mechanism can effectively reduce the network energy consumption while ensuring the delay performance of offloading service.

As a new type of neural network, Extreme Learning Machine (ELM) has extremely fast training speed and good generalization performance. Considering the problem that the Extreme Learning Machine has high computational complexity and huge memory demand when dealing with high dimensional data, a Batch inheritance Extreme Learning Machine (B-ELM) algorithm is proposed. Firstly, the dataset is divided into different batches, and the automatic encoder network is used to reduce the dimension of each batch. Secondly, the inheritance factor is introduced to establish the relationship between adjacent batches. At the same time, the Lagrange optimization function is constructed by combining the regularization framework to realize the mathematical modeling of batch ELM. Finally, the MNIST, NORB and CIFAR-10 datasets are used for the test experiment. The experimental results show that the proposed algorithm not only has higher classification accuracy, but also reduces effectively computational complexity and memory consumption.

Paroxysmal Atrial Fibrillation (PAF) is a kind of accidental arrhythmia, and its high missed detection rate leads to the increase of heart-related diseases. An automatic detection method is proposed based on kernel sparse coding, which can identify PAF attacks based only on short RR interval data. A special geometric structure is presented to analyze the high-dimensional characteristics of the data, and the covariance matrix is calculated as a feature descriptor to find the Riemannian manifold structure contained in the data; Based on the Log-Euclidean framework, a manifold method is used to map the manifold space to a high-dimensional renewable kernel Hilbert space to obtain a more accurate sparse representation to identify quickly PAF. After verification by the Massa-chusetts Institute of Technology-Beth Israel Hospital atrial fibrillation database, the sensitivity is 98.71%, the specificity is 98.43%, and the total accuracy rate is 98.57%. Therefore, this study has a substantial improvement in the detection of transient PAF and shows good potential for clinical monitoring and treatment.

To solve the failure of existing evaluation methods of infrared and visible fusion image caused by high brightness halation information in night vision halation scene, a novel fusion image quality evaluation method based on adaptive partition is proposed. In this method, the adaptive coefficient is automatically determined according to the halation degree of visible image, and then, the fusion image is divided into halo regions and non-halo region by iterative calculation of the critical halation gray value. In the halo region, the effectiveness of halation elimination is evaluated by halation elimination index designed, while in the non-halo region, the enhancement effect of detailed information such as texture and color is evaluated from three aspects including: characteristics of fusion image itself, retention degree of original image information and human visual effect. Based on evaluation and analysis of fusion images obtained by 4 different anti-halation algorithms, nine objective indexes are selected to construct a quality evaluation system of night vision anti-halation fused image. Experimental results in different night vision halation scenes show that the proposed method could evaluate anti-halation image quality of infrared and visible fusion comprehensively and reasonably, and could solve the problem that the more thorough halation elimination of fusion image, the worse objective evaluation results. This method could also be suitable for evaluating merits and demerits of different anti-halation fusion algorithms.

To eliminate the blocking effects in the dynamic recovery of the streaming signals observed from multiple tasks in time domain, a streaming multi-task sparse Bayesian learning based algorithm and its robust enhanced version are proposed in this paper, where the former extends Lapped Orthogonal Transform (LOT) sliding window in time domain to multi-task condition, and decouples the estimation of unknown noise accuracy from signal reconstruction by Bayesian probability modeling and omits it, the latter further introduces the measurement of reconstructed uncertainty, which improves the robustness of the algorithm and the ability to suppress the accumulation of errors. Experimental results based on measured meteorological data shows that the proposed algorithms have significantly higher reconstruction accuracy, success rate and running speed than the representative algorithms in the field of compressed sensing from multiple measurement vectors, namely, the Temporal Multiple Sparse Bayesian Learning (TMSBL) algorithm and the Multi-Task-Compressed Sensing (MT-CS) algorithm, under different conditions of Signal-to-Noise Ratios, number of observations and tasks.

Infrared thermal imaging system has obvious advantages in target recognition and detection at night, and the motion defocus blur caused by dynamic environment on mobile platform affects the application of the above imaging system. In order to solve the above problems, based on the research of infrared image restoration method after motion defocusing using generating confrontation network, a Infrared thermal image Multi scale deblurGenerative Adversarial Network (IMdeblurGAN) is proposed to suppress motion defocusing blurring effectively while preserving the image by using generating confrontation network to suppress the motion defocusing blurring of infrared image to hold the contrast of infrared image details, to improve the detection and recognition ability of night targets on motion platform. The experimental results show that compared with the existing optimal restoration methods for blurred images, Peak Signal to Noise Ratio (PSNR) of the image is increased by 5%, the Structure SIMilarity (SSIM) is increased by 4%, and the confidence score of YOLO for target recognition is increased by 6%.

Noise is one of the most important influences for clustering. Existing fuzzy clustering methods try to reduce the impact of noise by relaxing the constraint condition of membership. But there are still two basic problems to be solved. The first is how to evaluate the probability that a sample point is a noise. The second is how to retain the effect of normal points while suppressing the impact of noise. To solve these two problems, Robust Fuzzy C-Means based on Adaptive Relaxation (AR-RFCM) is proposed. The new model estimates the reliability of sample points by the method of the K-Nearest Neighbor (KNN). It adjusts adaptively the relaxation parameters to reduce the impact of noise, and keeps the effect of reliable sample points at the same time. In addition, AR-RFCM utilizes the sparsity of membership in K-means to improve the effect of reliable sample points. Therefore, the compactness of clusters is improved and the impact of noise is suppressed. Experiments demonstrate that AR-RFCM has a good robustness for noise, and also achieves higher rand index in all 25 UCI data sets, even averagely higher than FCM 7.7864%.

Considering the shortage of traditional medical ultrasound image despeckle methods, an adaptive multi-exposure fusion framework and feedforward convolutional neural network model image despeckle method is proposed. Firstly, an ultrasound image training data set is produced. Then, a multi-exposure fusion framework with adaptive enhancement factors is proposed to enhance the image for effective feature extraction.Finally, a speckle model is trained through the network and a speckle image is obtained. Experimental results show that, compared with the existing methods, this paper can more effectively remove speckle noise in medical ultrasound images and retain more image details.

In this paper, the S-box of Keccak is generalized into n-variable Keccak-like S-box, and the linear properties of n-variable Keccak-like S-box is studied. It is proved that all the values of correlation advantages of this kind of S-box are 0 or

, where

and

, and for any k in this range, there is an input mask and an output mask that make the correlation advantage be

. Furthermore, it is proved that when the output mask is fixed, the values of the nontrivial correlation advantages of the S-box are determined. Then, the necessary and sufficient condition are given when the count for the nontrivial correlation advantage is the maximum value

. Finally, the value distribution of the Walsh spectrum of Keccak-like S-box is presented.

Based on the characteristics of Whitened Swap−or−Not (WSN) construction, the maximum expected differential probability (MEDP) of Bent whItened Swap Or Not -like (BISON-like) algorithm proposed by Canteaut et al. is analyzed in this paper. In particular, the ability of BISON-like algorithm with balanced nonlinear components against linear cryptanalysis is also investigated. Notice that the number of iteration rounds of BISON algorithm is rather high (It needs usually to iterate 3n rounds, n is the block length of data) and Bent function (unbalanced) is directly used to XOR with the secret key bits. In order to overcome these shortcomings, a kind of balanced Boolean functions that has small absolute value indicator, high nonlinearity and high algebraic degree is selected to replace the Bent functions used in BISON algorithm. Moreover, the abilities of this new variant BISON algorithm against both the differential cryptanalysis and the linear cryptanalysis are estimated. It is shown that the new variant BISON algorithm only needs to iterate n-round function operations; If n is relative large (e.g. n=128 or n=256), Its abilities against both the differential  cryptanalysis and the linear cryptanalysis almost achieve ideal value. Furthermore, due to the balanced function is directly XORed with the secret key bits of the variant algorithm, it attains a better local balance indeed.

In encrypted email system, the public key searchable encryption technology can effectively solve the problem of searching for encrypted emails without decryption. In view of the complex key management problem of public key searchable encryption, an identity-based cryptosystem is introduced in the encrypted mail system. For the offline keyword guessing attack problem of searchable encryption, the method of encrypting keywords and generating trapdoors are adopted at the same time, and the server is designated to search for encrypted emails. At the same time, under the random oracle model, based on the decisional bilinear Diffie-Hellman assumption, the scheme is proved to satisfy the trapdoor and ciphertext indistinguishable security. The numerical experiments show that the scheme has higher computational efficiency than the existing schemes in the keyword trapdoor generation and keyword ciphertext test phase.