When the power of the mainlobe interference received by the receiver is at the same level as the power of the sidelobe interference power, the traditional eigen-projection interference suppression method has the problems of severe beam deformation and peak shift. Aiming at these problems, a beam pattern optimization method (BPOM) was proposed, which can suppress the interference well even when the mainlobe interference power is approximately equal to the sidelobe interference power. In the method, the mainlobe interference eigenvectors are firstly determined according to the correlation criterion. Then through the eigenvalue comparison, the sidelobe interference eigenvectors whose eigenvalues are approximately equal to the mainlobe interference eigenvalues are judged. After that, a projection matrix is constructed to filter out the mainlobe and sidelobe interference. Finally, the covariance matrix is reconstructed and the weight vector for beamforming is obtained. Simulation shows that BPOM has a better output performance than the existing algorithms in case that the power of the mainlobe interference is close to that of the sidelobe interference.

The manifold matrix of the received signals can be destroyed when the array is with the gain and phase errors,which will affect the performance of the traditional direction of arrival (DOA) estimation approaches. In this paper,a novel active array calibration method for the gain and phase errors based on a cascaded neural network(GPECNN) was proposed. The cascaded neural network contains two parts: signal-to-noise ratio ( SNR) classification network and two sets of error estimation subnetworks. Error calibration subnetworks are activated according to the output of the SNR classification network, each of which consists of a gain error estimation network(GEEN) and a phase error estimation network (PEEN), respectively. The disadvantage of neural network topology architecture is changing when the number of array elements varies is addressed by the proposed group calibration strategy. Moreover, due to the data characteristics of the input vector, the cascaded neural network can be applied to arrays with arbitrary geometry without repetitive training. Simulation results demonstrate that the GPECNN not only achieves a better balance between calibration performance and calibration complexity than other methods but also can be applied to arrays with different numbers of sensors or different shapes without repetitive training.

The research purpose of this paper is focused on investigating the performance of extra-large scale massive multiple-input multiple-output ( XL-MIMO) systems with residual hardware impairments. The closed-form expression of the achievable rate under the match filter (MF) receiving strategy was derived and the influence of spatial non-stationarity and residual hardware impairments on the system performance was investigated. In order to maximize the signal-to-interference-plus-noise ratio ( SINR ) of the systems in the presence of hardware impairments, a hardware impairments-aware minimum mean squared error (HIA-MMSE) receiver was proposed. Furthermore, the stair Neumann series approximation was used to reduce the computational complexity of the HIA-MMSE receiver, which can avoid matrix inversion. Simulation results demonstrate the tightness of the derived

analytical expressions and the effectiveness of the low complexity HIA-MMSE (LC-HIA-MMSE) receiver.

Joint sparse recovery (JSR) in compressed sensing (CS) is to simultaneously recover multiple jointly sparse vectors from their incomplete measurements that are conducted based on a common sensing matrix. In this study, the focus is placed on the rank defective case where the number of measurements is limited or the signals are significantly correlated with each other. First, an iterative atom refinement process is adopted to estimate part of the atoms of the support set. Subsequently, the above atoms along with the measurements are used to estimate the remaining atoms. The estimation criteria for atoms are based on the principle of minimum subspace distance. Extensive numerical experiments were performed in noiseless and noisy scenarios, and results reveal that iterative subspace matching pursuit (ISMP) outperforms other existing algorithms for JSR.

In intelligent education, most student-oriented learning path recommendation algorithms are based on either collaborative filtering methods or a 0-1 scoring cognitive diagnosis model. Unfortunately, they fail to provide a detailed report about the students'mastery of knowledge and skill and explain the recommendation results. In addition, they are unable to offer realistic learning path recommendations based on students'learning progress. Knowledge graph based memory recommendation algorithm (KGM-RA) was proposed to solve these problems. On the one hand, KGM-RA can provide more accurate diagnosis information by continuously fitting the students' knowledge and skill proficiency vector (SKSV) in a multi-level scoring cognitive diagnosis model. On the other hand, it also proposes the forgetting recall degree (FRD) according to the statistical results of the human forgetting phenomenon. It also calculates closeness centrality in the knowledge graph to achieve the recommended recall effect consistent with the human forgetting phenomenon. Experiments show that the KGM-RA can obtain the actual learning path recommendations for students, provides the adjustable ability of FRD, and has better reliability and interpretability.

The factors like production accuracy and completion time are the determinants of the optimal scheduling of the complex products work-flow, so the main research direction of modern work-flow technology is how to assure the dynamic balance between the factors. Based on the work-flow technology, restraining the completion time, and analyzing the deficiency of traditional minimum critical path algorithm, a virtual iterative reduction algorithm (VIRA) was proposed, which can improve production accuracy effectively with time constrain. The VIRA with simplification as the core abstracts a virtual task that can predigest the process by combining the complex structures which are cyclic or parallel, finally, by using the virtual task and the other task in the process which is the iterative reduction strategy, determines a path which can make the production accuracy and completion time more balanced than the minimum critical path algorithm. The deadline, the number of tasks, and the number of cyclic structures were used as the factors affecting the performance of the algorithm, changing the influence factors can improve the performance of the algorithm effectively through the analysis of detailed data. Consequently, comparison experiments proved the feasibility of the VIRA.

As a way of training a single hidden layer feedforward network (SLFN),extreme learning machine (ELM) is rapidly becoming popular due to its efficiency. However, ELM tends to overfitting, which makes the model sensitive to noise and outliers. To solve this problem, L2,1-norm is introduced to ELM and an L2,1-norm robust regularized ELM (L2,1-RRELM) was proposed. L2,1-RRELM gives constant penalties to outliers to reduce their adverse effects by replacing least square loss function with a non-convex loss function. In light of the non-convex feature of L2,1-RRELM, the concave-convex procedure (CCCP) is applied to solve its model. The convergence of L2,1-RRELM is also given to show its robustness. In order to further verify the effectiveness of L2,1-RRELM, it is compared with the three popular extreme learning algorithms based on the artificial dataset and University of California Irvine (UCI) datasets. And each algorithm in different noise environments is tested with two evaluation criterions root mean square error (RMSE) and fitness. The results of the simulation indicate that L2,1-RRELM has smaller RMSE and greater fitness under different noise settings. Numerical analysis shows that L2,1-RRELM has better generalization performance, stronger robustness, and higher anti-noise ability and fitness.

Aiming at the problem that the current encrypted traffic classification methods only use the single network framework such as convolutional neural network (CNN), recurrent neural network (RNN), and stacked autoencoder (SAE), and only construct a shallow network to extract features, which leads to the low accuracy of encrypted traffic classification, an encrypted traffic classification framework based on the fusion of vision transformer and temporal features was proposed. Bottleneck transformer network (BoTNet) was used to extract spatial features and bi-directional long short-term memory (BiLSTM) was used to extract temporal features. After the two sub-networks are parallelized, the feature fusion method of early fusion was used in the framework to perform feature fusion. Finally, the encrypted traffic was identified through the fused features. The experimental results show that the BiLSTM and BoTNet fusion transformer (BTFT) model can enhance the performance of encrypted traffic classification by fusing multi-dimensional features. The accuracy rate of a virtual private network (VPN) and non-VPN binary classification is 99.9%, and the accuracy rate of fine-grained encrypted traffic twelve-classification can also reach 97%.

To achieve the confidentiality and retrievability of outsourced data simultaneously, a dynamic multi-keyword fuzzy ranked search scheme (DMFRS) with leakage resilience over encrypted cloud data based on two-level index structure was proposed. The first level index adopts inverted index and orthogonal list, combined with 2-gram and location-sensitive Hashing (LSH) to realize a fuzzy match. The second level index achieves user search permission decision and search result ranking by combining coordinate matching with term frequency-inverse document frequency (TF-IDF). A verification token is generated within the results to verify the search results, which prevents the potential malicious tampering by cloud service providers (CSP). The semantic security of DMFRS is proved by the defined leakage function, and the performance is evaluated based on simulation experiments. The analysis results demonstrate that DMFRS gains certain advantages in security and performance against similar schemes, and it meets the needs of storage and privacy-preserving for outsourcing sensitive data.

White-box cryptography is critical in a communication system to protect the secret key from being disclosed in a cryptographic algorithm code implementation. The stream cipher is a main dataflow encryption approach in mobile communication. However, the research work about white-box cryptographic implementation for stream cipher is rare. A new white-box Zu Chongzhi-128 (ZUC-128) cryptographic implementation algorithm named WBZUC was proposed. WBZUC adopts lookup table and random coding in the non-linear function to make the intermediate value chaos without changing the final encryption result. Thus, the WBZUC algorithm's security gets improved compared with the original ZUC-128 algorithm. As for the efficiency, a test experiment on WBZUC shows that average speed of key generation, encryption, and decryption can reach at 33.74 kbit/ s, 23.31 kbit/ s, 24.06 kbit/ s respectively. Despite its running speed is relative a bit lower than original ZUC-128 algorithm, WBZUC can provide better security and comprehensive performance in mobile communication system environment.