A multi-layer dictionary learning algorithm that joints global constraints and Fisher discrimination (JGCFD-MDL) for image classification tasks was proposed. The algorithm reveals the manifold structure of the data by learning the global constraint dictionary and introduces the Fisher discriminative constraint dictionary to minimize the intra-class dispersion of samples and increase the inter-class dispersion. To further quantify the abstract features that characterize the data, a multi-layer dictionary learning framework is constructed to obtain high-level complex semantic structures and improve image classification performance. Finally, the algorithm is verified on the multi-label dataset of court costumes in the Ming Dynasty and Qing Dynasty, and better performance is obtained. Experiments show that compared with the local similarity algorithm, the average precision is improved by 3.34% . Compared with the single-layer dictionary learning algorithm, the one-error is improved by 1.00% , and the average precision is improved by 0.54% . Experiments also show that it has better performance on general datasets.

This paper studies the dynamic event-triggered leader-follower consensus of nonlinear multi-agent systems (MASs) under directed weighted graph containing a directed spanning tree, and also considers the effects of disturbances and leader of non-zero control inputs in the system. Firstly, a novel distributed control protocol is designed for uncertain disturbances and leader of non-zero control inputs in MASs. Secondly, a novel dynamic event-triggered control ( DETC) strategy is proposed, which eliminates the need for continuous communication between agents and reduces communication resources between agents. By introducing dynamic thresholds, the complexity of excluding Zeno behavior within the system is reduced. Finally, the effectiveness of the proposed theory is validated through numerical simulation.

This paper is devoted to investigate the consensus problems for the multi-agent systems with Lurie nonlinear dynamics in directed topology. Under some assumptions, some sufficient conditions for the systems reaching leaderless consensus and tracking consensus are established by using contraction analysis theory. Compared with the existing results, there is no need to formulate the multi-agent networks in compact form. These conditions are only related to the individual agent in lower-dimensional case and the communication topology of the network. Additionally, a generalized nonlinear function is introduced. Finally, three numerical examples are demonstrated to illustrate the effectiveness of the theoretical results.

   How to protect cultural retics is of great significance to the transmission and dissemination of history and culture. Digital 3-dimensional (3D) modeling of cultural relics is an effective way to preserve them. The efficiency and complexity of cultural relic model reconstruction algorithms are significant challenges due to redundant data. To tackle the above issue, a 3D reconstruction algorithm, named COLMAP + LSH, was proposed for movable cultural relics based on salient region optimization. COLMAP + LSH algorithm introduces saliency region detection and locality-sensetive Hashing (LSH) to achieve efficient, accurate, and robust digital 3D modeling of cultural relics. Specifically, 400 cultural model data were collected through offline and online collection. COLMAP + LSH algorithm detects the salient region interactively and reduces the number of images in the salient region by feature diffusion. Additionally, COLMAP + LSH algorithm utilizes LSH to calculate the image selection scores and employs the image selection scores to reduce the redundant image. The experiments on the self-constructed cultural relics dataset show that COLMAP + LSH algorithm can efficiently achieve image feature diffusion and ensure the quality of artifact reconstruction while selecting most of the redundant image data.

 Radio-frequency identification (RFID) antennas are critical components in wireless communication networks for the Internet of things (IoT). The RFID systems make it possible to realize the dynamic interconnection of various things. To better summarize the operating principles of the RFID antennas and associate antennas with specific complex applications, a review of RFID systems and antennas is necessary. In this paper, a review of reader antennas for ultra-high frequency ( UHF) RFID systems is presented, and the categories of RFID systems are summarized for the first time. The antennas are classified according to the reading region and operating principle. The reading region determines the most crucial performance that should be concentrated on when designing an antenna, while the operating principle affects the current distribution on the surface of the antenna, and further the electromagnetic radiation. By the summary of the RFID systems and antennas, the understanding of future researchers on the operating principles of the RFID antennas could be facilitated, which can be helpful in the advanced design and implementation of RFID antennas. In addition, taking engineering requirements into account, the future prospective of RFID applications is discussed, as well as the challenges to be addressed.

The signal is subjected to lots of interferences in vehicle-to-vehicle (V2V) channel propagation, resulting in receiving error codes. Two-dimensional (2D) and three-dimensional (3D) geometrical channel models are used to depict the wideband V2V multiple-input multiple-output (MIMO) channels. Using the channel model, Turbo code and low-density parity-check (LDPC) code are investigated for wideband V2V MIMO system, and the encoding and the decoding schemes are investigated. The bit error rate (BER), channel capacity and outage probability of wideband V2V MIMO system using Turbo code and LDPC code are analyzed at different typical speeds. The results show that the performance of wideband V2V MIMO system using Turbo code outperform that using LDPC code. The performance is affected by transmitting and receiving speeds with the same coding scheme. And the channel capacity of the 3D channel is larger than that of 2D channel.

   Few-shot named entity recognition (NER) aims to identify named entities in new domains using a limited amount of annotated data. Previous methods divided this task into entity span detection and entity classification, achieving good results. However these methods are limited by the imbalance between the entity and non-entity categories due to the use of sequence labeling for entity span detection. To this end, a point-proto network ( PPN) combining pointer and prototypical networks was proposed. Specifically, the pointer network generates the position of entities in sentences in the entity span detection stage. The prototypical network builds semantic prototypes of entity types and classifies entities based on their distance from these prototypes in the entity classification stage. Moreover, the low-rank adaptation ( LoRA) fine-tuning method, which involves freezing the pre-trained weights and injecting a trainable decomposition matrix, reduces the parameters that need to be trained and saved. Extensive experiments on the few-shot NER Dataset (Few-NERD) and Cross-Dataset demonstrate the superiority of PPN in this domain.

   As a subtask of open domain event extraction ( ODEE), new event type induction aims to discover a set of unseen event types from a given corpus. Existing methods mostly adopt semi-supervised or unsupervised learning to achieve the goal, which uses complex and different objective functions for labeled and unlabeled data respectively. In order to unify and simplify objective functions, a reliable pseudo-labeling prediction (RPP) framework for new event type induction was proposed. The framework introduces a double label reassignment ( DLR) strategy for unlabeled data based on swap-prediction. DLR strategy can alleviate the model degeneration caused by swap-predication and further combine the real distribution over unseen event types to produce more reliable pseudo labels for unlabeled data. The generated reliable pseudo labels help the overall model be optimized by a unified and simple objective. Experiments show that RPP framework outperforms the state-of-the-art on the benchmark.

Received signal strength (RSS) based positioning schemes ignore the actual environmental feature that the volatility of RSS increases as signal propagation distance grows. Therefore, RSS over long distance generally has relatively large measurement error and degrades the positioning performance. To reduce the negative impact of these RSSs over long distances, a weighted semidefinite programming (WSDP) positioning scheme was proposed. The WSDP positioning scheme first assesses the signal propagation quality using the average variance of all RSS sets. Then appropriate weighting factors are set based on the variance of each RSS set, and a weighted semidefinite programming optimizer is formulated to estimate the positions of target nodes. Simulation results show that the WSDP positioning scheme can effectively improve the positioning performance.

This paper proposes a novel method for the parameter optimization of complex networks established through coarsening and phase space reconstruction. Firstly, we identify the change-points of the time series based on the cumulative sum ( CUSUM) control chart method. Then, we optimize the coarse-graining parameters and phase space embedding dimension based on the evolution analysis of the global topology index ( betweenness) at the mutation point. Finally, we conduct a simulation analysis based on real-time data of Chinese copper spot prices. The results show that the delay of the copper spot prices in Chinese spot market is 1 day, and the optimal embedding dimension of the phase space reconstruction is 3. The acceptance level of the investors towards the small fluctuations in copper spot prices is 0.2 times than the average level of price fluctuations, which means that an average price fluctuation of 0.2 times is the optimal coarse-graining parameter.

In recent years, there has been considerable attention and research on the higher-order interactions that are prevalent in various real-world networks. Hypergraphs, especially in the study of complex systems, are proved effective in capturing these interactions. To better characterize the model in reality, this paper proposes a theoretical model of node interdependent percolation in multiplex hypergraphs, considering “ weak ” interdependence. The proposed model includes pairwise and higher-order interactions, where the removal of nodes triggers cascading failures. However, interdependent nodes connected to failed nodes experience partial loss of connections due to “ weak” interdependence, reflecting the self-sustaining capabilities of real-world systems. Percolation theory is applied to the analysis to investigate the properties of the percolation threshold and phase transition. Both analysis and simulation results show that as the strength of interdependence between nodes weakens, the network transitions from a discontinuous to a continuous phase, thereby increasing its robustness.

Traditional loss-based transports cannot meet the strict requirements of low latency and high throughput in data center networks (DCNs). Thus data center transmission control protocol (DCTCP) is proposed to better manage the congestion control in DCNs. To provide insight into improving the stability of the DCN, this paper focuses on the Hopf bifurcation analysis of a fluid model of DCTCP, and investigates the stability of the network. The round-trip time (RTT), being an effective congestion signal, is selected as the bifurcation parameter. And the network turns unstable and generates periodic solutions when the parameter is larger than the given critical value, which is given by explicit algorithms. The analytical results reveal the existence of Hopf bifurcation. Numerical simulations are performed to make a comparative analysis between the fluid model and the simplified model of DCTCP. The influence of other parameters on the DCN stability is also discussed.

In order to improve the reliability and resource utilization efficiency of vehicle-to-vehicle (V2V) communication system, in this paper, the fairness optimization and power allocation for the cognitive V2V network that takes into account the realistic three-dimensional (3D) channel are investigated. Large-scale and small-scale fading are considered in the proposed channel model. An adaptive non-orthogonal multiple access ( NOMA) / orthogonal multiple access (OMA) scheme is proposed to reduce the complexity of successive-interference-cancellation (SIC) in decoding and improve spectrum utilization. Also, a fairness index that takes into account each user’s requirements is proposed to indicate the optimal point clearly. In the imperfect SIC, the optimization problem of maximizing user fairness is formulated. Then, a subgradient descent method is proposed to solve the optimization problem with customizable precision. And the computational complexity of the proposed method is analyzed. The achievable rate, outage probability and user fairness are analyzed. The results show that the proposed adaptive NOMA / OMA (A-NOMA / OMA) outperforms both NOMA and OMA. The simulation results are compared with validated analysis to confirm the theoretical analysis.

This paper aims at solving the linear-quadratic optimal control problems ( LQOCP) for time-varying descriptor systems in a real Hilbert space. By using the Moore-Penrose inverse theory and space decomposition technique, the descriptor system can be rewritten as a new differential-algebraic equation (DAE), and then some novel sufficient conditions for the solvability of LQOCP are obtained. Especially, the methods proposed in this work are simpler and easier to verify and compute, and can solve LQOCP without the range inclusion condition. In addition, some numerical examples are shown to verify the results obtained.

In order to study the financial behavior of investors in the spot market, the transmission process of futures prices to spot prices is analyzed. Firstly, a coarse-graining method is proposed to construct a dual-layer coupled complex network of spot price and futures price. Then, to characterize the financial behavior of investors in the spot market, a price coupling strength indicator is introduced to capture investors' overreaction and underreaction behavior. The simulation results show that, despite the focus of researchers on arbitrage opportunities between futures and spot markets, investors in the spot market will not overreact or delay when the acceptance level of price fluctuations remains unchanged. On the contrary, when the stable coefficient of the price difference between the futures and spot markets remains unchanged, investors undergo a nonlinear process of overreaction followed by underreaction as their acceptance level of price fluctuations increases.

The extraction and description of image features are very important for visual simultaneous localization and mapping (V-SLAM). A rotated boosted efficient binary local image descriptor ( BEBLID) SLAM ( RB-SLAM) algorithm based on improved oriented fast and rotated brief (ORB) feature description is proposed in this paper, which can solve the problems of low localization accuracy and time efficiency of the current ORB-SLAM3 algorithm. Firstly, it uses the BEBLID to replace the feature point description algorithm of the original ORB to enhance the expressiveness and description efficiency of the image. Secondly, it adds rotational invariance to the BEBLID using the orientation information of the feature points. It also selects the rotationally stable bits in the BEBLID to further enhance the rotational invariance of the BEBLID. Finally, it retrains the binary visual dictionary based on the BEBLID to reduce the cumulative error of V-SLAM and improve the loading speed of the visual dictionary. Experiments show that the dictionary loading efficiency is improved by more than 10 times. The RB-SLAM algorithm improves the trajectory accuracy by 24.75% on the TUM dataset and 26.25% on the EuRoC dataset compared to the ORB-SLAM3 algorithm.

   Proxy re-encryption (PRE) allows users to transfer decryption rights to the data requester via proxy. Due to the current PRE schemes from lattice ( LPRE) cannot fulfill chosen-ciphertext attack ( CCA) security, an identity-based PRE (IB-PRE) scheme from learning with errors over ring ( RLWE) assumption with ciphertext evolution (IB-LPRE-CE) was proposed. IB-LPRE-CE generates the private key using the preimage sampling algorithm (SamplePre) and completes the ciphertext delegation using the re-encryption algorithm. In addition, for the problem of ciphertext delegation change caused by the long-term secret key update, the idea of PRE is used to complete ciphertext evolution and the modification of ciphertext delegation, which improves the efficiency of secure data sharing. In terms of security, IB-LPRE-CE is CCA security based on RLWE assumption. Compared with the current LPRE schemes, IB-LPRE-CE offers greater security and improves the computational efficiency of the encryption algorithm.

A geometry-based stochastic model ( GBSM) for unmanned aerial vehicle to vehicle ( UAV-V) multiple-input multiple-output (MIMO) wideband channel is proposed to investigate the characteristics of UAV-V channel. Based on the proposed model, a three-dimensional (3D) wideband channel matrix regarding channel numbers, time and delay is constructed. And some important channel characteristics parameters, such as power delay profile (PDP), root mean square ( RMS) delay spread, RMS Doppler spread, channel gain and Doppler power spectral density (PSD) are investigated with different vehicle velocities. It is much simpler and clearer compared with the complex analytical derivations. The results are compared with validated analysis to confirm the theoretical analysis.

 Network attacks evolved from single-step and simple attacks to complex multistep attacks. Current methods of multistep attack detection usually match multistep attacks from intrusion detection systems (IDS) alarms based on the correlation between attack steps. However, IDS has false negatives and false positives, which leads to incomplete or incorrect multistep attacks. Association based on simple similarity is difficult to obtain an accurate attack cluster, while association based on prior knowledge such as attack graphs is difficult to guarantee a complete attack knowledge base. To solve the above problems, a heuristic multistep attack scenarios construction method based on the kill chain (HMASCKC) model was proposed. The attack model graph can be obtained from dual data sources and heuristic multistep attack scenarios can be obtained through graph matching. The model graph of the attack and the predicted value of the next attack are obtained by calculating the matching value. And according to the purpose of the multistep attack, the kill chain model is used to define the initial multistep attack model, which is used as the initial graph for graph matching. Experimental results show that HMASCKC model can better fit the multistep attack behavior, the effect has some advantages over the longest common subsequence (LCS) algorithm, which can close to or match the prediction error of judge evaluation of attack intension ( JEAN) system. The method can make multistep attack model matching for unknown attacks, so it has some advantages in practical application.