Adverse imaging conditions such as fog and dust present substantial challenges to optical camera and Light Detection and Ranging (LiDAR), which in turn highlights millimeter-wave radar as a promising alternative for reliable mapping. However, reflection losses and severe multipath interference often render millimeter-wave radar pointcloud sparse and noisy, thereby limiting their applicability in 3D imaging. In this letter, we present a novel conditional Generative Adversarial Network (cGAN) for reconstructing dense, high-resolution 3D maps from sparse millimeter-wave radar pointcloud. The Non-Coherent Accumulation (NCA) and Synthetic Aperture Accumulation (SAA) strategies are designed to address the issues of raw pointcloud sparsity and low angular resolution, respectively. Then this framework employs the PointNet++-based generator-discriminator architecture, leveraging LiDAR-labeled NCA and SAA pointcloud as conditional input for pointcloud-to-pointcloud (Pcd2Pcd) translation. The proposed method is evaluated on the publicly available RadarEyes dataset, where experimental results demonstrate that it outperforms existing approaches, achieving higher resolution, improved accuracy, and superior generalization capability.

In this paper, we construct an air-sea collaborative system framework based on the integrated sensing and communication (ISAC) techniques, where the uncrewed aerial vehicle (UAV) and uncrewed surface vehicle (USV) jointly inspect targets of interest while keeping communication with each other simultaneously. We first demonstrate the unique challenges encountered in this collaborative system, i.e., the coupling and heterogeneity of the UAV/USV’s trajectories. By applying the hover-and-fly strategy, we formulate a total energy minimization problem to jointly optimize the trajectories, time durations, target scheduling, and beamforming, subject to the constraints of motion states, sensing quality, and communication rate requirements. To handle the strong coupling among variables, the problem is decomposed into two subproblems: hover-point selection and joint trajectory planning with beamforming design. The first subproblem is formulated as a novel bi-traveling salesman problem with neighborhoods (Bi-TSPN). To solve this NP-hard problem, we develop a three-step hierarchical method to successively determine hover-point location and target scheduling, optimize the visiting order of hover-points, and allocate the time duration. For second subproblem, the remaining trajectory planning and beamforming design are addressed in each hover-and-fly stage using semidefinite relaxation (SDR) and successive convex approximation (SCA) methods. Finally, we conduct a series of simulations to demonstrate the superiority of the proposed scheme over existing sequential access, leader–follower, and fly-and-sense strategies.

The increasing reliance on multi-sensor systems to enhance the robustness and accuracy of robotic systems introduces new challenges, particularly in calibrating multiple sensors. Traditional offline calibration techniques, which use targets like checkerboards, assume that extrinsic parameters between sensors remain constant during operation. However, this assumption does not account for drift caused by external factors such as vibration and deformation over time. In this letter, we introduce CalibOnline, a novel method for the online detection and correction of miscalibration in multi-sensor setups. First, we propose a unified representation of LiDAR and camera data as depth maps to reduce calibration uncertainties due to data modality discrepancies. We then explore the robust characteristic of depth discontinuity edges to facilitate efficient matching between depth maps. The impact of extrinsic variations on edge-matching constraints is analyzed, leading to the design of a miscalibration detection module to monitor extrinsic parameters. Finally, we frame the correction of extrinsic parameters as an on-manifold optimization problem, enhancing the convergence of the estimated parameters. Experimental results across various scenarios demonstrate the robustness and accuracy of our proposed method. Code is open-sourced at: https://github.com/cchester25/CalibOnline.git.

To position agents equipped with ultrawideband (UWB) devices without requiring continuous clock synchronization, the primary measurement currently used is the time of flight between agents, estimated through active two-way ranging (TWR). However, the cumbersome signal exchange mechanism in active TWR imposes a trade-off between the number of agents and the positioning frequency. To address this issue, this paper proposes a relative localization scheme using hybrid active-passive TWR to accommodate more agents without compromising the positioning refresh rate. Additionally, this scheme not only extends passive TWR into a dynamic form, thereby improving localization accuracy of mobile agents, but also presents a two-step relative positioning method to enhance computational efficiency. The feasibility of the scheme is validated through numerical simulations and real-world tests on a prototype system built with consumer-level UWB chips.

This article introduces a knowledge-empowered deep learning framework for direction of arrival (DOA) estimation in Internet of Unmanned Agents (IUA), addressing the critical challenge of direction-dependent mutual coupling in complex urban environments. The framework seamlessly integrates domain knowledge of electromagnetic coupling mechanisms with data-driven learning approaches, effectively combining the interpretability of traditional array signal processing with the robustness of modern deep learning techniques. By incorporating the structural properties of mutual coupling matrices and leveraging distributed learning architecture suitable for IUA deployment, our approach achieves substantial improvements in both estimation accuracy and computational efficiency. Extensive numerical simulations under various practical scenarios demonstrate that the proposed framework significantly outperforms conventional methods in terms of estimation precision, and robustness to environmental uncertainties, making it particularly suitable for dynamic IUA applications, such as unmanned agent localization and trajectory tracking.

Social media platforms connect users with users around the world and allow everyone to post their opinions freely. This facilitates the spread of rumors. Identifying rumors on social media becomes a thorny challenge. Existing research has achieved good results by using the propagation characteristics of rumors. However, these researches only consider the impact of rumor propagation and ignore users' feedback on rumors. In this paper, we propose a bottom-up node aggregation method based on user feedback trees and look at how to use pre-training to model the structure of feed back trees.

Communities are prevalent in large graphs such as social networks, protein networks, etc. Community search aims to find a cohesive subgraph that contains the query nodes. Existing community search algorithms often adopt community models to find target communities, and k-truss model is a popularly used one that provides structural constraints. However, the structural constraints presented by k-truss is so tight that the searching algorithm often can not find the target communities. There always exist some subgraphs that may not conform to k-truss structure but do have cohesive characteristics to meet users' personalized requirements. Moreover, the k-truss based community search algorithms can not meet users' real-time demands on large graphs. To address the above problems, this paper proposes the relaxed k-truss community search problem for the first time. Then we construct a relaxed k-truss index, which can help to find cohesive communities in linear time and provide flexible searching for nested communities. We also design an index maintenance algorithm to dynamically update the index. Furthermore, a community search algorithm based on the relaxed k-truss index is presented. Extensive experimental results on real datasets prove the effectiveness and efficiency of our model and algorithms.

Summary This research introduces a comprehensive suite of neural network models designed to tackle the challenging task of rumor stance detection within the framework of simulating complex rumor propagation systems. Our objective centers on accurately modeling the intricate structures of rumor dialogues and propagation patterns to identify user stances—whether they are in support, denial, questioning, or commenting on rumors. Unlike conventional methods that rely on simplistic keyword targeting and fail in the nuanced context of social networks, our models delve into the complexities of dialogue and propagation structures, offering a more precise and insightful analysis of rumor dynamics. In addressing the simulation and modeling of complex systems, our approach specifically focuses on the elaborate interaction networks that underpin rumor spread and reception. While our methodology does not directly engage with brain‐like computing paradigms, it reflects a similar level of sophistication in handling layered and complex information flows, analogous to cognitive processes in understanding and interpreting human communications. Employing a hierarchical attention mechanism, our models adeptly parse through multitiered dialogue sequences, effectively distinguishing between various indicators of user stances. This allows for a nuanced and detailed representation of the rumor ecosystem, significantly enhancing the accuracy of stance detection. Through rigorous testing on diverse datasets, our approach has demonstrated superior performance over existing models, thereby establishing a new benchmark in the field.

Augmented reality (AR), a prominent application within the Internet of Things (IoT) domain, demands high-performance pose estimation. Presently, the visual-inertial navigation system (VINS) is acknowledged as an essential method for providing 6-DoF poses. However, VINS builds the local frame at random during the system initialization stage, making it difficult to establish a connection with the global frame. In addition, VINS is prone to drifting. In this paper, we propose an innovative method that tightly couples markerless motion capture (Mocap) with vision and an IMU to achieve global and drift-free pose estimation for AR glasses. To address the issue of pose initialization and establish a connection between the IMU and Mocap, we introduce a coarse-to-fine initialization strategy, enabling data fusion for Mocap, vision, and the IMU under a unified global frame. Furthermore, we formulate the Mocap factor alongside the visual and inertial factors and integrate them into a factor graph framework to constrain the system states. With a spatiotemporal calibration method, the IMU-Mocap extrinsic parameter and time offset are calibrated online to improve the pose estimation accuracy. Experimental evaluations in real-world experiments demonstrate the capability of our method to accurately estimate drift-free poses in the global frame. Compared to the state-of-the-art VINS-Fusion, ORB-SLAM3, and GVIS, we achieve improvements of 81%, 42%, and 33% in translation accuracy and improvements of 58%, 33%, and 72% in rotation accuracy, respectively. Moreover, we also evaluate our system for the EuRoC dataset, further indicating the effectiveness of the proposed work.

In emergency situations, individuals often experience panic and may struggle to find their way to safety. In such cases, the presence of trained leaders is crucial to provide guidance for evacuation and ensure the safety of those evacuating. However, determining the optimal positions of leaders in complex environments to guide the maximum number of pedestrians presents a significant challenge. To address this problem, we propose an Entropy-Based Maximum Coverage Model(EMCM) to enhance evacuation guidance. Initially, we employ the social force model to describe pedestrian movement characteristics, while the environmental features of the evacuation area are delineated using a navigation network. Subsequently, we introduce an urgency model grounded in evacuation entropy to quantify the urgency experienced by pedestrians. Thirdly, we frame the problem of leader positioning as an optimization challenge focused on maximizing entropy coverage, thereby increasing the number of individuals a leader can effectively guide. Finally, we employ AnyLogic to construct simulation scenarios that visualize the leadership process. This innovative method holds the potential to offer valuable support in the formulation of effective evacuation strategies during emergency situations.

An architecture for vehicle position estimation is introduced in this paper to tackle the issue of vehicles positioning in traffic congestion of intelligent transportation system (ITS). The introduced architecture is related to three unmanned aerial vehicles (UAVs) equipped with uniform linear array (ULA), ITS center and the terminal of position estimation, in which the UAV collect data from the ULA, the ITS center store data and the terminal is responsible for executing the corresponding direction of arrival (DOA) estimation algorithm to estimate the vehicle position. In the introduced architecture, DOA estimation is the crucial issue, hence a robust sparse recovery framework based on the optimal weighted subspace fitting is put forward for DOA estimation in the presence of direction-dependent unknown mutual coupling. Firstly, a data model can be obtained by a new transformation approach. Then, a sparse recovery algorithm based on the weighted subspace fitting is used to estimate the desired DOAs. The proposed DOA estimation algorithm can achieve superior performance in both coherent and incoherent signals in the environment of direction-dependent mutual coupling, and this environment often occurs in traffic congestion. Based on the DOA estimation results obtained from the proposed efficient regularized sparse recovery algorithm, the position of vehicles is final estimated by a weighted three-points cross-positioning method. The results of various simulation experiments fully demonstrate the robustness and superiority of the proposed architecture and algorithm.

Anchor link prediction exacerbates the risk of privacy leakage via the de-anonymization of social network data. Embedding-based methods for anchor link prediction are limited by the excessive similarity of the associated nodes in a latent feature space and the variation between latent feature spaces caused by the semantics of different networks. In this article, we propose a novel method which reduces the impact of semantic discrepancies between different networks in the latent feature space. The proposed method consists of two phases. First, graph embedding focuses on the network structural roles of nodes and increases the distinction between the associated nodes in the embedding space. Second, a federated adversarial learning framework which performs graph embedding on each social network and an adversarial learning model on the server according to the observable anchor links is used to associate independent graph embedding approaches on different social networks. The combination of distinction enhancement and the association of graph embedding approaches alleviates variance between the latent feature spaces caused by the semantics of different social networks. Extensive experiments on real social networks demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of both precision and robustness.

Anchor link prediction across social networks plays an important role in multiple social network analysis. Traditional methods rely heavily on user privacy information or high-quality network topology information. These methods are not suitable for multiple social networks analysis in real-life. Deep learning methods based on graph embedding are restricted by the impact of the active privacy protection policy of users on the graph structure. In this paper, we propose a novel method which neutralizes the impact of users’ evasion strategies. First, graph embedding with conditional estimation analysis is used to obtain a robust embedding vector space. Secondly, cross-network features space for supervised learning is constructed via the constraints of cross-network feature collisions. The combination of robustness enhancement and cross-network feature collisions constraints eliminate the impact of evasion strategies. Extensive experiments on large-scale real-life social networks demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of precision, adaptability, and robustness for the scenarios with evasion strategies.

In the upcoming next-generation (5G-Advanced and 6G) wireless networks, sensing as a service will play a more important role than ever before. Recently, the concept of perceptive network is proposed as a paradigm shift that provides sensing and communication (S&C) services simultaneously. This type of technology is typically referred to as Integrated Sensing and Communications (ISAC). In this paper, we propose the concept of sensing quality of service (QoS) in terms of diverse applications. Specifically, the probability of detection, the Crámer-Rao bound (CRB) for parameter estimation and the posterior CRB for moving target indication are employed to measure the sensing QoS for detection, localization, and tracking, respectively. Then, we establish a unified framework for ISAC resource allocation, where the fairness and the comprehensiveness optimization criteria are considered for the aforementioned sensing services. The proposed schemes can flexibly allocate the limited power and bandwidth resources according to both S&C QoSs. Finally, we study the performance trade-off between S&C services in different resource allocation schemes by numerical simulations.

When sparse reconstruction (SR) space-time adaptive processing (STAP) strategy is employed for sea clutter suppression, the computational burden imposed by the spectrum reconstruction process is still a challenge. Hence, a low-complexity MIMO radar SR STAP strategy is developed via reducing model dimension and optimizing the sparse reconstruction scheme. On one hand, STAP echo model is projected into space and time domains owing to the uncoupled relationship, thus the computation burden involved with large measurement matrix can be lessened by low-dimension structure. Meanwhile, a coprime scheme is exerted to improve the degree of freedom (DOF) in sub models and ensure lower sampling consumption. On the other hand, in order to realize efficient spectral reconstruction, an improved SR algorithm is formulated via developing a prior matrix factor and a tradeoff factor. Convergence speed of the algorithm is promoted by tuning the factors reasonably, clutter spectrum is quickly accessible with less iterations so that adaptive filter can be constructed efficiently. According to the experiment results, high-efficiency spectral reconstruction is realized and effective clutter suppression performance can be ensured.

Convolutional neural network (CNN) models have achieved great success in many fields. With the advent of ResNet, networks used in practice are getting deeper and wider. However, is each layer non-trivial in networks? To answer this question, we trained a network on the training set, then we replace the network convolution kernels with zeros and test the result models on the test set. We compared experimental results with baseline and showed that we can reach similar or even the same performances. Although convolution kernels are the cores of networks, we demonstrate that some of them are trivial and regular in ResNet.

There is a new cache pollution attack in the information-centric network (ICN), which fills the router cache by sending a large number of requests for nonpopular content. This attack will severely reduce the router cache hit rate. Therefore, the detection of cache pollution attacks is also an urgent problem in the current information center network. In the existing research on the problem of cache pollution detection, most of the methods of manually setting the threshold are used for cache pollution detection. The accuracy of the detection result depends on the threshold setting, and the adaptability to different network environments is weak. In order to improve the accuracy of cache pollution detection and adaptability to different network environments, this paper proposes a detection algorithm based on gradient boost decision tree (GBDT), which can obtain cache pollution detection through model learning. Method. In feature selection, the algorithm uses two features based on node status and path information as model input, which improves the accuracy of the method. This paper proves the improvement of the detection accuracy of this method through comparative experiments.

Online social networks provide convenient conditions for the spread of rumors, and false rumors bring great harm to social life. Rumor dissemination is a process, and effective identification of rumors in the early stage of their appearance will reduce the negative impact of false rumors. This paper proposes a novel early rumor detection (ERD) model based on reinforcement learning. In the rumor detection part, a dual-engine rumor detection model based on deep learning is proposed to realize the differential feature extraction of original tweets and their replies. A double self-attention (DSA) mechanism is proposed, which can eliminate data redundancy in sentences and words at the same time. In the reinforcement learning part, an ERD model based on Deep Recurrent Q-Learning Network (DRQN) is proposed, which uses LSTM to learn the state sequence features, and the optimization strategy of the reward function is to take into account the timeliness and accuracy of rumor detection. Experiments show that, compared with existing methods, the ERD model proposed in this paper has a greater improvement in the timeliness and detection rate of rumor detection.

In social networks, some private information, such as the personal name, age gender, the number of friends, can be obtained by others. This paper defines a combination degree-neighborhood label matching attack model based on group maps obtained from multi-social networks. Based on the heuristic combination degree attack model, the target combination degree and neighborhood labels are used as the background knowledge of the attacker to obtain the candidate vertices set. The singularity of the sensitive label matching results will expose the sensitive information of the vertex being attacked. In order to solve this privacy attack, this paper proposes a group graph sensitive label generalization L diversity algorithm. This algorithm reduces the probability of sensitive labels being identified by designing a group map sensitive label generalization tree. According to the background knowledge, the number of sensitive labels in the candidate set and the number of sensitive labels obtained by matching are not less than L, so as to protect the sensitive information of the attacked target. The algorithm was evaluated by using three sets of data with different ratios. The experiment results show that the privacy protection algorithm effectively prevents sensitive label privacy attacks consisting of combination degree-domain label matching and better maintains the availability of graph data.

In this issue, we consider the location-aware targeted influence blocking maximization (LTIBM) problem, which plays a very important role in viral marketing and rumor control. LTIBM aims to find a set of positive seeds in a given social network to block the influence propagation of negative seeds over the targeted nodes located in a given region and having a preference on a given topic set as much as possible. We devise a simulation-based greedy algorithm based on monotone and submodular characteristics of influence function under the homogeneous independent cascade model. To improve the efficiency of the greedy algorithm, we propose LTIBM-H, a heuristic algorithm based on QT-tree and maximum influence arborescence (MIA). Experimental results show that the proposed LTIBM-H algorithm can achieve matching the blocking effect to the greedy algorithm and often performs better in terms of effectiveness than other baseline algorithms, while LTIBM-H is four orders of magnitude faster than the greedy algorithm.

Millimetre wave (mmWave) communications are the most promising candidate for the future fifth‐generation mobile broadband networks, which offer greater available spectrum than current cellular. However, the huge path loss and rain attenuation due to the characteristic of the mmWave channel make it difficult to realise. Thanks to the small wavelength of mmWave signals, massive multi‐input‐multi‐output (MIMO) systems can be leveraged to overcome the path loss. Unfortunately, in contrast to conventional MIMO systems, high‐power consumption and hardware cost make fully digital precoding impractical. In this study, the authors study the hybrid precoding structure for multiuser mmWave systems, which compromises the cost and performance. First, an extension algorithm from single user systems, which directly decomposes the optimal fully digital precoder into a baseband precoder and an analogue radio‐frequency (RF) precoder, is introduced according to the recent work. Then, the drawback of this kind of algorithm is analysed at high signal‐to‐noise ratios. Subsequently, a two‐stage hybrid precoding algorithm, which is based on the minimum mean‐squared error criterion is proposed, where the modified Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm is presented to reduce the reconstruction error of computing the analogue RF precoder. Simulation results show that the proposed precoding algorithm can significantly improve the performance of the system sum‐rate.

Web Security Gateway (WSG) is a new type of network security product that maintains the security of trusted networks. In this paper, a WSG model for evaluating WSG performance is presented. This paper advances discussion of previous studies on series services under multiple service windows. The proposed model consists of a two-stage queuing system. The first stage is a network layer simulation. The second stage is thus similar to a parallel hyper-Erlang distribution model. The results of a simulation test verified the feasibility and performance of the proposed model.

In this letter, we propose a robust direction-of-arrival (DOA) estimation algorithm in the context of sparse reconstruction, where some array sensors are miscalibrated. In this case, conventional DOA estimation algorithms suffer from degraded performance or even failed operations. In the proposed approach, the miscalibrated sensor observations are treated as outliers, and a weighting factor is adaptively optimized and applied to each sensor in order to effectively mitigate the effect of the outliers. An algorithm based on the maximum correntropy criterion is then developed to yield robust DOA estimation. The simulation results are presented to verify the effectiveness and superiority of the proposed approach compared with conventional DOA estimation algorithms.

A variable tap-length LMS algorithm based on adaptive parameters for tapped-delay-line (TDL) structure adaption is proposed in this paper. The objective of this work is to overcome some drawbacks of previous variable tap-length adaptive filter algorithms, including slow convergence speed and high sensitivity to noise. Novel tap-length iteration equation based on adaptive parameters and arctangent-limited value is proposed. The values of adaptive parameters can automatically change according to the filter state, which not only improves the convergence speed but also reduces the steady-state error. The arctangent-limited value can effectively reduce the instantaneous estimated error, which enhances the robustness to noise. Computer simulations are conducted to verify the performance of proposed algorithm.