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The algorithm proposed in this paper aims at solving the problem of star map matching in high-limiting-magnitude astronomical images, which is inspired by geometric voting star identification techniques. It is a two-step star map matching algorithm relying only on angular features, and adopts a reasonable matching strategy to overcome the problem of poor real-time performance of the geometric voting algorithm when the number of stars is large. The algorithm focuses on application scenarios where there are a large number of dense stars (limiting magnitude greater than 13, average number of stars per square degree greater than 185) in the image, which is different from the sparse star identification problem of the star tracker, which is more challenging for the robustness and real-time performance of the algorithm. The proposed algorithm can be adapted to application scenarios such as unreliable brightness information, centroid positioning error, visual axis pointing deviation, and a large number of false stars, with high accuracy, robustness, and good real-time performance. [ABSTRACT FROM AUTHOR]

In this paper we propose a new parameter estimator that ensures global exponential convergence of linear regression models requiring only the necessary assumption of identifiability of the regression equation, which we show is equivalent to interval excitation of the regressor vector. An extension to – separable and monotonic – nonlinear parameterisations is also given. The estimators are shown to be robust to additive measurement noise and – not necessarily slow-parameter variations. Moreover, a version of the estimator that is robust with respect to sinusoidal disturbances with unknown internal model is given. Simulation results that illustrate the performance of the estimator compared with other algorithms are given. [ABSTRACT FROM AUTHOR]

This paper introduces a robust procedure for controlling the false discovery rate utilizing empirical likelihood. Traditional approaches assume a normal or parametric distribution as the null distribution. However, it may be challenging to constrain the null distribution within specific parametric models. We focus on the cases where the null distribution may not precisely follow a normal distribution. Multiple testing procedures based on exact normality can lead to misleading outcomes. To address this issue, we adopt the empirical likelihood to estimate the null distribution. Additionally, we introduce the concept of a pilot distribution to establish constraints on the null distribution, which aids in estimating the empirical null distribution. We present a fast algorithm and provide theoretical justification for its efficiency. Furthermore, simulation studies demonstrate that our method outperforms existing approaches in controlling the false discovery rate. We also include examples involving gene expression data and compare the performance of different methods. [ABSTRACT FROM AUTHOR]

In recent years, Long-Term Evolution Vehicle-to-Everything (LTE-V2X) communication technology has received extensive attention. Timing synchronization is a crucial step in the receiving process, addressing Timing Offsets (TOs) resulting from random propagation delays, sampling frequency mismatches between the transmitter and receiver or a combination of both. However, the presence of high-speed relative movement between nodes and a low antenna height leads to a significant Doppler frequency offset, resulting in a low Signal-to-Noise Ratio (SNR) for received signals in LTE-V2X communication scenarios. This paper aims to investigate LTE-V2X technology with a specific focus on time synchronization. The research centers on the time synchronization method utilizing the Primary Sidelink Synchronization Signal (PSSS) and conducts a comprehensive analysis of existing algorithms, highlighting their respective advantages and disadvantages. On this basis, a robust timing synchronization algorithm for LTE-V2X communication scenarios is proposed. The algorithm comprises three key steps: coarse synchronization, frequency offset estimation and fine synchronization. Enhanced robustness is achieved through algorithm fusion, optimal decision threshold design and predefined frequency offset values. Furthermore, a hardware-in-the-loop simulation platform is established. The simulation results demonstrate a substantial performance improvement for the proposed algorithm compared to existing methods under adverse channel conditions characterized by high frequency offsets and low SNR. [ABSTRACT FROM AUTHOR]

This paper presents a cooperative game theory approach to improve the video steganography framework. Wherein, the video steganography framework comprises the following steps: (1) Cover video devising, (2) Secret image pre-processing, and (3) Embedding process. In the first step, the cover video is segmented using scene change detection method. Once the scenes are segmented the motion vectors are identified by Block Matching Motion Estimation Algorithm (BMMEA). Based on these motion vectors, the Region of Interest (ROI) is selected. The selected ROI is grouped using their momentum. In order to add an additional layer of security, the secret image is scrambled using pixel-wise Arnold Transform in the next step. Finally, based on the ROI groups, the scrambled secret data is embedded into the ROI's locations. An ideal and perfect video steganography system should be able to maintain the quality of the video after embedding the secret data. The quality factors of the video steganography approach include perceptible invisibility, payload capacity, and robustness. However, these quality factors are conflicted with each other. In order to address this issue, having these three quality factors as players a cooperative 3-players game theory approach is proposed. This technique provides an optimal solution for the video steganography framework using strategy adaption. The optimal solution is acquired by applying the Iterative Elimination of Strictly Dominant Strategies (IESDS) method. The achieved optimal solution comes up with the best trade-off between the quality factors. Experimental results have deduced the best optimal solution from the proposed three-player game theoretic approach that helps the video steganography approach to resolve the quality and security issues. It is also observed that the optimized proposed method outperforms the contemporary methods by attaining significant outcomes. [ABSTRACT FROM AUTHOR]

In recent years, network science has received significant attention and application. As an important mesoscale structure of networks, community structure reveals the inherent modular structure and potential functionality of networks. Therefore, maintaining the community structure during attacks is crucial. Existing studies on community robustness primarily focus on two attack behaviors: malicious attacks and random failures. However, little is known on the community robustness in the more practical situations where the attackers have limited ability to access precise network information, leading to gray information attack behaviors. Hence, in this paper, we investigate the robustness of network communities under gray information attacks and the enhancing algorithms. Following the community robustness measure for the sequential attacks, we establish a unified evaluation framework for attacks with gray information by introducing a gray attack coefficient, which treats the usual random failures and malicious attacks as the two special situations in the proposed framework. Several enhancing algorithms with local search strategy are exquisitely devised. The efficacy of our framework and algorithms which significantly improve the community robustness against gray attacks is demonstrated by the experimental results on a variety of real-world networks. Our findings have important implications not only in enhancing the community robustness of existing networks but also in designing robust ones from the scratch, which paves the way to further understand and seek strategies and solutions for network community robustness with gray information. [ABSTRACT FROM AUTHOR]

Representation‐based classification (RC) is an effective gauge of data similarity between a single instance and the whole dataset, which extends traditional individual‐wise distance metrics using representation coefficients. These coefficients show remarkable discrimination nature via various regularisation terms, but the interference from potentially uncorrelated objects involved in this single‐to‐global relation can degrade the effectiveness of the coefficients. In order to filter out those unproductive, or even counter‐productive, information from the decision making processes, this paper proposes a local representation‐based classification (LRC) algorithm to improve the classification accuracy or the RC approach. LRC uses a single‐to‐local relation induced by the local representation‐based neighbourhood (LRN) of each object, rather than the single‐to‐global relationship used by RC. Thanks to LRN, a compact and relevant dataset can be formed by selecting the most relevant data instances in the original dataset, to render a robust representation of a query. LRC was applied to multiple publicly available datasets, and the experimental results demonstrate the superiority of the proposed LRC algorithm as evidenced by the higher classification accuracy and more noise‐tolerant capability in reference to alternative RC approaches. Moreover, the sampling ability of LRN is also verified via a comparative study. [ABSTRACT FROM AUTHOR]

This paper introduces a novel design of a proportional filtered-integral (P-FI) controller whose parameters are auto-tuned using the backpropagation neural networks (BPNN) algorithm. The proposed controller is designed to address the main challenges posed by a class of complex systems characterized by uncertain high-gain and pure integrator dynamics, including high-frequency noise amplification and poor robustness against parameter variations. Designing such a controller involves three main steps: First, a proportional–integral–derivative (PID) controller is designed, with its parameters auto-tuned online using the BPNN algorithm, resulting in the primary (BPNN-PID) controller. Second, the obtained parameters of the previous controller are utilized to compute those of a low-pass filter offline. This filter is then cascaded with the integral parts of a PID controller, forming a P-FI controller structure. This configuration introduces a phase lead within a specific frequency range without amplifying high-frequency noise, overcoming the primary disadvantage of the derivative term. Finally, the parameters of the resulting P-FI controller are again auto-tuned online using the BPNN algorithm, resulting in the final robust (BPNN-P-FI) controller. This novel controller structure and its parameters tuning procedure, based on a two-stage BPNN learning approach, constitute the main contributions of this paper. Simulation results demonstrate the superiority of the proposed controller in terms of time-domain performance, sensor noise attenuation, and closed-loop robustness compared to those obtained with the BPNN-PID and optimally tuned PID controllers. [ABSTRACT FROM AUTHOR]

In unmanned aerial vehicle Ad hoc network (UANET), increasing the network size will increase the average number of hops between nodes, making network management and routing protocol operation more difficult. Clustering structure can be used to optimize network management and improve network scalability. This paper proposed a fast and stable weighted clustering algorithm to address the instability and poor robustness of cluster structures caused by the high mobility of drones. Compared with existing weighted clustering algorithms, this algorithm improved the selection of four indicators: link retention rate, node degree and relative speed. It proposed an efficient cluster maintenance mechanism to address the cluster oscillation caused by cluster head node disconnection in battlefield and emergency scenarios. Finally, it verified the performance of this algorithm through simulation. The results show that compared with existing improved weighted clustering algorithms, this algorithm can effectively reduce clustering time, and quickly recover in the event of cluster head nodes dropping, making it more suitable for network deployment in complex environments. [ABSTRACT FROM AUTHOR]

With the enhancement of people's security awareness, transmitting secret information securely has gradually become a demand for the public. Steganography is a technology of representing secret information within another carrier, aiming at transmitting secret information without causing suspicion. Most of the traditional steganographic algorithms hide secret information by modifying the statistical characteristics, which will leave traces to the carriers. Although these modifications are too tiny to be distinguished by human eyes, they can be detected by steganalysis algorithms. Differently, coverless steganography, also written as steganography without embedding, accomplish the process of information hiding by constructing the relationship between secret information and carriers. Due to no modification to the carriers, all of the steganalysis algorithms are expired. In this paper, more than 90 papers are included to provide a review in coverless steganographic algorithms, covering the major development process of coverless image and video steganographic algorithms. The main contribution of the existing methods is summarized. Besides, the current general issues of capacity, robustness, and security are discussed adequately for both image and video algorithms. Especially, the security of coverless steganography is discussed for the first time from theoretical analysis to actual investigation in this review. [ABSTRACT FROM AUTHOR]

With the rapid development of the city, urban metro network is growing rapidly, and the corresponding topology and the importance of stations are changing dynamically. It is of great significance to investigate the node importance and dynamic evolution of urban metro networks for scientific advice on urban metro construction, planning, and operation. In this paper, a new node importance algorithm RNEL (Node importance algorithm based on ReLU function considering Neighborhood force, Entropy and Local link similarity (LLS)) is proposed based on node force, entropy, and topology, and simulation experiments are carried out on the relevant datasets. In addition, the topology of Wuhan metro network from 2004 to 2022 is constructed using Space L, and the network topology parameters of 8 periods are analyzed, and the importance of stations is compared horizontally and vertically. The experimental results show that the proposed RNEL algorithm is superior to the nine node importance algorithms on four evaluation metrics for nine publicly available datasets and four artificial network models. With the spatiotemporal evolution of the Wuhan metro network, nodes become more and more dense, the network tends to be matched, the average distance between stations becomes longer and longer, and the importance of the different types of stations is changing over time. In addition, the importance of the Wuhan metro stations gradually spread from L-1 to L-3 northwest and L-5 southeast. The importance of a stations changes dynamically over time, driven by changes in the type of station, the stations connected to it, and the network environment. This study provides scientific suggestions and aids decision-making for the construction location, station setting and line extension direction of new lines in Wuhan Metro network. • A new node importance algorithm is proposed. • The validity of the algorithm is verified in Wuhan metro network and 13 data sets. • Dynamic analysis of Wuhan metro network in 8 periods. • The importance of the station is compared horizontally and vertically. [ABSTRACT FROM AUTHOR]

Hidden capacity, concealment, security, and robustness are essential indicators of hiding algorithms. Currently, hiding algorithms tend to focus on algorithmic capacity, concealment, and security but often overlook the robustness of the algorithms. In practical applications, the container can suffer from damage caused by noise, cropping, and other attacks during transmission, resulting in challenging or even impossible complete recovery of the secret image. An image hiding algorithm based on dynamic region attention in the multi-scale wavelet domain is proposed to address this issue and enhance the robustness of hiding algorithms. In this proposed algorithm, a secret image of size 256 × 256 is first decomposed using an eight-level Haar wavelet transform. The wavelet transform generates one coefficient in the approximation component and twenty-four detail bands, which are then embedded into the carrier image via a hiding network. During the recovery process, the container image is divided into four non-overlapping parts, each employed to reconstruct a low-resolution secret image. These low-resolution secret images are combined using dense modules to obtain a high-quality secret image. The experimental results showed that even under destructive attacks on the container image, the proposed algorithm is successful in recovering a high-quality secret image, indicating that the algorithm exhibits a high degree of robustness against various attacks. The proposed algorithm effectively addresses the robustness issue by incorporating both spatial and channel attention mechanisms in the multi-scale wavelet domain, making it suitable for practical applications. In conclusion, the image hiding algorithm introduced in this study offers significant improvements in robustness compared to existing algorithms. Its ability to recover high-quality secret images even in the presence of destructive attacks makes it an attractive option for various applications. Further research and experimentation can explore the algorithm's performance under different scenarios and expand its potential applications. [ABSTRACT FROM AUTHOR]

In the research of complex networks, how to find a set of nodes in the network with the most extensive range in the propagation process, i.e., the Influence Maximization (IM) problem, is one of the focal topics. Existing studies mainly consider the information dissemination process on networks and how to select diffusive nodes efficiently, but little attention has been paid to changes related to the network structure. In reality, networked systems are exposed to uncertain interferences and even destructive sabotages, and cascading failures are one common destruction that can cause networks to collapse even if only a small number of nodes fail. In the case of various complex environmental factors, how to select robust and influential nodes, i.e., the robust influence maximization (RIM) problem, is of great importance in promoting the realistic application of the influence maximization problem. This paper investigates the RIM problem under cascading failures to address the shortcomings in previous studies. Based on existing research, a new performance evaluation metric, R S-cf , is designed to assess the level of robust influence in a numerical form. For solving the seed determination problem, a Memetic algorithm towards the RIM problem under cascading failures, MA-RIM CF , is designed to find nodes with stable information propagation capability guided by R S-cf. Experiments have been conducted on both synthetic and realistic networks to validate the performance of the algorithm. Results indicate that MA-RIM CF can obtain competitive candidates over existing approaches, and seeds with robust and influential abilities are generated to solve diffusion dilemmas. [ABSTRACT FROM AUTHOR]

The smooth execution of tasks in the cloud manufacturing (CMfg) process can be significantly affected by various real-world uncertainties, resulting in inefficiencies or even failures of the composite manufacturing service (CMS). Nevertheless, current research primarily concentrates on post-exception emergency handling methods, heavily dependent on the planned CMS and real-time status of manufacturing resources, often leading to suboptimal temporary adjustments or even the inability to find a viable solution. Hence, there is an urgent necessity to enhance the robustness of the planned CMS during the service composition and optimal selection (SCOS) phase. In pursuit of this goal, the paper initially presents an adaptive robust service composition and optimization selection (ARSCOS) model. This model dynamically determines whether to await exception resolution or invoke an alternative service, considering the circumstances when a service exception arises and employing task delay time to characterize the robustness of a specific CMS. Subsequently, an enhanced multi-objective artificial hummingbird algorithm (EMOAHA) is devised for this model. Finally, the efficacy of EMOAHA is demonstrated by applying it to nine different scales of civilian unmanned aerial vehicle manufacturing SCOS problems and 17 benchmark functions. The results illustrate that the proposed EMOAHA exhibits superior convergence and diversity in comparison to the other 11 classical intelligent optimization algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a novel augmented space robust adaptive filter by reusing the errors for online applications. First, a batched augmented space constrained model (ASCM) is constructed to combat non-Gaussian noise. In ASCM, the errors are reused by k nearest neighbors (k -NN) estimation. Then, an augmented space recursive least-constrained-squares algorithm integrating with distance-based k -NN method (ARLCS- d k) is developed within the framework of ASCM for adaptive filtering. Finally, to curb the size of ever-growing error network, a sliding window ARLCS- d k (SW-ARLCS- d k) is proposed to reduce the computational burden. Theoretical analyses of excess mean square error (EMSE) and testing mean square error (TMSE) are carried out for performance evaluation. Examples on time-series prediction of simulated and real-world data are used to illustrate the advantages of proposed algorithms on robustness and prediction accuracy. • A batched augmented space constrained model (ASCM) with k nearest neighbors (k -NN) method is constructed to combat impulsive noise. • An online augmented recursive least-constrained-squares algorithm using distance-based k -NN method (ARLCS- dk) is developed for adaptive filtering. • A sliding window is designed for ARLCS- dk to reduce its computational complexity. • Theoretical results of mean square error are illustrated the effectiveness of ARLCS- dk in prediction of time-series. [ABSTRACT FROM AUTHOR]

Identifying template watermarks under severe geometric distortions is a significant scientific problem in the current watermarking research for remote sensing images. We propose a novel watermarking algorithm that integrates the ring-shaped template watermark with the multiscale local contrast measure (LCM) method. In the embedding stage, the ring-shaped template watermark is embedded into the discrete Fourier transform (DFT) magnitude coefficients, converting the watermark into small targets in the DFT domain. During the detection stage, the multiscale LCM, a classic infrared small target detection method, enhances these small targets and generates a contrast map. Peak detection is then performed on the contrast map to determine the radius of the template watermark. Finally, circular edge local binarization is applied to extract the watermark information. The proposed method enables synchronization recovery of watermarks under blind conditions. The experimental results demonstrate that the method possesses strong robustness against various geometric attacks such as rotation, scaling, translation, and cropping. It outperforms comparative algorithms in terms of robustness and also exhibits good imperceptibility. [ABSTRACT FROM AUTHOR]

• An original classification of attacks on robust watermarks is proposed. • A review of watermarking schemes designed to counter targeted attacks is presented. • The main methods of countering targeted attacks are noted. • Promising research directions are discussed. Image watermarking is an effective and promising technology. Robust watermarks that are resistant to various attacks allow authors and owners of digital images to protect their rights to digital content, control its distribution and confirm its authenticity. Most of the modern algorithms for robust image watermarking aim to achieve resistance to a large number of different attacks. However, some authors develop algorithms designed to counter targeted attacks. The study of such schemes allows developers of watermarking algorithms to evaluate special means of counteracting various attacks, and then use them to create new robust schemes, both targeted and universal ones. In this paper, we present an overview of robust image watermarking schemes in terms of countering targeted attacks. We review the state-of-the-art in the field of attacking robust watermarks and propose a four-level classification of attacks that includes different levels of attack implementation, including an attacker's intent, characteristics of actions, the main target and an attack type. The proposed classification considers a watermark as an object of attack and summarizes various characteristics of attacks in a hierarchical manner. We analyze the means of countering common attacks such as image processing attacks, geometric attacks, print-scan and screen capture attacks, collusion attacks, and ambiguity attacks. Based on the results of our review, we highlight the most common methods of countering attacks and formulate promising areas of research in the field of methods for improving security of embedding schemes. [ABSTRACT FROM AUTHOR]