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Abstract: Data hiding technique can facilitate security and the safe transmission of important information in the digital domain, which generally requires a high embedding payload and good stego image quality. Recently, a steganographic framework known as wet paper coding has been utilized as an effective strategy in image hiding to achieve the requirements of high embedding payload, good quality and robust security. In this paper, besides employing this mechanism as a fundamental stage, we take advantage of two novel techniques, namely, an efficient n-indicator and a fuzzy edge detector. The first is to increase the robustness of the proposed system to guard against being detected or traced by the statistics methods while allowing the receiver without knowledge of secret data positions to retrieve the embedded information. The second is to improve the payload and enhance the quality of stego image. The experimental results show that our proposed scheme outperforms its ability to reduce the conflict among three steganography requirements. [Copyright &y& Elsevier]

This paper introduces realizations of a reconfigurable finite-impulse-response (FIR) filter for simultaneous equalization and lowpass filtering. The main advantage of the proposed solutions is computational complexity reduction compared to existing solutions for a given performance, which leads to reduced hardware complexity. The proposed structures employ properties of both a variable bandwidth (VBW) filter and a variable equalizer (VE) with variable coefficients. The overall transfer function of the proposed reconfigurable lowpass equalizer (RLPE) is a weighted linear combination of fixed subfilters where the weights are directly determined by the bandwidth and one or several parameters of the channel needed to be equalized. The paper provides design procedures based on minimax optimization and introduces a fast design method for the filter with several variable parameters that can substantially decrease the design time. Filter order estimation expressions as well as complexity expressions are presented for all proposed realizations. Design examples include comparison of the RLPE structures and a common approach of using a regular FIR equalization filter requiring online redesign when the bandwidth or channel characteristics are changed. It is shown that the number of general multiplications can be reduced up to 91% using the proposed RLPE. • Three low-complexity realizations of variable FIR filters with simultaneously variable bandwidth and equalization. • No need for online design when the bandwidth or channel characteristics are modified. • Design procedure to obtain the overall filter with the lowest complexity. • Design procedure to obtain the overall filter with the lowest complexity for reduced design time. [ABSTRACT FROM AUTHOR]

Image enhancement techniques are commonly used to improve problems such as lack of brightness, high noise, and low contrast in low-light images. Notably, deep learning-based approaches have recently achieved substantial advancements in this domain. However, learning-based methods often require many parameters and multi-layer network structures to achieve high-quality enhancement effects, which limits their application in real-time image processing. To solve this problem, a lightweight Feature Activation Guided Multi-Receptive Field Attention Network (FAMANet) is designed in this paper. The Wavelet Feature Activation Block (WFAB) introduced in the network utilizes the discrete wavelet transform and residual connection to achieve selective activation of image features, thus reducing the redundant information in the feature map and improving the computational efficiency. In addition, the Multi-Receptive Field Attention (MRFA) introduced in this paper addresses the issue of inadequate pixel information and feature map loss stemming from a single input image by concentrating on the image structure, spanning from intricate details to the overall composition. By better-utilizing image information and distinguishing between global and local features, MRFA can improve the speed and efficiency of real-time image processing. After sufficient experimental validation, FAMANet significantly outperforms state-of-the-art methods in low-light image enhancement and exposure correction tasks. [ABSTRACT FROM AUTHOR]

Multimodal biometric recognition has attracted more and more attention in recent years because of its security and accuracy. Compared with the single use of fingerprint or finger vein feature recognition, the multi-modal feature recognition method based on fingerprint and finger vein significantly improves the recognition performance. However, most of the multi-modal feature recognition networks have the disadvantages of large number of parameters and high training cost. In this paper, a narrow-channel lightweight network NLNet for fingerprint and finger vein recognition is proposed. The network adopts asymmetric narrow channel structure for lightweight design, and combines shallow network to improve the discriminating nature of the extracted features, which significantly reduces the model parameters and computation. In addition, a lightweight feature extraction module for building feature extraction branches is designed for NLNet. This module takes dimensional transformation feature extraction as the backbone, and the joint extension module and attention mechanism obtain low-redundancy multi-scale feature information. In terms of feature fusion, a feature fusion method based on PatchPooling is proposed. This method combines the characteristics of modal images, and uses Spatial dimension local mapping to increase the utilization rate of low-dimensional features, which effectively improves the richness of classified features. In this paper, experiments were carried out on the SDUMLA-HMT, NUPT-FPV, FVC HKP and HDPR-310 multimodal finger datasets, and the recognition accuracy was high as 97.72 %, 99.10 %, 99.67 % and 99.74 %, respectively. In addition, the effectiveness of the model is verified by comparing with other advanced methods. [ABSTRACT FROM AUTHOR]

In most multi-extended target tracking scenarios, the target detection probability is usually unknown and time-varying, which leads to biased estimation of the state and cardinality of extended targets in online filtering. In addressing the challenge, this paper presents the unknown detection probability extended target trajectory Poisson multi-Bernoulli mixture (U-TPMBM) filter. Compared to the existing extended target Poisson multi-Bernoulli Mixture (PMBM) filter, the U-TPMBM is firstly based on sets of trajectories, which allows for direct output of target trajectory and can lead to improved trajectory estimation performance. Besides, the U-TPMBM filter integrates the unknown detection probability with the target trajectory state and thus obtains the augmented state space. By recursively estimating the augmented states via multi-target filtering approaches, it successfully realizes online and joint estimates of the unknown detection probability and the target trajectory. Finally, the U-TPMBM filter is implemented by the Beta-Gamma Gaussian Inverse Wishart (BGGIW) mixture method, especially the BGGIW-TPMBM filter. The Beta distribution is utilized to propagate densities of the unknown detection probability and the GGIW distribution to propagate densities of the target trajectory. Based on the BGGIW distribution, the trackers's recursive and closed solutions are derived in detail. The simulation experiments demonstrate that the BGGIW-TPMBM proposed in this paper can achieve robust tracking performance, even when dealing with unknown detection probabilities. [ABSTRACT FROM AUTHOR]

Picture fuzzy C-means clustering is a novel computational intelligence method that has some advantages over fuzzy clustering in pattern analysis and machine intelligence. However, picture fuzzy clustering is easily affected by noise and weighting exponent, which seriously limits its widespread application. To address this issue, this paper proposes a new robust possibilistic clustering method called "interval type-2 possibilistic picture C-means clustering with local information". This method combines interval type-2 fuzzy sets with possibilistic C-means clustering based on picture fuzzy sets, strengthening the noise resistance of picture fuzzy clustering. Firstly, this paper creatively extends an improved possibilistic clustering with double weighing exponents to picture fuzzy sets, solving the problem of consistency clustering in existing possibilistic picture clustering. Second, this paper originally introduces a new picture local information factor in possibilistic picture clustering and further enhances the anti-noise robustness of the method by using spatial possibilistic picture partition information. Finally, this paper skillfully extends this clustering method to interval type-2 fuzzy sets, which can handle more flexibly high-order uncertainties than type-1 clustering method. Experimental results indicate that this proposed method has better segmentation performance and stronger noise suppression ability compared with existing picture fuzzy clustering and interval type-2 fuzzy clustering. In summary, this work has made significant contributions to the development of picture fuzzy clustering theory and its applications. [ABSTRACT FROM AUTHOR]

This paper introduces a related model of membrane calculation in the defect detection and positioning of industrial components. It has the characteristics of distributed and parallel computing, and can efficiently search for better solutions in a given feature space. Inspired by the membrane clustering algorithm, this paper proposes a greedy membrane clustering algorithm and names it GMCA. GMCA is applied after the extraction of local features of normal samples. It uses a greedy strategy to construct a sub-feature set that describes the local characteristics of normal samples. During training, GMCA can learn the membrane cluster center of normal image blocks and each sub-feature within the cluster. At test time, the anomaly map is obtained by calculating the distance from the test sample block to the corresponding cluster center and the maximum distance from the cluster center to the nearest neighbor in the training sample. This solves the limitation of traditional algorithms requiring dataset alignment. In the unsupervised dataset MvTec AD, samples can be divided into object categories and texture categories according to the background of images. The pixel-level anomaly location index (AUROC) of this method on object category data reaches 98.3%. The image-level anomaly detection index (AUROC) on texture category data reaches 99.1%. • We design a computational model of membrane clustering using the evolutionary mechanisms and communicative mechanisms of cells. • GMCA has the global optimization characteristics of high accuracy and fast convergence of the membrane clustering algorithm. • GMCA has the local optimization characteristics of the greedy strategy. • GMCA solves the limitation of traditional defect detection and positioning methods that require dataset alignment. • Numerous experiments show the proposed GMCA performs competitively in industrial defect detection and location prediction. [ABSTRACT FROM AUTHOR]

Generative models have emerged as potential tools for creating high-quality images, videos, and text. This paper explores the application of generative models in automating vlog content creation. It addresses both static and dynamic visual elements, eliminating the need for human intervention. Traditional vlogs often require specific environmental conditions and proper lighting for the vlog creation. To streamline this process, an automated system utilizing the generative models is proposed here. Generative models excel at generating realistic content that seamlessly integrates with real-world content. They enhance overall video quality and introduce creative elements by generating new scenes and backgrounds. This paper categorizes various generative modeling techniques based on frame elements and foreground-background conditions. It offers a comparative analysis of different generative model variants tailored for specific objectives. Furthermore, the paper reviews existing research on generative models for video and image content generation, visual quality enhancement, diversity, and coherence outcomes. Additionally, the paper highlights practical uses of the generative model for content creation in various contexts, such as face swapping, scene translation, and virtual content insertion. The paper also examines the public datasets used to train generative models. These datasets contain diverse visual content such as celebrity images, urban landscapes, and everyday scenes. [ABSTRACT FROM AUTHOR]

In some telecommunication and magnetic/digital recording applications, bits/symbols tend to be lost in the transmission due to the interference. In this paper, we consider a segmented burst deletion channel where in a block of L consecutive bits at most a single burst deletion of length up to D bits exists. Existing synchronization approaches either provide a poor synchronization performance or suffer from a high computational complexity. For example, the reduced state Forward Backward Algorithm (FBA) incurs high time and space complexities, i.e., O (n 3 2 ) and O (n) , respectively, where n denotes the sequence length. In this paper, we discover binary marker patterns which require the minimum D + 1 bits redundancy to detect the burst deletion with the length up to D bits for the segmented burst deletion channel, and propose an optimal algorithm to resynchronize the corrupted bit sequence that minimizes the expected bit error rate. As compared to the reduced state FBA, the time and space complexities of our proposed algorithm are reduced to O (n) and O (1) , respectively. Theoretical analysis and simulations verify the optimality of our proposed algorithm, and demonstrate that the expected bit error rate introduced in our proposed scheme is lower than that in the existing synchronization error detection schemes and that in the FBA under segmented burst deletion channels. [ABSTRACT FROM AUTHOR]

Parameter selection in high-dimensional models is typically fine-tuned in a way that keeps the (relative) number of false positives under control. This is because otherwise the few true positives may be dominated by the many possible false positives. This happens, for instance, when the selection follows from a naive optimisation of an information criterion, such as AIC or Mallows's C p. It can be argued that the overestimation of the model comes from the optimisation process itself changing the statistics of the selected variables, in a way that the information criterion no longer reflects the true divergence between the selected model and the data generating process. Using lasso, the overestimation can also be linked to the shrinkage estimator, which makes the selection too tolerant of false positive selections. For these reasons, this paper works on refined information criteria, carefully balancing false positives and false negatives, for use with estimators without shrinkage. In particular, the paper develops corrected Mallows's C p criteria for structured selection in trees and graphical models. [ABSTRACT FROM AUTHOR]

Gaussian pulse streams can be characterized by a finite number of unit-time parameters, and classical Finite Rate of Innovation (FRI) sampling enables sub-Nyquist sensing of these signals. However, prior knowledge of its shape factor is required, limiting FRI's applicability. This paper proposes a solution to the FRI sampling problem of Gaussian pulse streams with an unknown pulse shape factor. We aim to fit pulse shape information from sub-Nyquist samples and reconstruct parameters using spectral estimation methods. We first demonstrate the feasibility of fitting the shape factor from sub-Nyquist samples and provide the fitting algorithm and related fitting errors in detail. This paper also provides the Cramer-Rao lower bound (CRLB) on parameter estimation accuracy of Gaussian pulse streams under analog white Gaussian noise, offering a statistical perspective of our proposed information fitting method's performance. We qualitatively demonstrate that the information-fitting method can also be applied to a wider range of FRI pulse stream forms. Simulation experiments show that our proposed information fitting method achieves high accuracy in parameter estimation of the signal when the pulse shape factor is unknown. [ABSTRACT FROM AUTHOR]

Compared with the traditional pulse repetition interval (PRI) parameters, PRI pattern can describe the temporal characteristics of radar more completely, and the method based on PRI pattern is more suitable for tasks of deinterleaving and identification. Therefore, effective reconstruction of PRI pattern is an important task in radar signal processing. However, previous PRI pattern reconstruction methods either set too many subjective parameters or have high requirements for the number of pulses, making it not very practical for analyzing actual radar signals. In this paper, the temporal rule of pulse train is modeled from the perspective of radar timing state switching. Then the pulses of different timing states in the pulse train are clustered based on state merging algorithms in model learning. The transitions between different timing states are established based on the pulse sets obtained by clustering. Finally, the PRI pattern of radar is reconstructed after processing interferential pulses and missing pulses. Based on the above method, this paper further proposes a method to reconstruct PRI patterns of all radars in interleaved pulse trains. Simulation results verify the effectiveness of the proposed PRI pattern reconstruction method. • PRI pattern reconstruction framework based on model learning. • Pulse clustering algorithm based on state merging. • Radar emitter division method for interleaved pulse trains. • Better adaptability to the data noises, PRI pattern order and small pulse number. [ABSTRACT FROM AUTHOR]

Channel quantization, particularly designing optimal quantizers maximizing the mutual information between channel input and quantizer output, plays a great role in communications. This paper focuses on the mutual information-maximizing quantizer and explores stochastic resonance (SR) effect on quantization performance when the channel is constructed by a noise-injected threshold array. First, we present the structure of an optimal quantizer. Such a quantizer is determined by using optimal boundaries to partition the set of channel output into disjoint subsets consisting of consecutive integers. Next, the optimal binary quantizer is examined and the optimal noise in the array is derived. For non-optimal Gaussian noise, we find that noise helps to improve mutual information when the threshold is greater than the amplitude of input signal. This means SR occurs in subthreshold case. Moreover, optimal non-binary quantizers are obtained based on dynamic programming. In this case, the Gaussian noise's effect on enhancing mutual information is also demonstrated. At the same time, the impact of the number of threshold units or the quantization levels is explored. Finally, a non-Gaussian noise, i.e., Cauchy noise, is considered, and its SR effect is displayed as well. These results in this paper may be useful for channel coding. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of target tracking using multiple acoustic sensors to observe the target state with unknown propagation delays. This problem occurs because the measurements received by multiple acoustic sensors located at different positions are from different unknown emission times of the acoustic signal even if the sensors receive measurements simultaneously; thus, they cannot be stacked up directly for centralized fusion as usual. However, the target states at different unknown emission times can be aligned to a common measurement received time by the retrodiction of state prediction. On this basis, herein we propose the centralized fusion of multiple acoustic sensors via sequential processing, namely, sequential centralized fusion (SCF). First, the measurement received time is chosen as the target state time, and the target state is predicted to this time for tracking. Second, state prediction is retrodicted to the signal emission times by solving augmented implicit nonlinear equations through Wegstein's method. Third, the state prediction is updated with acoustic measurements sequentially at measurement received time. Compared with the existing distributed fusion methods, our proposed SCF method has smaller computational complexity and better tracking performance. Illustrative examples demonstrate that SCF outperforms covariance intersection and the largest ellipsoid approximation. • Multiple acoustic sensors with unknown delays for target tracking are considered. • A sequential centralized fusion method is proposed in this paper. • Choosing measurement received time as target state time reduces extra prediction step. • The Wegstein's method avoiding to calculate Jacobians is used. • Sequential update decreases computational complexity, and improves tracking accuracy. [ABSTRACT FROM AUTHOR]

In this paper, the problem of secure spectrum sharing and power allocation for the vehicle to vehicle communication has been investigated. The information transmitted in the network might be overheard by the eavesdropper. The aim of this paper is to share the vehicle to infra structure frequency bands with the vehicle to vehicle links in order to maximize the sum rate of the network as well as minimizing the data received by the eavesdropper. To achieve this goal, we have suggested to leverage some friendly jammers to prevent the leakage of information to the eavesdropper. A multi-agent reinforcement learning based approach has been developed to smartly determine the power level, frequency band, and jammer number in a way that the secure rate is maximized. All the agents would cooperate in making the decision in every state which might change over time. The simulation results confirm the superiority of the suggested scheme over its counterparts in various scenarios. The security provided by the proposed method is much higher than those of the other schemes. [ABSTRACT FROM AUTHOR]

Staircase recognition is of great significance for exoskeleton robot mode switching and mobile robot foothold calculation, which can improve the overall performance of the robot in the staircase scene. As a common terrain, stairs are quite difficult for mobile robots or people with lower limb disabilities and visual impairment. However, there are still some problems from the sensor's characteristics and external interference limiting the development of this technology. Despite the growing demand for recognition in this area and the emergence of a large number of related methods, there is a lack of a systematic and comprehensive review. Therefore, this paper reviews and compares the advantages and disadvantages of various methods, and provides the next research hotspots and directions. This paper first analyzes and summarizes the current mainstream perception hardware from the perspective of scene information acquisition, including wearable sensors, photoelectric sensors, multi-sensor fusion and ultrasonic sensors, which can be installed on the head, chest, waist, knees and legs, and soles of feet, respectively. Then, the existing recognition methods of ascending and descending stairs are compared and analyzed from four aspects of sensor type, installation location, processing algorithm and recognition accuracy. The research progress of staircase scene recognition in auxiliary motion is introduced in detail. Finally, the application prospects and fields of staircase scene recognition are analyzed, and the future development direction is prospected. [ABSTRACT FROM AUTHOR]

The maturity of 5G technology provides a guarantee for increasingly large communication networks, while the resources required for communication and computation are also increasing, and reasonable resource allocation can improve the efficiency of network communication and reduce the consumption of communication resources. Existing deep learning methods have been able to predict network traffic to a certain extent, so as to solve the communication efficiency and resource consumption problems in the field of integrated sensing, communication and computation (ISCC) through rational resource allocation. However, the following problems still exist: (1) The feature learning ability of the prediction model is insufficient, and the prediction accuracy needs to be improved. (2) Powerful and complex deep learning methods lead to an increase in the prediction cost of the model. To address these problems, this paper proposes a deep learning method based on a lightweight hybrid attention network. In order to capture the key features of 5G data more effectively, an efficient hybrid attention mechanism (EHA) is proposed. After this attention is applied to convolution, the key information can be well enhanced. We use depthwise separable convolution in feature extraction, which greatly improves the efficiency of lightweight convolution layer (LC) in feature extraction. Combined with the efficient hybrid attention mechanism (EHA), the proposed model has better lightweight properties. Experimental results show that the model proposed in this paper has lower RMSE and MAE values on the three datasets, as well as fewer parameters and computational effort compared to the baseline scheme. [ABSTRACT FROM AUTHOR]

This paper focuses on the beam-space target height estimation for bistatic multiple-input multiple-output (MIMO) radars, which is greatly affected by the multipath effect in low-elevation areas. The beam-space technique compresses the data and reduces computation, making it an ideal solution for this problem. However, there is a lack of research on beam-space target height estimation for bistatic MIMO radar, which this paper aims to address. In order to obtain the target height parameters accurately, we propose bistatic MIMO radar height estimation method based on adaptive beam-space refined maximum likelihood (RML) data fusion. First, we analyze and simplify the signal model, and obtain rough estimation of direction of departure (DOD) and direction of arrival (DOA) using digital beamforming (DBF) scanning technique; then, we convert target signals from the element space to the beam-space, separates the transmitter and the receiver signals, and obtain two target height estimations using the beam-space RML algorithm; finally, the minimum mean square error (MSE) criterion is used to fuse the two height estimations of the transmitter and the receiver. On this basis, we also analyze the application and advantages of RML algorithm in complex terrain through the measured data. In addition, the computational complexity of the proposed algorithm and the comparison algorithm is also given. Through some simulation results, it is not difficult to find that the proposed algorithm has good estimation accuracy and robustness. [ABSTRACT FROM AUTHOR]

As wireless communication rapidly evolves and the demand for intelligent connectivity grows, the need for precise sensing integrated with efficient communication becomes paramount. While traditional Integrated Sensing and Communication (ISAC) methods have laid foundational groundwork, they grapple with environmental limitations and significant propagation losses. Visible Light Communication (VLC) emerges as a transformative solution characterized by its high-speed transmission, minimal latency, cost-efficiency, and seamless installation. This paper introduces the Lighting, Sensing, and Communication (LiSAC) concept for VLC and systematically reviews the technical aspects, such as channel characteristics, modulation techniques, and system design. Specifically, this paper presents the evolution of the LiSAC system, its integration with other communication technologies, its applications in various fields, and its challenges. At the end of this paper, we outlooked LiSAC in the future, in which high-quality communication will integrate pinpoint sensing accuracy. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of decentralized classification algorithm in sensor networks, i.e., the data is captured by privacy sensor or the data is not suitable for publication. Therefore, we may maintain the privacy of the data captured and processed by each sensor. The number of the sensors can be selected based on actual application situations. In addition, even if some sensors break down, the classification process still works and thus the proposed scheme is robust against the traditional center scheme. The contributions of this paper are: i) two new classification algorithms are proposed based on the sparse representation and constrained fractional programming. One is for the centralized environment while the other is for the decentralized environment, where the decentralized network node is able to process its own data to extract useful information by implementing some local computation, communication, and storage operations; ii) to reduce the redundant features and noisy data of the original data is helpful to improve the speed of algorithm, we form a new classification strategy by combining the sparsity transform with the classifier; iii) to improve the robustness of the classifiers in abnormal and dangerous situations, we construct a constrained fractional programming to enforce the discriminant ability of the classifier so that the transformed coefficient vector should be closer to the class center of itself but being far away from centers of other class; iv) to handle the proposed centralized/decentralized classification problems, we decouple the constrained fraction via the Dinkelbach algorithm and alternating minimization. Finally, numerical examples are provided to verify the proposed algorithms realized in a distributed manner have the same recognition rate with the centralized algorithm. [ABSTRACT FROM AUTHOR]

The paper takes an in-depth analysis of the channel estimate model (CEM) in passive radar using the orthogonal frequency division multiplexing (OFDM) waveforms. The CEM has been intensively exploited in OFDM passive radar, where the channel estimates (CE) are obtained from the original digitized received signal (ODRS), and target detection is performed based on CEs. However, traditional CEM is derived neglecting the inter-carriers interference (ICI). The influence of the ICI on target detection has rarely been discussed previously. In fact, target with large power and Doppler frequency can induce strong ICI, which increases the noise floor, and thus imposes significant influence on the detection of the other targets, especially the weak targets. In this paper, we rederive the CEM taking the ICI into consideration, and obtain a new CEM. In the new CEM, a specific target has two components, i.e., the useful signal part, and ICI. We derive the useful signal to noise ratio (SNR) and ICI to noise ratio (ICINR) theoretically, and provide compact expressions. We reveal the relationship between the SNR and ICINR in the CE, and the SNR in the ODRS. Based on the theoretical derivation, the influence of ICI is analysed. We also discuss the elimination of the ICI. The influence of ICI can be eliminated by cancelling the target signal that induces the ICI from the CEM. A target signal cancellation method is developed based on the new CEM. Simulations demonstrate the effectiveness of the theoretical analysis of the CEM and the proposed cancellation method. [ABSTRACT FROM AUTHOR]

• The paper presents a lightweight model with satisfactory performance in complex industrial noise environments. • Data fusion using data from multiple sensors for more complete fault information. • Design a unique grayscale feature map based on IMF components to obtain multi-sensor and multi-frequency band fault features. • Design a UL-GoogLeNet lightweight fault diagnosis model based on GoogLeNet and ULSAM. As artificial intelligence and sensor technology develop rapidly, intelligent fault diagnosis methods based on deep learning are widely used in industrial production. However, in practical industrial applications, the complex noise environment affects the performance of the diagnostic model, and the huge model parameters cannot meet the requirements of low cost and high performance in industrial production. To address the above problems, this paper proposes a lightweight intelligent fault diagnosis model using multi-sensor data fusion that not only meets the lightweight requirements of "small, light, and fast", but also realizes high accuracy diagnosis in noisy environments. Firstly, the vibration signals from different sensors of rolling bearings are processed using the variational mode decomposition (VMD) to design a unique method of constructing grayscale feature maps based on each intrinsic modal function (IMF) component. Then, the ultra-lightweight GoogLeNet model (UL-GoogLeNet) is constructed to adjust the traditional GoogLeNet structure, while the Ultra-lightweight subspace attention module (ULSAM) is introduced to reduce the model parameters and enhance the feature extraction capability. UL-GoogLeNet is trained and tested by dividing the grayscale feature maps into training and testing sets to realize the intelligent recognition of different fault types in rolling bearings. Experiments are conducted on two datasets and compared with multiple methods, and the final experimental results prove the effectiveness and superiority of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

In this paper, a novel narrowband interference-plus-noise covariance matrix (INCM) reconstruction method is proposed for robust adaptive beamforming (RAB). To enhance the effectiveness of the INCM reconstruction, we offer a prior selection of the dimension of a quasi-signal subspace (QSS) based on the signal subspace proximity employing two subsample covariance matrices (SSCMs). The QSS herein can provide estimations of all relevant factors of interference for the INCM. Moreover, by employing the eigenvectors of the SSCM, the noise power can be estimated precisely through a nonlinear regularization scheme. Finally, the signal-of-interest steering vector is corrected by an optimization problem exploiting the reconstructed INCM. The proposed RAB method can capitalize on the benefits of the QSS without prior knowledge of the number of sources. The results of the simulations demonstrate the effectiveness of the proposed method in terms of various mismatches. [ABSTRACT FROM AUTHOR]

Concept factorization (CF) is a powerful tool in subspace learning. Recently graph-based CF and local coordinate CF have been proposed to exploit the intrinsic geometrical structure of data, and have been shown quite successful in improving performance. However, these methods have limited robustness and might be sensitive to noises and disturbances in practical applications. In this paper, we propose a novel robust sparse CF framework (RSCF) for subspace learning. Specifically, we present a robust loss function to effectively eliminate the impact of the large outliers. The local coordinate constraint and the graph regularization term are incorporated into RSCF to simultaneously guarantee the sparsity of the coefficient matrix and maintain the local structure of the data. We prove that the local coordinate constraint implies the orthogonality of the coefficient matrix. By using the half-quadratic optimization technique, we transform the objective function of RSCF into a quadratic form and provide the iterative updating rules and the convergence analysis. Extensive experiments demonstrate the robustness and superiority of the proposed RSCF in comparison to state-of-the-art CF methods. [ABSTRACT FROM AUTHOR]

This paper is concerned with the information fusion estimation problems for stochastic uncertain systems with quantized measurements and mixed network attacks including random deception attack and denial-of-service (DoS) attack. The cross-correlated random parameter matrices and addictive noises simultaneously exist in the studied system. By resorting to the optimal prediction compensation mechanism for DoS attack, an optimal centralized fusion filter in the linear minimum variance sense is proposed using an innovation analysis approach. In addition, the Kalman-like recursive distributed optimal linear fusion predictor and filter without feedback are presented based on local estimators from single-sensor subsystems. The estimation error cross-covariance matrices between two arbitrary local estimators, and those between local and prior fusion estimators are derived. They have good flexibility due to the parallel structure. However, they have lower accuracy than the centralized fusion estimators. To further improve the estimation accuracy, the distributed optimal linear fusion predictor and filter with feedback are also presented. They avoid the calculation of cross-covariance matrices. Moreover, it has been mathematically proved that they have the same estimation accuracy as the centralized fusion estimators. A simulation example demonstrates the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This study investigates event-triggered (ET) mechanism-based sequential fusion filtering for nonlinear multi-sensor systems with composite correlated noise and random packet dropout. Composite correlated noise means that the observation noise at the same time is correlated, while the observation noise is also correlated with the process noise at the previous and the same instants. To minimize the energy loss within the system, the ET mechanism is used to reduce the communication rate of the sensors. The equation for the local filtering process is derived based on whether the ET mechanism has been triggered. To improve the accuracy of the filter, a noise estimator for local observations is developed using the ET mechanism. A two-step prediction method is used to calculate the estimate state vector of the main filter for time updating. In this paper, the sequential fusion filtering algorithm under the Bayesian framework is derived to solve the nonlinear systems with above problems, and the numerical implementation of the algorithm is given by the square-root cubature Kalman filter (SCKF). Finally, simulation experiments demonstrate that the developed algorithm enables a reduced communication rate of the sensor system while maintaining powerful filter estimation performance. [ABSTRACT FROM AUTHOR]

In recent years, lightweight object detection networks have been increasingly applied to remote sensing platforms due to their fast inference speed and flexible deployment advantages. Knowledge distillation methods have been widely used to reduce the performance gap between large and small models, and many studies have combined knowledge distillation with object detection. However, existing knowledge distillation methods often overlook the transfer of localization knowledge. Therefore, this paper proposes a method called Discretized Position Knowledge Distillation (DPKD) to improve the use of knowledge distillation in object detection. Specifically, the DPKD method incorporates a Discretization Algorithm Module (DAM), which leverages both general probability distribution and cross-Gaussian distribution to transfer high-quality bounding box position and pose information. Additionally, the Position Knowledge Distillation (PKD) method splits the target and non-target bounding boxes to form the distillation loss function, addressing the issue of missing background knowledge transfer during the distillation process. To further enhance the learning of high-quality bounding boxes, a Region Weighting Module (RWM) based on EIoU is introduced in DPKD, assigning weights to the various bounding boxes in the teacher's output. The effectiveness of DPKD in the field of remote sensing image object detection in multi-modal scenarios was verified through multi-modal training on the publicly available DOTA and HRSID datasets. [ABSTRACT FROM AUTHOR]

Infrared and visible image fusion strives to effectively combine the advantageous information from input images to achieve a fused result. In this paper, we propose a novel multi-directional fractional-order variation to fuse infrared-visible images. First, each fidelity term of each source image is modeled straightforwardly by Euclidean norm, ensuring robust optimization. Then, the detail regularization term is formulated based on fractional variation in two, four, and eight directions instead of integral variation, which enables the capture of more comprehensive detail while avoiding the undesirable staircase effect. Furthermore, the fused image is enhanced by transferring the highest level of brightness from the source images in the luminance regularization. Finally, based on the extensive experiments, the proposed method exhibits superior performance in both subjective and objective evaluations compared to existing others. Moreover, our method is further expanded to the multi-modal medical image fusion, achieving promising performance preliminarily. [ABSTRACT FROM AUTHOR]

Coprime array motions are known to improve direction of arrival (DOA) estimation due to the synthetic aperture technique and the concept of difference coarray (DCA) are combined to achieve an increased number of degrees of freedom (DOFs). In this paper, an improved moving scheme for coprime array is presented, where the array spans a carefully designed large displacement. The benefit by doing this is that the overlapping lags induced by the original DCA and the shifted DCA can be avoided as much as possible, thereby significantly enhancing the number of consecutive DOFs (cDOFs). Then, the closed-form expressions of the displacement, cDOFs, and DOFs are derived. We also prove that the synthetic array has the same robustness against mutual coupling as the original array. It shows that the proposed moving scheme is more attractive than the other existing schemes. Finally, numerical examples are provided to demonstrate the superiority of the proposed moving scheme in both the absence and presence of mutual coupling. [ABSTRACT FROM AUTHOR]

• The mixture generalized minimum error entropy (MGMEE) criterion is designed. • A new MGMEE-DLKF is proposed based on MGMEE criterion and lattice samples. • Complexity analysis and convergence condition of the MGMEE-DLKF are given. The Gaussian kernel function-based Minimum Error Entropy (MEE) criterion is effective for special types non-Gaussian noise. However, non-Gaussian noise distributions and shapes are diverse in practice, the traditional MEE methods are difficult to fit non-Gaussian effectively due to the shape parameters of MEE cannot be adjusted. In this paper, the Mixture Generalized Minimum Error Entropy (MGMEE) criterion is proposed by a mixture generalized Gaussian kernel function. Then, a new Mixture Generalized Minimum Error Entropy-based Distributed Lattice Kalman Filter (MGMEE-DLKF) is proposed for multi-sensor nonlinear systems with non-Gaussian noise. The complexity analysis and convergence condition of proposed MGMEE-DLKF algorithm are derived. In the end, the target tracking simulations are verified for systems with mixture Gaussian noise, Rayleigh distribution noise and α − stable distribution noise. The simulation results demonstrate that the proposed filter has the smallest root mean square error. [ABSTRACT FROM AUTHOR]

Information transmission is easily affected by obstacles or other factors, which makes it challenging to achieve perfect channel state information (CSI) and successive interference cancellation (SIC) in practical applications. Therefore, in this paper, a dual-hop underwater wireless optical communication-radio frequency (UWOC-RF) system with non-orthogonal multiple access (NOMA) in the presence of both imprecise CSI and imperfect SIC is investigated. In particular, the underwater source sends a superimposed optical NOMA signal to a pair of land-based users via a decode-and-forward relay on the surface, which decodes the received signal and encodes it into the RF one before broadcasting it to the terrestrial users. Considering a scenario that both the land-based users are equipped with multiple antennas and adopt a maximal-ratio combining technique to deal with those multiple copies of received signals, we first format the expressions of probability density function, cumulative distribution function, and the p -th moment of the imperfect channel gain in the UWOC link. And then the analytical and asymptotic expressions for outage probability as well as the closed-form expression for average achievable rate of each user are obtained by utilizing the Meijer's- G and the Fox's- H functions. Finally, Monte-Carlo simulations are performed to validate the accuracy of those analytical expressions. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative bilevel optimization approach to elevate the deblurring process. By integrating a weights variable nonlocal model with a spatially varying attached term, the methodology aims to achieve enhanced restoration outcomes. Theoretical scrutiny is dedicated to unraveling the solution of the model, paving the way for the development of an efficient algorithm meticulously crafted to compute the clean image. This algorithm excels in learning both the weights parameter and the balanced L 2 - L 1 attached parameter concurrently, thereby ensuring optimal performance. Through careful parameter selection, the proposed nonlocal deblurring model showcases superior effectiveness, surpassing existing models in terms of both performance and efficacy. [ABSTRACT FROM AUTHOR]

Monocular depth estimation from a single image is an ill-posed problem for computer vision due to insufficient reliable cues as the prior knowledge. Besides the inter-frame supervision, namely stereo and adjacent frames, extensive prior information is available in the same frame. Reflections from specular surfaces, informative intra-frame priors, enable us to reformulate the ill-posed depth estimation task as a multi-view synthesis. This paper proposes the first self-supervision for deep-learning depth estimation on water scenes via intra-frame priors, known as reflection supervision and geometrical constraints. In the first stage, a water segmentation network is performed to separate the reflection components from the entire image. Next, we construct a self-supervised framework to predict the target appearance from reflections, perceived as other perspectives. The photometric re-projection error, incorporating SmoothL1 and a novel photometric adaptive SSIM, is formulated to optimize pose and depth estimation by aligning the transformed virtual depths and source ones. As a supplement, the water surface is determined from real and virtual camera positions, which complement the depth of the water area. Furthermore, to alleviate these laborious ground truth annotations, we introduce a large-scale water reflection scene (WRS) dataset rendered from Unreal Engine 4. Extensive experiments on the WRS dataset prove the feasibility of the proposed method compared to state-of-the-art depth estimation techniques. • Proposes an intra-frame-supervised depth estimation by specular reflections, comprising water segmentation and depth estimation. • Introduces the Photometric Adaptive SSIM for aligning reflections with source patterns, emphasizing local contrast and structural details. • Develops the Water Reflection Scene dataset to address the lack of reflection scenes depth estimation. [ABSTRACT FROM AUTHOR]

• A fusion positioning scenario: closed corridor between buildings (With the development of urbanization, the positioning scene is gradually complicated.). • A novel fusion localization algorithm. (The adaptive factor function is established by using the median of residual, and the prior error covariance matrix is weighted to realize the balances the trust of the filter estimation value to the observation value and the state prediction value, then change the optimal estimation of the position.). • The proposed algorithm is compared with LS-KF, EKF, UKF and the improved LS-AKF algorithm proposed in reference [20] , and the results show that the proposed algorithm in this paper has a better effect. • Simulation experiment. To verify the stability of the fusion algorithm in the face of interference. According to the minimum detectable bias (MDB) obtained by internal reliability, different interference types and sizes are introduced, the proposed algorithm has stronger anti-interference ability. Due to the blocking and reflection of buildings, the received Beidou satellite navigation signal (BDS) in the closed corridor will be getting weaker, which results in poor positioning accuracy or positioning failure. Introducing Ultra-Wide Band (UWB) positioning technology and establishing BDS/UWB integrated positioning system is an effective method to achieve seamless positioning. The positioning accuracy obtained by the Least Square-Kalman Filter (LS-KF) algorithm is below m level. However, the prior error covariance matrix of the KF correction process is easily contaminated, which affects the proportion of system observations and state prediction values to the optimal position estimation. A LS-KF fusion positioning algorithm based on adaptive error covariance matrix is proposed, the algorithm adjusts the state prior error covariance matrix by constructing an adaptive factor, improves the Kalman gain and the optimal estimation of the position, balances the trust of the filter estimation value to the observation value and the state prediction value of the integrated positioning system. Theoretical analysis and experimental results show that compared with LS-KF, EKF, UKF and improved LS-KF algorithm, the proposed LS-AKF algorithm not only has higher positioning accuracy, stronger anti-interference ability, but also has faster convergence speed. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the constrained independent vector analysis integrated with the bounded multivariate generalized Gaussian mixture model (cIVABMGGMM) to tackle the limitations of independent component analysis (ICA) when applied to multivariate data, accompanied by its adaptive version, the acIVABMGGMM, aimed at alleviating associated constraints. The acIVABMGGMM employs a full covariance matrix that considers feature correlations, effectively addressing the challenges posed by ICA and independent vector analysis (IVA) models when analyzing multivariate data. The innovative acIVABMGGMM framework merges the adaptability inherent in data-driven methods with the capability to manage noise and other artifacts often encountered in model-based approaches. This technique effectively employs prior knowledge to guide the solution, avoiding the imposition of inaccurate constraints. To overcome these challenges, our set of two constrained methods incorporates prior source information into the IVA model, effectively mitigating its limitations in data with a high number of sources. We assess the efficacy of our proposed models through three distinct ECG separation experiments, which include heartbeat separation, fetal ECG extraction, and arrhythmia detection. Notably, the performance metrics demonstrate the superiority of our models over the baseline approaches in the conducted experiments. [ABSTRACT FROM AUTHOR]

Most Siamese trackers based on graph attention models focus on the topology between nodes when extracting features. These approaches ignore contextual information regarding the overall structure of the target, resulting in a diminished capacity for target discrimination. In this paper, to resolve this issue, we propose a global semantic-guided graph attention network for Siamese tracking. Firstly, in order to balance the node constraints with the overall information of the target, a global semantic awareness module (GSA) is proposed. It utilizes the attention strategy to focus on the overall characteristics of the target, aiming to mine the relationship of the target context. Incorporating it into the semantic-guided graph attention network through cross-correlation operations can avoid the problem of focusing only the information between nodes without being able to effectively characterize the target. Secondly, to enhance the accuracy of single-point feature regression results, a novel extreme point feature aggregation module (EPA) is proposed. We obtain feature information of the extreme points on the bounding box and combine them with single-point features for the bounding box feature representation to improve the regression accuracy. Finally, to establish a connection or bridge between the classification and regression subnetworks, a categorical regression alignment network is designed. An IOU-guided ranking loss is introduced to align the classification confidence with the IOU of the corresponding positive sample localization prediction to solve the problem of misalignment of the classification branch with the regression branch effectively. Experiments conducted on five challenging benchmarks datasets, namely GOT-10K, OTB-100, LaSOT, UAV123, and TC128, demonstrate that our approach exhibits superior performance compared to other tracking methods in terms of tracking accuracy and execution efficiency. [ABSTRACT FROM AUTHOR]

Orthogonal Time Frequency Space (OTFS) modulation has significant Doppler resistance in non-geostationary orbit (NGSO) satellite communications, but is extremely affected by single-tone interference (STI). In this paper, a Fast Harris Hawk Optimization-based Energy Concentration Interference Suppression (FHHO-ECIS) algorithm is proposed to remove STI. First, the algorithm designs the translation matrices in the Delay-Doppler (DD) domain based on the interference center. The STI energy is concentrated by the translation matrices to a single point in the OTFS data block and removed by a zero-setting operation. Subsequently, an interference center pre-estimation module is proposed to improve the accuracy and speed of the HHO algorithm for interference center estimation. Finally, the theoretical analysis of the algorithm's interference suppression gain and the effect of the zero-setting operation on the data is performed. Simulation results show that the FHHO-ECIS algorithm improves the signal to interference plus noise ratio (SINR) by more than 1.7 dB and the bit error rate (BER) performance is improved by more than 0.3 dB. [ABSTRACT FROM AUTHOR]

In conventional sparse Space-Time Adaptive Processing (STAP) algorithms, the l 0 -norm is often used instead of the l 1 -norm to relax convexity constraints. The approach presented in this paper can be mathematically reformulated as a linear problem, which can be efficiently solved using various convex optimization techniques. While this method effectively circumvents the NP-Hard complexity associated with the l 0 -norm, it does come with certain potential challenges. For example, the observation matrix must meet the criteria of the Restricted Isometry Property (RIP) and other stringent conditions. In this research, a novel STAP algorithm is introduced, which is based on a non-convex penalty function. This innovative algorithm substitutes the traditional l 0 -norm with a custom-designed non-convex penalty function and is applied to the Direct Filter Processor (DFP), solved using the recursive least squares (RLS) algorithm. The resulting algorithm, known as g p -RLS, is demonstrated through Monte Carlo simulations to outperform other l 1 -based and reduced-rank STAP algorithms in terms of faster convergence, improved signal-to-clutter-plus-noise ratio (SCNR), and enhanced target detection performance. [ABSTRACT FROM AUTHOR]

In order to suppress random noise and remove stripe interference in ISAR imaging, a Compressive Sensing method is proposed for super-resolution ISAR imaging in this paper, which is named LR-ASTV (Low-rank and Anisotropic Spatial Total Variation) algorithm here. In this algorithm, the original echo HRRP is transformed into echo HRRP of MMV model with radial interpolation processing at first. Subsequently, an optimization objective function based on Compressive Sensing framework is formulated which includes the sparsity constraint and low-rank constraint, and this optimization problem is solved by JLRS algorithm to generate an initial ISAR image from which the random noise has been eliminated. The initial image is then subjected to further refinement using the Anisotropic Spatial Total Variation (ASTV) processing, ultimately yielding the final ISAR image with stripe interference removed. To validate the effectiveness of the LR-ASTV algorithm, ISAR imaging experiments based on simulated and measured data at different SNRs are completed, and the imaging results of the LR-ASTV algorithm are compared with those of other three algorithms including the LSM-ME2 algorithm, the JLRS algorithm and the LRPB algorithm. It can be found that the LR-ASTV algorithm has obvious superiority in suppressing random noise and removing stripe interference, and can provide ISAR images of higher clarity. The quality evaluation for ISAR images also shows that the LR-ASTV algorithm has lower image entropy index and higher image contrast index than the other three ISAR imaging algorithms. [ABSTRACT FROM AUTHOR]

As a blind equalization algorithm, the fractional lower-order statistics based multimodulus algorithm (FLOS-MMA) is suitable for equalizing multilevel quadrature amplitude modulation (QAM) signals in impulsive noise. However, the equalization performance of FLOS-MMA is severely degraded in strong impulsive noise because the first-order gradient of the cost function constrains the ability of FLOS to suppress large outliers in the equalizer output signal and is sensitive to large outliers in the equalizer input signal. To enhance the robustness of multimodulus blind equalization algorithm against impulsive noise, a FLOS based momentum fractional-order multimodulus algorithm (FLOS-MFOMMA) is proposed in this paper. The proposed algorithm mitigates the adverse effects of impulsive noise on the updating of equalizer coefficients by utilizing the fractional-order gradient and accelerates the convergence speed by incorporating a momentum term. Then, the steady-state excess mean square error and the computational complexity of FLOS-MFOMMA are analyzed theoretically. Finally, numerical simulations, for equalizing 16-QAM signals under impulsive noise environments, show that the proposed algorithm is superior to FLOS-MMA in terms of robustness and convergence speed. [ABSTRACT FROM AUTHOR]

Dynamic point clouds can effectively describe 3D objects and natural scenes, providing users with an immersive visual experience, but their huge amount of data requires efficient compression tools. To this end, the video-based point cloud compression (V-PCC) standard was developed, however, it may lead to cracks in the compressed point cloud at low bitrates. To solve the problem, this paper proposes a cracks-suppression perceptual geometry coding method for dynamic point cloud. Firstly, considering that the edge regions of 2D geometry patches in V-PCC are prone to cracks, a prior knowledge based crack region detection and preprocessing scheme is designed to reduce cracks. Secondly, considering the unique perceptual geometry characteristics of point cloud, a projected geometry curvature based structural similarity (PGC-SSIM) is proposed to evaluate geometry quality of point clouds, which contains the geometric perception information of point cloud, and is more consistent with the human visual perception. Finally, based on the PGC-SSIM, an adaptive quantization parameter adjustment strategy is designed for rate-distortion optimization in geometry coding of dynamic point clouds. The experimental results show that the proposed method can effectively reduce cracks in the compressed point cloud without reducing the coding performance compared to the V-PCC anchor. Moreover, the presented PGC-SSIM can be used to improve the visual quality of the compressed point cloud. [ABSTRACT FROM AUTHOR]

The low-rank approximation of big data matrices and tensors plays a pivotal role in many modern applications. Although, a truncated version of the singular value decomposition (SVD) furnishes the best approximation, its computation is challenging on modern, multicore architectures. Recently, the randomized subspace iteration has shown to be a powerful tool in approximating large-scale matrices. In this paper we present a two-sided variant of the randomized subspace iteration. Novel in our work is the exploitation of the unpivoted QR factorization, rather than the SVD, for factorizing the compressed matrix. Hence our algorithm is a randomized rank-revealing URV decomposition. We prove the rank-revealingness of our algorithm by establishing bounds for the singular values as well as the other blocks of the compressed matrix. We further provide bounds on the error of the low-rank approximations of the proposed algorithm, in both 2- and Frobenius norm. In addition, we employ the proposed algorithm to efficiently compute low rank tensor decompositions: we present two randomized algorithms, one for the truncated higher-order SVD, and the other for the tensor SVD. We conduct numerical tests on (i) various classes of matrices, and (ii) synthetic tensors and real datasets to demonstrate the efficacy of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The task of producing high-quality, realistic, and diverse images based on a few instances of newly emerging or long-tail categories is known as few-shot image generation. Despite prior works showing outstanding results, the quality and diversity of the outputs are still limited. In this paper, we tackle this problem by presenting a range of innovative fusion techniques based on attention mechanisms with generative adversarial networks. Our proposed framework introduces global skip attention that links matching residual blocks of symmetric encoder-decoder pairs to generate new instance objects. Additionally, we incorporate an alignment algorithm based on spatial transformer networks into our pipeline encoder to address feature misalignment. In the decoding phase of our attention-based decoder, we propose a novel attention mechanism within each fusion residual block, which leads to capturing long-range dependencies in feature maps. An attention reconstruction loss function has been proposed to balance adversarial training between the generator and discriminator, mitigate mode collapse, and guide the generator to focus on specific regions of interest within images. Finally, we apply a back summation to the decoding outputs, resulting in unified features through a weighted combination of similar characteristics. Extensive experiments conducted on five few-shot image datasets demonstrate the effectiveness of our proposed model. The source code of the proposed model can be found on GitHub https://github.com/Aldhubri/SAGAN. [ABSTRACT FROM AUTHOR]

Many action recognition methods require significant computational resources to achieve good results on unedited videos. However, their performance on infrared videos, which contain less information, is often unsatisfactory. In this paper, we propose a multi-scale difference joint key frame extraction algorithm for action recognition in infrared surveillance videos. To evaluate our algorithm, we have created a self-built dataset comprising 1200 unedited infrared surveillance videos categorized into 10 action categories. Our algorithm extracts key frames by jointly analyzing the global and local differences between adjacent frames. Experimental analysis demonstrates that by using only 10 frames, our algorithm improves the accuracy of generic action recognition algorithms by more than 10% on both our self-built dataset and the Infrared-Visible dataset. Moreover, our proposed method achieves high recognition accuracy with minimal computational cost, even when using a small number of frames. It outperforms state-of-the-art methods by 1.82% on the Infrared-Visible dataset and 1.35% on the InfAR dataset. These results highlight the effectiveness of our algorithm as a preprocessing module to significantly enhance the accuracy of action recognition algorithms prior to employing generic models. [ABSTRACT FROM AUTHOR]

In recent years, neural operators have shown great advantages in solving partial differential equations by learning mappings between function spaces. In this paper, we propose a novel neural operator network to further improve the efficiency of operator learning from parameter space to solution space. We introduce Dual-view block in the architecture, which can better extract and fuse potential features under different visual fields. In addition, we also introduce the homeomorphic mapping and parameterized integration kernels directly in Fourier space, which is beneficial to capturing the specific frequency information required during function iteration. The test results on the standard benchmark (Representative Partial Differential Equations Benchmark) demonstrate that our neural operator not only has better accuracy than the baseline but also has a huge advantage in inference speed. In summary, we provide a new option for using data-driven machine learning methods to solve partial differential equations quickly, robustly, and accurately. [ABSTRACT FROM AUTHOR]

Crypto-currency markets are known to exhibit inefficiencies, which presents opportunities for profitable cyclic transactions or arbitrage, where one currency is traded for another in a way that results in a net gain without incurring any risk. Quantum computing has shown promise in financial applications, particularly in resolving optimization problems like arbitrage. In this paper, we introduce a differential evolution (DE) optimization algorithm for Variational Quantum Eigensolver (VQE) using Qiskit framework. We elucidate the application of crypto-currency arbitrage using different VQE optimizers. Our findings indicate that the proposed DE-based method effectively converges to the optimal solution in scenarios where other commonly used optimizers, such as COBYLA, struggle to find the global minimum. We further test this procedure's feasibility on IBM's real quantum machines up to 127 qubits. With a scenario of three currencies, the algorithm converged in 417 steps over a 12 hour period on the "ibm_geneva" machine. These results suggest the potential to achieve a quantum advantage in solving increasingly complex problems. • We introduce a differential evolution (DE) optimization algorithm for Variational Quantum Eigensolver (VQE) using Qiskit. • We validate this method using crypto-currency arbitrage, as an example of problem with many local minima. • We test this procedure's feasibility on IBM's real quantum machines up to 127 qubits. [ABSTRACT FROM AUTHOR]

This paper proposes a dense network composed of an improved Transformer network, which successfully restores low-light images to high-quality normal-light images, alleviating issues such as low brightness, high noise, and missing critical information in low-light images. The entire network architecture is based on the improved Transformer network and builds a dense network with a combination of long and short connections. While retaining the self-attention mechanism of the Transformer network, it achieves multi-level fusion and utilization of shallow and deep features, providing the network with rich image features and enabling the restoration of low-light images to high-quality normal-light images. Additionally, a spatial-domain and frequency-domain combined loss function is designed, considering both pixel-level and frequency domain losses, effectively constraining the image restoration process and avoiding spectral biases. Lastly, a multi-scale hybrid gate feedforward network is designed to replace the traditional feedforward network in the Transformer, facilitating feature selection and forward propagation. These designs effectively enhance the richness of meaningful image features, alleviate spectral biases, and improve the visual quality of low-light images. Experimental results demonstrate the superiority of our method over state-of-the-art networks on various typical image enhancement datasets. Taking the most commonly used low-light dataset LOLv1 as an example, our method achieves improvements of 1.3% and 3.07% in PSNR and SSIM, respectively, compared to the best-performing network, showing favorable qualitative and quantitative evaluation results. The proposed method effectively addresses the issue of insufficiently realistic results in low-light image restoration, providing a reliable reference for practical applications. [ABSTRACT FROM AUTHOR]

Obtaining location information of the multi-sensor internet of things (IoT) is a fundamental requirement. But, two kinds of the incomplete measurement data enhance the difficulty of positioning: 1) The location information is partially missing. 2) The errors of the measurement. Furthermore, the complex environmental impact and the characters of the measurement methods make the error model more changeable at the same time. This paper tries to study a universal method to address these problems, through combining IoT positioning and affinity graph assessment with arbitrary model of the measurement errors or the missing measurement data. First of all, the relationship between the graph signal and the combinatorial graph Laplacian matrix is constructed to link the graph topology learning and the IoT localization problem. Later, the Gaussian mixture model is applied to describe the measurement errors as a general model. Then, the calculations of the graph signal, Laplacian matrix and hyper parameters are obtained via variational Bayesian inference and message passing. The numerical results show the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a novel Extended Logistic Chaotic Map (ELCM) with two control parameters is proposed. Overcoming the major drawback of the standard Logistic and other existing chaotic maps, the ELCM not only has infinite chaotic range as well as good ergodicity, but also has a simple structure just like the Logistic map, which greatly facilitates its practical implementation and becomes very suitable for today's real-time applications. Moreover, a new color image encryption scheme based on chaos concept, DNA (Deoxyribonucleic Acid) encoding and Convolutional Neural Network (CNN) is also presented. To perform a chaotic scrambling, the ELCM is however used at different stages of the encryption process. In addition, a pre-trained AlexNet CNN is used to generate a public key. After XORing with the secret key, the latter is used, on the one hand, to generate the initials values and the control parameters of the ELCM. On the other hand, it will be split into two keys in order to generate a random grayscale image, which will then be XORed with the three components (i.e., R, G and B) of the color plaintext image. Afterward, a permutation operation, DNA encoding, diffusion operation as well as bit reversion operation are then applied to the R, G and B components. The performance evaluation demonstrates that the ELCM not only has an infinite chaotic parameter range, but also exhibits a high chaotic complexity. Besides, experimental results as well as security analysis confirm that with NPCR of 99.6287%, the designed color image encryption scheme achieves an average entropy of 7.9975 and a near zero correlation (-0.0027). Furthermore, the proposed encryption scheme is in fact of higher security level in comparison to other schemes recently presented in the literature, thus making it highly suitable for today's real-time applications. [ABSTRACT FROM AUTHOR]

This paper presents two novel methods based on geometric algebra (GA) to estimate two-dimensional (2D) direction-of-arrival (DOA) of non-circular (NC) signals for uniform rectangular array (URA). Traditional methods treat the received NC signals as a long vector which will inevitably lose orthogonality inside each electromagnetic vector sensor (EMVS) and thus miss some information of second-order statistical properties. Furthermore, the computational complexity will also increase. By contrast, the GA-based estimating signal parameter via rotational invariance techniques (ESPRIT) and propagation method (PM) algorithms are proposed to estimation DOA of NC signals. Taking advantage of GA, the relationship among multidimensional signals can be maintained. First, the six components of the EMVS are represented as a multivector in GA space. Then, we construct the GA-based extended covariance matrix to utilize the signal information more completely. The DOA parameters can be estimated through the ESPRIT and PM principle. The proposed GA-based estimation of signal parameter via rotational invariance techniques for NC signals processing (GANC-ESPRIT) can estimate DOA with high estimation accuracy. The proposed GA-based propagation method for NC signals estimation (GANC-PM) uses linear transformation to calculate angle parameters. Our model has much lower memory requirements and less computational burden compared with long vector models. Simulation results demonstrate the robustness and superiority of the proposed GANC-ESPRIT algorithm in terms of angular resolution. Complexity analysis shows that the proposed GANC-PM algorithm performances better with less computations. [ABSTRACT FROM AUTHOR]