Search

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]

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]

Low probability of intercept (LPI) radar signal identification plays an important role in electronic warfare, but most existing algorithms are proposed under the condition of sufficient samples, ignoring the problem of a small amount of labeled data in the actual electromagnetic environment. To solve the problem, in this paper, a semi-supervised learning model All Mean Teacher (AMT) based on Mean Teacher (MT) is proposed. First, the LPI radar signal is transformed into Time-frequency images (TFIs) by using the Choi-Williams distribution, and Random Erasing is used for TFIs which improves the generalization ability of the model. Then the Multi-headed Self-Attention Network (MSA-Net) is aimed to extract features, combined with AMT to realize the automatic waveform recognition of radar signals. MSA-Net facilitates feature information propagation by computing contrast costs on TFIs between the student and teacher networks. It solves the problem that TFIs are not easy to train for small amounts of labeled data, improving the accuracy of signal recognition in semi-supervised learning scenarios. Experimental results show that the average recognition accuracy of the proposed method is up to 85.7% at a signal-to-noise ratio of -8 dB. • Designing a novel SSL model for the automatic recognition of LPI radar signals. • Introducing an improved multi-head attention mechanism enables the model to capture global information more effectively. • Incorporating an extra contrast cost into the overall loss function. • Introducing noise to the samples through the data augmentation technique of random erasing for better consistency training. [ABSTRACT FROM AUTHOR]

This paper introduces a novel construction method for Type-II Z complementary code set (ZCCS) based on the Kronecker product between complete complementary code and mutually orthogonal sequences. Type-II ZCCS offer significant advantages over their Type-I counterparts, boasting larger zero correlation zone (ZCZ) width and a larger number of codes with identical constituent sequences and lengths. Moreover, we propose a strategy to reduce column peak-to-mean envelope power ratio (PMEPR) to a low values upto smaller than 2. This construction framework also facilitates the creation of Z complementary pairs (ZCPs) with flexible parameters. Furthermore, we present a Type-II ZCCS based multi-carrier code division multiple access (MC-CDMA) system and conduct performance comparisons against existing Type-I ZCCS based counterparts. Simulation results demonstrate the superiority of the proposed system, showcasing improved reliability and efficiency. Additionally, we establish a novel relationship among set size, code size, sequence length, and ZCZ width for Type-II ZCCS. [ABSTRACT FROM AUTHOR]

Due to the need for the real-time participation of a large number of professionals, existing decision-making methods for radar interference suppression are characterized by a slow decision-making speed, unstable decision-making effects, and insufficient decision-making intelligence. In this paper, a deep reinforcement learning (DRL)-based decision-making method for radar interference suppression is proposed. This method has a fast decision speed and a stable and accurate decision effect, and can complete decision-making tasks by itself with high intelligence. To enhance the ability of the agent to acquire high-value experiences, the variable greedy algorithm (VGA) is proposed. The VGA adjusts the fixed greedy value in the original action strategy into a declining greedy curve that mimics the human learning process via a combination of the ideas in the win or learn fast–policy hill-climbing (WoLF-PHC) algorithm and the Ebbinghaus forgetting curve. To improve the efficiency of the agent in utilizing high-value experiences, the double-depth prioritized experience replay (DDPER) algorithm is proposed. The DDPER algorithm changes the uniform random experience replay into prioritized experience replay (PER), and performs sorting and extraction learning based on experience values in the form of additive trees to achieve better learning results. Further, the accuracy and speed of decision-making are improved via the double-depth experience replay system. The findings of a simulation experiment show that the agent can efficiently learn the most optimal radar interference suppression method by knowing that the interference suppression algorithm library contains algorithms that can deal with current environmental interference signals. Furthermore, compared to the PER–double deep Q-Network (PER-DDQN) presented by Zhang, the average accuracy, speed, and stability of decision-making are respectively increased by 6.4%, 2.51%, and 102.12%. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output (MIMO) can significantly improve the energy efficiency and spectral efficiency of wireless communication systems, and obtaining accurate channel state information is a key prerequisite. However, traditional channel estimation techniques often have high computational complexity, poor channel estimation performance, and do not fully utilize the information in the communication scene. In this paper, a channel estimator (PrePD-CNN) with convolutional neural network (CNN) driven by predicted position information is proposed based on high-precision localization technology for 6G. The channel estimation performance is improved by rationally transforming the sensing information and accurately reconstructing the quasi-channel state as auxiliary information. Simulation results show that the BER performance and MSE performance of PrePD-CNN are significantly lower than that of traditional channel estimation algorithms. Therefore, the proposed scheme combines stronger robustness and good generalization, which not only reduces the complexity of the algorithm but also improves the performance. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of spectrum sensing (SS) in cognitive radio networks utilizing multiple-input multiple-output (MIMO) technology, even when the receivers are not calibrated. The study develops a framework by formulating various composite hypothesis testing SS problems based on different assumptions about the parameter space of the alternative hypothesis. To accomplish this, four new detectors are designed using the likelihood ratio test (LRT), Rao, and matrix information geometry (MIG) principles. We demonstrate analytically that three of the proposed detectors maintain a constant false alarm rate (CFAR) despite noise covariance matrix uncertainty. For the non-CFAR detectors, we introduce a novel analytical threshold-setting method to ensure that their false alarm probabilities remain below a predetermined level, thereby making them useful for practical situations. Finally, extensive simulations are conducted to verify the analytical results, assess false alarm regulation, and compare the proposed SS algorithms with existing approaches in terms of detection probability. [ABSTRACT FROM AUTHOR]

Signal recovery on graphs is attracting more and more attentions. Based on the smoothness assumption, the signal recovery problem can be formulated as an unconstrained optimization model. Although the model can be solved analytically, the computational cost is too expensive. In this paper, to reduce the computational complexity, we skillfully construct a kernel function, such that the smoothness item is equivalent to the norm of the signal modulo a constant in the reproducing kernel Hilbert space. Based on this, by using the Representer Theorem of reproducing kernel Hilbert space, the optimization problem for recovering the signal from a given bandlimited space, can be solved in a lower-dimensional subspace corresponding to the known labeled set. Experiments for the recovery of artificial graph signals in GSPBOX and real data in the recommendation system show very encouraging results. [ABSTRACT FROM AUTHOR]

The learning-based method has been widely applied in target localization. However, the performance of learning-based localization is mostly validated by simulation experiments and lacks theoretical analysis. In this paper, we investigate the performance of learning-based multi-target localization in a multiple-input multiple-output (MIMO) radar system with widely separated antennas. A mean squared error bound (MSEB) for learning-based multi-target localization is derived, which contains all the system parameters, including radar-related parameters and learning network-related parameters. Therefore, the MSEB can be employed as the metric to optimize any parameter of interest, such as those associated with the learning network architecture, which is usually determined with the trial-and-error method by minimizing the training loss. Leveraging the MSEB, we can design the optimal learning network architecture which maximizes the testing performance and this optimal architecture can be obtained before training. Numerical results are presented to verify the correctness of the MSEB and effectiveness of the MSEB-based learning network architecture design method. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the physical-layer security performance for a reconfigurable intelligent surface (RIS) assisted free-space optical (FSO) communication-radio frequency (RF) system with non-orthogonal multiple access (NOMA). In particular, the source (S) transmits an optical NOMA signal to the relay (R), which forwards the optical-to-electrical converted signal to both users in the presence of an eavesdropper (E). Furthermore, for the far user, the scenario that there is no direct path from R and it can only receive the signal from R via the RIS is considered. While for the near user, three signal transmission scenarios are taken into account: 1) only the direct path from R exists; 2) only the reflected paths from RIS exist; 3) both the direct and the reflected paths exist and superimpose together. In addition, for the first hop of information transmission, E may or may not locate in the region of laser beam divergences of S. Therefore, both scenarios that the first hop may or may not be overheard by E are taken into consideration. Considering the FSO link is with a Gamma-Gamma distribution and the RF channels experience the Nakagami- m fading, the analytic and asymptotic expressions of secrecy outage probability for both users are presented. Monte-Carlo simulations are also conducted to validate the accuracy of the derived expressions. [ABSTRACT FROM AUTHOR]

In the realm of radar systems, the proliferation of new modulation techniques introduces an increased level of complexity in the identification of both existing and potentially novel modulation schemes. Conventional supervised recognition methodologies, which rely heavily on labeled datasets, exhibit limitations in distinguishing amongst a multitude of unlabeled signals. This paper introduces a novel framework that synergizes Subspace Clustering (SC) with Multi-Block Time-Frequency Representations (TFRs, denoted as mT), referred to as SC-mT. This framework leverages subspace clustering for the unsupervised categorization of radar modulations and employs an innovative multi-block strategy to augment classification precision. The process commences with the generation of TFR datasets for radar signals, followed by the construction of two distinct multi-block models, Model-A and Model-B. These models segment the radar TFRs into overlapping multi-block sets, utilizing the concept of random receptive fields. The subspace clustering algorithm is then applied to each block within the sub-TFR sets to procure an affinity matrix. The culmination of this process involves the aggregation of the affinity matrices from all blocks, facilitating the derivation of classification outcomes via spectral clustering. Empirical analyses affirm that the sequential integration of the SC-mT algorithm surpasses the classification efficacy of various conventional and cutting-edge algorithms. Notably, this algorithm attains a classification accuracy surpassing 90% for ten unlabeled signals, even in scenarios where the signal-to-noise ratio (SNR) is as low as -4 dB. [ABSTRACT FROM AUTHOR]

Direction-of-arrival (DOA) estimation is a critical issue in array signal processing, especially in radar applications. However, the array aperture of high-frequency radars is constrained by the site and platform, which in turn leads to limited DOA estimation performance. In this paper, inspired by the inverse synthetic aperture radar, we design a novel DOA estimation method based on motion-guided large-aperture uniform linear array (ULA). Referring to the concept of inverse synthetic aperture, the novel method utilizes the relative motion between the radar and the target to construct a larger ULA. We demonstrate that the key condition for reconstructing ULAs is suitable data segmentation and reshaping within the coherent integration time. However, the correct segmentation position is related to the target's motion trajectory. For non-cooperative targets, the uncertain segmentation position is regarded as a mismatch problem between the steering and weighting vectors, such that the spatial spectrum becomes a product of two sinc-like functions. With the analysis of the main lobe and grating lobes, the maximum peak-to-grating-lobe ratio criterion is introduced to find the ambiguity-free DOA. The experimental results demonstrate the performance advantages of the proposed DOA estimation algorithm in terms of noise robustness and resolution. • Inspired by the inverse synthetic aperture, a trading time for space framework is developed to enhance DOA estimation. • The array aperture is extended by the relative motion between radar and target. Its key condition is proper data reshaping. • For steering mismatch problem with noncooperative targets, the spatial spectrum becomes the product of two sinc functions. • The maximum peak-to-grating-lobe ratio criterion is introduced to find the ambiguity-free DOA. • A fixed-size doubled synthetic aperture can be implemented by manipulating the cross-velocity set. [ABSTRACT FROM AUTHOR]

The paper is devoted to the rhythm-adaptive statistical estimation methods of cyclic random processes probabilistic characteristics. The main goal of the article is development and research of rhythm-adaptive methods of cyclic random processes statistical processing. Based on the fundamental fact of invariance of probabilistic characteristics of a cyclic random process to time shifts determined by its rhythm function, rhythm-adaptive statistical estimations of initial, central and mixed moment functions of cyclic random processes of discrete parameter were obtained. The article analytically proves the unbiasedness and consistency of rhythm-adaptive statistical estimates of the mathematical expectation and the initial moment functions of the higher order of cyclic random process. By conducting a series of computer simulation experiments, it has been demonstrated, that the absolute errors of rhythm-adaptive methods of statistical estimation are significantly smaller than the corresponding errors for classical non-rhythm adaptive methods of statistical estimation of cyclic stochastic signals probabilistic characteristics. The effectiveness of using rhythm-adaptive methods of statistical estimation of probabilistic characteristics of ECG signals has been demonstrated. The results obtained in the work clearly demonstrate the essential advantages of rhythm-adaptive methods of statistical processing of cyclic stochastic signals with a variable (irregular) rhythm in comparison with non-rhythm-adaptive analogues. [ABSTRACT FROM AUTHOR]

Vision-guided coal mine robots often encounter challenges in low-light environments, capturing images marked by poor visibility and significant loss of detail, which complicates the advancement of smart mine safety production. Despite various advancements, achieving high visibility and detailed images through robotic vision sensors remains a formidable task. This paper introduces a conditional generative model incorporating a skip-connection structure to address these issues. We present a novel approach utilizing conditional Generative Adversarial Networks (GNAs) aimed at improving the visibility of image content. This involves an encoder-decoder framework with skip-connections functioning as the generator, and the discriminator, both tailored to enhance image detail. In addition, we implement loss functions integrated with boundary equilibrium constraints to counter the issue of model collapse in conditional GANs, thereby enhancing training stability and image fidelity. Our experimental findings demonstrate that our model delivers competitive results against other state-of-the-art low-light image enhancement models, alongside a standardized training regimen that ensures rapid and consistent convergence. [ABSTRACT FROM AUTHOR]

Chemical safety accidents cause significant socio-economic and environmental hazards, and safety accidents caused by unsafe worker behaviors are preventable. Detection of workers' smoking and phone use behaviors in chemical parks can avoid such safety accidents from the root. Aiming at the issues of reduced accuracy in detecting small objects and low arithmetic capability within industrial equipment. This paper proposes a GD-YOLO network based on YOLOv7 for detecting smoking and phone use behaviors. First, an efficient feature extraction module, D-LAN, is designed to solve the problem of difficult extraction of small object features. Second, a lightweight G-LAN module is proposed to reduce the complexity of the model. Third, WIoU and Mish are introduced to improve the model's performance further, creating a more efficient model. The experimental results show that the mean average precision (mAP) and the number of computational parameters increased by 16.80% and decreased by 21.54%, respectively, compared with YOLOv7. GD-YOLO performs better than other existing mainstream methods in precision and complexity. [ABSTRACT FROM AUTHOR]

Identify authentication is an important component of computer security systems and has received extensive research and attention. Computer authentication techniques, such as passwords, fingerprint recognition, and face recognition, are widely used. However, the above authentication methods are vulnerable to copying and theft attacks. Past proposals to solve this limitation are based on mouse trajectory movement curves, which leverage the user's dynamic behavioral characteristics and are difficult to counterfeit. This paper proposes an intelligent authentication method based on mouse trajectory and wireless signal. Unlike previous work, our design uses two commercial wireless devices as transceivers to identify users. The insight is that when different users draw the same mouse trajectory, the impact of the trajectory on the wireless signal is different, which can be seen as fingerprints to identify users. Butterworth bandpass filtering and principal components analysis (PCA) is used to denoise and reduce the dimension of CSI measurements, and then time-frequency domain and multi-scale energy features are extracted, and then the ReliefF algorithm is leveraged to select features that can differentiate different users, and machine learning algorithm is used to authenticate and identify users. Experimental results show that average accuracy of 97.34% for user authentication and 93.90% for user identification. • Preferring features in each principal component separately retains more unique information about the user. • Experiments show that entropy eigenvalues are suitable for authentication. • Implemented and compared the accuracy, precision, and F-scores of eight machine learning classifiers. • Extensive experiments show that the proposed the WiMouse performs competitively in authentication and identification. [ABSTRACT FROM AUTHOR]

Low-light images incur several complicated degradations such as low brightness, poor contrast, and higher noise. Retinex theory, which decomposes a low-light image into illumination and reflection components based on local image derivatives, is widely used in the field of shimmering image enhancement. In this paper, we propose a new low-light image enhancement model based on the Retinex theory to reduce the above distortions. To be more specific, we first introduce a new structural texture extractor (NSTE) based on the local image derivatives properties to generate the texture and structure maps of the observed images. Compared to existing structural texture extractors, NSTE has two key designs: anisotropic weighting and adaptive exponential term. The aim is to generate structural maps with clear edge and texture maps with salient details. Subsequently, these two images are used to regularize the illumination and reflection components of the variational decomposition model, respectively. Finally, we developed a luminance correction function and a detail enhancement function to enhance the illumination and reflectance, respectively. This aims to improve the viewing experience, mitigate over-enhancement, and minimize detail distortion. We choose the alternating direction method of multipliers (ADMM) to solve the optimization model. Experimental results on several low-light datasets demonstrate that our proposed method can achieve better quantitative and qualitative performance than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The semantic segmentation of remote sensing images is vital for Earth observation purposes. However, its performance can decline significantly due to differences in dataset distributions between training (source) and deployment (target) settings. Unsupervised domain adaptation can be used to counter this problem by leveraging the knowledge acquired from the labelled source domain and by adapting it to the unlabelled target domain. Existing methods focus on either input-level or feature-level alignments, which can be sub-optimal for addressing large domain gaps. To this end, this paper introduces a new unsupervised domain adaptation method that employs concurrently two levels of alignment: first, at the input level, an adaptive Fourier-based image-to-image translation approach is utilised to generate target-styled source images with class-based low-amplitude changes. Then, at the feature level, an adaptive fine-grained domain discriminator is introduced that incorporates class information into two parallel discriminators, for source vs. target and target-styled source image vs. target settings. Experimental results indicate that the proposed method improves significantly cross-domain semantic segmentation performance with respect to the state-of-the-art. [ABSTRACT FROM AUTHOR]

Active contour model (ACM) is an important branch in the field of image segmentation, since it adapts to different image types and scenes. However, traditional ACMs rely on local image information, which makes them sensitive to initial contours and time-consuming. In this paper, a Laplace operator based ACM (LOACM) with improved image edge detection performance is proposed. LOACM combines the global pre-fitting function which is constructed by using the image's second-order gradient information with the adaptive boundary indicator function to build a hybrid model. Its compact code size allows it to be executed on a wide range of edge devices. To substantiate the segmentation capability of the LOACM model, the BSD500 dataset was utilized for experimentation. The results indicate that LOACM exhibits excellent performance, with average IOU and DSC scores of 0.861 and 0.924 respectively. Additionally, it consistently handles processes within an average time of less than 0.3 s, significantly less than that of comparative models, providing compelling evidence of LOACM's exceptional efficiency and precision. [ABSTRACT FROM AUTHOR]

In contrast to the method based on motion equations, image registration proves effective for tracking moving objects in missions where establishing a motion model is unfeasible. However, in cases where the object is captured at varying imaging angles or against a similar background, a large number of outliers may arise during the process of image feature extraction. Consequently, when confronted with a large number of outliers, image registration methods exhibit a drawback in accurate outlier filtering, especially when spatial constraints on feature matching pairs are constrained. Ultimately, these factors can render object tracking inaccurate or even unattainable. In light of the aforementioned circumstances, this paper proposes TRESAC++, which based on a triplet relation feature matching method to effectively filter outliers. To enhance the operational efficiency of the algorithm, Principal Component Analysis is employed for dimensionality reduction, aiming to decrease the incidence of triple matches and subsequently mitigate execution time. To fortify the algorithm's robustness, a triplet relation linked with an initial data subset selection strategy is additionally proposed. Experimental results on five challenging experiments show that TRESAC++ rivals current state-of-the-art techniques in terms of computational cost, while the implementation exhibits better outlier filtering. Additionally, Experiment 4 and Experiment 5 use moving object datasets to confirm TRESAC++'s superior capability in precise object tracking. [ABSTRACT FROM AUTHOR]

Accurate localization of a person or object is a significant aspect in the field of industrial internet of things (IoT). Ultra-wideband (UWB) localization system, a promising technology, has been widely studied due to its high accuracy in 2D and 3D scenarios. Moreover, in narrow corridors and deep-well environments, the 1D localization is also an important application with the advantage of resource saving, however which attracts less attention. In this paper, we focus on the problem of 1D localization and propose a practical localization model with time difference of arrivals (TDOA). To solve the nonlinear 1D localization problem, we design an algorithm that integrates the dichotomous method and Newton's iterative method, which realizes an efficient solution. Meanwhile, in order to cope with the random errors in the real scenes, we introduce a sliding window algorithm to filter out the undesirable solutions. By observing the localization error map, we design a weighted sliding window algorithm to achieve further improvement of the localization accuracy. In addition, we derive the Cramér-Rao lower bound (CRLB) for 1D localization of TDOA. Simulations and practical experiments show that our proposed algorithm greatly benefits the localization accuracy of 1D TDOA and provides strong support for its practical application. [ABSTRACT FROM AUTHOR]

In this paper, the problem of optimal low earth orbit satellite (LEO-Sat) selection by unmanned aerial vehicle (UAV) in space air ground integrated networks (SAGINs) will be investigated. The primary objective is to maximize the UAV's achievable data rate while ensuring a prolonged remaining visible time for the selected LEO-Sat. This problem poses significant challenges due to the highly dynamic nature caused by the substantial relative velocity between UAV and LEO-Sats, making LEO-Sats' features, e.g., elevation angle, remaining visible time, and the available bandwidth strongly time-dependent. Moreover, some LEO-Sats will appear/disappear from UAV visibility within short time intervals. An online learning approach will be proposed to efficiently address this highly dynamic problem, where it will be modeled as contextual multi-armed bandit (MAB) with dynamic arms. Through this modeling, the time varying LEO-Sats' features as well as their dynamic appearance/disappearance will be taken into account while selecting the best LEO-Sat. Through extensive numerical analysis, the performance of the proposed approach nearly matches that of the highly complex optimal performance based on exhaustive searching with negligible time complexity, and it outperforms conventional schemes that individually maximize one of the LEO-Sats' features. [ABSTRACT FROM AUTHOR]

To tackle the challenges posed by the insensitivity of current multi-scale networks to image detailed information and their limited capacity to model contextual relationships, our paper proposes a novel semantic segmentation network called LEFNet. It is based on Parallel Feature Enhancement (PFE) and Adaptive Weighted Feature Fusion (AWFF). In the coding stage, the PFE module performs posterior enhancement of multi-scale features by Detail Sharpening Attention (DSA) and High-level Dilation Fusion (HDF) methods. The DSA guides the learning of detailed information in low-level features. The HDF broadens the perceptual field, enabling the acquisition of richer high-level features. In the decoding stage, the AWFF module supersedes the conventional feature fusion methods. The AWFF module constructs perceptual factors for each multi-scale feature map, enabling weighted learning of features. It emphasizes features with stronger semantic information, making them more decisive in pixel classification. It integrates features more reasonably based on the relevance of global contextual information, fully releasing the expressive potential of encoded features. Our method achieves mIoU scores of 82.8%, 49.3%, and 45.4% on the Cityscapes, ADE 20 K, and COCO-Stuff 164 K datasets, respectively, reaching an advanced level on popular benchmarks. The experimental results show that LEFNet alleviates the challenge of insensitivity of multi-scale networks to image detailed information, improves its ability to model contextual relationships, and significantly improves segmentation performance. [ABSTRACT FROM AUTHOR]

Large-apertured wearable and deformable microphone arrays have shown their better performance than conventional small wearable and rigid microphone arrays for speech extraction tasks, especially when the desired speech is far away from the arrays or there exist a great number of directional noises in practical applications. Most existing beamforming methods that require the information of the desired sound direction often cannot be used directly on deformable arrays because their shape might continuously change over time. To tackle the problem of utilizing partially deformable microphone arrays for target speech extraction, this paper derives a frame-wise speech extraction method, which is referred to as the recursive multichannel Wiener filter with generalized Gamma priors. In this method, the complex spectrum of the desired speech is assumed to follow a zero-mean complex Gaussian distribution with time-varying variance, and the reciprocal of this time-varying variance known as the precision is further assumed to follow a Gamma distribution, while the complex spectrum of the noise is assumed to follow a zero-mean complex Gaussian distribution. Under these two assumptions, we derive the recursive expectation maximization algorithm to extract the desired speech in a frame-by-frame manner. Experimental results show the promising performance of the proposed method in terms of multiple objective metrics. [ABSTRACT FROM AUTHOR]

NOMA allows for the simultaneous transmission of multiple user signals and full-duplex (FD) communication enables concurrent transmit-and-receive operations, both improving spectral efficiency. Additionally, NOMA networks utilize energy harvesting (EH) to efficiently harvest energy from radio frequency sources, contributing to improved energy efficiency. In addition, deploying multiple antennas at NOMA users facilitates energy transfer and harvesting, as well as reliable information transmission through antenna array processing. Moreover, jamming is presented as a useful method to secure wireless transmission. Therefore, multiple-input multiple-output (MIMO) NOMA transmission with EH-aided FD jammer (MMnOehFD) is envisioned to acquire high performance like energy efficiency, security, reliability, and throughput. The paper numerically assesses the reliability and security of MMnOehFD where evaluation metrics include energy efficiency, throughput, and outage probability. Further, practical factors such as erroneous channel information (ECI) and multi-antenna users are taken into account in the evaluation. Results indicate that the ECI significantly degrades system performance, and this degradation is influenced by multiple parameters. Also, the proposed MMnOehFD can avoid complete outage and attain optimal performance with appropriate system configuration. The performance of the MMnOehFD is notably improved with an increasing number of antennas. Further, the MMnOehFD outperforms two baseline schemes (MIMO OMA transmission with EH-aided FD jammer and MIMO NOMA transmission without jammer), highlighting the effectiveness of combining jammer and NOMA. [ABSTRACT FROM AUTHOR]

Due to providing greater degrees-of-freedom (DOFs) and securer communication guarantees than traditional scalar array, polarized massive multiple-input multiple-output (MIMO) technique offers a prospective insight into millimeter-wave (mmWave) communication. In this paper, a compressed two-dimensional (2D) direction-of-arrival (DOA) and polarization estimation algorithm combining the compressive sampling propagator method with reduced-dimension multiple signal classification (CSPM-RDMUSIC) is developed for mmWave polarized massive MIMO systems. First, a synchronous compressive network is constructed to compress the high-dimensional output into a low-dimensional one. Thereafter, a propagator is structured to acquire the signal subspace. Subsequently, accurate 2D-DOA and polarization estimation are achieved using a coarse-refined strategy, where the vector cross-product technology is investigated for coarse 2D-DOA estimation and then RDMUSIC is exploited to perform 2D local search for accurate parameter estimation. Owing to integrating the CSPM framework with the RDMUSIC strategy, the developed approach provides high-precision parameter estimation with relative computational economy, supporting mmWave communication demands. What's more, it's suitable for arbitrary array geometry with high flexibility. Several experimental results validate the superiorities of the developed framework. • A synchronous compressive network is combined with propagator to compress high-dimensional data and avoid matrix decomposition. • A coarse-refined strategy using rotational invariance and RDMUSIC is explored for accurate 2D-DOA and polarization estimation. • The developed approach provides high-precision parameter estimation with relative computational economy. • The developed method is suitable for arbitrary geometry with high flexibility, supporting practical engineering applications. [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]