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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 investigates Filter Bank MultiCarrier (FBMC) modulation implemented with frequency spreading and polyphase network-based in terms of the introduced quantization noise. As FBMC is considered one of the future candidates for 5G/6G communication systems due to its advantageous spectral properties, the introduced quantization noise in the implementation is an essential design criterion. Analytical expressions for fixed- and floating-point Quantization Noise Power (QNP) in FBMC transmitter schemes are given. Based on the results, it can be stated that the total QNP depends on the number of carriers, overlapping symbols, and the resolution of the quantizer. The results are verified through simulations. Estimating the quantization noise in FBMC systems in the function of the selected bit resolution and keeping it at an acceptable level is an essential design step. The results can be directly employed in the preliminary hardware design of FBMC transmitters, where the choice of the arithmetical units and the bit resolution is a key factor. • Different FBMC transmitter architectures are compared in terms of quantization noise. • Analytical formulas for the QNP at the output of FBMC transmitters are derived. • The effect of the Quantization noise on the PSD of the FBMC signal is analyzed. [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]

Voice activity detection (VAD) is essential for multiple microphone arrays processing, in which massive potential devices, such as microphone devices for far-field voice-based interaction in smart home environments, will be activated when sound sources appear. Therefore, the VAD can save a lot of computing resources in massive microphone arrays processing for the sparsity in sound source activity. However, it may not be feasible to obtain an accurate VAD in harsh environments, such as far-field, time-varying noise field. In this paper, the long-term speech information (LTSI) and the log-energy are modeled for deriving a more accurate VAD. First, the LTSI can be obtained by measuring the differential entropy of long-term smoothed noisy signal spectrum. Then, the LTSI is used to get labeled data for the initialization of a Gaussian mixture model (GMM), which is used to fit the log-energy distribution of noise and (noisy) speech. Finally, combining the LTSI and the GMM parameters of noise and speech distribution, this paper derives an adaptive threshold, which represents a reasonable boundary between noise and speech. Experimental results show that our VAD method has a remarkable improvement for a massive microphone network. [ABSTRACT FROM AUTHOR]

This paper presents a novel closed-form analytical expression for Riesz fractional order derivative in the Fractional Fourier domain. The expression is obtained in the terms of higher transcendental functions such as Parabolic Cylinder Function as well as Confluent Hypergeometric Function. The presented work is analyzed in the discrete domain by using the properties of Discrete Fractional Fourier Transform (DFrFT). The proposed algorithm is capable of preserving the texture and edge information without any phase distortion. The design example discussed in the paper shows the efficacy of the proposed algorithm for a signal with high frequency chirp noise. The design flexibility of the proposed approach is confirmed due to the fact that it provides an optimal value of performance metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) corresponding to the variation of the fractional order of Riesz derivative and fractional parameter in the rotation angle of Fractional Fourier Transform (FrFT). The proposed algorithm provides better results in terms of minimum RMSE of 0.115136 and MAE of 0.094223 for the optimal fractional order of 0.43 at a rotation angle of 0.45 π. [ABSTRACT FROM AUTHOR]

Sensor position and velocity uncertainties are known to be able to degrade the source localization accuracy significantly. This paper focuses on the problem of locating multiple disjoint sources using time differences of arrival (TDOAs) and frequency differences of arrival (FDOAs) in the presence of sensor position and velocity errors. First, the explicit Cramér–Rao bound (CRB) expression for joint estimation of source and sensor positions and velocities is derived under the Gaussian noise assumption. Subsequently, we compare the localization accuracy when multiple-source positions and velocities are determined jointly and individually based on the obtained CRB results. The performance gain resulted from multiple-target cooperative positioning is also quantified using the orthogonal projection matrix. Next, the paper proposes a new estimator that formulates the localization problem as a quadratic programming with some indefinite quadratic equality constraints. Due to the non-convex nature of the optimization problem, an iterative constrained weighted least squares (ICWLS) method is developed based on matrix QR decomposition, which can be achieved through some simple and efficient numerical algorithms. The newly proposed iterative method uses a set of linear equality constraints instead of the quadratic constraints to produce a closed-form solution in each iteration. Theoretical analysis demonstrates that the proposed method, if converges, can provide the optimal solution of the formulated non-convex minimization problem. Moreover, its estimation mean-square-error (MSE) is able to reach the corresponding CRB under moderate noise level. Simulations are included to corroborate and support the theoretical development in this paper. [ABSTRACT FROM AUTHOR]

The problem of detecting a subspace signal embedded in subspace Gaussian interference and thermal noise is studied in this paper. In this problem, both the signal-independent and signal-dependent interferences are assumed to be present, therefore the overall interference subspace covers the signal subspace. The approach of this paper extends previous works involving either of those two kinds of interferences. A set of secondary data containing only interference plus noise is employed to estimate the interference covariance matrix and the noise power. Three new detectors are designed via the generalized likelihood ratio (GLR), Rao and Wald tests, respectively. Their probabilities of false alarms (PFAs) and detections are analytically derived. The PFAs show that the new detectors have the constant false alarm rate (CFAR) property against the interference and noise. Numerical results show that the new detectors outperform their counterparts for the studied problem. Furthermore, the new detectors are less sensitive to the secondary data size and to the mismatched subspace signal than some other detectors, such as the GLR detector (GLRD), the adaptive matched filter (AMF), the adaptive subspace detector (ASD), etc. [ABSTRACT FROM AUTHOR]

• A new method is proposed to dynamic texture analysis. • The video is modeled into directed networks. • The dynamic texture is characterized by the diffusion over the networks. • The experiments on three dynamic texture datasets validate its effectiveness. Dynamic texture is a field of research that has gained considerable interest from computer vision community due to the explosive growth of multimedia databases. In addition, dynamic texture is present in a wide range of videos, which makes it very important in expert systems based on videos such as medical systems, traffic monitoring systems, forest fire detection system, among others. In this paper, a new method for dynamic texture characterization based on diffusion in directed networks is proposed. The dynamic texture is modeled as a directed network. The method consists in the analysis of the dynamic of this network after a series of graph cut transformations based on the edge weights. For each network transformation, the activity for each vertex is estimated. The activity is the relative frequency that one vertex is visited by random walks in balance. Then, texture descriptor is constructed by concatenating the activity histograms. The main contributions of this paper are the use of directed network modeling and diffusion in network to dynamic texture characterization. These tend to provide better performance in dynamic textures classification. Experiments with rotation and interference of the motion pattern were conducted in order to demonstrate the robustness of the method. The proposed approach is compared to other dynamic texture methods on two very well known dynamic texture database and on traffic condition classification, and outperform in most of the cases. [ABSTRACT FROM AUTHOR]

To solve direction-finding problems for nested arrays under conditions of impulse noise and gain–phase errors, this paper proposes a direction-of-arrival (DOA) estimation method. First, a correntropy with an adaptive kernel length combined with an infinite norm of a variable weight value is proposed to suppress impulse noise. Then, the auxiliary array elements are used to realize the initial calibration of the amplitude and phase errors. On this basis, a DOA estimation method without a spectral peak search is proposed; this method is robust for arrays that still have residual gain–phase errors after calibration. Our method overcomes the difficulties of DOA estimation for nested arrays under conditions of gain–phase errors and impulse noise. Compared with traditional methods, the proposed method does not require prior knowledge, does not require a spectral peak search and has high superiority and robustness; a series of experiments have verified its superiority. In addition, a Cramér‒Rao bound (CRB) for angle estimation under conditions of gain–phase errors and impulse noise is proposed in this paper. [ABSTRACT FROM AUTHOR]

The paper presents a predictive filter based frequency estimation algorithm (PF-FE) for multiple complex sinusoids corrupted by additive white Gaussian noise. The proposed frequency estimator combines the noise suppression capability of an FIR filter with a least squares approach minimizing the sum of the squared errors of a prediction filter. The FIR filter coefficients are chosen such that the sinusoidal components are predicted with minimum distortion. Computer simulation results are given to contrast the performance of the proposed PF-FE method with six other state-of-the-art frequency-estimators and the Cramer-Rao lower bound. Furthermore, the paper provides a complexity analysis of the PF-FE method and compares it with those of the conventional MUltiple SIgnal Classification algorithm and some selected state-of-the-art methods from the literature. Performance of the proposed PF-FE method is validated with effective mean square error (EMSE) plots for model orders of three and five and under different observation lengths and signal-to-noise ratio values. Finally, the paper demonstrates how EMSE varies for the proposed method when the prediction filter length is increased from 20 to 75. After extensive simulations, it is observed that when filter length is larger than half the observation length the EMSE will start to degrade. [ABSTRACT FROM AUTHOR]

Recent vision Transformer has been applied to human pose estimation and has achieved excellent performance by two-order spatial interaction with self-attention. However, it is still unclear whether higher-order spatial interaction can facilitate pose estimation. In this paper, we propose a novel approach based on multi-order spatial interactions and confirm that the combination of different orders is beneficial for human pose estimation task. We first build a Triple Interaction Module (TIM) by pure convolutions to make spatial information interactions three times. In contrast to Transformer, the TIM is compatible with several pure convolutions and extends two-order interaction in Transformer to triple-order without extensive additional computation, which makes it easier to explore inter-related features between keypoints in the human body. In addition, we combine TIM with traditional CNN and Transformer to form Multi-order Spatial Interaction Network (MSIN). This paper takes advantage of MSIN to extract keypoint heatmaps and certifies that the order-by-order structure can enhance the overall performance of locating human keypoints. Experimental results demonstrate that MSIN performs favorably against the most state-of-the-art CNN-based and Transformer-based counterparts on the COCO and MPII datasets, while being more lightweight. • The Triple Interaction Module can be obtained by pure convolutions to make spatial information interactions three times. • Combining the Triple Interaction Module with traditional CNN and Transformer can improve the performance of locating keypoints. • The Triple Interaction Module can effectively better to explore inter-related features between keypoints in the human body. • The order-by-order structure can enhance the overall performance of locating human keypoints. [ABSTRACT FROM AUTHOR]

In Kalman filter-based spacecraft autonomous celestial navigation, Inertial/Celestial integrated navigation system and asynchronous multi-sensor data fusion, complex environments and sensor alignment errors are likely to lead to inaccurate statistical priors for the noise covariance matrices and the non-zero measurement noise mean vector (MNMV). To address this issue, this paper firstly proposes a Multi-Normal-Inverse Wishart (MNIW) mixture distribution modelling the joint probability density function (PDF) for one-step prediction and measure likelihood, then the MNIW mixture distribution is decomposed into a Gaussian hierarchy, and finally the posterior estimates of the state and variables are obtained using variational Bayesian technique. In this paper, a Multi-Normal-Inverse Wishart mixture distribution-based variational Bayesian extended Kalman filter (VB-EKF) is proposed, in which a first-order Taylor expansion is used to address the non-linear problem. The proposed filter can be used to address non-linear filtering problem with inaccurate noise covariance matrices and measurement bias. Simulations of spacecraft autonomous celestial navigation validate the effectiveness and superiority of the proposed filter. [ABSTRACT FROM AUTHOR]

Target area search in the standard Bayesian information-theoretic formulation consists of a repetitive cycle of sensing, recursive Bayesian estimation and motion control. The paper formulates the problem of target area search in the framework of imprecise probability theory using probability sets. The rationale is that the measurement model parameters, such as the probability of detection or the probability of false alarm, are rarely known as precise values. Instead, we adopt probability intervals to express beliefs. The paper formulates the Bayes-like state estimation equations as well as the reward function for motion control. The reward function is based on an uncertainty measure which takes into account both randomness and epistemic uncertainty. [ABSTRACT FROM AUTHOR]

An online dictionary learning algorithm for kernel sparse representation is developed in the current paper. In this framework, the input signal nonlinearly mapped into the feature space is sparsely represented based on a virtual dictionary in the same space. At any instant, the dictionary is updated in two steps. In the first step, the input signal samples are sparsely represented in the feature space, using the dictionary that has been updated based on the previous data. In the second step, the dictionary is updated. In this paper, a novel recursive dictionary update algorithm is derived, based on the recursive least squares (RLS) approach. This algorithm gradually updates the dictionary, upon receiving one or a mini-batch of training samples. An efficient implementation of the algorithm is also formulated. Experimental results over four datasets in different fields show the superior performance of the proposed algorithm in comparison with its counterparts. In particular, the classification accuracy obtained by the dictionaries trained using the proposed algorithm gradually approaches that of the dictionaries trained in batch mode. Moreover, in spite of lower computational complexity, the proposed algorithm overdoes all existing online kernel dictionary learning algorithms. [ABSTRACT FROM AUTHOR]

The adoption of three-dimensional (3D) measurement technology for parts surface defect detection can improve inspection reliability. For online inspection purposes, 3D measurement technologies must possess the characteristics of high speed and high efficiency. The photometric stereo method is a potential 3D measurement method with high speed and low cost. However, the traditional photometric stereo method is unsuitable for dynamic scenes due to its initial design for static scenes. In this paper, we propose a novel dynamic photometric stereo method based on an image registration method. To achieve fast speed and high efficiency, we reduce the computational cost by automatically generating regions of interest (ROI). Additionally, we innovatively map the depth information (the surface normal vectors) to a mean curvature map of the surface and use it to detect defects, which combines the robustness of 3D methods and the fast speed of 2D methods. We designed experiments and the results showed that our method can detect defects on the surfaces of aluminum alloy castings accurately and robustly in an online manner. This paper also aims to reveal the importance of utilizing multidimensional information in high-speed online inspections. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) imaging is generally characterized by a large amount of data and a high sampling rate. The traditional one-bit compressed sensing will generate virtual targets when recovering images at a low SNR, resulting in low reconstruction accuracy and obvious noise impact of the algorithm. In this paper, a one-bit SAR imaging algorithm based on the Minimax Concave (MC) penalty function and the Total Variation (TV) norm is proposed, which can potentially improve the reconstruction accuracy. At the same time, to solve the problem of speckle noise generated in the imaging process, this paper proposes a Fast Low-rank Sparse Decomposition (FLRSD) algorithm based on the one-bit contaminated image, which potentially improves the anti-noise performance and reconstruction efficiency. The results of simulation and measured data show that the proposed algorithms have better reconstruction accuracy and focusing performance than other algorithms. Even at low SNR, they also have good reconstruction effect. [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]