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

Retinal vessel segmentation is a crucial focus within the realm of medical image analysis, playing a pivotal role in early disease diagnosis, notably retinopathy. Deep learning has exhibited remarkable segmentation capabilities for retinal blood vessels, leveraging the advantages of contextual feature learning. However, there are still some shortcomings in fine retinal vessel segmentation due to the loss of semantic information due to too many pooling operations or limited receptive fields due to fewer pooling operations. In response to the nuanced balance required for expanding the receptive field while preserving information integrity during multiple downsampling operations, this paper introduces DCNet (Dilated Convolution Net), a novel lightweight three-layer dilated-convolution-based network tailored for retinal blood vessel segmentation. This three-layer architecture autonomously extracts crucial segmentation features from various levels of the feature map. Each layer comprises a dilated convolution Positive Sequence Block (PSB) and a dilated convolution Reverse Sequence Block (RSB). The dilated convolution operation is strategically exploited for its capacity to extend the receptive field, facilitating effective feature information extraction. Simultaneously, to alleviate semantic information loss within the deep network's feature map, this paper proposes the Nonlinear Feature Extraction Module (NFEM) to supplement shallow network feature information. Furthermore, to comprehensively leverage information from various scale features, a Feature Fusion Module (FFM) is introduced for multiscale vascular feature extraction, ultimately enhancing segmentation accuracy. DCNet undergoes rigorous evaluation on four publicly accessible retinal vascular datasets – DRIVE, STARE, CHASE_DB1, and HRF. Experimental results unequivocally demonstrate that DCNet achieves superior segmentation performance with fewer model parameters compared to existing state-of-the-art methods. The code for DCNet can be accessed on the following website: https://github.com/ChunhuiYu1/DCNet. [ABSTRACT FROM AUTHOR]

In a companion paper [32] , a sparse array called the dilated nested array (DNA) was introduced, which owns the same number of uniform degrees of freedom (DOFs) as the nested array but has the first and third constituent sub-arrays dense, since their sensors are uniformly spaced out by the critical inter-sensor spacing (2 × λ / 2). Therefore, in this paper we introduce two further dilations to address this shortcoming. In the first dilation - referred to as the one-sided dilated nested array (OS-DNA), Q f possible high-order extensions starting from the 2nd-order extension (Q = 2) to the (Q f + 1) th-order extension are applied to the third parent sub-array, and in the second dilation - referred to as the two-sided dilated nested array (TS-DNA), an additional extension is applied to the first parent sub-array. While the high-order extensions of the OS-DNA as well as the TS-DNA strictly preserve the same number of uniform DOFs of the parent DNA, the first extension of the OS-DNA eliminates all the sensor pairs with separation 2 in the third parent sub-array, whereas the Q th-order extensions (for 2 < Q ≤ Q f + 1) increase the number of nonuniform DOFs as Q increases. The TS-DNA then eliminates all the sensor pairs with spacing 2 in the first sub-array of the (Q f + 1) th-order extension and concurrently maintains the higher number of nonuniform DOFs of this parent order. As such, the TS-DNA, named the super dilated nested array (SDNA) as well, still retains the number of uniform and nonuniform DOFs of the highest-order extension of the OS-DNA and at the same time enjoys the ideal critical weights of the co-prime array. Many theoretical properties are proved and extensive simulations are included to demonstrate the superior performance of these arrays. [ABSTRACT FROM AUTHOR]

Interference mitigation has been a vital area of focus within the realm of automotive radar research, holding paramount importance for augmenting advanced driver-assistance capabilities, particularly in vehicle-dense scenarios. This paper presents a novel interference mitigation scheme based on continuous wavelet transform and morphological component analysis (CWT-MCA). This method, in essence, transforms the time-domain signal into the time-frequency domain, subsequently enabling the direct separation of target and interference components, thereby circumventing the necessity for interference detection and identification. Additionally, this paper introduces a series of MCA algorithms grounded in various transform domains, which are subsequently compared with the traditional linear prediction based approach. We show through comprehensive numerical simulations and real-data experiments that CWT-MCA algorithm exhibits markedly superior interference mitigation performance while offering lower computational complexity, which makes it well-suited for real-time implementation in automotive radar systems. • Continuous wavelet transform and morphological component analysis are applied to mitigate interference for automotive radars. • Fast algorithm is proposed to accelerate computation. • Numerical studies and real-data experiments are conducted to validate the algorithm performance. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RISs) are characterized by low cost and strong survivability and can change the electromagnetic characteristics of signals. Based on these characteristics, this paper deploys an RIS near a monostatic radar. The radar can receive direct signals from the target (jammer) and reflected signals from the RIS, thereby achieving multiview monostatic radar anti-main lobe suppression interference. This paper first establishes an RIS-aided radar echo signal model and then systematically analyzes the ability of the RIS-aided radar to resist main lobe suppression interference. Subsequently, the phase shift vector of the 1-bit encoded RIS is obtained using a genetic algorithm to achieve beam control. The simulation results showed that introducing an RIS to assist monostatic radar can effectively improve the anti-main lobe suppression interference ability of monostatic radar at a relatively low cost. [ABSTRACT FROM AUTHOR]

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

This paper addresses the joint estimation problem of directions of arrival of sources and towed array shape. Ocean currents and the maneuvering of the towing platform often cause the towed array to bend rather than stay in a straight line, so the array shape must be estimated to avoid performance degradation due to array mismatch. Existing joint estimation methods suffer from high computational complexity or the need for external sensors installed on the array. Therefore, this paper proposes a fast joint estimation algorithm that solely utilizes received acoustic data. The joint estimation is transformed into a sparse reconstruction optimization problem within the Bayesian estimation framework. Firstly, by decomposing the marginal likelihood function, a closed-form solution for the signal variance in the spatial domain is obtained. Then, the towed array shape is introduced as a hyperparameter into the marginal likelihood function and optimized using the quasi-Newton method. In this way, we accomplish joint estimation and reduce computational complexity. Multi-source simulation results show that the proposed method achieves high resolution and fast computational speed. The robustness and efficiency of the proposed joint estimation method are demonstrated by experimental results in the South China Sea. [ABSTRACT FROM AUTHOR]

• The generalized approach for design of the harmonic analyzers and filter banks is given. • The primary part is the resonator filter structure with cascaded resonators. • Frequency responses of the harmonic filters are reshaped by preprocessing or postprocessing filters. • Filter coefficients of resulting IIR filters are designed through the linear optimization techniques. • Optimization of the frequency response of one harmonic ensures the same shapes for all harmonic filters. The cascaded-resonator (CR)-based filter structure provides high attenuation in the stopbands thanks to the serially coupled resonators, which is an advantage in many applications of harmonic analyses and/or filtering. In addition to that, it has good properties provided by its parallel structure. On the other side, there are applications which besides the high stopband attenuation need wider (or even flat-top) passbands, lower group delays, etc. These performances can be modified/improved by preprocessing and postprocessing through reshaping of the primary CR-based filter bank frequency responses. The numerical complexity is also an important aspect where infinite impulse response (IIR) filters provide implementation with a smaller number of numerical operations, but involving nonlinearity of the phase responses and stability control issue. Although most of these issues have been discussed in the previous papers dealing with the design of the CR-based harmonic filtering and/or filter banks, the aim of this paper is to highlight them all together. In addition, some improvements of the previously described design techniques are suggested too. [Display omitted] [ABSTRACT FROM AUTHOR]

Gait recognition based on multiple wearable sensors has received widespread attention in recent years. Collecting data from various types of multimodal sensors allows for a more comprehensive capture of gait information. This effectively enhances gait recognition performance. However, the increase in the number of sensors also leads to the possibility of generating defective data during the process of data collection and processing, particularly data redundancy and data anomaly. Traditional solutions often involve complex data analysis and selection, which may result in important information being filtered out and high computational demands. To address these issues, this paper proposes a new method that combines the powerful feature extraction capabilities of convolutional neural networks. To address the issue of data redundancy, the Channel Multiscale-aware (CMSA) Module is designed. It extracts information with different receptive fields from different channels of the same feature map to directly reduce similarities in information. To address the issue of data anomaly, the Feature Combination based on Local Channel Attention (FCLCA) Module is designed to select channel segments least affected by data anomaly from the feature map. Attention mechanisms are incorporated into both the CMSA and FCLCA modules to facilitate the model in adapting and learning more crucial features. Additionally, improvements are made to their pooling structures. After numerous experiments, it was demonstrated that our model achieved superior performance in gait recognition tasks. • A novel deep learning method is proposed to address the issue of defective data arising from the use of multiple sensors. • To address data redundancy and anomaly in defective data, CMSA and FCLCA modules are proposed respectively. • The CMSA and FCLCA modules use attention mechanisms with improved pooling structures to adaptively learn crucial gait features. • Extensive experiments demonstrate the superior performance of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

In the point cloud segmentation tasks, the existing local feature aggregation methods in the up-sampling and down-sampling stages still rely on Euclidean distance to constrain the local aggregation process. However, this approach is susceptible to the influence of abnormal points leading to inaccuracies in fitting the original geometric shape of the point cloud. Therefore, this paper proposes a local gradient aggregation module, which incorporates gradient information of neighboring points during the aggregation process. This enables the model to capture fine-grained geometric information and extract richer local features. Additionally, we introduce a symmetric sampling strategy to improve computational efficiency. The same original mapping indices were used for both up-sampling and down-sampling aggregation. Thus, a large number of additional k-nearest neighbor calculations are avoided. Furthermore, this paper introduces a position-aware encoding in the attention mechanism to address the positional cues for short-term and long-term contexts, facilitating positional-aware communication between points. Numerous comparative experiments prove the effectiveness of the method in this letter. It obtained 72.1% mIoU on ScannetV2, and 72.4% mIoU on S3DIS. [ABSTRACT FROM AUTHOR]

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]

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]

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 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]

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]

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]

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]

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]

RGB-D food nutrition assessment entails the direct prediction of nutritional content in pairs of RGB and depth food images using signal processing technology. However, existing methods present numerous challenges related to accuracy and computational complexity. In this paper, we introduce the Ingredient-guided Multi-modal Interaction and Refinement Network (IMIR-Net), a novel framework for RGB-D food nutrition assessment. We first utilize a visual grounding model to accurately identify the dish region containing food in the input image with complex semantics. The identified RGB-D dish regions are processed through a multi-modal interaction and refinement module to compute the fused embedding. Concurrently, we introduce an ingredient guidance module to effectively capture ingredient textual information, thereby facilitating the acquisition of the enhanced fused embedding. Finally, we design a decoupled nutrition predictor to predict nutrition from the fused RGB-D embedding. The IMIR-Net can capture the rich multi-modal semantics for RGB-D food nutrition assessment and achieves state-of-the-art performances on the Nutrition5k dataset. Specifically, the percentage of mean absolute error (PMAE) of Calories, Mass, Fat, Carb and Protein reached 14.5%, 10.4%, 21.8%, 20.4% and 20.0%, respectively. Compared with the existing best method, our IMIR-Net demonstrated superior prediction accuracy, with a mean PMAE value of 17.4%, an improvement of 1.1%. In terms of computational complexity, the floating-point operations (FLOPs) and parameters reached 63.87 and 105.11 via our method, which is improved by 5.2% and 7.4%, respectively. The codes, data, and model of our method are publicly available at https://github.com/nianfd/IMIR-Net-DSP. [ABSTRACT FROM AUTHOR]

This paper considers a bearings-only tracking problem with unknown measurement noise statistics. It is assumed that the measurement noise follows a Gaussian probability density function where the mean and the covariance of the noise are unknown. Here, an adaptive nonlinear filtering technique is proposed, where the joint distribution of the measurement noise mean and its covariance are considered to follow a normal inverse Wishart (NIW) distribution. Using the variational Bayesian (VB) approximation, joint distribution of the target state, the measurement noise mean and covariance is factorized as the product of their individual probability density function (pdf). Minimizing the Kullback-Leibler divergence (KLD) between the factorized and true joint pdfs, probability distributions of the noise mean, covariance and the target states are evaluated. The estimation of states with the proposed VB based method is compared with the maximum a posteriori (MAP) and the maximum likelihood estimation (MLE) based adaptive filtering. Deterministic sigma points are used to realize the filtering algorithms. The proposed adaptive filter with VB approximation is found to be more accurate compared to their corresponding MAP-MLE based counterparts. [ABSTRACT FROM AUTHOR]

In this paper, we propose an algorithm for the estimation and compensation of second-order nonlinearity in Wireless Sensor Networks (WSNs) within a distributed estimation framework. First, we investigate the impact of second-order nonlinearity on the performance of the Diffusion Least Mean Square (DLMS) algorithm and derive an upper bound for the l 2 -norm of the error caused by nonlinearity. Next, we suggest a distributed algorithm incorporating additional nonlinearity estimation and compensation units. Furthermore, considering second-order nonlinearity, we calculate the Cramer-Rao Bound for estimating both the unknown vector and the nonlinearity coefficient vector, with the Fisher information matrix derived in a closed-form formula. Simulation results demonstrate the effectiveness of the proposed algorithm in enhancing the performance of distributed estimation in the presence of nonlinear sensors in a WSN. [ABSTRACT FROM AUTHOR]

Financial market noise greatly limits portfolio optimization by concealing key patterns and resulting in inaccurate asset selection. In this paper, we propose a novel approach that leverages dictionary learning, specifically the modified K-SVD algorithm, for denoising financial time series in the context of multivariate portfolio selection. Our method, which considers highly correlated short datasets, trains the dictionary simultaneously for multiple assets, resulting in more robust and adaptive denoising. We next use a vector auto-regressive process on the denoised data to estimate covariance matrices and build optimal portfolios using the minimum variance approach. Extensive computer simulations are conducted to assess the impact of our denoising method on portfolio performance in terms of several metrics, such as cumulative returns, Sharpe ratio, and model accuracy. The findings indicate how dictionary learning can improve the robustness of investment portfolios in the face of market noise and volatility. [ABSTRACT FROM AUTHOR]

Dates sealed by embossed stamps (stamped dates) on pill packaging boxes are crucial for people to notice the expiration dates. Thus, manufacturers must ensure the accuracy of stamped dates on the pill packaging boxes. However, the low contrast between the stamped dates and the surface of the container and the lack of clear character outlines often lead to light interference, which hampers the stamped dates detections by various deep learning models. This article presents a method for detecting stamped characters that combines YOLO-Shallow Feature Detection (YOLO-SFD) and image fusion. YOLO-SFD enhances YOLOv8 by introducing a Shallow Feedback Feature Pyramid Network (SF-FPN) that includes shallow feature paths and feedback paths for the original input. This enables the model to pay more attention to the shallow features, which include a more detailed character contour. Secondly, this paper presents a lightweight convolutional structure to enhance the model's portability without sacrificing accuracy. Experimental results indicate that SF-FPN outperforms the original feature pyramid network in terms of accuracy and model size. On the stamped date dataset, YOLO-SFD achieves an average accuracy of 99.02 %, 2.57 % higher than the original model, with 29 % fewer parameters. In addition, a 1.5 % mAP improvement is achieved on the Street View House Numbers Dataset. This shows that this method can better fulfill the requirements of pill packaging box stamped date detection and provides new solutions and ideas for related character detection or low contrast image detection. [ABSTRACT FROM AUTHOR]

In Massive multiple-input multiple-output (MIMO) systems, channel estimation is crucial. The large size of the antennas causes a significant pilot and feedback overhead, making it challenging to estimate channels in massive MIMO systems. Besides, the studies have shown that mmWave channels are sparse due to the limited number of dominant propagation paths. Therefore, the motivation of this paper is to exploit the inherent sparsity of the massive mmWave MIMO channels to develop a semi-blind channel estimation with reduced number of pilots. To this goal, an expectation maximization (EM) based technique has been developed which leverages the sparsity of the underlying channel for its better estimation. An iterative approach is proposed to solve the modeled problem which simultaneously updates the channel coefficients and data symbols using available data and system structure at each iteration. The proposed method imposes sparsity with the aid of Smoothed L 0 norm (SL0) in the M-step. The simulation results demonstrate the proposed method have quick convergence and lower channel estimation error compared to the existing methods. As a quantitative evaluation, the proposed method attains the normalized mean square error of 9 × 10 − 2 and 7 × 10 − 3 at SNR = 5 dB and SNR = 15 dB , respectively. [ABSTRACT FROM AUTHOR]

Road cracks pose a persistent challenge in road maintenance, with timely detection and repair crucial for enhancing road safety. However, determining which cracks require repair can be difficult, necessitating a quantitative analysis approach. This paper proposes a deep learning-based method, Multiscale Feature Fusion and Superimposed U-Net (MPSU-Net), for precisely this purpose. The method employs MPSU segmentation, which quantifies crack interpretation by analyzing the black and white pixels in binary images. Within the segmentation algorithm, Attention Connection is introduced to fuse features across different layers, while the PSU Block amalgamates feature information within the same layer, incorporating ASPP, CBAM, and the superimposed U-Net. The superimposed U-Net is designed to enhance PSU's feature extraction capabilities. Furthermore, a new Conv block is introduced to bolster feature extraction by replacing all convolutions in the superimposed U-Net and decoder. To address imbalanced positive and negative crack samples, we adopt the Jaccard loss metric based on experimental results. We enhance dataset diversity by leveraging data augmentation and amalgamating data from multiple datasets, resulting in the comprehensive Crack500 and Crack datasets. Experimental findings showcase the significant efficacy of MPSU-Net in enhancing F1-score and Recall metrics. On the Crack500 dataset, MPSU-Net achieves F1-scores and Recall rates of 85.32% and 78.68%, respectively, representing notable improvements of 6.19% and 6.43% over the U-Net baseline performance. Similarly, on the Crack dataset, MPSU-Net attains F1-scores and Recall rates of 74.54% and 46.66%, marking enhancements of 8.25% and 8.64% over the U-Net baseline. These results underscore the high performance of MPSU-Net and its potential to aid in quantitative road crack analysis. [ABSTRACT FROM AUTHOR]

The utilization of constant modulus beamforming technology is prevalent in multiple-input multiple-output (MIMO) radar systems and MIMO wireless communication systems. This paper investigates the problem of phase-only pattern control and synthesis in linear antenna array systems. It imposes a fixed amplitude constraint on the excitation signal, which only involves a phase constraint. However, this constraint is NP-hard and non-convex. To effectively address this challenging problem, we propose an iterative solution based on the scaled form of ADMM. This solution provides a means to rapidly synthesize patterns and significantly contributes to mitigating interference in other directions. In addition, the convergence of the proposed method is proved theoretically, and it is further demonstrated that the algorithm converges to the KKT stationary point of the original problem. Finally, simulation results show that the proposed method is superior to the most advanced methods in terms of pattern synthesis and convergence speed. [ABSTRACT FROM AUTHOR]

With the fast development of jamming technology, strong coherent jamming based on digital radio frequency memory (DRFM) technology poses serious threats to radar detection capabilities due to their flexibility and deception characteristics. However, the extremely high isolation between transmitter and receiver is difficult to implement in practice, and jammers typically operate in a transmit/receive time-sharing mode. Consequently, the duty cycle of jamming signal received by radar is less than 100% in the time domain, enabling the radar systems to detect targets amidst jamming. To generate a full duty cycle jamming signal, a new oversampling-based full-duplex DRFM jamming generation method is proposed in the transmit/receive time-sharing mode. This method requires that the intercepting width of jammer must be less than the reciprocal of value which is the product of forwarding times incremented by one and the radar signal bandwidth. Therefore, the jammer first intercepts extremely short segments from the radar signal using oversampling, with lengths measured in nanoseconds. Then, the jammer rapidly re-modulates and re-transmits the intercepted signal segments within the corresponding interception period. The jamming signal is filtered by the radar receiver. Then, the spectrum of the signal only contains the complete fundamental component, and the pulse width of the signal matches that of the transmitted signal, achieving a duty cycle of 100% and realizing the full-duplex jamming effect. In addition, the frequency modulation technique is utilized to generate dense false targets corresponding to the transmitted signal, thereby improving jamming efficiency. This paper elucidates the generation mechanism and ramifications of the jamming signal through rigorous signal analysis, delineating the relationship between intercepting length and full-duplex radar jamming generation across various DRFM jamming styles. Finally, the efficacy of full-duplex jamming is substantiated through theoretical analysis and simulation. • The derivation of boundary conditions for generating full-duplex jamming is proposed based on DRFM jammers. • There is very little distinction between full-duplex jamming and the radar signal in both the time and frequency domains. • By employing frequency modulation, full-duplex jamming can generate 'false' targets at any position around the actual target. [ABSTRACT FROM AUTHOR]

The contribution of this paper is two-fold. Firstly, the well-known two-point Interpolated Discrete Fourier Transform (IpDFT) algorithm based on a Maximum Sidelobe Decay (MSD) window is generalized to the estimation of the frequency and the damping factor of a real-valued damped sinusoids. Analytical expressions for the detrimental contributions on the obtained estimators due to the image frequency component and wideband noise are then derived and their accuracies are verified through computer simulations. Secondly, leveraging on the derived expression, an improved version of the IpDFT algorithm is proposed which ensure more accurate estimates by compensating the contribution of the image frequency component. The performance of the improved IpDFT algorithm is compared with those provided by various state-of-the-art algorithms through meaningful computer simulations. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet technology, the copyright protection and authentication of multimedia data have attracted more and more attention. Recently, a dual watermarking framework for industrial image content authentication and tampering localization (DWFCAT) has been proposed, in which the main innovation is the double encrypted robust watermark is embedded into the transform domain by exploiting the DCT energy compression characteristics and block coefficient correlation mechanism, the fragile watermark is embedded in the spatial domain to ensure data authentication and tamper location. However, through security analysis, we found that DWFCAT has security holes, the dual encryption algorithm of the robust watermarks cannot resist KPA attacks and prevent robust watermarks from being damaged, stolen, and replaced, even when the fragile watermark is unaware. Theoretical analysis and simulation results verify the effectiveness of the method. To improve the security of the original watermarking scheme and the ability to tampering with location, and restore the robust watermark that has been tampered with, an improved scheme is also given in this paper. [ABSTRACT FROM AUTHOR]

To address the issues of poor segmentation accuracy and insensitivity to details in semantic segmentation, this paper proposes a novel image semantic segmentation framework SEFANet. Specifically, SEFANet adopts encoder-decoder structure, and incorporates a novel perceptual enhancement mechanism called Multi-scale Spatial Integration Module (MSIM) at the encoder. MSIM is based on group convolution to boost spatial semantic features and refine spatial-gradient semantic features within the multi-scale structure. This module enhances feature extraction across different network levels, leading to improved edge detection and segmentation abilities. In the decoder, SEFANet introduces a pixel-level Interleaved Feature Alignment Module (IFAM), which leverages rich semantic information in low-dimensional features and the strategy of Semantic Offset Field. Meanwhile, IFAM warps the high-dimensional feature map into low-dimensional features, completing the calibration process through convolution operations. Experimental results on the Pascal VOC2012 val dataset and the Cityscapes val dataset confirm the effectiveness and generalization of the proposed semantic segmentation. Additionally, the results further demonstrate that SEFANet improves the poor segmentation accuracy and insensitivity to details, and achieves a competitive performance compared with other semantic segmentation methods. [ABSTRACT FROM AUTHOR]

The optimization algorithm plays a decisive role in the performance of deep learning models. Nevertheless, the task, the data properties, and the model's architecture directly influence this latter's effectiveness. Hence, analyzing the effect of different optimizers on specific tasks, such as change detection, is very advantageous due to the ambiguity surrounding the method selection. This paper is twofold. First, we investigate the performance of five first-order optimization methods, namely Momentum GD, NAG, AdaGrad, RMSProp, and Adam, in the context of change detection using a U-Net and DenseU-Net architectures for remote sensing images. To the best of our knowledge, no comparative study involving optimization methods has been conducted for change detection. The results reveal that the Adam optimizer is the preferred choice to train a U-Net like architecture for this specific task. Challenges like dataset size and irregular change patterns warrant further exploration and optimization. The second part of this study focuses on a detailed analysis of the Adam optimizer. We specifically examine its sensitivity to both model and data sparsity. To do this, we train several Convolutional Neural Network (CNN) model architectures, each with a different degree of sparsity, on several datasets with various levels of sparsity. The experimental results provide valuable insights, showing that the Adam optimizer is more influenced by model sparsity than data sparsity, and suggesting its suitability for effectively training densely connected models. [ABSTRACT FROM AUTHOR]

Due to the lack of specific shapes and textures and susceptibility to background clutter and noise interference, infrared small target detection has always been a challenging task. The nonlinear spiking neural P (NSNP) system is a type of neural computing model inspired by the nonlinear spiking mechanisms of biological neurons. It can better identify the internal relationships between small objects, and based on this. This paper presents a variant of NSNP systems, called nonlinear spiking neural P systems with two states or NSNP-TS systems. Based on this variant, a new method for infrared small target detection is proposed. The core idea is the employment of the nonlinear spiking mechanism of the NSNP-TS system. Moreover, a preprocessing component is introduced to find potential targets from the original infrared image, with the aim of making the firing mechanism of the NSNP-TS system more effective. The experimental results on real sequences from 8 different environmental backgrounds indicate that the proposed method outperforms other comparative methods. [ABSTRACT FROM AUTHOR]