By leveraging the inherent ability of reconfigurable intelligence surface (RIS) to enhance wireless communication channels, the integration of RIS into simultaneous wireless information and power transfer (SWIPT) enabled vehicle-to-vehicle (V2V) systems presents a promising solution to jointly enhance communication performance and energy harvesting efficiency. Building on this potential, an RIS-assisted V2V dual-hop relay system is constructed, which deploys RIS on the gate of relay vehicle user equipment (VUE), enabling efficient signal refraction from source to relay VUEs. To address the critical challenge of information freshness in such RIS-assisted systems, age of information (AoI) is adopted as the key metric. And the AoI optimization problem is formulated that jointly considers energy/data buffer capacity limitations, relay sustainability, and real-time packet freshness. To effectively resolve this optimization problem under dynamic vehicular conditions, an prioritized experience replay – dueling double deep Q network (PER-D3QN) scheme based on deep reinforcement learning (DRL) is proposed to make the optimal relay decision for AoI minimization. Numerical results demonstrate that the average AoI using the proposed PER-D3QN scheme is reduced by 20 percent compared with the existing schemes.

Synthetic aperture radar (SAR) is a high-resolution remote sensing technology widely employed for ground and sea surface target detection. However, due to the unique imaging mechanism and information representation of SAR images, conventional spatial-domain feature extraction methods often struggle to fully capture their discriminative features. To address this limitation, this letter introduces the wavelet domain as an additional feature extraction space and proposes a dual-domain feature-enhanced network based on wavelet attention for SAR ship detection. Specifically, two wavelet attention modules are designed to independently and jointly compute attention for high-frequency and low-frequency features in the wavelet domain. Meanwhile, an embedding grouping strategy is adopted to reduce computational costs while enhancing the model’s detailed perception and global understanding of ship targets. Furthermore, a dynamic domain fusion module is proposed to more effectively integrate wavelet-domain and spatial-domain information, enriching feature representation. Comprehensive experiments on two widely used SAR ship datasets demonstrate that the proposed method outperforms many other state-of-the-art detectors. The source code is available at https://github.com/Wenjing-Jiang-hbu/DFWA-Net.

Micro-expressions (MEs) are tiny muscular movements on the face that conceal an individual's genuine emotions. However, the micro-expression recognition (MER) task faces challenges like short duration, low motion intensity, and a scarcity of training data. To solve these problems and obtain a good recognition effect, a Channel Self-Attention Residual Network (CSARNet) is proposed for extracting micro-expression discriminative information from motion stream images with augmented local features. Firstly, based on the offset frames of micro-expressions, a local feature augmentation strategy is devised to augment the local feature representations of motion flow images, thus effectively suppressing the interference of motions that are not related to micro-expressions. Second, aiming to mitigate the risk of model overfitting resulting from the dataset's limited size, CSARNet with a lightweight backbone network structure is designed to streamline the model's complexity and decrease computation time, which also accurately extracts the discriminative information of micro-expressions across channel and spatial dimensions, enabling the effective recognition of emotions. The proposed method was extensively tested on three benchmark datasets (SMIC, CASME II, SAMM) and the composite 3DB dataset, with experimental results clearly showcasing its superiority.

Despite the high spectral resolution and abundant information of hyperspectral images (HSI), their spatial resolution is relatively low due to limitations in sensor technology. Sensors often need to sacrifice some spatial resolution to ensure accurate light energy measurement when pursuing high spectral resolution. This trade-off results in HSI’s inability to capture fine spatial details, thereby limiting its application in scenarios requiring high-precision spatial information. HSI and multispectral images (MSI) fusion is a commonly used technique for generating high-resolution HSI (HR-HSI). However, many deep learning-based HSI-MSI fusion algorithms ignore correlation and multi-scale information between input images. To address this issue, we propose an asymptotic multi-scale symmetric fusion network (AMSF-Net) for hyperspectral and multispectral image fusion. AMSF-Net consists of two parts: the multi-level feature fusion (MFF) module and the progressive cross-scale spatial perception (PCP) module. The MFF module uses multi-stream feature extraction branches to perform information interaction between HSI and MSI at the same scale layer by layer, compensating for the spatial details lacking in HSI and the spectral details absent in MSI. The PCP module combines the input and output features of MFF, utilizes multi-scale bidirectional strip convolution and deep convolution to further refine edge features, and reconstructs HR-HSI by learning the features of different expansion roll branches by connecting across scales. Comparative experiments with several state-of-the-art HSI-MSI fusion algorithms on four publicly available datasets, CAVE, Chikusei, Houston and WorldView-3 are conducted to validate the effectiveness and superiority of AMSF-Net. On the Chikusei dataset, improvements were 9.1%, 12.5%, and 5.1%, respectively, on the indicators RMSE, ERGAS, and SAM, compared to the suboptimal method.

Electroencephalogram (EEG) is widely utilized in emotion recognition owing to its unique advantages. To achieve more optimal cross-subject emotion recognition, a cross subject emotion recognition method based on interconnection dynamic domain adaptation (IDDA) is proposed. In IDDA, dynamic graph convolution (DGC) is employed to dynamically learn the intrinsic relationships between different EEG channels and to extract domain invariant features. And dynamic domain adaptation (DDA) is employed to align the source domain and target domain, at the same time the emotional sub-domains is aligned, achieving more optimal cross subject emotion recognition. To select suitable subjects as the source domain, a multi-source selection algorithm is incorporated before dynamic adaptive computation reducing migration noise and achieving interconnection between DGC and DDA. IDDA enhances the emotion discrimination ability of domain invariant features, thereby improving the accuracy of cross-subject EEG emotion recognition. This method achieves classification results of 85.75% and 72.36% in cross subject experiments on SEED and SEED-IV.

Synthetic aperture radar (SAR) tends to be seriously affected by speckle noise due to its inherent imaging characteristics, which brings great challenges to the high-level visualization task of SAR images. Therefore, speckle suppression plays a crucial role in remote sensing image processing. Attention-based SAR image denoising algorithms frequently struggle to capture rich feature information and face challenges in balancing the trade-off between denoising and preserving texture details. To solve the above problems, this paper constructs a local and global dual-branch network (LG-DBNet) for SAR image denoising. This network can effectively suppress speckle noise while fully retaining the detail information of the original image. Firstly, the shallow features are extracted through simple convolution. Then, a dual-branch structure constructed using different attention modules is used to extract deep features from SAR images. Specifically, one branch performs local deep feature extraction of an image through a hybrid attention module built by a convolutional neural network (CNN), while the other branch utilizes a superposition of self-attention mechanisms for global deep feature extraction of the image. Finally, the final denoised image is generated through global residual learning. LG-DBNet can effectively extract the local and global image information through the dual-branch structure, and further focus on the noise information, which can better retain the texture information of the image while effectively denoising. The experimental results show that compared with the state-of-the-art SAR image denoising algorithms, the proposed algorithm not only improves on various objective indexes, but also shows great advantages in the visual effect after denoising.

Recently, methods based on deep learning have been successfully applied to ship detection for synthetic aperture radar (SAR) images. However, most current ship detection networks rely too much on the anchor mechanism. These methods have low accuracy and poor generalization ability for multiscale ship detection. To solve the aforementioned problems, an anchor-free framework for multiscale ship detection in SAR images based on a balance attention network (BANet) is proposed. First, due to the diversity of scales and rotation angles of ships, deformable convolution is introduced to build a local attention module (LAM) to better obtain local information of ships and effectively enhance the robustness of the network. Second, a nonlocal attention module (NLAM) is designed to extract the nonlocal features of the SAR image, so as to balance the local features and nonlocal features acquired by the entire network. Finally, the feature pyramid network (FPN) is used to detect ships of different sizes at different scales. The detection results on three datasets demonstrate that the detection precision of our method is higher than that of all comparison methods, and this method achieves the most advanced performance.

Deep learning-based detection methods have achieved great success in ship target detection in synthetic aperture radar (SAR) images. However, due to the interference of imaging mechanism, speckle noise, and sea and land clutter, ship detection in SAR images still suffers from difficult interpretation. It is found that most ship detection algorithms focus on object-level detection while ignoring pixel-level information. In order to further improve the recognition effectiveness and positioning accuracy of ships in SAR images, we present a novel ship detection method based on a feature interaction network (FINet) in SAR images from the perspective of object-level and pixel-level. FINet consists of an object-level detection network and a pixel-level detection network. The information of the two branches is fused through the feature interaction module (FIM), and then the object-level information and pixel-level information are enhanced by the feature guidance module (FGM). Finally, FINet utilizes object-level and pixel-level detection heads for prediction and regression to obtain object-level classification accuracy, positioning bounding box coordinates, and pixel-level binary classification results. The experimental results demonstrate that the classification effectiveness and localization accuracy of FINet are better than those of the comparison algorithms, and FINet achieves the best performance.

Synthetic aperture radar (SAR), due to its inherent characteristics, will produce speckle noise, which results in the deterioration of image quality, so the removal of speckle in SAR image is very important for the subsequent high-level image processing. In order to balance the relationship between denoising and texture preservation, we propose a multiscale residual dense dual attention network (MRDDANet) for SAR image denoising. This algorithm can effectively suppress the speckle while fully retaining the texture details of the image. In MRDDANet, shallow features are extracted from the noisy images by multiscale modules with different kernel sizes, and then, the extracted shallow features are mapped to the residual dense dual-attention network to obtain the deep features of SAR image. Finally, the final denoising image is generated through global residual learning. MRDDANet has advantages of both multiscale blocks and residual dense dual attention networks. The dense connection can fully extract features in the image, and the dual-channel attention enables MRDDANet to pay more attention to noise information, which is beneficial to remove noise and keep the details of the original image at the same time. Compared with state-of-the-art algorithms, the results of the experiment indicate that our method not only improves various objective indicators but also shows great advantages in visual effects.

Compared with traditional remoting image, there is a large amount of spectral information in the hyperspectral image (HSI), which makes HSI better reflect the actual condition of surface features. However, due to the limitations of imaging conditions, HSI tends to have a lower spatial resolution. In order to overcome this issue, we propose a spectral-spatial attention-based U-Net named SSAU-Net for HSI and multispectral image (MSI) fusion. The SSAU-Net constructs a spectral-spatial attention module by a coordinate-attention (CA) module and an efficient pyramid split attention (ESPA) module, which can enhance the image’s spectral information and spatial information. Meanwhile, the proposed network fully extracts the shallow and deep features of the images, and finally generates high-resolution (HR) hyperspectral images. Compared with state-of-the-art HSI-MSI fusion methods, the experimental results verify that the proposed method has a better subjective and objective fusion effect.

Since electroencephalogram (EEG) signals can truly reflect human emotional state, emotion recognition based on EEG has turned into a critical branch in the field of artificial intelligence. Aiming at the disparity of EEG signals in various emotional states, we propose a new deep learning model named three-dimension convolution attention neural network (3DCANN) for EEG emotion recognition in this paper. The 3DCANN model is composed of spatio-temporal feature extraction module and EEG channel attention weight learning module, which can extract the dynamic relation well among multi-channel EEG signals and the internal spatial relation of multi-channel EEG signals during continuous period time. In this model, the spatio-temporal features are fused with the weights of dual attention learning, and the fused features are input into the softmax classifier for emotion classification. In addition, we utilize SJTU Emotion EEG Dataset (SEED) to appraise the feasibility and effectiveness of the proposed algorithm. Finally, experimental results display that the 3DCANN method has superior performance over the state-of-the-art models in EEG emotion recognition.

In this paper, an active-user cooperative scheme for overlay cognitive radio (OCR) vehicle-to-vehicle (V2V) communication system based on three-dimensional (3D) channel model is proposed. Based on the proposed cooperative scheme, the achievable rate regions of the primary users (PU) and secondary users (SU) with outdated channel state information (CSI) are analyzed. According to the tradeoff between the achievable rates of PU and SU, three power allocation schemes are proposed using outdated CSI. The performance of PU and SU in terms of outage events are analyzed. Based on the analytical framework, the simulation results for achievable rate and outage probabilities are provided. And the impact of power allocation coefficient of PU and SU, outdated CSI, signal-to-noise ratio(SNR) and the codes cross-correlation on the proposed active-user cooperation is analyzed. The simulation results are compared with validated analysis to confirm the theoretical analysis.

Affective computing is one of the key technologies to achieve advanced brain-machine interfacing. It is increasingly concerning research orientation in the field of artificial intelligence. Emotion recognition is closely related to affective computing. Although emotion recognition based on electroencephalogram (EEG) has attracted more and more attention at home and abroad, subject-independent emotion recognition still faces enormous challenges. We proposed a subject-independent emotion recognition algorithm based on dynamic empirical convolutional neural network (DECNN) in view of the challenges. Combining the advantages of empirical mode decomposition (EMD) and differential entropy (DE), we proposed a dynamic differential entropy (DDE) algorithm to extract the features of EEG signals. After that, the extracted DDE features were classified by convolutional neural networks (CNN). Finally, the proposed algorithm is verified on SJTU Emotion EEG Dataset (SEED). In addition, we discuss the brain area closely related to emotion and design the best profile of electrode placements to reduce the calculation and complexity. Experimental results show that the accuracy of this algorithm is 3.53 percent higher than that of the state-of-the-art emotion recognition methods. What's more, we studied the key electrodes for EEG emotion recognition, which is of guiding significance for the development of wearable EEG devices.

Synthetic aperture radar (SAR) images often interfere with speckle artifacts that have a great impact on subsequent processing and analysis operations. To remove speckle artifacts, this article introduces a hybrid denoising approach by using a convolutional neural network (CNN) and consistent cycle spinning (CCS) in the nonsubsample shearlet transform (NSST) domain. First, we apply NSST to a noisy SAR image to gain low- and high-frequency coefficients. Second, we adopt a learned deep CNN model to eliminate the speckle noise in the low-frequency coefficients, which retains more contour information. Third, we employ CCS to enhance the high-frequency coefficients, which preserves more details of the original SAR image. Finally, we obtain the denoised image by using inverse NSST applied to the denoised coefficients. Compared with state-of-the-art algorithms, the results of the experiment indicate that our method not only achieves better speckle removal performance but also maintains more detailed information retention.

As one of the important tools of epilepsy diagnosis, the electroencephalogram (EEG) is noninvasive and presents no traumatic injury to patients. It contains a lot of physiological and pathological information that is easy to obtain. The automatic classification of epileptic EEG is important in the diagnosis and therapeutic efficacy of epileptics. In this article, an explainable graph feature convolutional neural network named WTRPNet is proposed for epileptic EEG classification. Since WTRPNet is constructed by a recurrence plot in the wavelet domain, it can fully obtain the graph feature of the EEG signal, which is established by an explainable graph features extracted layer called WTRP block . The proposed method shows superior performance over state-of-the-art methods. Experimental results show that our algorithm has achieved an accuracy of 99.67% in classification of focal and nonfocal epileptic EEG, which proves the effectiveness of the classification and detection of epileptic EEG.

Coherent imaging systems are greatly affected by speckle noise, which makes visual analysis and features extraction a difficult task. In this paper, we propose a speckle suppression algorithm based on weighted nuclear norm minimization (WNNM) and Grey theory. First, we use logarithmic transformation to the noisy images such that the speckle noise is transformed into additive noise. Second, by matching the local blocks based on Grey theory, we will get approximate low-rank matrices grouped by the similar blocks of the reference patches. We then estimate the noise variance of the noisy images with the wavelet transform. Finally, we use WNNM method to denoise the image. The results show that our algorithm not only effectively improves the visual effect of the denoised image and preserves the local structure of the image better but also improves the objective indexes values of the denoised image.

The low-rankness property of the Hankel matrix formulated from the clean seismic data corresponding to a few number of linear events has been successively leveraged in many low-rank (LR) approximation methods for seismic data denoising. The common scheme in these rank-reduction methods is to compute the best LR approximation of the formulated Hankel matrix and then obtain the denoised data from the LR matrix. However, without utilizing the Hankel structure when computing the LR approximation, if we rearrange the denoised data into a Hankel matrix, it is in general not exactly LR as expected. In this paper, we propose a Hankel LR (HLR) approximation method to simultaneously exploit both the Hankel structure and the LR property underlying the clean seismic data. The formulated HLR approximation problem is solved by an alternating-minimization-based algorithm. We provide rigorously convergence analysis of the proposed algorithm. The superior performance of the proposed HLR approximation method is demonstrated on both synthetic and field seismic data.

How to suppress speckle noise effectively has become one of the key problems in remote sensing image processing. This problem also restricts the development of key technology severely, especially in military applications and so on. To overcome the shortcoming that the optimal solution of image denoising based on sparse representation does not have one-to-one mapping of the original signal space, in this paper, we propose a novel synthetic aperture radar (SAR) image denoising via sparse representation in Shearlet domain based on continuous cycle spinning. First, the Shearlet transform is applied to the noised SAR image. Second, a new optimal denoising model is constructed using the sparse representation model based on the cycle spinning theory. Finally, the alternate iteration algorithm is used to solve the optimal denoising model to obtain the denoised image. The experimental results show that the proposed method not only effectively suppresses the speckle noise and improves the peak signal-to-noise ratio of denoising SAR image, but also obviously improves the visual effect of the SAR image, especially by enhancing the texture of the SAR image.