Pinching antennas are a promising technology for enhancing the performance of future indoor communication systems by leveraging spatial degrees of freedom. This paper pioneers the application of pinching antenna arrays in integrated sensing and communication (ISAC) systems and investigates dynamic array partitioning strategies. To maximize the secrecy sum rate (SSR), a partitioned array optimization problem under binary constraints is formulated, while satisfying sensing performance requirements and transmit power limitations. Specifically, the antenna partitioning constraints are modeled as minimum and maximum numbers of transmit antennas, along with binary constraints determining whether each antenna element functions in transmit or receive mode. To solve the non-convex optimization problem, a beamforming algorithm integrating semidefinite relaxation, generalized Rayleigh quotient, and minimum mean square error is proposed. Then, an element-wise iterative optimization method and a deep Q-network (DQN)-based partitioning approach are respectively developed to optimize the array configuration, thereby enhancing security performance under guaranteed sensing constraints. Simulation results demonstrate that the DQN-based approach outperforms the conventional iterative optimization method. In terms of security performance, the pinching antenna array can achieve a 69.70% reduction in the number of antennas and a 30.16% saving in transmit power compared to conventional fixed-position antenna (FPA) systems. Moreover, the pinching antenna system attains a 35.72% improvement in SSR performance, surpassing traditional FPA configurations.

In processes of refining and chemical productions, alarm systems are generally centralized alarm management systems for process parameters. However, in order to address the challenges of advanced manipulation and maintenance during emergencies, there has been limited research on timely alarming for individual critical process parameters. This paper proposes a method based on the combination of power spectral density and statistical characteristics, which can quickly and accurately diagnose large‐scale trend changes and short‐term nonstationary abnormal trends in process parameters. First, the method employs incremental data from historical records of critical process parameters for volatility analysis. Second, the historical data of critical process parameters are segmented into multiple appropriately sized datasets. We employ a combined analysis of power spectral density and statistical characteristics to extract features from multitude of incremental data. Meanwhile, we have designed a tuning scheme for critical frequencies and their threshold parameters, which can be used for testing and online diagnostics. Experimental validation is performed using actual critical process parameters data from Chinese refineries. The experimental results indicate that the method can detect large‐scale trends and short‐term nonstationary abnormal trends in process parameters, demonstrating good diagnostic performance.

Iris recognition is widely regarded as one of the most reliable biometric identification technologies. Traditional methods, such as the Daugman algorithm typically normalize the annular iris region into a rectangular format during the preprocessing stage, followed by feature extraction and matching. However, these preprocessing steps often introduce distortions and struggle to adapt to multiresolution images, leading to inaccurate feature encoding. In response to these limitations, we propose a weak preprocessing algorithm for iris recognition that effectively preserves both grayscale and structural information of the iris. This approach is highly adaptable to varying image resolutions by leveraging a multiscale structural information extraction framework. It demonstrates significant improvements, achieving a matching accuracy of 96.67% on our proprietary dataset and 90% on the CASIA‐IrisV4 dataset. Compared to the Daugman and OsIris 4.0 algorithm using weak preprocessing schemes, our approach improves accuracy by 15.55% and reduces matching time by 16%. More importantly, this method presents a new idea that is different from traditional preprocessing methods with wider adaptability. It offers considerable potential for real‐world applications in security, with promising prospects for further integration with deep learning techniques.

This study focuses on the latest research advancements in the field of semantic communication. Traditional communication systems prioritize the transmission of raw data, whilst semantic communication emphasizes conveying the meaning represented by the data. However, the extracted semantic information is often ambiguous and subject to subjective evaluation. To address this problem, this study proposes a model that combines a convolutional neural network (CNN) with a Transformer, called DeepSC‐CT. The model utilizes a CNN to extract semantic information from the data, followed by a Transformer model to capture spatial relationships and contextual information within the semantic content. We utilize federated learning to train the model and propose an adaptive aggregation algorithm to accelerate the convergence process. Moreover, we expand the single‐modality semantic communication model to encompass multiple modalities, such as texts, audio, and images. Furthermore, this study introduces a learnable position‐encoding method for the Transformer. The experimental results and visual effects of audio and image restoration demonstrate that the proposed method exhibits impressive performance and that the proposed model shows robust data restoration capabilities under various signal‐to‐noise ratio conditions.

The channel estimation technique is crucial for the development of wireless communication systems. By accurately estimating the channel state, transmission parameters such as power allocation, modulation schemes, and encoding strategies can be optimized to maximize system capacity and transmission rate. In this paper, we propose a hybrid deep learning model for channel estimation in multiple‐input multiple‐output (MIMO) wireless communication system. By combining the advantages of convolutions and gated recurrent units (GRUs), the generalization capability of deep learning models across various wireless communication scenarios can be fully utilized. Furthermore, a series of regularization techniques such as data augmentation and structural complexity constraints have been introduced to avoid overfitting problems. The stochastic gradient descent (SGD) based on error backpropagation is used to iteratively train the model to convergence. During the simulation process, we have validated the effectiveness of the hybrid deep learning model on two wireless channel conditions, including quasi‐static block fading and time‐varying fading condition. All the samples are generated offline with SNRs from 10 to 40 dB with a step size of 5 dB. The comparison results with a series of conventional methods and deep learning models have proven the effectiveness of the proposed method.

As an essential technology for spectrum sensing and dynamic spectrum access, automatic modulation classification (AMC) is a critical step in intelligent wireless communication systems, aiming at automatically recognizing the modulation schemes of received signals. In practice, AMC is challenging due to the influence of communication environment and signal parameters, such as unknown channels, noise, symbol rate, signal length, and sampling frequency. In this survey, we investigated a series of typical AMC methods, including key technology, performance comparisons, advantages, challenges, and future key development directions. According to the methodology and processing flow, AMC methods are divided into three categories: likelihood‐based (Lb) methods, feature‐based (Fb) methods, and deep learning methods. The technical details of various types of methods are introduced and discussed, such as likelihood distributions, artificial features, classifiers, and network structures. Then, extensive experimental results of state‐of‐the‐art AMC methods on public or simulated datasets are compared and analyzed. Despite the achievements that have been made, there are still limitations of the individual methods, including generalization capability, reasoning efficiency, model complexity, and robustness. In the end, we summarized the severe challenges faced by AMC and key future research directions.

Hyperspectral image (HSI) clustering is a fundamental yet challenging task that typically operates without training labels. Recent advancements in deep graph clustering methods have shown promise for HSI due to their ability to effectively encode spatial structural information. However, limitations such as inadequate utilization of structural information, poor feature representation, and weak graph update capabilities hinder their performance. In this article, we propose an adaptive homophily structure graph clustering (AHSGC) method for HSI. Our approach begins with the generation of homogeneous regions to process HSI and construct the initial graph. Next, we design an adaptive filter graph encoder that captures both high and low-frequency features for subsequent processing. We then develop a graph embedding clustering self-training decoder using KL Divergence to generate pseudo-labels for network training. To enhance graph learning, we introduce homophily-enhanced structure learning, which updates the graph based on the clustering task. This involves estimating node connections through orient correlation estimation and dynamically adjusting graph edges via graph edge sparsification. Finally, we implement joint network optimization to facilitate self-training and graph updates, with K-means used to express latent features. The clustering accuracy on three datasets is 83.60%, 63.65%, and 86.03%, the FLOPs are 3.57G, 30.62G, and 2.95G. The source code will be available athttps://github.com/DY-HYX.

Saliency object detection has been widely used in computer vision tasks such as image understanding, semantic segmentation, and target tracking by mimicking the human visual perceptual system to find the most visually appealing object. The U2Net model has shown good performance in salient object detection (SOD) because of its unique U‐shaped residual structure and the U‐shaped structural backbone incorporating feature information of different scales. However, in the U‐shaped structure, the global semantic information computed from the topmost layer may be gradually interfered by the large amount of local information dilution in the top‐down path, and the U‐shaped residual structure has insufficient attention to the features in the salient target region of the image and will pass redundant features to the next stage. To address these two shortcomings in the U2Net model, this paper proposes improvements in two aspects: to address the situation that the global semantic information is diluted by local semantic information and the residual U‐block (RSU) module pays insufficient attention to the salient regions and redundant features. An attentional gating mechanism is added to filter redundant features in the U‐structure backbone. A channel attention (CA) mechanism is introduced to capture important features in the RSU module. The experimental results prove that the method proposed in this paper has higher accuracy compared to the U2Net model.

The detection of small infrared targets is still a challenging task and efficient and accurate detection plays a key role in modern infrared search and tracking military applications. However, small infrared targets are difficult to detect due to their weak brightness, small size and lack of shape, structure, texture, and other information elements. In this paper, we propose a target detection method. First, to address the problem that the proximity of targets to high‐brightness clutter leads to missed detection of candidate targets, a Gaussian differential filtering preprocessed image is used to suppress high‐brightness clutter. Second, a density‐peaked global search method is used to determine the location of candidate targets in the preprocessed image. We then use local contrast to the candidate target points to enhance the gradient features and suppress background clutter. The Facet model is used to compute multidirectional gradient features at each point. A new efficient surrounding symmetric region partitioning scheme is constructed to capture the gradient characteristics of targets of different sizes in eight directions, followed by weighting the candidate target gradient characteristics using the standard deviation of the symmetric region difference. Finally, an adaptive threshold segmentation method is used to extract small targets. Experimental results show that the method proposed in this paper has better detection accuracy and robustness compared with other detection methods.

Cover Caption: The cover image is based on the Research Article MR-DCAE: Manifold regularization-based deep convolutional autoencoder for unauthorized broadcasting identification by Qinghe Zheng et al., https://doi.org/10.1002/int.22586.

Nowadays, radio broadcasting plays an important role in people's daily life. However, unauthorized broadcasting stations may seriously interfere with normal broadcastings and further disrupt the management of civilian spectrum resources. Since they are easily hidden in the spectrum and are essentially the same as normal signals, it still remains challenging to automatically and effectively identify unauthorized broadcastings in complicated electromagnetic environments. In this paper, we introduce the manifold regularization-based deep convolutional autoencoder (MR-DCAE) model for unauthorized broadcasting identification. The specifically designed autoencoder (AE) is optimized by entropy-stochastic gradient descent, then the reconstruction errors in the testing phase can be adopted to determine whether the received signals are authorized. To make this indicator more discriminative, we design a similarity estimator for manifolds spanning various dimensions as the penalty term to ensure their invariance during the back-propagation of gradients. In theory, the consistency degree between discrete approximations in the manifold regularization (MR) and the continuous objects that motivate them can be guaranteed under an upper bound. To the best of our knowledge, this is the first time that MR has been successfully applied in AE to promote cross-layer manifold invariance. Finally, MR-DCAE is evaluated on the benchmark data set AUBI2020, and comparative experiments show that it achieves state-of-the-art performance. To help understand the principle behind MR-DCAE, convolution kernels and activation maps of test signals are both visualized. It can be observed that the expert knowledge hidden in normal signals can be extracted and emphasized, rather than simple overfitting.