Recent advancements in zero-/few-shot anomaly detection have demonstrated the efficiency of contrastive learning approaches. However, existing methods struggle with imprecise perception of anomaly details and often lack focus on identifying anomaly types. To address these challenges, we propose a flexible reconstruction-refined framework based on contrastive learning, which comprises three core components: a cross-modal alignment network, a reconstruction module, and a dual-attention refinement module. The reconstruction module captures fine-grained anomaly embeddings and fidelity scores, enabling flexible module switching based on task requirements. The attention module directs the cross-modal alignment network to focus on fine-grained anomaly information for accurate segmentation. Our framework leverages the strengths of current anomaly detection algorithms, and excels in zero-/few-shot tasks across industrial datasets, recognizing 43 anomaly types without separate training for each category, and significantly outperforms existing methods, particularly on the challenging MPDD dataset. Our code has been released at https://github.com/AiArt-Gao/ReCLIP.

With digitization and globalization in the era of 5G and beyond, research on high-throughput satellites (HTS) to increase communication capacity and improve flexibility is becoming essential. To achieve efficient resource utilization and dynamic traffic demand matching, the multi-dimensional resource management (MDRM) problem of the HTS communication system has been studied in this paper. Since the MDRM problem is a non-convex mixed integer problem, we decompose it into two tractable sub-problems. First, the beam-domain resource configuration problem is formed to enable on-demand coverage. Next, the user-domain resource allocation problem is modeled to enable on-demand communication. Considering the two-domain optimization problem, a two-stage framework is developed based on the combination of self-supervised learning and deep reinforcement learning. Specifically, in the first stage, a maximum co-channel interference based self-supervised learning method is proposed to perform traffic demand matching through demand awareness. In the second stage, a soft frequency reuse based proximal policy optimization approach is presented to further increase the system capacity through interference coordination. The simulation results demonstrate that our proposed two-stage algorithm outperforms the benchmark schemes in terms of spectrum efficiency and demand satisfaction.

In vehicle‐to‐vehicle (V2V) communications, achieving reliable physical layer performance is a challenging task due to the highly dynamic nature of V2V propagation channels. Frame timing estimation, as one of the most critical signal processing procedures that rely on channel statistics, has to be appropriately enhanced to tackle this challenge. This study presents a novel frame timing estimation scheme based on both the available periodical preambles in IEEE 802.11p standard. By designing the fourth‐order statistics‐based correlation and differential normalisation functions, the proposed timing metric not only is capable of possessing an extensible correlation length, but also achieves the robustness to multipath effect and large carrier frequency offset. From the standpoints of hypothesis testing and classification, the proposed approach can effectively increase the distinction between correct and wrong timing indexes in terms of the class‐separability criteria, and consequently has a significantly improved timing estimation performance compared with the existing methods. Simulation results consist with theoretical analysis under the typical V2V channel model, and demonstrate that the proposed method can significantly reduce both the probabilities of false alarm and missed detection, and make the selection of a suitable threshold for frame detection much easier.

Reasonable design and effective detection of the random access preamble has become a challenging task due to the unique characteristics of mobile satellite communications. To tackle this challenge, we first design a universal long sequence structure by concatenating multiple short Zadoff-Chu sequences that are insensitive to carrier frequency offset (CFO), and then propose the new principles of parameter selection for short sequences to ensure the minimum utilization of root sequence and the independence of the cyclic shift offset on the beam radius. To further reduce the detection complexity and improve the multi-user access performance, a fast timing detection approach is also presented by leveraging the piecewise cumulative detection and the multi-peaks joint estimation to obtain an accurate timing advance for each access user. Simulation results and complexity analysis validate the effectiveness of the new preamble in a typical satellite communication environment, and reveal that the proposed timing detection can achieve the robustness to CFO and offer outstanding performance improvements especially in multi-user scenarios while having a notably reduced computational complexity.

This study investigates the problem of frame timing estimation in orthogonal frequency division multiplexing systems. Conventional timing estimation methods, which take advantage of the correlation property of a given preamble, always experience performance degradation in multipath fading channels with severe channel dispersion. To achieve accurate timing estimation, the authors propose a robust threshold‐based timing detection method independent of the preamble structure. Based on the autocorrelation and cross‐correlation, a novel timing metric with an extended correlation length is proposed to mitigate noise and resist large carrier frequency offsets. Due to the superior statistical property of the proposed timing metric, the threshold can be easily determined with no need for the process of noise variance estimation. Simulation results under different multipath fading channels demonstrate that the proposed method achieves a remarkably improved timing accuracy compared to the existing methods.

With the rapid growth of multimedia message exchange and digital communication, multimedia big data has become a research hotspot in various fields. The storage and transmission of multimedia big data have high requirements for security. Images, covering the highest proportion of multimedia data, should be processed and transmitted with high security. Compressive sensing (CS) has a beneficial property for the encryption that the image can be recovered with fewer samples than conventional approaches use. In recent years, CS has been studied not only to reduce the resource requirements for signal acquisition but also to ensure the security of data. It is still an open challenge to improve security and enhance the quality of the decrypted image simultaneously using the key with small size. In this article, a CS-based encryption method is presented that associates the quantization with random measurement permutation. An enormous number of experiments have been conducted on both standard test images and face images chosen from the big database LFW. Experimental results show that our proposal has dramatic improvements on ensuring the security, enhancing the quality of the decrypted image, and raising the efficiency. Additionally, this proposal remarkably reduces storage and transmission resources. Accordingly, this encryption scheme can be applied to ensure the security of multimedia transmission.

The recently introduced theory of compressed sensing (CS) enables the recovery of sparse or compressible signals from a small set of non‐adaptive measurements, and furthermore, it holds promise for substantially improving the performance by leveraging more signal structures that go beyond simple sparsity. In this study, the authors study the weighted l 1 minimisation problem for CS reconstruction when partial support information is available. Firstly, they focus on the coherence‐based performance guarantees and show that if an estimated support can be obtained with its accuracy and relative size satisfying certain coherence‐related conditions, the weighted l 1 minimisation is then stable and robust under weaker sufficient conditions than that of the analogous standard l 1 optimisation. Meanwhile, better upper bounds on the reconstruction error could also be achieved. Besides, a novel adaptive alternating direction method of multipliers with iterative support detection is outlined to solve the weighted l 1 minimisation problem. Simulation results show that the authors’ method achieves good convergence, and obtains improved reconstruction performance in comparison with the conventional methods.

Compressed sensing is a novel technology that exploits sparsity of a signal to perform sampling below the Nyquist rate, and thus has great potential in low-complexity video sampling and compression applications, due to the significant reduction of the sampling rate ( SR) and computational complexity. However, most current work about compressive video sampling (CVS) has focused on real-valued measurements without being quantized, and thus is not applicable to engineering practices. Moreover, in many circumstances, the total number of bits is often constrained. Therefore, how to achieve a compromise between the number of measurements and the number of bits per measurement to maximize the visual quality is a great challenge for CVS, which has still not been addressed in literature. In this paper, we first present a novel distortion model that reveals the relationship between distortion, SR, and quantization bit-depth ( B). Then, using this model, we propose a joint SR - B optimization algorithm, by which we are able to easily derive the values of SR and B. Finally, we present an adaptive and unidirectional CVS framework with rate-distortion (RD) optimized rate allocation, wherein we use video characteristics extracted from partial sampling to allocate the required bits for each block, and then implement “optimized” video sampling and measurement quantization with the estimated SR and B, respectively. Simulation results show that our proposal offers comparable RD performance to the conventional method, with a 4.6 dB improvement in the average PSNR.

This letter presents a novel adaptive alternating direction method of multipliers with support/signal value detection for compressed sensing. The support/signal value detection in our algorithm can achieve an efficient reconstruction by leveraging more information that goes beyond simple sparsity. Especially for time-correlated signals in large-scale problems, our proposal performs better than conventional methods, since more accurate signal information could be estimated from prior knowledge during initialization. Simulation results show that our method can improve the average PSNR by 1.02-2.05 dB for undersampled video sequences.