IET Signal Processing最新文献

Next-generation 6G networks will significantly advance the development of integrated sensing, communication, and computing (ISSC) systems, particularly in collection and processing of point cloud data. High bandwidth and low latency offered by 6G enable sensors to generate high-resolution point cloud data more efficiently, providing precise geometric information for tunnel lining inspections. As a key application within ISSC systems, tunnel lining detection has garnered widespread attention in the transportation and infrastructure sectors, helping to enhance the structural stability of tunnels and ensure their long-term safe operation. However, current tunnel inspection methods often require extensive experimental data and struggle to effectively extract features from tunnel objects. In this article, we propose a novel point cloud semantic segmentation (PCSS) network built upon few-shot learning for tunnel detection, capable of segmenting various essential elements within the tunnel, such as bolts, pipes, and tracks. First, due to the prevalent issue of sample imbalance in tunnel point cloud data, we introduce few-shot learning to tackle this challenge, enabling the model to perform effective semantic segmentation with limited data samples. Second, recognizing that different objects and structures within the tunnel scene may exhibit significant scale variations, we employ multiembedding networks to capture features at various scales within the point cloud data. Additionally, we propose a heterogeneous feature interaction (HFI) module to merge features derived from distinct embedding networks.

In compressed sensing (CS), a sparse measurement matrix with few nonzero entries is more competitive than a dense matrix in reducing the number of multiplication units. Recent studies indicate that an optimized measurement matrix having low coherence with a specified dictionary can significantly improve the reconstruction performance. This paper considers the optimization problem of the sparse measurement matrix. The optimized sparse measurement matrix is formulated by minimizing the Frobenius norm of the difference between the Gram matrix of the sensing matrix and the target Gram matrix. First, the approach for updating the target Gram matrix is designed to reduce the maximal, average, and global coherence simultaneously. Then, an improved momentum gradient algorithm for updating the sparse measurement matrix is derived to accelerate convergence. On the basis of alternating minimization, two optimization algorithms are proposed. The experimental results show that the proposed algorithms outperform several state-of-the-art methods in terms of reconstruction performance.

Precise frequency detection is one of the key problems to be solved in a high-accuracy transfer of time and frequency. The solution to this problem is helpful in improving the precision of the phase noise measurement, atomic frequency standard, and time synchronization, which plays a strong role in the whole precision measurement physics fields. A high-accuracy frequency detection and analysis based on adaptive frequency standard tracking are proposed for time–frequency signal processing without frequency normalization. First, an adaptive frequency standard signal is generated by using an FPGA to control the DDS based on the measured signal. This signal can achieve phase comparison with the measured signal under any frequency relationships including complex and large-frequency difference relationships, widening a frequency measurement range. Second, the frequency standard signal is put off by the delay chains. The rough time delaying can generate many phase coincidences, which can shorten the gate switch time to achieve fast time response. The finer delaying can provide a very high measurement resolution without transforming the frequency relationships between the measured and reference signals. And then, a differential synchronization is performed between the measured and reference signals after shaping and conditioning the two signals. The obtained optimal phase coincidences, that is, fuzzy zone edge pulses, are used as the gate signals. A precise frequency measurement for the measured signals can then be realized by counting the measured and reference signals without gap in the gate time. The testing results show that the frequency measurement accuracy of the system can reach 1.7 × 10⁻¹³/s.

In order to accurately detect and give alerts to the anomalies in visual images, this paper proposes an image anomaly detection method. For the complex background in the image, a multiframe differential superposition algorithm is proposed to denoise the target image; a feature extraction method is given to extract features for the target image, and then a more complete image with target features is obtained after filtering; a normal behavior model is established to extract the motion information of the target from a single frame of the image; an abnormal detection method is proposed to determine whether it belongs to abnormal behavior. The experimental results show that the accuracy of the abnormal behavior detection method proposed in this paper can better discern the beginning and end of behavior occurrence, abnormal behavior prediction, behavior online detection, and other aspects from the visual image data stream, and the correct detection rate is more than 90%, which reduces the consumption of human resources. At the same time, compared with the existing anomaly detection methods, this anomaly detection presented in this paper not only has higher accuracy, faster speed, and stronger anti-interference ability but also has a better detection effect. These researches advance in this paper can provide a new method and decision support for abnormal behavior detection and identification in a variety of scenarios.

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.

Power line communication (PLC) can realize low-cost IOT access and is widely used in home and new energy applications. To meet the requirements of low-latency services such as remote control and demand-side response, a joint optimal allocation algorithm of subcarriers and their power based on diversity grouping and channel prediction is proposed. First, considering the influence of channel estimation and prediction errors, a resource allocation model is established with the constraints of subcarrier data volume and transmission power, and the objective is to minimize the total delay of multiple slots. The optimal power allocation under the condition of a single slot is realized by subcarrier diversity grouping and improved genetic algorithm, and then the subcarrier power below the rate threshold is recycled and allocated to the slot with good prediction performance. Finally, the performance of the algorithm is compared and analyzed by simulation. The results show that the proposed algorithm can reduce the rate fluctuation and improve the system delay performance and deterministic transmission ability under the condition of ensuring the average rate optimization.

Active noise control (ANC) algorithms have been developed within the adaptive algorithm framework. However, multichannel ANC systems, which include numerous reference sensors, control speakers, and error microphones, require a very long control filter converging time for control filter estimation. Traditional system identification methods, such as the Wiener filter method, are better suited for such systems because of their relatively shorter converging time. However, they require large amounts of data to achieve accurate statistical estimation. Therefore, this article proposes a control filter estimation method that requires only a short length of data. An iterative Wiener filter solution using Kronecker product decomposition for multichannel ANC systems converts the filter estimation process by breaking down the extensive control filter into multiple shorter control filters through Kronecker product decomposition. This decomposition effectively reduces the high-dimensional system identification problem into manageable low-dimensional ones. Numerical simulations demonstrate the superiority of the proposed method over conventional Wiener filter techniques, especially in scenarios when limited data are available for control filter estimation.

Data loss is a common problem in intelligent transportation systems (ITSs). And the tensor-based interpolation algorithm has obvious superiority in multidimensional data interpolation. In this paper, a Bayesian robust tensor decomposition method (MBRTF) based on the Markov chain Monte Carlo (MCMC) algorithm is proposed. The underlying low CANDECOMP/PARAFAC (CP) rank tensor captures the global information, and the sparse tensor captures local information (also regarded as anomalous data), which achieves a reliable prediction of missing terms. The low CP rank tensor is modeled by linear interrelationships among multiple latent factors, and the sparsity of the columns on the latent factors is achieved through a hierarchical prior approach, while the sparse tensor is modeled by a hierarchical view of the Student-t distribution. It is a challenge for traditional tensor-based interpolation methods to maintain a stable performance under different missing rates and nonrandom missing (NM) scenarios. The MBRTF algorithm is an effective multiple interpolation algorithm that not only derives unbiased point estimates but also provides a robust method for the uncertainty measures of these missing values.

Tacit understanding refers to the ability of team members to work together seamlessly and intuitively without explicitly communicating in detail. This ability is crucial for effective teamwork in complex situations that involve both manned and unmanned aerial vehicles (UAVs). Existing collaborative tasks between manned and unmanned aircraft focus mainly on optimizing communication and the UAVs’ flight paths but neglect the benefits of tacit and intelligent operational cooperation with pilots. To address this limitation, we propose a tacit collaborative attack method that utilizes the UAVs’ capacity for tacit understanding to infer human intent and select the appropriate targets for collaborative attack missions. A learning framework incorporating intention prediction and reinforcement learning paradigms is developed to teach the UAV to generate corresponding collaborative attack actions. Finally, we present results from extensive simulation experiments in a homemade game environment to demonstrate the efficiency and scalability of our method within the proposed framework. The video can be found at https://www.youtube.com/watch?v=CjXhkD7ko14.

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.