IET Signal Processing最新文献

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.

Since distributed sensing, storage, and computing are the frontiers for future sixth-generation (6G) communication systems, user terminal (UT) localization based on received signal strength (RSS) data from wireless sensor networks (WSNs) has received widespread attention because of its low energy consumption and ease of operation. Most of the existing work focused on the single-source localization problem. However, multiple UT localization is a more realistic problem that has not been well addressed. In this paper, we proposed a novel multiple UT localization scheme. Specifically, based on the log-normal property of spatial shadowing, the RSS is approximated as a random variable obeying a log-normal distribution, and the objective function is derived via maximum likelihood estimation. Then, aiming to better solve the objective function, a radio map is constructed to narrow search area, and a meta-heuristic algorithm with global search capability is adopted. Compared with the state-of-the-art methods through simulation experiments, it is proved that the method proposed in this paper has the best localization performance.

Securing communication between multiple users efficiently while there are too many potential eavesdroppers has become an important issue with the rise of the Internet of Things (IoTs). This paper extends on earlier research, moving from a single-user and single-eavesdropper scenario to a complex multiuser and multieavesdropper context, and incorporates an advanced physical layer security (PLS) technique for the first time. Using reconfigurable intelligent surfaces (RISs) enhances the strength and quality of signals for intended users, while those to the unintended users are suppressed. Real-time control of the RIS phase shifts is enabled through a deep deterministic policy gradient (DDPG) algorithm and this control significantly changes the trade-off between security and energy wastage. The simulation results demonstrate that the developed approach can scale up in densely populated urban centers, while increasing the bit error rate (BER) performance and the overall energy efficiency across different wireless mobile channels.

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.