To deal with pattern synthesis of antenna arrays, a chaotic particle swarm optimization (CPSO) is presented to avoid the premature convergence. By fusing with the ergodic and stochastic chaos, the novel algorithm explores the global optimum with the comprehensive learning strategy. The chaotic searching region can be adjusted adaptively. To evaluate the performance of CPSO, several representative benchmark functions are minimized using various optimization algorithms. Numerical results demonstrate that the proposed approach improves the performance of the algorithm significantly, in terms of both the convergence speed and exploration ability. Moreover, CPSO was applied to array synthesis examples, including the equally spaced linear array, unequally spaced linear array and conformal array, compared with other optimization methods. Experimental results show its high performance in the pattern synthesis with low side lobe, multi-nulls and shaped beam.

The remote photoplethysmography (rPPG) based on cameras, a technology for extracting pulse wave from videos, has been proved to be an effective heart rate (HR) monitoring method and has great potential in many fields; such as health monitoring. However, the change of facial color intensity caused by cardiovascular activities is weak. Environmental illumination changes and subjects’ facial movements will produce irregular noise in rPPG signals, resulting in distortion of heart rate pulse signals and affecting the accuracy of heart rate measurement. Given the irregular noises such as motion artifacts and illumination changes in rPPG signals, this paper proposed a new method named LA-SSA. It combines low-rank sparse matrix decomposition and autocorrelation function with singular spectrum analysis (SSA). The low-rank sparse matrix decomposition is employed to globally optimize the components of the rPPG signal obtained by SSA, and some irregular noise is removed. Then, the autocorrelation function is used to optimize the global optimization results locally. The periodic components related to the heartbeat signal are selected, and the denoised rPPG signal is obtained by weighted reconstruction with a singular value ratio. The experiment using UBFC-RPPG and PURE database is performed to assess the performance of the method proposed in this paper. The average absolute error was 1.37 bpm, the 95% confidence interval was −7.56 bpm to 6.45 bpm, and the Pearson correlation coefficient was 98%, superior to most existing video-based heart rate extraction methods. Experimental results show that the proposed method can estimate HR effectively.

With the rapid development of urban rail transit, the safety of subway sliding plug doors has become a great concern. To improve the operational reliability of the sliding plug door, we developed a fault detection system based on the adaptive empirical mode decomposition (AEMD). Firstly, we designed a hardware acquisition device and analysis software to collect motor current signal data during the opening and closing of the sliding plug door. Secondly, to address the impact of noise on signal analysis, the AEMD denoising method is proposed. This method employs EMD to obtain intrinsic mode functions (IMFs), and select the appropriate IMF components for reconstruction based on the adaptive threshold of Hausdorff distance, resulting in improved denoising effectiveness. Thirdly, waveform segments of different faults are sliced to reduce the amount of computation and effectively improve recognition accuracy. Meanwhile, this paper utilizes feature selection methods and machine learning techniques to classify the 12 subway sliding plug door faults. It is worth noting that most of these faults have not been extensively studied in previous classification research. The experimental results show that the identification accuracy reaches 98.96% on the practical platform. Moreover, the effectiveness and robustness of our proposed method are further validated through practical tests, ablation experiments, and comparisons with other relevant literature.

Objective . Sleep staging is the basis for sleep quality assessment and diagnosis of sleep-related disorders. In response to the inadequacy of traditional manual judgement of sleep stages, using machine learning techniques for automatic sleep staging has become a hot topic. To improve the performance of sleep staging, numerous studies have extracted a large number of sleep-related characteristics. However, there are redundant and irrelevant features in the high-dimensional features that reduce the classification accuracy. To address this issue, an effective hybrid feature selection method based on the entropy weight method is proposed in this paper for automatic sleep staging. Approach . Firstly, we preprocess the four modal polysomnography (PSG) signals, including electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG) and electromyogram (EMG). Secondly, the time domain, frequency domain and nonlinear features are extracted from the preprocessed signals, with a total of 185 features. Then, in order to acquire characteristics of the multi-modal signals that are highly correlated with the sleep stages, the proposed hybrid feature selection method is applied to choose effective features. This method is divided into two stages. In stage I, the entropy weight method is employed to combine two filter methods to build a subset of features. This stage evaluates features based on information theory and distance metrics, which can quickly obtain a subset of features and retain the relevant features. In stage II, Sequential Forward Selection is used to evaluate the subset of features and eliminate redundant features. Further more, to achieve better performance of classification, an ensemble model based on support vector machine, K-nearest neighbor, random forest and multilayer perceptron is finally constructed for classifying sleep stages. Main results . The experiment using the Cyclic Alternating Pattern (CAP) sleep database is performed to assess the performance of the method proposed in this paper. The proposed hybrid feature selection method chooses only 30 features highly correlated to sleep stages. The accuracy, F1 score and Kappa coefficient of 6 class sleep staging reach 88.86%, 83.15% and 0.8531%, respectively. Significance . Experimental results show the effectiveness of the proposed method compared to the existing state-of-the-art studies. It greatly reduces the number of features required while achieving outstanding auto-sleep staging results.