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Abstract Accurately model recognition of mobile device is of great significance for identifying copycat device and protecting intellectual property rights. Although existing methods have realized high-accuracy recognition about device’s category and brand, the accuracy of model recognition still needs to be improved. For that, we propose Recognizer, a high-accuracy model recognition method of mobile device based on weighted feature similarity. We extract 20 features from the network traffic and physical attributes of device, and design feature similarity metric rules, and calculate inter-device similarity further. In addition, we propose feature importance evaluation strategies to assess the role of feature in recognition and determine the weight of each feature. Finally, based on all or part of 20 features, the similarity between the target device and known devices is calculated to recognize the brand and model of target device. Based on 587 models of mobile devices of 17 widely used brands such as Apple and Samsung, we carry out device recognition experiments. The results show that Recognizer can identify the device’s brand and model than existing methods more effectively. In average, the model recognition accuracy of Recognizer is 99.08% (+ 9.25%↑) when using 20 features and 92.08% (+ 29.26%↑) when using 13 features.

Abstract In coal mining areas, surface subsidence poses significant risks to human life and property. Fortunately, surface subsidence caused by coal mining can be monitored and predicted by using various methods, e.g., probability integral method and deep learning (DL) methods. Although DL methods show promise in predicting subsidence, they often lack accuracy due to insufficient consideration of spatial correlation and temporal nonlinearity. Considering this issue, we propose a novel DL-based approach for predicting mining surface subsidence. Our method employs K-means clustering to partition spatial data, allowing the application of a gate recurrent unit (GRU) model to capture nonlinear relationships in subsidence time series within each partition. Optimization using snake optimization (SO) further enhances model accuracy globally. Validation shows our method outperforms traditional Long Short-Term Memory (LSTM) and GRU models, achieving 99.1% of sample pixels with less than 8 mm absolute error.

Abstract Obstructive sleep apnea (OSA) is a common sleep respiratory disease. Previous studies have found that the wakefulness electroencephalogram (EEG) of OSA patients has changed, such as increased EEG power. However, whether the microstates reflecting the transient state of the brain is abnormal is unclear during obstructive hypopnea (OH). We investigated the microstates of sleep EEG in 100 OSA patients. Then correlation analysis was carried out between microstate parameters and EEG markers of sleep disturbance, such as power spectrum, sample entropy and detrended fluctuation analysis (DFA). OSA_OH patients showed that the microstate C increased presence and the microstate D decreased presence compared to OSA_withoutOH patients and controls. The fifth microstate E appeared during N1-OH, but the probability of other microstates transferring to microstate E was small. According to the correlation analysis, OSA_OH patients in N1-OH showed that the microstate D was positively correlated with delta power, and negatively correlated with beta and alpha power; the transition probability of the microstate B → C and E → C was positively correlated with alpha power. In other sleep stages, the microstate parameters were not correlated with power, sample entropy and FDA. We might interpret that the abnormal transition of brain active areas of OSA patients in N1-OH stage leads to abnormal microstates, which might be related to the change of alpha activity in the cortex.

The original version of this Article contained an error in Reference 34, which was incorrectly given as: