Fetal electrocardiograph (fECG) R-wave detection from maternal abdominal ECGs (aECGs) is challenging as it is easily distorted and often overwhelmed by a variety of unavoidable interference signals. In this study, we combined RR time-series smoothing with a template-matching (TM) approach to detect fECG R waves from a single-lead aECG. According to the quasi-periodicity of the R wave, we analyzed the cases of misaligned and missed R wave detection in the RR time series and developed an amendment algorithm to distinguish and correct them. The fECG R wave detection consisted of two stages. Firstly, an fECG R wave, which did not overlap with the maternal ECG QRS complex (mQRS), was detected. Subsequently, the amendment algorithm corrected the misaligned and missed R waves and predicted the approximate region of the fECG R wave that overlapped with the mQRS. A TM algorithm was implemented to detect the fECG R wave that overlapped with the mQRS in the approximate region. The fECG QRS complex (fQRS) and mQRS templates were built from the detected fQRS and mQRS that did not overlap with the fQRS, respectively, using singular value decomposition. The TM algorithm then determined the accurate location when the most optimal correlation occurred between the actual mQRS and the superposition of the mQRS and fQRS templates in the approximate region. The performance of the proposed method was assessed using F1 measure and Bland-Altman analysis. High accuracy values were obtained corresponding to 95.

Due to the complexity of human emotions, there are some similarities between different emotion features. The existing emotion recognition method has the problems of difficulty of character extraction and low accuracy, so the bidirectional LSTM and attention mechanism based on the expression EEG multimodal emotion recognition method are proposed. Firstly, facial expression features are extracted based on the bilinear convolution network (BCN), and EEG signals are transformed into three groups of frequency band image sequences, and BCN is used to fuse the image features to obtain the multimodal emotion features of expression EEG. Then, through the LSTM with the attention mechanism, important data is extracted in the process of timing modeling, which effectively avoids the randomness or blindness of sampling methods. Finally, a feature fusion network with a three-layer bidirectional LSTM structure is designed to fuse the expression and EEG features, which is helpful to improve the accuracy of emotion recognition. On the MAHNOB-HCI and DEAP datasets, the proposed method is tested based on the MATLAB simulation platform. Experimental results show that the attention mechanism can enhance the visual effect of the image, and compared with other methods, the proposed method can extract emotion features from expressions and EEG signals more effectively, and the accuracy of emotion recognition is higher.

Deep learning has been widely used in the field of image classification and image recognition and achieved positive practical results. However, in recent years, a number of studies have found that the accuracy of deep learning model based on classification greatly drops when making only subtle changes to the original examples, thus realizing the attack on the deep learning model. The main methods are as follows: adjust the pixels of attack examples invisible to human eyes and induce deep learning model to make the wrong classification; by adding an adversarial patch on the detection target, guide and deceive the classification model to make it misclassification. Therefore, these methods have strong randomness and are of very limited use in practical application. Different from the previous perturbation to traffic signs, our paper proposes a method that is able to successfully hide and misclassify vehicles in complex contexts. This method takes into account the complex real scenarios and can perturb with the pictures taken by a camera and mobile phone so that the detector based on deep learning model cannot detect the vehicle or misclassification. In order to improve the robustness, the position and size of the adversarial patch are adjusted according to different detection models by introducing the attachment mechanism. Through the test of different detectors, the patch generated in the single target detection algorithm can also attack other detectors and do well in transferability. Based on the experimental part of this paper, the proposed algorithm is able to significantly lower the accuracy of the detector. Affected by the real world, such as distance, light, angles, resolution, etc., the false classification of the target is realized by reducing the confidence level and background of the target, which greatly perturbs the detection results of the target detector. In COCO Dataset 2017, it reveals that the success rate of this algorithm reaches 88.7%.

Electronic noses are one of the predominant technological choices for gas mixture detection, but their application in real-world atmospheric environments still leaves several issues to be resolved. The key bottleneck is the effect of turbulence caused by the diffusion of gases in the atmosphere on the quantitative and qualitative analytical performance of the electronic nose. In light of this, this paper presents a quantitative and qualitative analysis strategy for gas mixture monitoring. This strategy adopts baseline manipulation of the raw sensor data to reduce drift interference, and then performs feature extraction on the multidimensional response signals of the MOS gas sensor array using principal component analysis (PCA). In order to improve gas mixture recognition accuracy, the whale optimization algorithm (WOA) is used to optimize the network structure of the long short-term memory (LSTM) model for turbulent gas mixture composition recognition. The least squares support vector machine (LSSVM) algorithm is adopted to implement turbulent gas mixture concentration prediction. This paper focuses on two aspects of hyper-parameter optimization for the development of an LSSVM with particle swarm optimization (PSO) and for improved training sample selection for the LSSVM which should subsequently increase the accuracy of concentration estimation. The effectiveness of the proposed strategy is evaluated with a dataset from a chemical sensor array exposed to turbulent gas mixtures. Experimental results revealed that the proposed strategy for turbulent gas mixtures has satisfactory outcomes for both qualitative gas composition recognition and quantitative gas concentration prediction.