Arrhythmia is a significant cause of death, and it is essential to analyze the electrocardiogram (ECG) signals as this is usually used to diagnose arrhythmia. However, the traditional time series classification methods based on ECG ignore the nonlinearity, temporality, or other characteristics inside these signals. This paper proposes an electrocardiogram classification method that encodes one-dimensional ECG signals into the three-channel images, named ECG classification based on Mix Time-series Imaging (EC-MTSI). Specifically, this hybrid transformation method combines Gramian angular field (GAF), recurrent plot (RP), and tiling, preserving the original ECG time series’ time dependence and correlation. We use a variety of neural networks to extract features and perform feature fusion and classification. This retains sufficient details while emphasizing local information. To demonstrate the effectiveness of the EC-MTSI, we conduct abundant experiments in a commonly-used dataset. In our experiments, the general accuracy reached 93.23%, and the accuracy of identifying high-risk arrhythmias of ventricular beats and supraventricular beats alone are as high as 97.4% and 96.3%, respectively. The results reveal that the proposed method significantly outperforms the existing approaches.

Brick elements are widely used for everything from designing toys to architectural designs. This article proposes an intelligent method to construct complex 3D brick models automatically. The proposed method is designed based on a set of core algorithms to generate a brick layout in accordance with a given 3D voxel model. In the system, each 3D model is sliced into tiers of flat vector polygons at first. Then, the vector polygons are converted to raster data for each tier. The bricks are built in a single tier until all tiers are completed. The proposed model is validated using two case studies: one is a series of solid sculpture models and the other is a building model. The results show that the proposed intelligent building system successfully builds visual models from brick assembly models. The proposed system can help engineers build large block models to improve the efficiency of constructing complex 3D building models.