Electrocardiogram (ECG) signals are often used to diagnose cardiac status. However, most of the existing ECG diagnostic methods only use the time-domain information, resulting in some obviously lesion information in frequency-domain of ECG signals are not being fully utilized. Therefore, we propose a method to fuse the time and frequency domain information in ECG signals by convolutional neural network (CNN). First, we adapt multi-scale wavelet decomposition to filter the ECG signal; Then, R-wave localization is used to segment each individual heartbeat cycle; And then, the frequency domain information of this heartbeat cycle is extracted via fast Fourier transform. Finally, the temporal information is spliced with the frequency domain information and input to the neural network for classification. The experimental results show that the proposed method has the highest recognition accuracy (99.43%) of ECG singles compared with state-of-the-art methods.

Existing infrared small target detection algorithms often lack adaptability in complex scenes and heavily rely on parameter configurations. To address this limitation, we propose a novel infrared small target detection method based on adaptive region growing algorithm with iterative threshold analysis that leverages the homogenous compactness of the small target and discontinuity with its surroundings. Initially, the image undergoes adaptive splitting into multiple regions using an automatic seeded region growing (ASRG) algorithm, eliminating the need for preassigned seed points. Next, the segmentation results at each threshold are utilized to calculate the relative residual map (RRM) and local dissimilarity map (LDM), contributing to the selection of the optimal threshold. Finally, RRM and LDM corresponding to the optimal threshold are integrated to accurately characterize the small target signal while effectively removing background clutter. Experimental results show that the proposed method is effective in clutter removal and small target detection in diverse complex scenes, and is robust to the shape and size of targets.

Owing to the large range cell migration (RCM) and fast time-variant Doppler frequency modulation (DFM) generated by rapidly spinning targets, it is difficult to efficiently obtain well-focused inverse synthetic aperture radar (ISAR) images via conventional algorithms because of the multidimensional search requirement. Inspired by the inherent azimuth spatial invariance in strip-map synthetic aperture radar (SAR) imaging mode, an efficient range-Doppler domain ISAR imaging method for rapidly spinning targets is proposed in this article. First, echo signal is transformed into range-Doppler domain and its precise analytical expression is derived according to the principle of stationary phase (POSP). Second, the energy of scatterers distributed in different range cells is extracted along the rotating radius. By doing so, the energy is concentrated in the same range cell. After that, the high-order phase terms of the signal are compensated and the CLEAN technique is also applied to reduce the sidelobes of a strong scatterer. Finally, 3-D ISAR image of the spinning target is reconstructed by projecting the spatial parameters to 3-D cylindrical coordinates. Furthermore, in this article, the output signal-to-noise ratio (SNR) gain, anti-noise performance, the mismatched phase error, and the computational complexity analyses are also provided. Compared with existing approaches, the proposed method has advantages in the computational complexity and low SNR environment thanks to the only 1-D search and the coherent integration gain obtained. Both the theoretical derivations and the simulated results demonstrate the effectiveness of the proposed method.

High-dimensional data often cause the “curse of dimensionality” in data processing. Dimensionality reduction can effectively solve the curse of dimensionality and has been widely used in high-dimensional data processing. However, the existing dimensionality reduction algorithms neglect the effect of noise injection, failing to account for the datasets of large variance within classes and not effectively considering the stability of dimensionality reduction. To solve the problems, this paper proposes a weighted local discriminant preservation projection algorithm based on an ensemble imbedded mechanism with micro-noise injection (nwLPPD). The proposed algorithm aims to overcome the problem of large variance within classes and introduces an ensemble projection matrix via Bayesian fusion mechanism with micro-noise to enhance the antijamming capability of the model. Ten public datasets were used to verify the proposed algorithm. The experimental results demonstrated that the proposed algorithm is significantly effective, especially for the case of small sample datasets with high intraclass variance. The classification accuracy is improved by at least 10% compared to the case without dimensionality reduction. Even compared with some representative dimensionality reduction algorithms, the proposed nwLPPD has significantly superior classification performance.