Signal modulation classification is an important research subject in both military and civilian field. This study proposed a novel blind modulation classification method based on the time–frequency distribution and convolutional neural network (CNN). This is the first attempt to treat the time–frequency map as a picture and use an outstanding (CNN‐based) algorithm in computer vision area for signal recognition. The combination offers a novel feature extraction strategy, to some extent, which also conforms to intuition. Simulation results show that the method proposed in this study is efficient and robust and enables a high degree of automation for extracting features, training weights and making decisions. Additionally, a remarkable performance emerges with small samples and repeated training, which distinguishes this method from many other classification methods.

Since 2000, the research of uncertainty quantification (UQ) has been successfully applied in many fields and has been highly valued and strongly supported by academia and industry. This review firstly discusses the sources and the types of uncertainties and gives an overall discussion on the goal, practical significance, and basic framework of the research of UQ. Then, the core ideas and typical methods of several important UQ processes are introduced, including sensitivity analysis, uncertainty propagation, model calibration, Bayesian inference, experimental design, surrogate model, and model uncertainty analysis.

Glioma is a kind of brain disease with high incidence, high recurrence rate, high mortality, and low cure rate. To obtain accurate diagnosis results of brain glioma, doctors need to manually compare the imaging results of different modalities many times, which will increase the diagnosis time and reduce the diagnostic efficiency. Image fusion technology has been widely used in recent years to obtain information on multimodal medical images. This paper proposes a novel image fusion framework, target information enhanced image fusion network (TIEF), using cross-modal learning and information enhancement techniques. The framework consists of a multi-sequence feature extraction block, a feature selection block, and a fusion block. The multi-sequence feature extraction block consists of multiple sobel dense conv leaky ReLu block (SDCL-block). SDCL-block mainly realizes the extraction of edge features, shallow features, and deep features. The feature selection block identifies the feature channels with rich texture information and strong discrimination ability through the effective combination of global information entropy criterion and feature jump connection. The feature fusion block mainly comprises multi-head and spatial attention mechanisms, which can realize the fusion of intra-modality and inter-modality features. On this basis, considering the influence of tumor spatial location and structure information on the fusion results, a loss function is designed, which is a weighted combination of texture loss, structure loss, and saliency loss so that texture information from multimodal magnetic resonance imaging (MMRI) and saliency information from different anatomical structures of the brain can be fused at the same time to improve the expression ability of features. In this paper, the TIEF algorithm is trained and validated on the MMRI and (Single-Photon Emission Computed Tomography-MRI) SPECT-MRI datasets of glioma and generalized on the (Computed Tomography-MRI) CT-MRI dataset of meningioma to verify the performance of the TIEF algorithm. In the image fusion task, quantitative results showed that TIEF exhibited optimal or suboptimal performance in information entropy, spatial frequency, and average gradient metrics. Qualitative results indicate that the fused images can highlight tumor and edematous features. A downstream image segmentation task was used for evaluation to further verify TIEF’s effectiveness. TIEF achieved the best results in both (Dice similarity coefficient) Dice and (Hausdorff distance 95%) HD95 segmentation metrics. In the generalization task, quantitative results indicated that TIEF obtained more information in the meningioma dataset. In conclusion, TIEF can effectively achieve cross-domain information acquisition and fusion and has robustness and generalization ability.

Artificial intelligence-based models for global weather forecasting have advanced rapidly, but research on high-resolution data-driven limited area models remains scarce. Here we introduce a limited area artificial intelligence-based weather forecasting model with 3 km and 1 h resolutions, utilizing parallel global-local structures to capture multiscale meteorological features. The model is trained on high-resolution regional analysis data, and utilizes the global artificial intelligence-based model forecasts for the lateral boundary condition during prediction, and operates much faster than the dynamical forecast model. In two selected limited areas, it outperforms dynamical forecast models in surface wind speed forecasting but underperforms in surface temperature and pressure. Skills in surface temperature and pressure can be further improved comparable to the dynamical forecast model by providing better lateral boundary conditions. Issues related to lateral boundary conditions, such as selecting width of lateral boundary regions and combining finer and coarser resolution predictions in the regions, are also studied.

Epilepsy, as a serious neurological disorder, can be detected by analyzing the brain signals produced by neurons. Electroencephalogram (EEG) signals are the most important data source for monitoring these brain signals. However, these complex, noisy, nonlinear and nonstationary signals make detecting seizures become a challenging task. Feature-based seizure detection algorithms have become a dominant approach for automatic seizure detection. This study presents an algorithm for automatic seizure detection based on novel features with clinical and statistical significance. Our algorithms achieved the best results on two benchmark datasets, outperforming traditional feature-based methods and state-of-the-art deep learning algorithms. Accuracy exceeded 99.99% on both benchmark public datasets, with the 100% correct detection of all seizures on the second one. Due to the interpretability and robustness of our algorithm, combined with its minimal computational resource requirements and time consumption, it exhibited substantial potential value in the realm of clinical application. The coefficients of variation of datasets proposed by us makes the algorithm data-specific and can give theoretical guidance on the selection of appropriate random spectral features for different datasets. This will broaden the applicability scenario of our feature-based approach.

Gliomas have the highest mortality rate of all brain tumors. Correctly classifying the glioma risk period can help doctors make reasonable treatment plans and improve patients’ survival rates. This paper proposes a hierarchical multi-scale attention feature fusion medical image classification network (HMAC-Net), which effectively combines global features and local features. The network framework consists of three parallel layers: The global feature extraction layer, the local feature extraction layer, and the multi-scale feature fusion layer. A linear sparse attention mechanism is designed in the global feature extraction layer to reduce information redundancy. In the local feature extraction layer, a bilateral local attention mechanism is introduced to improve the extraction of relevant information between adjacent slices. In the multi-scale feature fusion layer, a channel fusion block combining convolutional attention mechanism and residual inverse multi-layer perceptron is proposed to prevent gradient disappearance and network degradation and improve feature representation capability. The double-branch iterative multi-scale classification block is used to improve the classification performance. On the brain glioma risk grading dataset, the results of the ablation experiment and comparison experiment show that the proposed HMAC-Net has the best performance in both qualitative analysis of heat maps and quantitative analysis of evaluation indicators. On the dataset of skin cancer classification, the generalization experiment results show that the proposed HMAC-Net has a good generalization effect.