The research progress on COF solid-state electrolytes for lithium batteries.

Lithium metal batteries have garnered significant attention due to their high energy density and broad application prospects. However, the practical use of traditional liquid electrolytes is constrained by safety and stability challenges. In the exploration of novel electrolytes, solid-state electrolyte materials have emerged as a focal point. Covalent organic frameworks (COFs), with their large conjugated structures and unique electronic properties, are gradually gaining attention as an emerging class of solid-state electrolyte materials. In recent years, outstanding electrochemical performance has been achieved through the design and synthesis of various types of COF-based solid-state electrolytes, along with successful integration with other functional materials. This review will provide an overview of the research progress on COFs as solid-state electrolyte materials for lithium metal batteries and offer insights into their future potential in battery technology.

STS-TransUNet: Semi-supervised Tooth Segmentation Transformer U-Net for dental panoramic image.

In this paper, we introduce a novel deep learning method for dental panoramic image segmentation, which is crucial in oral medicine and orthodontics for accurate diagnosis and treatment planning. Traditional methods often fail to effectively combine global and local context, and struggle with unlabeled data, limiting performance in varied clinical settings. We address these issues with an advanced TransUNet architecture, enhancing feature retention and utilization by connecting the input and output layers directly. Our architecture further employs spatial and channel attention mechanisms in the decoder segments for targeted region focus, and deep supervision techniques to overcome the vanishing gradient problem for more efficient training. Additionally, our network includes a self-learning algorithm using unlabeled data, boosting generalization capabilities. Named the Semi-supervised Tooth Segmentation Transformer U-Net (STS-TransUNet), our method demonstrated superior performance on the MICCAI STS-2D dataset, proving its effectiveness and robustness in tooth segmentation tasks.

Time-Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems.

Blind modulation classification is an important step in implementing cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information about channel parameters and the overlapping of signals in MIMO systems, the traditional likelihood-based and feature-based approaches cannot be applied in these scenarios directly. Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time-frequency analysis method based on the windowed short-time Fourier transform was used to analyze the time-frequency characteristics of time-domain modulated signals. Then, the extracted time-frequency characteristics are converted into red-green-blue (RGB) spectrogram images, and the convolutional neural network based on transfer learning was applied to classify the modulation types according to the RGB spectrogram images. Finally, a decision fusion module was used to fuse the classification results of all the receiving antennas. Through simulations, we analyzed the classification performance at different signal-to-noise ratios (SNRs); the results indicate that, for the single-input single-output (SISO) network, our proposed scheme can achieve 92.37% and 99.12% average classification accuracy at SNRs of -4 and 10 dB, respectively. For the MIMO network, our scheme achieves 80.42% and 87.92% average classification accuracy at -4 and 10 dB, respectively. The proposed method greatly improves the accuracy of modulation classification in MIMO networks.