The research was based on the image recognition technology of artificial intelligence, which is expected to assist physicians in making correct decisions through deep learning. The liver dataset used in this study was derived from the open source website (LiTS) and the data provided by the Kaohsiung Chang Gung Memorial Hospital. CT images were used for organ recognition and lesion segmentation; the proposed Residual-Dense-Attention (RDA) U-Net can achieve high accuracy without the use of contrast. In this study, U-Net neural network was used to combine ResBlock in ResNet with Dense Block in DenseNet in the coder part, allowing the training to maintain the parameters while reducing the overall recognition computation time. The decoder was equipped with Attention Gates to suppress the irrelevant areas of the image while focusing on the significant features. The RDA model was used to identify and segment liver organs and lesions from CT images of the abdominal cavity, and excellent segmentation was achieved for the liver located on the left side, right side, near the heart, and near the lower abdomen with other organs. Better recognition was also achieved for large, small, and single and multiple lesions. The study was able to reduce the overall computation time by about 28% compared to other convolutions, and the accuracy of liver and lesion segmentation reached 96% and 94.8%, with IoU values of 89.5% and 87%, and AVGDIST of 0.28 and 0.80, respectively.

Unmanned aerial vehicles (UAVs) have emerged as a promising solution for enhancing wireless networks, especially in challenging environments. However, recent studies that integrate reconfigurable intelligent surfaces (RIS) with UAVs tend to focus on limited aspects, such as single-UAV deployments or partial optimization of system parameters, thereby neglecting a comprehensive system-level design. To overcome these limitations, we propose a multi-user MISO communication network that leverages RIS-assisted UAVs to maximize both sum-rate and energy efficiency as two distinct objectives. Our approach stands out by considering multiple UAVs and incorporating four critical constraints: UAV flying areas, power limitations, transmit beamforming, and RIS requirements. We formulate separate optimization problems for sum-rate and energy efficiency, and address them using deep reinforcement learning (DRL) algorithms, namely proximal policy optimization (PPO) and deep deterministic policy gradient (DDPG). By jointly optimizing UAV coordinates, the transmit beamforming matrix, and RIS phase shifts, our method achieves significant performance improvements under dynamic environmental conditions. Extensive simulations show that our comprehensive strategy delivers higher sum-rates and enhanced energy efficiency, underscoring its practical potential for next-generation RIS-assisted UAV communication systems.

Due to the fact that previous studies have rarely investigated the recognition rate discrepancy and pathology data error when applied to different databases, the purpose of this study is to investigate the improvement of recognition rate via deep learning-based liver lesion segmentation with the incorporation of hospital data. The recognition model used in this study is H-DenseUNet, which is applied to the segmentation of the liver and lesions, and a mixture of 2D/3D Hybrid-DenseUNet is used to reduce the recognition time and system memory requirements. Differences in recognition results were determined by comparing the training files of the standard LiTS competition data set with the training set after mixing in an additional 30 patients. The average error value of 9.6% was obtained by comparing the data discrepancy between the actual pathology data and the pathology data after the analysis of the identified images imported from Kaohsiung Chang Gung Memorial Hospital. The average error rate of the recognition output after mixing the LiTS database with hospital data for training was 1%. In the recognition part, the Dice coefficient was 0.52 after training 50 epochs using the standard LiTS database, while the Dice coefficient was increased to 0.61 after adding 30 hospital data to the training. After importing 3D Slice and ITK-Snap software, a 3D image of the lesion and liver segmentation can be developed. It is hoped that this method could be used to stimulate more research in addition to the general public standard database in the future, as well as to study the applicability of hospital data and improve the generality of the database.

In this paper, we advocate the use of Grassmann manifolds for discovering object images in different states (e.g., unripe, peeled, etc.). We propose a novel dictionary learning algorithm, which derives the subspaces on a Grassmann manifold for describing each object state. By our introduced geodesic-flow constraint, our Grassmann manifold exhibits excellent capabilities in relating objects in distinct states (i.e., subspaces on the derived manifold), while the geodesics connecting different states can be viewed as transformations between the associated states. This is the reason why the use of our proposed Grassmann manifold can be applied to perform object state classification with improved performance. In our experiments, we provide quantitative and qualitative results to verify the effectiveness of our proposed method.