This paper presents a new scheme to improve the performance of finger-vein identification systems. Firstly, a vein pattern extraction method to extract the finger-vein shape and orientation features is proposed. Secondly, to accommodate the potential local and global variations at the same time, a region-based matching scheme is investigated by employing the Scale Invariant Feature Transform (SIFT) matching method. Finally, the finger-vein shape, orientation and SIFT features are combined to further enhance the performance. The experimental results on databases of 426 and 170 fingers demonstrate the consistent superiority of the proposed approach.

The application of edge computing combined with the Internet of Things (edge-IoT) has been rapidly developed. It is of great significance to develop a lightweight network for gearbox compound fault diagnosis in the edge-IoT context. The goal of this paper is to devise a novel and high-accuracy lightweight neural network based on Legendre multiwavelet transform and multi-channel convolutional neural network (LMWT-MCNN) to fast recognize various compound fault categories of gearbox. The contributions of this paper mainly lie in three aspects: The feature images are designed based on the LMWT frequency domain and they are easily implemented in the MCNN model to effectively avoid noise interference. The proposed lightweight model only consists of three convolutional layers and three pooling layers to further extract the most valuable fault features without any artificial feature extraction. In a fully connected layer, the specific fault type of rotating machinery is identified by the multi-label method. This paper provides a promising technique for rotating machinery fault diagnosis in real applications based on edge-IoT, which can largely reduce labor costs. Finally, the PHM 2009 gearbox and Paderborn University bearing compound fault datasets are used to verify the effectiveness and robustness of the proposed method. The experimental results demonstrate that the proposed lightweight network is able to reliably identify the compound fault categories with the highest accuracy under the strong noise environment compared with the existing methods.

With the emergence of vehicular edge computing (VEC) and electric vehicles (EVs), integrating computation and charging tasks presents challenges due to limited resources and dynamic vehicular networks. This research focuses on the joint optimization of computation offloading and charging scheduling in VEC networks. Specifically, we optimize the offloading factor, charging association variable, and charging rates to minimize the system delay and energy consumption by leveraging the multi-attributes of EVs in both information and energy networks. Considering the dynamic environment, we model the problem as a Markov Decision Process, and use the Multi-Agent Reinforcement Learning (MARL) algorithm MADDPG, with its centralized training and distributed execution mechanisms. Simulation results demonstrate that this approach significantly improves utility while reducing energy consumption and latency.

Image segmentation plays a crucial role in breast ultrasound (BUS) for breast cancer detection. However, due to the heavy speckle noise, low contrast and shadowing effects of BUS images, it's a challenging task to develop an accurate and robust segmentation algorithm. In this paper, we present a novel algorithm for breast ultrasound image segmentation which is based on hybrid of level set and graph cuts. Firstly, speckle reducing anisotropic diffusion. Subsequently, the initial contour is achieved by graph cut. To smooth the boundaries, a boundary item is added into the energy function of level set. At last, the final contour converges to the objective boundary quickly and accurately after finite steps of iteration of level set. Comparing with the traditional level set method, the requirement of the costly re-initialization procedure is completely eliminated and the local minima are effectively avoided in our method. The experiment results show that the processing time for level set based method to BUS image can be substantially reduced while getting promising segmentation results.

In large-scale public security equipment projects, long-term equipment operation often leads to equipment failures and other problems, so it is particularly important to choose the appropriate operation and maintenance (O&M) scheme based on the content of the equipment work orders. However, there are a variety of equipment models in the work orders; not only is the data complex, but also due to the long project cycle, there are often problems such as loss of content, which bring great challenges to the O&M work. This paper defines these problems as “3-No problems”: inconsistency, inaccuracy, and incompleteness. In this paper, an improved DIKWP model is proposed and combined with a random forest classifier to construct data graphs, information graphs, knowledge graphs, and wisdom graphs. Through the above model, the 3-No problem in equipment work orders can be solved, and the importance of each equipment model can be obtained. Eventually, combined with the purpose graph, the selection of models, the bid score calculation, and the selection of O&M schemes are carried out based on the obtained conclusion in a purpose-driven manner to achieve the evaluation of O&M efficiency and safety. Finally, an example is assumed to illustrate the application of the method in actual projects, which provides a certain reference value for the selection of an O&M scheme for large-scale equipment projects.