The advantages of non-contact and embedded palm vein recognition technology in terms of security, privacy, and convenience are highlighted. Performance factors include error rate, rejection rate, and real-time processing, while engineering factors include small size, scalability, and cost-effectiveness. However, the non-contact sampling mode is susceptible to uncontrollable factors such as palm placement and posture, as well as external environmental influences like complex backgrounds and strong lighting. This paper proposes an omni-direction palm vein recognition (oPVR) method that integrates geometric and brightness multi-dimensional image quality feedback (MIQF), including integrity judgment, scale assessment, rotation angle detection. The method involves hand detection network, palm single-shot multibox detection (P-SSD) network for region of interest(ROI), and vein feature extraction network. These enhancements effectively improve recognition performance under environmental disturbances and pose variations. Based on a real-world database and a real-time demonstration system, a series of comparative tests and ablation experiments were conducted to validate the algorithm's accuracy, speed and usability for large-scale applications.

With the rapid development of unmanned aerial vehicle (UAV) technology, its applications in military and civilian fields are becoming increasingly widespread. However, realizing autonomous navigation of UAVs in complex environments still faces numerous challenges, especially the three-dimensional path planning problem. This paper proposes an improved parrot optimization algorithm (IPO) by introducing SPM chaotic mapping, adaptive switching factor, and hybrid Cauchy and Gaussian mutation strategies to enhance the algorithm's global search capability and convergence speed. The improved IPO is combined with the MATLAB simulation platform to construct a complete three-dimensional path-planning solution framework. Extensive simulation experiments demonstrate that, compared with the standard parrot optimization and other optimization algorithms, this algorithm significantly improves optimization accuracy, convergence speed, and path smoothness, showing good potential for engineering applications. The experimental results indicate that under the complex three-dimensional environment modeling of the Beijing International Studies University (BISU) campus, the improved IPO algorithm can quickly and accurately find the optimal collision-free path with minimal flight cost, which demonstrates excellent prospects for engineering applications.

Cross-view Image Geo-localization is the process of determining the geographic location of a ground-view query image by matching it with geotagged satellite or unmanned aerial vehicle (UAV) captured images. In the context of ground images characterized by a constrained field of view (FoV), the query image exhibits a reduced coverage area, limited scene content, and an unknown imaging direction. Furthermore, reference satellite images from the same location may contain significant feature redundancy. The existing methods are not able to achieve a high degree of accuracy in the positioning of cross-sight terrain images, which is a problem given the importance of this task. The above issues result in the existing methods providing low accuracy for cross-view localization of ground images with a limited FoV. We propose a cross-view image geo-localization method based on attention weight mask alignment. The Coordinate Attention (CA) mechanism, when integrated into a lightweight ResNet18 network to generate weight masks, enables precise alignment between limited FoV ground images and satellite image feature maps. This process aims to remove redundant areas in satellite images and improve localizing accuracy. Given that feature maps at different levels describe images at different granularities, this work also introduces a multi-scale feature fusion strategy. It generates more representative image descriptors by combining features from different convolutional layers. Experimental results on the CVUSA and CVACT_val benchmark datasets demonstrate that when the FoV of ground images to be located is 70° and 90° with a random imaging direction, the proposed method significantly improves location accuracy.

Currently, developers are often obligated to enhance code quality. High-quality code is often accompanied with comprehensive summaries, including code documentation and function explanations, which are invaluable for maintenance and further development. Regrettably, few software projects provide sufficient code comments owing to the high costs associated with human labeling. Contemporary researchers in software engineering concentrate on the methods for automated comment generating. Initial algorithms depended on handwritten templates or information retrieval methods. With the advancement of machine learning, researchers construct automated models for machine translation instead. Nonetheless, the produced code comments remain inadequate owing to the significant disparity between code structure and normal language. This study introduces a unique deep learning model, At-ComGen, which utilizes hybrid attention for the automated creation of source code comments. Utilizing two separate LSTM encoders, our approach integrates essential tokens from source code functions with the code structure, represented by a corresponding Abstract Syntax Tree. In contrast to earlier data-driven models, our methodology utilizes code syntax and semantics in the generation of comments. The hybrid attention method, used for comment creation for the first time to our knowledge, enhances the quality of code comments. The tests demonstrate that At-ComGen is efficacious and surpasses other prevalent methodologies. Machine comments from Seq2Seq and CODE-NN disregard code structure underlying DeepCom and At-ComGen. At-ComGen has 59.3%, 36.4%, 43.3%, and 13.1% higher comment BLEU values than baseline models for a 5-line function. Even though model performance reduces with comment length, At-ComGen's comments often outperform others. 5–10-word machine comments work best. For reference length 10, At-ComGen has 38.2%, 23.7%, 9.3%, and 4.4% greater BLEU values than the other baseline models.

Pipelines used for the hydraulic lifting of gas hydrate particles in deep-sea gas hydrates consume a large quantity of energy, so the level of efficient resource exploitation is very low and it is challenging to meet an efficient gas supply. Therefore, the article aims to optimize and analyze a process used for rigid pipe hydraulic lifting, an essential part of a deep-sea gas hydrate extraction system. First, the objective function is constructed considering the relationship between the extraction system’s parameters, and a specific energy consumption is set when the deep-sea gas hydrate extraction is under consideration. Then, the range of each parameter is determined according to the extraction system's actual situation. Secondly, the improved crow search algorithm with a hybrid strategy covering dynamic perception probability, Levy flight, and Cauchy variation mechanism is employed to solve the optimization model. Finally, the improved crow search algorithm is applied to the experimental settings and compared with other optimization algorithms. The experimental results show that the proposed method, which is, the improved crow search algorithm, has a good computational efficiency, can effectively realize the optimization of the parameters of the deep-sea natural gas hydrate system, and is robust to numerical fluctuations of the parameters. Thus, the performance of the pipeline is improved and the energy consumption of the system is effectively reduced. Eventually, a theoretical reference is provided for the development of deep-sea gas hydrate. The proposed algorithm, I-CSA, can effectively deal with larger sample data and maintain high computational efficiency with fewer MAPE results when the sample sizes increase. Eventually, it is helpful for the deep exploitation and utilization of deep-sea gas hydrate.

Facial expression analysis is one of the most essential tools for behavior interpretation and emotion modeling in Intelligent Human-Computer Interaction (HCI). Although humans can easily interpret facial emotions, computers have great difficulty doing so. Analyzing changes and deformations in the face is one of the methods through which machines can interpret facial expressions. However, maintaining great precision while being accurate, stable, and quick is still challenging in this field. To address this issue, this research presents an innovative and novel method to fully automatically extract critical features from a face during a facial expression. Various machine learning models are used on these features to analyze emotions. We used the optical flow algorithm to extract motion vectors divided into sections on the subject’s face. Finally, each section and its symmetric section were used to calculate a new vector. The final features produce a state-of-the-art accuracy of over 98% in emotion classification in the Extended Cohen-Kanade (CK+) facial expression dataset. Furthermore, we proposed an algorithm to filter the most important features with an SVM classifier and achieved an accuracy of over 97 % by only looking at 15% of the face area.

To meet the requirement of 3D object detection task , an efficient point clouds correlation enhancement RCNN(EPCE-RCNN) is proposed. The proposed method reduces the computational complexity and time consumption of the network through a lightweight proposal generation module, and accelerates the generation of the 3D proposal box. Meanwhile, during region of interest feature coding, the relevance among different grid points is enhanced through an efficient self-attention pooling module, so that the limitation that the pooling operation is influenced by the radius of a neighborhood query sphere is addressed. In addition, the combination of an attention mechanism and a feedforward network ensures the nonlinearity of the model, so that the model can perform feature expression better. Thus, the synchronous improvement of the network detection efficiency and the detection precision is realized. On the KITTI dataset, the detection accuracy of three difficulty levels reaches 89.99%, 81.69% and 77.17% respectively. Compared with the baseline Voxel-RCNN, the detection efficiency of EPCE-RCNN is improved by 12%. To verify the generalization and application value of the proposed method, a power equipment dataset with 3D label information is constructed, the 3D label frame information of the YCB dataset is also supplemented. Experiments are carried out on these datasets. In the experimental results of the verification set, the mAP of a mug, gelatin box, single clip, wedge clip and C clip can reach 37.67%, 40.06%, 35.63%, 30.01% and 37.31% respectively. Compared with the baseline, the proposed algorithm has a significant improvement and its generalization has been fully verified.

With the prosperity and development of the Maritime Silk Road, China's maritime industry has reached a new height. While the maritime transport industry has been vigorously developed, it has also brought great challenges to safe navigation. To realize intelligent navigation, effectively prevent maritime collision accidents, and improve navigation safety, a structural model of intelligent navigation obstacle avoidance platform based on Internet of Things technology is first proposed. Then the research combines the analytic hierarchy process, artificial neural network and BP neural network algorithm, and introduces environmental factors to design an optimized intelligent navigation obstacle avoidance algorithm, so that the algorithm can make real-time intelligent adjustment strategies according to the changes of the actual environment. Finally, the collision risk at the location of the research ship is judged, and the priority list of obstacle avoidance is constructed by the risk value between different ships and the research ship, providing reference for the pilot. The research results show that the prediction accuracy of I-INOA algorithm is 97.83%. In the two obstacle avoidance experiments, the decision-making efficiency of the four ships based on I-IONA algorithm is the highest, which is 1. In practical application, the priority list of obstacle avoidance is P, O and S2. In conclusion, I-INOA algorithm has better performance and practicability, enabling the research ship to respond more intelligently and quickly.

This paper introduces a method to enhance 3D human pose estimation accuracy by leveraging human topological structure and temporal information, addressing inaccuracies due to occlusion and complex poses. It proposes a spatiotemporal Transformer network that aggregates local temporal information to predict 3D poses for video frames, reducing sequence length through cross-step convolution. To further handle occlusion and information loss, the paper suggests a spatiotemporal graph attention network that incorporates spatial constraints and graph convolution with an improved adjacency matrix to emphasize local information in pose inference. A temporal convolutional network is also employed to model time, and the network alternates between temporal and spatial attention modules to prevent spatiotemporal information loss. Experiments on Human3.6m and HumanEva datasets demonstrate that the proposed method outperforms other approaches in prediction accuracy.

Brain tumor segmentation in medical image analysis is a challenging task. Deep learning techniques have recently shown promise in resolving a variety of computer vision problems, such as semantic segmentation and image classification. Brain MRI (magnetic resonance imaging) requires precise brain image segmentation for effective, rapid diagnosis and treatment planning. However, it is quite difficult to manually segment the brain image rapidly and accurately in low-quality, noisy images. This paper proposes a U-Net and combined attention mechanism-based method. This research enhances the segmentation of images of tumors in the brain using modified U-net. Traditional U-net segmentation techniques are still widely used in the medical field, but they have a number of limitations when dealing with small targets and fuzzier boundaries. To address this issue, we made the following modifications to U-net: We propose attention mechanisms to assist the network in concentrating on important regions. The multiscale feature fusion strategy improves the efficacy of network segmentation at various scales. Cross-entropy loss function and data augmentation improve the performance of the network further. Our method was validated using the Brats2019 dataset. The experimental results demonstrate that our proposed methodology exhibits superior speed and efficiency compared to existing techniques in the context of brain image segmentation. The dice coefficients for the multiple branch TS-U-net model were 0.876, 0.868, and 0.814 in the tumor subregions of WT, TC, and ET, respectively. This exemplifies the feasibility and potential of our methodology for the segmentation of medical images.