计算机科学最新文献

In this paper, through the study on the transformation of lung sound signal into image feature signal processing, we further mastered the processing process of lung sound signal, and used the new neural network model to identify and diagnose the image features of lung sound, effectively improving the effect of clinical AI-assisted diagnosis. To solve the problem that the traditional neural network model cannot obtain the temporal and spatial characteristics of lung sound signals at the same time, we propose a DCCLSTM (Dual-Channel Convolutional neural network for Long- and Short-Time Memory) to obtain spatial information and temporal information features of lung sound simultaneously. New features are generated by weighted fusion, which can effectively make up for the problem that the resolution of the feature map extracted by the traditional neural network model is reduced. This report presents the results of studies conducted on the lung sound dataset, and the accuracy rate of Dalal_CNN with the best effect was 89.56%. The DCCLSTM proposed in this study has a recognition accuracy of 97.40%. Experiments show that the DCCLSTM method is more accurate than the Dalal_CNN method.

mpisee is a lightweight profiling tool designed to track MPI communication operations per communicator, providing fine-grained insights into MPI applications that use communicators to partition MPI communication. While existing profiling tools offer valuable information, they may limit detailed analysis and optimization for such MPI applications, as they do not associate MPI communication with their communicator. Additionally, mpisee categorizes MPI communication operations based on message size, offering more granular information. It uses an SQLite database to efficiently store the profiling data, enabling users to analyze the application's profile from various perspectives, focusing on specific MPI ranks, operations, and more. Our analysis shows that mpisee incurs less than 5% overhead, performing on par with other state-of-the-art profilers. We demonstrate mpisee 's effectiveness by profiling and analyzing an FFT application, revealing potential performance bottlenecks related to the MPI_Alltoallv collective operation on small communicators and insights not available by other profilers. Leveraging this detailed information, we improved the application's overall performance by selecting different algorithms for MPI_Alltoallv and measuring their performance on different communicators with mpisee. This study illustrates mpisee 's utility and highlights the significant advantages of a communicator-centric approach in MPI profiling.

This article proposes a data-driven model predictive control (MPC) method for multirotor collision avoidance, considering uncertainties and the unknown dynamics caused by a payload. To address this challenge, sparse identification of nonlinear dynamics (SINDy) is employed to derive the governing equations of the multirotor system. SINDy is capable of discovering the equations of target systems from limited data, under the assumption that a few dominant functions primarily characterize the system's behavior. In addition, a data collection framework that combines a baseline controller with MPC is proposed to generate diverse trajectories for model identification. A candidate function library, informed by prior knowledge of multirotor dynamics, along with a normalization technique, is utilized to enhance the accuracy of the SINDy-based model. Using data-driven model from SINDy, MPC is used to achieve accurate trajectory tracking while satisfying state and input constraints, including those for obstacle avoidance. Simulation results demonstrate that SINDy can successfully identify the governing equations of the multirotor system, accounting for mass parameter uncertainties and aerodynamic effects. Furthermore, the results confirm that the proposed method outperforms conventional MPC, which suffers from parameter uncertainty and an unknown aerodynamic model, in both obstacle avoidance and trajectory tracking performance.

Accurate land cover classification of urban aerial imagery presents significant challenges, particularly in recognising small objects and similar-appearing features (e.g., flat land, prepared land for cultivation, crop growing areas and built-up regions along with ground water resource areas). These challenges arise due to the irregular scaling of extracted features at various rates from complex urban scenes and the mismatch in feature information flow across channels, ultimately affecting the overall accuracy (OA) of the network. To address these issues, we propose the scale-wise interaction fusion network (SIFN) for land cover classification of urban scene imagery. The SIFN comprises four key modules: multi-scale feature extraction, scale-wise interaction, feature shuffle-fusion and adaptive mask generation. The multi-scale feature extraction module captures contextual information across different dilation rates of convolutional layers, effectively handling varying object sizes. The scale-wise interaction module enhances the learning of multi-scale contextual features, while the feature shuffle-fusion module facilitates cross-scale information exchange, improving feature representation. Lastly, adaptive mask generation ensures precise boundary delineation and reduces misclassification in transitional zones. The proposed network significantly improves boundary masking accuracy for small and similar objects, thereby enhancing the overall land cover classification performance.