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To make unmanned surface vehicles that are better applied to the field of environmental monitoring in inland rivers, reservoirs, or coasts, we propose a global path-planning algorithm based on the improved A-star algorithm. The path search is carried out using the raster method for environment modeling and the 8-neighborhood search method: a bidirectional search strategy and an evaluation function improvement method are used to reduce the total number of traversing nodes; the planned path is smoothed to remove the inflection points and solve the path folding problem. The simulation results reveal that the improved A-star algorithm is more efficient in path planning, with fewer inflection points and traversing nodes, and the smoothed paths are more to meet the actual navigation demands of unmanned surface vehicles than the conventional A-star algorithm. [ABSTRACT FROM AUTHOR]

In addressing the challenge of ships being prone to occlusion in multi‐target situations during ship target detection, leading to missed and false detections, this paper proposes an enhanced ship detection algorithm for YOLOv7‐tiny. The proposed method incorporates several key modifications. Firstly, it introduces the Convolutional Block Attention Module in the Backbone section of the original model, emphasizing position information while attending to channel features to enhance the network's ability to extract crucial information. Secondly, it replaces standard convolution with GSConv convolution in the Neck section, preserving detailed information and reducing computational load. Subsequently, the lightweight operator Content‐Aware ReAssembly of Features is employed to replace the original nearest‐neighbour interpolation, mitigating the loss of feature information during the up‐sampling process. Finally, the localization loss function, SIOU Loss, is utilized to calculate loss, expedite training convergence, and enhance detection accuracy. The research results indicate that the precision of the improved model is 91.2%, mAP@0.5 is 94.5%, and the F1‐score is 90.7%. These values are 3.7%, 5.5%, and 4.2% higher than those of the original YOLOv7‐tiny model, respectively. The improved model effectively enhances detection accuracy. Additionally, the improved model achieves an FPS of 145.4, meeting real‐time requirements. [ABSTRACT FROM AUTHOR]

Abstract: A continuous and analytic channel potential solution for undoped (lightly doped) surrounding-gate (SRG) MOSFETs is presented in this article. It is based on the exact solution of Poisson’s equation, allowing the surface potential to be adequately described from the sub-threshold to strong inversion region. It is demonstrated that the analytic channel potential characteristics agree with the Newton–Raphson iterative solutions for all ranges of gate and quasi-Fermi-potential. [Copyright &y& Elsevier]

Abstract: An explicit current–voltage model for undoped double-gate MOSFETs based on an accurate analytic approximation to the carrier concentration is presented in this paper. An analytic approximation solution to the carrier (electron) concentration is derived from the Taylor expansion of the exact solution of the Poisson equation. This analytic approximation gives a highly accurate result of the electron concentration when compared with that evaluated by Newton–Raphson iterative. The resulting electron concentration is then coupled to the Pao–Sah current equation to produce an explicit current–voltage model for symmetric undoped double-gate MOSFETs. The model predictions have been extensively validated by numerical simulations. [Copyright &y& Elsevier]