扫描覆盖问题一直是无线传感网络的热点问题,目前大多数研究主要集中于同构无人机扫描覆盖问题,目标为无人机数量最小的情况下对区域节点达到全覆盖。近年来,扫描覆盖问题衍生出新的方向,即MTMC(min-time max-coverage)问题,即使用有限...扫描覆盖问题一直是无线传感网络的热点问题,目前大多数研究主要集中于同构无人机扫描覆盖问题,目标为无人机数量最小的情况下对区域节点达到全覆盖。近年来,扫描覆盖问题衍生出新的方向,即MTMC(min-time max-coverage)问题,即使用有限无人机对区域节点进行扫描覆盖,使得覆盖率尽可能大的同时任务时间尽可能小。在考虑了无人机异构性的基础上,分析了MTMC问题的数学模型,提出了CWBGAA(CW Based on Genetic Annealing Algorithm optimization)算法解决MTMC问题。上述算法分为两阶段解决问题,第一阶段基于启发式插入算法生成每架无人机对应的飞行路径,第二阶段基于遗传退火算法对生成后路径进行路径优化,使得无人机的飞行时间降低。仿真结果表明,CWBGAA算法相较于其它算法拥有更好的性能,提升覆盖率的同时降低了任务执行时间。展开更多
Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art performance.Recent efforts have e...Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art performance.Recent efforts have explored the incorporation of Transformers to augment network performance in SISR.However,the high computational cost of Transformers makes them less suitable for deployment on lightweight devices.Moreover,the majority of enhancements for CNNs rely predominantly on small spatial convolutions,thereby neglecting the potential advantages of large kernel convolution.In this paper,the authors propose a Multi-Perception Large Kernel convNet(MPLKN)which delves into the exploration of large kernel convolution.Specifically,the authors have architected a Multi-Perception Large Kernel(MPLK)module aimed at extracting multi-scale features and employ a stepwise feature fusion strategy to seamlessly integrate these features.In addition,to enhance the network's capacity for nonlinear spatial information processing,the authors have designed a Spatial-Channel Gated Feed-forward Network(SCGFN)that is capable of adapting to feature interactions across both spatial and channel dimensions.Experimental results demonstrate that MPLKN outperforms other lightweight image super-resolution models while maintaining a minimal number of parameters and FLOPs.展开更多
本文提供的用 Turbo-C 实现的软件集成器能把在物理上分散的软件,从逻辑上集成于一个统一的界面之下。采用控制与数据分离的办法,用户不必修改和重编译 C 程序,而只需用文字处理器修改菜单文本文件和一个批处理文件,就能适应用户的特殊...本文提供的用 Turbo-C 实现的软件集成器能把在物理上分散的软件,从逻辑上集成于一个统一的界面之下。采用控制与数据分离的办法,用户不必修改和重编译 C 程序,而只需用文字处理器修改菜单文本文件和一个批处理文件,就能适应用户的特殊需要.展开更多
文摘扫描覆盖问题一直是无线传感网络的热点问题,目前大多数研究主要集中于同构无人机扫描覆盖问题,目标为无人机数量最小的情况下对区域节点达到全覆盖。近年来,扫描覆盖问题衍生出新的方向,即MTMC(min-time max-coverage)问题,即使用有限无人机对区域节点进行扫描覆盖,使得覆盖率尽可能大的同时任务时间尽可能小。在考虑了无人机异构性的基础上,分析了MTMC问题的数学模型,提出了CWBGAA(CW Based on Genetic Annealing Algorithm optimization)算法解决MTMC问题。上述算法分为两阶段解决问题,第一阶段基于启发式插入算法生成每架无人机对应的飞行路径,第二阶段基于遗传退火算法对生成后路径进行路径优化,使得无人机的飞行时间降低。仿真结果表明,CWBGAA算法相较于其它算法拥有更好的性能,提升覆盖率的同时降低了任务执行时间。
文摘Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art performance.Recent efforts have explored the incorporation of Transformers to augment network performance in SISR.However,the high computational cost of Transformers makes them less suitable for deployment on lightweight devices.Moreover,the majority of enhancements for CNNs rely predominantly on small spatial convolutions,thereby neglecting the potential advantages of large kernel convolution.In this paper,the authors propose a Multi-Perception Large Kernel convNet(MPLKN)which delves into the exploration of large kernel convolution.Specifically,the authors have architected a Multi-Perception Large Kernel(MPLK)module aimed at extracting multi-scale features and employ a stepwise feature fusion strategy to seamlessly integrate these features.In addition,to enhance the network's capacity for nonlinear spatial information processing,the authors have designed a Spatial-Channel Gated Feed-forward Network(SCGFN)that is capable of adapting to feature interactions across both spatial and channel dimensions.Experimental results demonstrate that MPLKN outperforms other lightweight image super-resolution models while maintaining a minimal number of parameters and FLOPs.
