An energy-efficient ACO-based multipath routing algorithm (EAMR) is proposed for energy-constrained wireless sensor networks. EAMR is a hybrid multipath algorithm, which is reactive in path discovery and proactive in route maintenance. EAMR has improvement and innovation in the ant packet structure, pheromone update formula, pheromone update mode, and the mechanism of multipath. Average energy consumption and congestion of path make pheromone update formula more reasonable. Incremental pheromone update mode may easily lead to local optimum. The pheromone will be thoroughly updated when a node receives a backward ant. EAMR makes an innovation in multipath mechanism which becomes more reasonable to multipath between source node and destination node. Probabilistic routing mechanism is designed to make stream flow into network more balanced. The simulation results show that the proposed algorithm achieves an improvement in energy efficiency, packet delivery ratio, and endto-end delay.
Tomatoes are among the very important crops grown worldwide. However, tomato diseases can harm the health of tomato plants during growth and reduce tomato yields over large areas. The development of computer vision technology offers the prospect of solving this problem. However, traditional deep learning algorithms require a high computational cost and several parameters. Therefore, a lightweight tomato leaf disease identification model called LightMixer was designed in this study. The LightMixer model comprises a depth convolution with a Phish module and a light residual module. Depth convolution with the Phish module represents a lightweight convolution module designed to splice nonlinear activation functions with depth convolution as the backbone; it also focuses on lightweight convolutional feature extraction to facilitate deep feature fusion. The light residual module was built based on lightweight residual blocks to accelerate the computational efficiency of the entire network architecture and reduce the information loss of disease features. Experimental results show that the proposed LightMixer model achieved 99.3% accuracy on public datasets while requiring only 1.5 M parameters, an improvement over other classical convolutional neural network and lightweight models, and can be used for automatic tomato leaf disease identification on mobile devices.
With the continuous advancement in computer vision, image segmentation has achieved fruitful results in many applications such as medical image processing. In recent years, UNet and Dual Path Network (DPN) have achieved promising results in medical image segmentation. UNet cannot effectively obtain new features and reuse features. Also, DPN cannot effectively transfer the contour information of shallow blocks to the subsequent deep blocks. This paper proposes a dual path U-shaped network with the attention mechanism (Attention-DPU); taking advantage of the two networks. In the proposed network, the ordinary convolutional layer is replaced by the micro block with a dual path. Also, the attention mechanism is adopted to improve the efficiency and accuracy of segmentation.
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