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A novel strategy for segmenting mpox virus images is proposed to address the challenge of distinguishing lesion areas from muscle tissue and other regions of infection. This strategy leverages a multiscale inflated convolutional feature fusion and attentional Swin-Unet approach. In this method, a multi-scale extended convolution module is employed in the coding stage of the Swin-Unet network to enhance complementary features while preserving different features at different scales. Additionally, a triple attention module is integrated into the downsampling process to address the issue of inter-channel independence. Finally, the Swin Transformer Block is utilized to modulate the network segmentation performance by adjusting the iteration count in the encoding and decoding regions. Experimental results on a self-constructed mpox dataset demonstrate that the proposed network achieves a pixel segmentation accuracy of 90.4% and an average intersection-over-union ratio of 80.3%. These values represent improvements of 8.6% and 14.6%, respectively, compared to the original Swin-Unet. This enhancement provides valuable support for the ancillary diagnosis of mpox.

There are many decisions which are usually made heuristically both in single object tracking (SOT) and multiple object tracking (MOT). Existing methods focus on tackling decision-making problems on special tasks in tracking without a unified framework. In this paper, we propose a decision controller (DC) which is generally applicable to both SOT and MOT tasks. The controller learns an optimal decision-making policy with a deep reinforcement learning algorithm that maximizes long term tracking performance without supervision. To prove the generalization ability of DC, we apply it to the challenging ensemble problem in SOT and tracker-detector switching problem in MOT. In the tracker ensemble experiment, our ensemble-based tracker can achieve leading performance in VOT2016 challenge and the light version can also get a state-of-the-art result at 50 FPS. In the MOT experiment, we utilize the tracker-detector switching controller to enable real-time online tracking with competitive performance and 10× speed up.

Event-triggered consensus problem is studied for a class of heterogeneous first-order multi-agent system, which contains multiple single integrators with different nominal velocities, and an adaptive consensus algorithm in the leader-following structure is adopted. Under the event-triggered mechanism, the agents' inputs are only updated at discrete times determined by a centralized event-triggered function. By constructing proper Lyapunov function, sufficient consensus conditions are gained for the asymptotic consensus convergence. Furthermore, the consensus problem subject to identical input delay is considered, and consensus conditions in the form of linear matrix inequalities are obtained. Numerical simulations show the effectiveness of the event-triggered consensus control strategy.