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Biomedical image segmentation plays a pivotal role in medical imaging, facilitating precise identification and delineation of anatomical structures and abnormalities. This review explores the application of the Denoising Diffusion Probabilistic Model (DDPM) in the realm of biomedical image segmentation. DDPM, a probabilistic generative model, has demonstrated promise in capturing complex data distributions and reducing noise in various domains. In this context, the review provides an in-depth examination of the present status, obstacles, and future prospects in the application of biomedical image segmentation techniques. It addresses challenges associated with the uncertainty and variability in imaging data analyzing commonalities based on probabilistic methods. The paper concludes with insights into the potential impact of DDPM on advancing medical imaging techniques and fostering reliable segmentation results in clinical applications. This comprehensive review aims to provide researchers, practitioners, and healthcare professionals with a nuanced understanding of the current state, challenges, and future prospects of utilizing DDPM in the context of biomedical image segmentation. [ABSTRACT FROM AUTHOR]

With the rapid development of space programs in various countries, the number of satellites in space is rising continuously, which makes the space environment increasingly complex. In this context, it is essential to improve space object identification technology. Herein, it is proposed to perform intelligent detection of space objects by means of deep learning. To be specific, 49 authentic 3D satellite models with 16 scenarios involved are applied to generate a dataset comprising 17,942 images, including over 500 actual satellite Palatino images. Then, the five components are labeled for each satellite. Additionally, a substantial amount of annotated data is collected through semi-automatic labeling, which reduces the labor cost significantly. Finally, a total of 39,000 labels are obtained. On this dataset, RepPoint is employed to replace the 3 × 3 convolution of the ElAN backbone in YOLOv7, which leads to YOLOv7-R. According to the experimental results, the accuracy reaches 0.983 at a maximum. Compared to other algorithms, the precision of the proposed method is at least 1.9% higher. This provides an effective solution to intelligent recognition for spatial target components. [ABSTRACT FROM AUTHOR]

In this paper, the finite-time trajectory tracking control problem of a flexible link manipulator (FLM) system with unknown parameters is investigated in joint space. An adaptive nonsingular terminal sliding mode (ANTSM) controller based on an extended state observer (ESO) is proposed to ensure that the tracking error converges to a small neighborhood of zero. Firstly, an adaptive ESO is introduced, which is used to estimate unknown system parameters, including the unknown rigid-flexible coupling coefficient and the unknown control gain. Then, an ESO based on the parameters estimated above is proposed to estimate the unmodeled dynamics and external disturbances of the system in real-time. Finally, an ANTSM controller based on ESO is studied to achieve finite-time trajectory tracking control and vibration suppression without any prior knowledge of system uncertainties and external disturbances. The finite-time convergence ability of the closed-loop system is proved by Lyapunov theory. Experimental results on the Quanser Flexible Link System verify the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]