To achieve the Internet of Things (IoT) vision, Mobile Edge Computing (MEC) is a promising technology aimed at providing low-latency computing services to user equipment (UE). However, terrestrial MEC network struggles to provide service to UEs in remote and maritime region. Low Earth Orbit (LEO) satellite networks have the potential to overcome geographical restrictions and provide seamless global coverage for UEs. In this paper, we provide the first attempt to use stochastic geometry to investigate the performance of implementing space caching with LEO satellites (SATs) in the MEC network. We study a LEO satellite-assisted space caching MEC network, and LEO SATs can be equipped with servers to enable space caching, with the advantage of seamless coverage to assist terrestrial CSs for serving UEs in remote or maritime reigon. Using stochastic geometry and queuing theory, we establish an analytical framework for this MEC network. Meanwhile, we develop association strategies for UEs to connect with LEO SATs or CSs and utilize stochastic geometry to derive uplink and downlink coverage probabilities, considering the diversity of task and service types. On this basis, we employ the queuing theory to calculate the average delay to evaluate the system performance. Through Monte Carlo simulations and numerical results, the system performance is evaluated. The results show the potential of SAT spatial caching in improving the performance of the MEC network. Additionally, our results reveal useful insights such as the significant impact of the altitude and number of LEO SATs on the average delay of the network, providing helpful systemlevel recommendations for the design and configuration of the space-caching MEC network.

Unsupervised domain adaptation (UDA) is crucial for semantic segmentation of remote sensing images (RS-SS), particularly when data distributions differ between source and target domains. Existing prototype-based UDA methods struggle with complex land cover class distributions and spatial information capture. To address these limitations, the Neighborhood-Assisted Multi-Prototype Group (NAPG) model is proposed. This model enhances cross-domain adaptability and spatial context richness by dynamically determining the number of prototype features and integrating neighborhood similarity gradients. Specifically, NAPG employs the Cross-Domain Representation of Multi-Prototype Group (CDR-MPG) module to generate multi-prototype group, capturing land cover complexity more effectively. Additionally, the Gradient Neighborhood Consistency Estimation (GNCE) module improves spatial representation by reducing intra-class variance and alleviating feature inconsistency. Experiments demonstrate that the proposed NAPG model outperforms state-of-the-art UDA methods across multiple datasets, achieving an mean intersection over union (mIoU) improvement of 3%. The source code is publicly available at https://github.com/Fanrongbo/NAPG-UDA-RS-SS.

Retractable worm robots possess hyper-flexibility, allowing them to work in confined spaces that are difficult for humans. However, the spatial locomotion control of these robots remains challenging due to the robots’ large degrees of freedom. To address this challenge, we propose a phase synthesis (PS) scheme for retractable worm robots. The scheme combines an undulating gait inspired by caterpillars with three-dimensional movement commands. We first introduce the kinematics model and real-world prototype of our retractable worm robot, called RW-Robot, and then we introduce footstep phases to express the timing of segments’ spatial movement. According to the length of movement periods, we classify the movement into short-term movements and long-term movements and compress their patterns in the frequency domain. Our PS scheme aligns the patterns according to the footstep phases to generate new gaits of spatial locomotion. We evaluate the scheme in real-world experiments, including steering and climbing a slope. The experimental results indicate that our scheme allows the RW-Robot to perform flexible spatial locomotion from simple user input.

Remote sensing (RS) image change detection (CD) is a basic application in the military and people’s livelihood. The imaging results of multitemporal and multispectral RS images have interference effects such as spectral information difference, imaging light difference, sensor noise, so the accuracy and robustness of traditional CD algorithms are restricted. In this study, we propose an unsupervised fully connected multivariate alteration detection (UFMAD) algorithm. First, UFMAD is used to extract the deep difference feature of normalized multitemporal RS images. The weighted map was obtained by using the chi-square distance of the deep difference feature and weighted with the input data. Then, the UFMAD is iteratively optimized under the three proposed constraints of variance maximization, similarity maximization, and uncorrelation of deep features. Finally, the deep feature was clustered to get the change results. By analyzing the experimental results of six advanced CD algorithms on three sets of data, the highest relative detection accuracy of UFMAD has increased by 7.0%. It is verified that the algorithm proposed in this thesis has obvious performance advantages.

Registration of pelvic CT and MRI is highly desired as it can facilitate effective fusion of two modalities for prostate cancer radiation therapy, i.e., using CT for dose planning and MRI for accurate organ delineation. However, due to the large inter-modality appearance gaps and the high shape/appearance variations of pelvic organs, the pelvic CT/MRI registration is highly challenging. In this paper, we propose a region-adaptive deformable registration method for multi-modal pelvic image registration. Specifically, to handle the large appearance gaps, we first perform both CT-to-MRI and MRI-to-CT image synthesis by multi-target regression forest (MT-RF). Then, to use the complementary anatomical information in the two modalities for steering the registration, we select key points automatically from both modalities and use them together for guiding correspondence detection in the region-adaptive fashion. That is, we mainly use CT to establish correspondences for bone regions, and use MRI to establish correspondences for soft tissue regions. The number of key points is increased gradually during the registration, to hierarchically guide the symmetric estimation of the deformation fields. Experiments for both intra-subject and inter-subject deformable registration show improved performances compared with state-of-the-art multi-modal registration methods, which demonstrate the potentials of our method to be applied for the routine prostate cancer radiation therapy.

Computed tomography (CT) is widely used for dose planning in the radiotherapy of prostate cancer. However, CT has low tissue contrast, thus making manual contouring difficult. In contrast, magnetic resonance (MR) image provides high tissue contrast and is thus ideal for manual contouring. If MR image can be registered to CT image of the same patient, the contouring accuracy of CT could be substantially improved, which could eventually lead to high treatment efficacy. In this paper, we propose a learning-based approach for multimodal image registration. First, to fill the appearance gap between modalities, a structured random forest with auto-context model is learnt to synthesize MRI from CT and vice versa. Then, MRI-to-CT registration is steered in a dual manner of registering images with same appearances, i.e., (1) registering the synthesized CT with CT, and (2) also registering MRI with the synthesized MRI. Next, a dual-core deformation fusion framework is developed to iteratively and effectively combine these two registration results. Experiments on pelvic CT and MR images have shown the improved registration performance by our proposed method, compared with the existing non-learning based registration methods.