Low-Earth-orbit(LEO) satellite communication, as a key technology in the future networking field, which can provide ubiquitous mobile communication services. However, the LEO constellation faces multiple challenges such as low throughput of onboard switching systems, large transmission delay, and the limited computing and storage resources on satellites, making it difficult to meet the requirement of the time-sensitive applications such as the Internet of Things and industrial Internet. To realize LEO-based time-sensitive networking (TSN), this paper proposes a deadline guaranteed scheduling algorithm based on time-ordered queues for time-sensitive LEO satellite networks. The key idea of the proposed algorithm is to simplify the deadline guaranteed scheduling problem of multiple periods into a single period. Thus, the problem of high storage complexity of scheduling table caused by period extension is solved successfully. To do this, the Push-In-First-Out(PIFO) queues, usually used in terrestrial networks, are configured at the output end of the onboard switching fabric. Through a reordering mechanism based on the deadline priority, packets are pushed into the appropriate position for waiting and then output in time order. Simulation results indicate that compared with traditional scheduling algorithms, the proposed algorithm saves 45% - 97% of the time flow table storage resources of the onboard switching system under the same conditions, effectively improving resource utilization. The throughput performance of the onboard switch is enhanced by approximately 15% - 24%, and the average delay is reduced by around 17% - 49%.

Neural implicit representations are increasingly used to depict three-dimensional (3D) shapes owing to their inherent smoothness and compactness, contrasting with traditional discrete representations. Yet, the multilayer perceptron–based neural representation, because of its smooth nature, rounds sharp corners or edges, rendering it unsuitable for representing objects with sharp features like computer-aided design (CAD) models. Moreover, neural implicit representations need long training times to fit 3D shapes. While previous works address these issues separately, we present a unified neural implicit representation called Patch-Grid , which efficiently fits complex shapes, preserves sharp features delineating different patches, and can also represent surfaces with open boundaries and thin geometric features. Patch-Grid learns a signed distance field (SDF) to approximate an encompassing surface patch of the shape with a learnable patch feature volume. To form sharp edges and corners in a CAD model, Patch-Grid merges the learned SDFs via the constructive solid geometry (CSG) approach. Core to the merging process is a novel merge grid design that organizes different patch feature volumes in a common octree structure. This design choice ensures robust merging of multiple learned SDFs by confining the CSG operations to localized regions. Additionally, it drastically reduces the complexity of the CSG operations in each merging cell, allowing the proposed method to be trained in seconds to fit a complex shape at high fidelity. Experimental results demonstrate that the proposed Patch-Grid representation is capable of accurately reconstructing shapes with complex sharp features, open boundaries, and thin geometric elements, achieving state-of-the-art reconstruction quality with high computational efficiency within seconds.

While tooth alignment is crucial for digital dentistry, especially in orthodontic treatment, existing computer-aided methods mainly focus on the 3D dental crown but overlook the entire teeth, which is essential for applications in orthodontics. Besides, clinical orthodontic rules are not fully considered in these methods, i.e., there should be no collisions and gaps between teeth, the upper jaw and lower jaw should have correct occlusion relationships, the teeth should comply with a reasonable dental arch curve, etc. To generate optimal tooth alignment results, we propose a rule-based optimization method for solving the tooth alignment problem that takes into consideration the clinical rules functionally and aesthetically. We optimize rule-driven objective functions by adjusting the 6-DoF transformations of each tooth. Besides, our optimization formulation supports customization for different clinical scenarios by specifying the various energy terms. Extensive experiments, ablation studies, and user studies have been conducted to validate the effectiveness of our method. Quantitative and qualitative comparisons demonstrate that our method generates better tooth alignments than previous methods.

Nowadays, orthodontics has become an important part of modern personal life to assist one in improving mastication and raising self-esteem. However, the quality of orthodontic treatment still heavily relies on the empirical evaluation of experienced doctors, which lacks quantitative assessment and requires patients to visit clinics frequently for in-person examination. To resolve the aforementioned problem, we propose a novel and practical mobile device-based framework for precisely measuring tooth movement in treatment, so as to simplify and strengthen the traditional tooth monitoring process. To this end, we formulate the tooth movement monitoring task as a multi-view multi-object pose estimation problem via different views that capture multiple texture-less and severely occluded objects (i.e. teeth). Specifically, we exploit a pre-scanned 3D tooth model and a sparse set of multi-view tooth images as inputs for our proposed tooth monitoring framework. After extracting tooth contours and localizing the initial camera pose of each view from the initial configuration, we propose a joint pose estimation scheme to precisely estimate the 3D pose of each individual tooth, so as to infer their relative offsets during treatment. Furthermore, we introduce the metric of Relative Pose Bias to evaluate the individual tooth pose accuracy in a small scale. We demonstrate that our approach is capable of reaching high accuracy and efficiency as practical orthodontic treatment monitoring requires.

Automatic tooth alignment target prediction is vital in shortening the planning time of orthodontic treatments and aligner designs. Generally, the quality of alignment targets greatly depends on the experience and ability of dentists and has enormous subjective factors. Therefore, many knowledge-driven alignment prediction methods have been proposed to help inexperienced dentists. Unfortunately, existing methods tend to directly regress tooth motion, which lacks clinical interpretability. Tooth anatomical landmarks play a critical role in orthodontics because they are effective in aiding the assessment of whether teeth are in close arrangement and normal occlusion. Thus, we consider anatomical landmark constraints to improve tooth alignment results. In this article, we present a novel tooth alignment neural network for alignment target predictions based on tooth landmark constraints and a hierarchical graph structure. We detect the landmarks of each tooth first and then construct a hierarchical graph of jaw-tooth-landmark to characterize the relationship between teeth and landmarks. Then, we define the landmark constraints to guide the network to learn the normal occlusion and predict the rigid transformation of each tooth during alignment. Our method achieves better results with the architecture built for tooth data and landmark constraints and has better explainability than previous methods with regard to clinical tooth alignments.

Performance degradation due to domain shift remains a major challenge in medical image analysis. Unsupervised domain adaptation that transfers knowledge learned from the source domain with ground truth labels to the target domain without any annotation is the mainstream solution to resolve this issue. In this paper, we present a novel unsupervised domain adaptation framework for cross-modality cardiac segmentation, by explicitly capturing a common cardiac structure embedded across different modalities to guide cardiac segmentation. In particular, we first extract a set of 3D landmarks, in a self-supervised manner, to represent the cardiac structure of different modalities. The high-level structure information is then combined with another complementary feature, the Canny edges, to produce accurate cardiac segmentation results both in the source and target domains. We extensively evaluate our method on the MICCAI 2017 MM-WHS dataset for cardiac segmentation. The evaluation, comparison and comprehensive ablation studies demonstrate that our approach achieves satisfactory segmentation results and outperforms state-of-the-art unsupervised domain adaptation methods by a significant margin.

Abstract Rig‐space physics a finite element method(FEM) based simulation technique that aims at adding secondary motion on a character while maintaining seamless cooperation with traditional animation pipelines. We enhance the rig‐space physics by introducing several techniques, including general field interaction, proportional‐derivative control, and improved material control. This allows an animator to perform various interferences to the simulation process and create more abundant animation effects. Moreover, we also improve the numerical stability of the simulation algorithm by prepending a conjugate gradient procedure. Copyright © 2016 John Wiley & Sons, Ltd.

The Onboard system is a typical safety-critical system, in which any fault can lead to huge human injury or wealth losing. Function testing method which is mainly focus on the conformance relation between the specification and the SUT has been widely used in testing the Onboard system in the past few years. However, most of the test cases are manually generated which can't be reused and leads to repeat works when the specification is changed. To improve the testing efficiency and quality, Model-based testing method is introduced. We use a tool chain to generate test case automatically based on Timed Automata theory and apply in function testing of the Onboard system. EBD-TR timed automata network model is established using tool Uppaal. And based on the EBD-TR model, two kinds of coverage criteria (all-location coverage, and all-edge coverage) are used in tool of CoVer to generate test case automatically. Different test suits of the Onboard system are acquired and a complete model transition function test suit is derived which is proven very useful for testing the Onboard system.