Prior Position- and ZWD-Constrained PPP for Instantaneous Convergence in Real-Time Kinematic Application

PPP using Kalman filter typically takes half an hour to achieve high positioning precision, which is required for small movements detection. Many dataset gaps due to temporary GPS receiver signal loss challenge the feasibility of PPP in GPS applications for kinematic precise positioning. Additional convergence time is needed before PPP reaches the required precision again. In this study, Partial parameters were estimated by using the position and ZWD as prior constraint. The solved partial parameters were applied to initialize the Kalman filter for PPP instantaneous re-convergence. A set of bridge GPS data with logging gaps were used to validate the re-convergence performance of improved PPP. The results show that the displacements from position-constrained PPP with initialized variance are 0.14 m, 0.09 m and 0.05 m, which are much better than those from standard PPP. The precision of displacement from position- and ZWD-constrained PPP with initialized variance is slightly improved when compared with that from position-constrained PPP with initialized variance at all 3 surveying points. The bridge experiment verifies that the displacement time series of improved PPP instantaneously converges at the first epoch of all signal reacquired, in contrast, standard PPP deviates by meters. This finding suggests that improved PPP can successfully deal with the GPS data logging gaps for instantaneous convergence.

The deontological perspective of sustainable development

The idea of sustainable development as a normative concept emphasizes the necessity for a wider consensus on meeting human needs, ensuring social equity, and respecting planetary boundaries. The purpose of the article focuses on the deontological orientation in perceiving sustainable development. It is expected that looking at sustainability from the deontological perspective might increase individuals’ awareness of responsibility towards respecting the needs of the world’s poor, environmental boundaries, and moral equity, which emphasizes that all people are equal. Any attempt to achieve sustainability demands, first of all, rational action placed on moral duties/obligations before individual people or institutions can achieve their particular desires and goals. According to this perspective, sustainability should be treated as a prior constraint in obtaining economic maximization.

Comparison of Two Bayesian Methods in Evaluation of the Absence of the Gold Standard Diagnostic Tests

Objective. The Bayesian model plays an important role in diagnostic test evaluation in the absence of the gold standard, which used the external prior distribution of a parameter combined with sample data to yield the posterior distribution of the test characteristics. However, the correlation between diagnostic tests has always been a problem that cannot be ignored in the Bayesian model evaluation. This study will discuss how different Bayesian model, correlation scenarios, and prior distribution affect the outcome. Methods. The data analyzed in this study was gathered during studies of patients presenting to the Nanjing Chest Hospital with suspected tuberculosis. The diagnostic character of T-SPOT.Tb and KD38 tuberculosis antibody test were evaluated in different Bayesian model, and discharge diagnosis as a gold standard was used to verify the model results in the end. Result. The comparison of four models under the conditional independence situation found that Bayesian probabilistic constraint model was consistent with the Conditional Covariance Bayesian model. The results were mainly affected by prior information. The sensitivity and specificity of the two tests in Conditional Covariance Bayesian model in prior constraint situation were considerably higher than the Bayesian probabilistic constraint model in prior constraint situation. The results of the four models under the conditional dependence situation were similar to the conditional independence situation; pD was also negative with no prior constraint situation in both model Bayesian probabilistic constraint model and Conditional Covariance Bayesian model. The Deviance Information Criterion of Bayesian probabilistic constraint model was close to model Conditional Covariance Bayesian model, but pD of Conditional Covariance Bayesian model in Prior constraint situation (pD=2.40) was higher than the Bayesian probabilistic constraint model in Prior constraint situation (pD=1.66). Conclusion. The result of Conditional Covariance Bayesian model in prior constraint with conditional independence situation was closest to the result of gold standard evaluation in our data. Both of the two Bayesian methods are the feasible way for the evaluation of diagnostic test in the absence of the gold standard diagnostic. Prior source, priority number, and conditional dependencies should be considered in the method selection, the accuracy of posterior estimation mainly depending on the prior distribution.

Three-Dimensional Face Reconstruction Using Multi-View-Based Bilinear Model

Face reconstruction is a popular topic in 3D vision system. However, traditional methods often depend on monocular cues, which contain few feature pixels and only use their location information while ignoring a lot of textural information. Furthermore, they are affected by the accuracy of the feature extraction method and occlusion. Here, we propose a novel facial reconstruction framework that accurately extracts the 3D shapes and poses of faces from images captured at multi-views. It extends the traditional method using the monocular bilinear model to the multi-view-based bilinear model by incorporating the feature prior constraint and the texture constraint, which are learned from multi-view images. The feature prior constraint is used as a shape prior to allowing us to estimate accurate 3D facial contours. Furthermore, the texture constraint extracts a high-precision 3D facial shape where traditional methods fail because of their limited number of feature points or the mostly texture-less and texture-repetitive nature of the input images. Meanwhile, it fully explores the implied 3D information of the multi-view images, which also enhances the robustness of the results. Additionally, the proposed method uses only two or more uncalibrated images with an arbitrary baseline, estimating calibration and shape simultaneously. A comparison with the state-of-the-art monocular bilinear model-based method shows that the proposed method has a significantly higher level of accuracy.

Improving Galileo's Carrier-Phase Time Transfer Based on Prior Constraint Information

The Carrier-Phase (CP) technique used in the Global Positioning System (GPS) has proved to be a useful spatial tool for remote precise time transfer. Galileo is a Global Navigation Satellite System like GPS. However, currently, given the low number of satellites at any one observation epoch, Galileo's accuracy and continuity of time transfer leave much to be desired. To achieve better performance of time transfer for Galileo, this study has developed a new approach for Galileo CP time transfer, using prior constraint information such as precise coordinates and troposphere zenith delay constraints. The new approach was applied for precise time transfer in real-time mode and post-processed mode for short baseline and long baseline observations. For the short baseline time link in real-time mode, compared with the standard Galileo CP, the standard deviation improved by 51·4% for the troposphere zenith delay constraint, 47·6% for the station coordinates constraint, and 49·5% when considering both constraints simultaneously. At a 10,000 s time interval, in comparison to the standard CP, the three constraint approaches show stable results as well as improvements of nearly an order of magnitude. In post-processed mode, the constraint approach for Galileo time transfer showed little improvement compared to the standard CP technique for both the short baseline and long baseline time links.

Natural Finger Interaction for CAD Assembly Modeling

In current CAD software the process of assembly modeling is hindered by a large number of separate rotation and translation actions necessary, especially in case of larger assemblies. Additionally matching faces, edges or points must be selected by clicking to define the appropriate constraint. In contrast to that, the process of assembling two normal sized physical parts in the real world seems to be rather simple. That is because we know how to grasp and move objects with our hands intuitively from our everyday experience. The idea behind this contribution is to enable the product developer to assemble CAD parts in a virtual environment through natural finger interaction like in reality. Therefore we present an overall method that combines the natural finger interaction with virtual objects and the insertion of constraints between rotationally symmetric CAD parts. The developed algorithms identify matching surfaces on the basis of the geometry as well as position and orientation of the parts in 3D space. This paper highlights the method to use a combination of real-time physics simulation and a heuristic approach to achieve an intuitive interaction interface. Additionally, we describe the detection algorithms developed to find assembly relationships between rotationally symmetric CAD parts without prior constraint definition. We also present a prototype system to demonstrate the functionality of the overall method. Furthermore, challenges for future research, such as extending the functionality of the detection algorithms on additional part types, like non-rotationally symmetric shapes, are discussed.