Dissemination of High-Resolution Ionosphere Information from VTEC B-spline Expansions for Single-Frequency Positioning

<p>The ionospheric signal delay is one of the largest error sources in GNSS applications and may cause in case of a single-frequency receiver a positioning error of up to several meters. To avoid such an inaccuracy some of the Ionosphere Associated Analysis Centers (IAAC) of the International GNSS Service (IGS) provide the user the Vertical Total Electron Content (VTEC) as Real-Time Global Ionosphere Maps (RT-GIM) via streaming formats. Currently, the only data format used for the dissemination of these ionospheric corrections is based on the State Space Representation (SSR) message and the RTCM standards.</p><p>Mathematically most of the RT-GIMs are based on modeling VTEC as series expansions in spherical harmonics (SH) up to a highest degree of n = 15 which corresponds to a spatial resolution of 12° in latitude and longitude and is therefore, too low for modern GNSS applications such as autonomous driving. However, the SSR VTEC message allows the dissemination of SH coefficients only up to a maximum degree of n = 16.</p><p>To avoid the drawbacks of expanding VTEC in SHs other approaches such as a voxel representation or a B-spline series expansion have been proven to be appropriate candidates for global and regional modelling with an enhanced resolution. In order to provide in these cases the significant model parameters to the user, the application of the SSR VTEC message requires a transformation of the model parameters into SH coefficients. In this contribution a methodology will be presented which describes a fast transformation of the B-spline approach into a SH representation with high accuracy by minimizing the information loss.</p><p>To test the method, a high-resolution VTEC GIM modeled as a series expansion in B-splines is transformed into SH representations of different highest degree values; the results are validated via dSTEC analysis as well as via an example of single frequency positioning and show a significantly improved accuracy compared to the IGS GIMs.</p>

ON SELECTING IMAGES FROM AN UNAIMED VIDEO STREAM FOR PHOTOGRAMMETRIC MODELLING

In this paper, we illustrate how convolutional neural networks and voxel-based processing together with voxel visualizations can be utilized for the selection of unaimed images for a photogrammetric image block. Our research included the detection of an ear from images with a convolutional neural network, computation of image orientations with a structure-from-motion algorithm, visualization of camera locations in a voxel representation to detect the goodness of the imaging geometry, rejection of unnecessary images with an XYZ buffer, the creation of 3D models in two different example cases, and the comparison of resulting 3D models. Two test data sets were taken of an ear with the video recorder of a mobile phone. In the first test case, a special emphasis was taken to ensure good imaging geometry. On the contrary, in the second test case the trajectory was limited to approximately horizontal movement, leading to poor imaging geometry. A convolutional neural network together with an XYZ buffer managed to select a useful set of images for the photogrammetric 3D measuring phase. The voxel representation well illustrated the imaging geometry and has potential for early detection where data is suitable for photogrammetric modelling. The comparison of 3D models revealed that the model from poor imaging geometry was noisy and flattened. The results emphasize the importance of good imaging geometry.

VOXEL-BASED INDOOR RECONSTRUCTION FROM HOLOLENS TRIANGLE MESHES

Current mobile augmented reality devices are often equipped with range sensors. The Microsoft HoloLens for instance is equipped with a Time-of-Flight (ToF) range camera providing coarse triangle meshes that can be used in custom applications. We suggest to use these triangle meshes for the automatic generation of indoor models that can serve as basis for augmenting their physical counterpart with location-dependent information. In this paper, we present a novel voxel-based approach for automated indoor reconstruction from unstructured three-dimensional geometries like triangle meshes. After an initial voxelisation of the input data, rooms are detected in the resulting voxel grid by segmenting connected voxel components of ceiling candidates and extruding them downwards to find floor candidates. Semantic class labels like ’Wall’, ’Wall Opening’, ’Interior Object’ and ’Empty Interior’ are then assigned to the room voxels in-between ceiling and floor by a rule-based voxel sweep algorithm. Finally, the geometry of the detected walls and their openings is refined in voxel representation. The proposed approach is not restricted to Manhattan World scenarios and does not rely on room surfaces being planar.

Geometric Modeling of Objects’ Thermal Characteristics by the Functional-Voxel Method

In this paper the influence of objects’ thermal processes on their correspondence to a given geometry has been considered, and an alternative apparatus for geometric modeling of bodies’ temperature stress and thermal expansion after effect of a heat source, based on a functional-voxel approach, has been proposed as well. A discrete geometric model of temperature stress at a point of thermal loading in an isotropic heat-conducting body for a functional-voxel representation has been developed, allowing simulate a single action of a heat source to obtain local geometric characteristics of thermal stress in the body. This approach, unlike traditional approaches based on the FEM, allows apply the temperature load at the object’s point taken by itself. A discrete geometric model for expansion at the point of thermal loading in an isotropic heat-conducting body for a functional-voxel representation has been developed, which allows simulate the change of an object’s local geometric characteristics during the process of material expansion from a single effect of a heat source to obtain a value upon the body volume changing. This approach, unlike traditional approaches based on the FEM, allows simulate a change in the body’s surface geometry from thermal expansion at a point taken by itself without errors arising from calculations using a mesh. Have been proposed algorithms for functional-voxel modeling of temperature stress and expansion under distributed thermal loading. These algorithms allow construct a loading region of complex configuration based on the spatial distribution and scaling of the temperature stress’s geometric model for a single point of thermal loading, uniformly form a contour (surface) after material expansion, and obtain information about the change in products’ length (volume) based on information about each point of functional space. Has been presented an example of using the proposed approach for solving a processing tool’s correction problem based on the temperature in the cutting zone and material thermal reaction. The geometric model can be used to the automated design of a processing tool path for parts cutting on CNC machines.

Rapid Neural Reorganization during Retrieval Practice Predicts Subsequent Long-term Retention and False Memory

Active retrieval can induce changes to the strength and content of a memory, yielding enhanced or distorted subsequent recall. But how consolidation influences these retrieval-induced seemingly contradictory outcomes remains unknown. Here we show rapid neural reorganization over eight runs of retrieval practice predicted subsequent recall. Behaviorally, retrieval practice boosted memory following a 24-hour (long-term) but not a 30-minute delay, and increased false memory at both delays. Long-term retention gains were predicted by multi-voxel representation distinctiveness in the posterior parietal cortex that increased progressively over retrieval practice. False memory was predicted by unstable representation distinctiveness in the medial temporal lobe during retrieval practice. Memory-related neural networks gradually reconfigured over retrieval practice, with the ventrolateral and medial prefrontal cortex acting as hubs for functional connections that predicted long-term retention gains and false memory outcomes respectively. Our findings demonstrate dynamic neural reorganization during retrieval practice, through which memories are arranged into discrete yet malleable representations for subsequent consolidation.

A Voxel Model-Based Process-Planning Method for Five-Axis Machining of Complicated Parts

This paper presents a new process planning method for five-axis machining, which is particularly suitable for parts with complex features or weak structures. First, we represent the in-process workpiece as a voxel model. Facilitated by the voxel representation, a scalar field called subtraction field is then established between the blank surface and the part surface, whose value at any voxel identifies its removal sequence. This subtraction field helps identify a sequence of intermediate machining layers, which are always accessible to the tool and are free of self-intersection and the layer redundancy problem as suffered, respectively, by the traditional offset layering method and the morphing method. Iso-planar collision-free five-axis tool paths are then determined on the interface surfaces of these machining layers. In addition, to mitigate the deformation of the in-process workpiece and avoid potential dynamic problems such as chattering, we also propose a new machining strategy of alternating between the roughing and finishing operations, which is able to achieve a much higher stiffness of the in-process workpiece. Ample experiments in both computer simulation and physical cutting are performed, and the experimental results convincingly confirm the advantages of our method.

Genetic Algorithm Based Topology Optimization of Heat Exchanger Fins Used in Aerospace Applications

Topology Optimization (TO) in the design of structural components is commonly used and well explored. However, its usage in the design of complex thermo-fluid equipment used in aerospace applications is limited and relatively new. This is because the coupling between the fluid dynamics, heat transfer, and the shape is complex and nonlinear. Furthermore, the resulting geometry from a TO analysis is often very complex and difficult to manufacture due to the free forms that can occur. With the advent of Additive Manufacturing (AM), however, it has become possible to directly manufacture complex geometries. This study develops a new Genetic Algorithm (GA) based TO combined with Computational Fluid Dynamics (CFD) to produce optimized fin shapes for heat exchangers used in aerospace applications. To implement this approach, a rectangular shaped baseline fin geometry was created using voxel representation. An initial population is generated by mutating the baseline fin a random number of times. The CFD package OpenFOAM is then used to evaluate the performance of each design, after which the optimization algorithm is applied. The GA sorts the designs using a composite fitness function that is comprised of the overall heat transfer and pressure drop, and generates new generations based on mutation and carryover of top performing designs. The study also explores the sensitivity of the GA to the various GA parameters as well as the effect of varying flow Reynolds number. In general, as Reynolds number increases, the percent improvement in the optimum relative to the baseline increases, with potentially a 60% performance improvement. Overall, the approach enables generation of novel freeform designs that may open new performance space for heat transfer applications.

3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders

We propose a data-driven 3D shape design method that can learn a generative model from a corpus of existing designs, and use this model to produce a wide range of new designs. The approach learns an encoding of the samples in the training corpus using an unsupervised variational autoencoder-decoder architecture, without the need for an explicit parametric representation of the original designs. To facilitate the generation of smooth final surfaces, we develop a 3D shape representation based on a distance transformation of the original 3D data, rather than using the commonly utilized binary voxel representation. Once established, the generator maps the latent space representations to the high-dimensional distance transformation fields, which are then automatically surfaced to produce 3D representations amenable to physics simulations or other objective function evaluation modules. We demonstrate our approach for the computational design of gliders that are optimized to attain prescribed performance scores. Our results show that when combined with genetic optimization, the proposed approach can generate a rich set of candidate concept designs that achieve prescribed functional goals, even when the original dataset has only a few or no solutions that achieve these goals.

Tractostorm: Rater reproducibility assessment in tractography dissection of the pyramidal tract

Investigative studies of white matter (WM) brain structures using diffusion MRI (dMRI) tractography frequently require manual WM bundle segmentation, often called “virtual dissection”. Human errors and personal decisions make these manual segmentations hard to reproduce, which have not yet been quantified by the dMRI community. The contribution of this study is to provide the first large-scale, international, multi-center variability assessment of the “virtual dissection” of the pyramidal tract (PyT). Eleven (11) experts and thirteen (13) non-experts in neuroanatomy and “virtual dissection” were asked to perform 30 PyT segmentation and their results were compared using various voxel-wise and streamline-wise measures. Overall the voxel representation is always more reproducible than streamlines (≈70% and ≈35% overlap respectively) and distances between segmentations are also lower for voxel-wise than streamline-wise measures (¾3mm and ¾ûmm respectively). This needs to be seriously considered before using tract-based measures (e.g. bundle volume versus streamline count) for an analysis. We show and argue that future bundle segmentation protocols need to be designed to be more robust to human subjectivity. Coordinated efforts by the diffusion MRI tractography community are needed to quantify and account for reproducibility of WM bundle extraction techniques in this era of open and collaborative science.