PointSCNet: Point Cloud Structure and Correlation Learning Based on Space-Filling Curve-Guided Sampling

Geometrical structures and the internal local region relationship, such as symmetry, regular array, junction, etc., are essential for understanding a 3D shape. This paper proposes a point cloud feature extraction network named PointSCNet, to capture the geometrical structure information and local region correlation information of a point cloud. The PointSCNet consists of three main modules: the space-filling curve-guided sampling module, the information fusion module, and the channel-spatial attention module. The space-filling curve-guided sampling module uses Z-order curve coding to sample points that contain geometrical correlation. The information fusion module uses a correlation tensor and a set of skip connections to fuse the structure and correlation information. The channel-spatial attention module enhances the representation of key points and crucial feature channels to refine the network. The proposed PointSCNet is evaluated on shape classification and part segmentation tasks. The experimental results demonstrate that the PointSCNet outperforms or is on par with state-of-the-art methods by learning the structure and correlation of point clouds effectively.

Ultrasound simulation of blood with different red blood cell aggregations and concentrations

BACKGROUND: Considerable progress of ultrasound simulation on blood has enhanced the characterizing of red blood cell (RBC) aggregation. OBJECTIVE: A novel simulation method aims at modeling the blood with different RBC aggregations and concentrations is proposed. METHODS: The modeling process is as follows: (i) A three-dimensional scatterer model is first built by a mapping with a Hilbert space-filling curve from the one-dimensional scatterer distribution. (ii) To illustrate the relationship between the model parameters and the RBC aggregation level, a variety of blood samples are prepared and scanned to acquire their radiofrequency signals in-vitro. (iii) The model parameters are determined by matching the Nakagami-distribution characteristics of envelope signals simulated from the model with those measured from the blood samples. RESULTS: Nakagami metrics m estimated from 15 kinds of blood samples (hematocrits of 20%, 40%, 60% and plasma concentrations of 15%, 30%, 45%, 60%, 75%) are compared with metrics estimated by their corresponding models (each with different eligible parameters). Results show that for the three hematocrit levels, the mean and standard deviation of the root-mean-squared deviations of m are 0.27 ± 0.0026, 0.16 ± 0.0021, 0.12 ± 0.0018 respectively. CONCLUSION: The proposed simulation model provides a viable data source to evaluate the performance of the ultrasound-based methods for quantifying RBC aggregation.

Space-Filling Curve Resistor on Ultra-Thin Polyetherimide Foil for Strain Impervious Temperature Sensing

Monitoring process parameters in the manufacture of composite structures is key to ensuring product quality and safety. Ideally, this can be done by sensors that are embedded during production and can remain as devices to monitor structural health. Extremely thin foil-based sensors weaken the finished workpiece very little. Under ideal conditions, the foil substrate bonds with the resin in the autoclaving process, as is the case when polyetherimide is used. Here, we present a temperature sensor as part of an 8 µm thick multi-sensor node foil for monitoring processing conditions during the production and structural health during the lifetime of a construction. A metallic thin film conductor was shaped in the form of a space-filling curve to suppress the influences of resistance changes due to strain, which could otherwise interfere with the measurement of the temperature. FEM simulations as well as experiments confirm that this type of sensor is completely insensitive to the direction of strain and sufficiently insensitive to the amount of strain, so that mechanical strains that can occur in the composite curing process practically do not interfere with the temperature measurement. The temperature sensor is combined with a capacitive sensor for curing monitoring based on impedance measurement and a half-bridge strain gauge sensor element. All three types are made of the same materials and are manufactured together in one process flow. This is the key to cost-effective distributed sensor arrays that can be embedded during production and remain in the workpiece, thus ensuring not only the quality of the initial product but also the operational reliability during the service life of light-weight composite constructions.

Acoustic space filling curve metamaterials for grazing flow in Jet engine inlets

Acoustic metamaterials research has grown exponentially in the past 10 years driven by the advances in manufacturing and an increased understanding of damaging environment noise. 2020 was the first noise reduction target as set by Advisory Council for Aircraft Research and Innovation in Europe with a relative 50% decrease. This was missed by current Jet engine noise control technology; however, metamaterials offer an encouraging alternative. Space Filling Curves (SFC) have the potential to provide a lightweight, thin, high performance acoustic liner. SFC have a history in mathematical geometry dating back to the 1890's but are a comparatively new addition to acoustics. They are designed with a sub-wavelength curled cross-section creating a maze-like pattern which slows acoustic wave propagation through the liner enabling characteristics such as negative refraction and low frequency attenuation. This paper contains a comparison of some of the most promising SFC metamaterial acoustic liner designs, in terms of the fundamental theory of the design category and a discussion of the reflection, absorption and transmission characteristics in terms of a grazing flow conditions. Computer simulation and impedance tube based experimental testing compares the designs. The paper concludes with future application for aeroacoustics with particular focus on the engine inlet.

The Effect of Space-filling Curves on the Efficiency of Hand Gesture Recognition Based on sEMG Signals

Over the past few years, Deep learning (DL) has revolutionized the field of data analysis. Not only are the algorithmic paradigms changed, but also the performance in various classification and prediction tasks has been significantly improved with respect to the state-of-the-art, especially in the area of computer vision. The progress made in computer vision has produced a spillover in many other domains, such as biomedical engineering. Some recent works are directed towards surface electromyography (sEMG) based hand gesture recognition, often addressed as an image classification problem and solved using tools such as Convolutional Neural Networks (CNN). This paper extends our previous work on the application of the Hilbert space-filling curve for the generation of image representations from multi-electrode sEMG signals, by investigating how the Hilbert curve compares to the Peano- and Z-order space-filling curves. The proposed space-filling mapping methods are evaluated on a variety of network architectures and in some cases yield a classification improvement of at least 3%, when used to structure the inputs before feeding them into the original network architectures.

Microbiome Maps: Hilbert Curve Visualizations of Metagenomic Profiles

MotivationAbundance profiles from metagenomic sequencing data synthesize information from billions of sequenced reads coming from thousands of microbial genomes. Analyzing and understanding these profiles can be a challenge since the data they represent is complex. Particularly challenging is their visualization, as existing techniques are inadequate when the taxa number in the thousands. We present a technique for succinct visualization of abundance profiles using a space-filling curve that transforms a profile into an interpretable 2D image.ResultsJasper is a tool for visualizing profiles from metagenomic whole-genome sequencing and 16S, and orders taxa along a space-filling Hilbert curve. The result is a “Microbiome Map”, where each position in the image represents the abundance of a single taxon from a reference collection. Jasper can order the taxa in one of two ways, and depending on the ordering, the microbiome maps can highlight “hot spots” of microbes that are either dominant in taxonomic clades or to the biological conditions under study.We use Jasper to visualize samples from the Human Microbiome Project and from a Chronic Kidney Disease study, and discuss a variety of ways in which the microbiome maps can be an invaluable tool to visualize spatial, temporal, disease, and differential profiles. Our approach can create detailed microbiome maps involving hundreds of thousands of microbial reference genomes with the potential to unravel latent relationships (taxonomic, spatio-temporal, functional, and other) that could remain hidden using traditional visualization techniques. The maps can be converted into animated movies that bring to life the dynamicity of microbiomes.AvailabilityJasper will be available as free software from the Mac App Store and biorg.cs.fiu.edu/jasperSupplementary informationSupplementary materials are available at biorg.cs.fiu.edu/jasper

Partitioning Wide Area Graphs Using a Space Filling Curve

Graph structure is a very powerful tool to model system and represent their actual shape. For instance, modelling an infrastructure or social network naturally leads to graph. Yet, graphs can be very different from one another as they do not share the same properties (size, connectivity, communities, etc.) and building a system able to manage graphs should take into account this diversity. A big challenge concerning graph management is to design a system providing a scalable persistent storage and allowing efficient browsing. Mainly to study social graphs, the most recent developments in graph partitioning research often consider scale-free graphs. As we are interested in modelling connected objects and their context, we focus on partitioning geometric graphs. Consequently our strategy differs, we consider geometry as our main partitioning tool. In fact, we rely on Inverse Space-filling Partitioning, a technique which relies on a space filling curve to partition a graph and was previously applied to graphs essentially generated from Meshes. Furthermore, we extend Inverse Space-Filling Partitioning toward a new target we define as Wide Area Graphs. We provide an extended comparison with two state-of-the-art graph partitioning streaming strategies, namely LDG and FENNEL. We also propose customized metrics to better understand and identify the use cases for which the ISP partitioning solution is best suited. Experimentations show that in favourable contexts, edge-cuts can be drastically reduced, going from more 34% using FENNEL to less than 1% using ISP.

Methods for optimizing routes in digital logistics

The current problem of digital logistics is investigated - the calculation of optimal routes for freight transportation by computer means to reduce time and distance. Heuristic methods used in logistics for constructing optimal routes are considered. A comparative analysis of ten methods for solving the optimization problem of the “nondeterministic polynomial time” complexity class traveling salesman is carried out. The study performs a comparative analysis of the following methods: “convex hull, cheapest insertion and angle selection”, “greedy”, “greedy-cycle”, “integer-linear-programming”, “or-opt”, “or-zweig”, “remove crossings”, “space filling curve”, “simulated annealing”, “two-opt”. A computational experiment is performed, on the basis of which the accuracy and computational complexity of the considered methods are estimated. The results of the computational experiment show the construction of the optimal route by the “integer-linear-programming” method and the highest computation speed for the “greedy” method. Application of the “integer-linear-programming” method in logistics is the most accurate at the optimal time for calculating efficient routes of freight traffic.

Inverse Space Filling Curve Partitioning Applied to Wide Area Graphs

The most recent developments in graph partitioning research often consider scale-free graphs. Instead we focus on partitioning geometric graphs using a less usual strategy: Inverse Spacefilling Partitioning (ISP). ISP relies on a space filling curve to partition a graph and was previously applied to graphs essentially generated from Meshes. We extend ISP to apply it to a new context where the targets are now Wide Area Graphs. We provide an extended comparison with two state-of-the-art graph partitioning streaming strategies, namely LDG and FENNEL. We also propose customized metrics to better understand and identify the use cases for which the ISP partitioning solution is best suited. Experimentations show that in favourable contexts, edge-cuts can be drastically reduced, going from more 34% using FENNEL to less than 1% using ISP.