Independent 2D Research Articles

Reconstruction of High-Resolution 3D GPR Data from 2D Profiles: A Multiple-Point Statistical Approach

Ground-penetrating radar (GPR) is a popular geophysical tool for mapping the underground. High-resolution 3D GPR data carry a large amount of information and can greatly help to interpret complex subsurface geometries. However, such data require a dense collection along closely spaced parallel survey lines, which is time consuming and costly. In many cases, for the sake of efficiency, a choice is made during 3D acquisitions to use a larger spacing between the profile lines, resulting in a dense measurement spacing along the lines but a much coarser one in the across-line direction. Simple interpolation methods are then commonly used to increase the sampling before interpretation, which can work well when the subsurface structures are already well sampled in the across-line direction but can distort such structures when this is not the case. In this work, we address the latter problem using a novel multiple-point geostatistical (MPS) simulation methodology. For a considered 3D GPR dataset with reduced sampling in the across-line direction, we attempt to reconstruct a more densely spaced, high-resolution dataset using a series of 2D conditional stochastic simulations in both the along-line and across-line directions. For these simulations, the existing profile data serve as training images from which complex spatial patterns are quantified and reproduced. To reduce discontinuities in the generated 3D spatial structures caused by independent 2D simulations, the target profile being simulated is chosen randomly, and simulations in the along-line and across-line directions are performed alternately. We show the successful application of our approach to 100 MHz synthetic and 200 MHz field GPR data under multiple decimation scenarios where survey lines are regularly deleted from a dense 3D reference dataset, and the corresponding reconstructions are compared with the original data.

A dose rate independent 2D Ce-doped YAG scintillating dosimetry system for time resolved beam monitoring in ultra-high dose rate electron “FLASH” radiation therapy

Preclinical studies have shown that radiotherapy treatments benefit from ultra-high dose rate (UHDR) irradiations. These trigger the FLASH-effect, resulting in a strong decrease in normal tissue toxicity with conserved tumor control probability, compared with conventional irradiations. However, the beam parameters to trigger the FLASH-effect and the radiobiological mechanisms behind it remain to be elucidated. A limiting factor in many studies is the lack of accurate real time dosimetry. In this work an innovative solution for 2D real time dosimetry in UHDR electron beams is presented and characterized.The in-house developed ImageDosis system consists of a scientific camera, with high temporal resolution, and a coating, containing 12% of Y3Al5O12:Ce3+ as scintillating material. Reference dosimetry was performed by means of radiochromic film, and a (C38H34P2)MnBr4 point scintillator was used to validate the pulse discrimination properties of the ImageDosis system. Irradiations were performed in two centers (Antwerp and Orsay), with an ElectronFlash accelerator. The ImageDosis system was tested with varying number of pulses, pulse length, pulse repetition frequency (PRF) and energy. In addition, its temporal resolution and 2D properties were investigated.The ImageDosis system showed negligible dose, dose rate and energy dependence for doses up to 13 Gy and average dose rates up to 140 Gy/s, with a dose per pulse up to 2 Gy and a PRF up to 300 Hz. The system is capable of discriminating and measuring the dose of individual pulses and shows promising 2D characteristics that need further optimization.

Combined force decomposition approach and CFD simulation methods for 2D water entry and exit problem

The force decomposition approach is extended to solve the two-dimensional (2D) water entry and exit problems. Two typical scenarios, direct water exit and continuous water entry and exit, are examined. It reveals that the total force is irrelevant to the body velocity but to the acceleration. More specifically, the force can be expressed as the multiplication of constant values, body acceleration and a non-dimensional coefficient, and the coefficient is related only to the displacement and immersion condition of the body while independent of its motion condition. Two typical body shapes, a widely used wedge model and a practical ship section model, are examined here. CFD simulation is employed here to directly extract the non-dimensional coefficients, which avoids complex calculations for the wetted length of body by analytical models. The force decomposition approach with the obtained coefficients provides a means to quickly and accurately predict the hydrodynamic force acting on body for water exit scenarios. The proposed method is verified by comparing its results with CFD simulations in the complicated motion cases with varying acceleration. Furthermore, this work provides a potential tool to calculate the total force acting on any entire 3D hull that is divided into several independent 2D slices.

Three-dimension holographic numerical simulation of gravity anomalies

In gravity-anomaly-based prospecting, it is difficult to achieve large-scale and high-precision inversion imaging of complex geological models. To address this issue, this paper proposes a three-dimensional holographic numerical simulation method for gravity anomalies. This method transforms the 3D partial differential equation of gravity potential into many independent 1D differential equations with different wavenumbers by performing a 2D Fourier transform along the horizontal direction. It decomposes a large-scale 3D numerical modeling problem into many 1D numerical modeling problems, which greatly reduces the computation and memory requirements of modeling, and each 1D differential equation is independent, so it has high parallelism. The vertical direction is reserved as the spatial domain, which has strict upper and lower boundary conditions; The horizontal 2D Fourier transform uses a holographic Fourier transform (Holo-FT) with a full interval of integration, consistent with the physical boundary in the horizontal direction, thus the physical information of the gravity potential described by the three-dimensional partial differential equation is fully and accurately simulated. Using the high parallelism of the algorithm, the CPU is used for parallel solving ordinary differential equations, while the GPU is used for parallel computing of Holo-FT, which implements the CPU-GPU parallel acceleration scheme and further improves the efficiency of this algorithm.

Clustered jamming and antenna beam-width fluctuations for UAV-assisted cellular networks

Jamming devices have the capability to deliberately disrupt legitimate communications. Clustered jamming pertains to the deployment of jammers that are concentrated within specific clusters to amplify the interference. This paper models the impact of clustered jamming interference on unmanned aerial vehicles (UAV)-assisted cellular networks, taking into account fluctuations of the 3-dimensional (3D) beam-width of low altitude platform (LAP) antennas. The distribution of UAVs and macro base station (MBS) is modeled by independent 3D and 2D homogeneous Poisson point processes, respectively, while jammers are modeled using an independent Matern cluster process. Antenna 3D beam-width fluctuations are modeled according to Normal distribution. The modeling centers on the probability of association as well as coverage and spectral efficiency for line-of-sight LAP, non-line-of-sight LAP, and MBS links. This modeling investigation is conducted under multiple jamming clusters and the antenna 3D beam-width fluctuations of UAV. Analytical expressions are derived to characterize association, coverage, and SE performance in the context of clustered jamming and UAV beam-width variation. The obtained results indicate that UAV-assisted cellular networks experience a notable decline in the proximity of clustered jammers. Furthermore, this performance degradation is significantly exacerbated when the antenna 3D beam-width exhibits fluctuations. Consequently, to enhance the performance of UAV-assisted cellular networks, it is imperative for system designers to consider the implementation of robust anti-jamming techniques.

A hybrid spectral-spatial formulation for the calculation of the transient eddy-current response

A new approach for the solution of transient eddy-current problems involving pieces with one direction of symmetry is presented. The approach is applicable to pieces with translational or rotational symmetry. A Fourier series decomposition of the solution is introduced in the direction of symmetry, which converts the original 3D numerical problem into a series of independent 2D problems. The decomposition has significant benefits in terms of computational time and numerical noise reduction, and is inherently parallelisable.

Analysis of Fluctuating Antenna Beamwidth in UAV-Assisted Cellular Networks

This paper investigates a cellular network assisted by unmanned aerial vehicles (UAVs) in the presence of a fluctuating 3-dimensional (3D) antenna beamwidth. The primary objective is to perform an analysis of typical user equipment (T-UE) performance with a specific focus on coverage probability and spectral efficiency (SE) in the presence of fluctuations of 3D antenna beamwidth. Within this analytical framework, the macro base stations (MBSs) are meticulously characterized through the application of an independent 2D homogeneous Poisson point process (PPP), while the low-altitude platforms (LAPs) are described using an independent 3D PPP. The study entails the derivation of association probabilities, determining the likelihood of the T-UE associating with MBSs, line-of-sight LAPs, and non-line-of-sight LAPs. Through rigorous mathematical analysis, the paper formulates precise analytical expressions that encapsulate the association and coverage probabilities, taking into account the inherent variability in the UAV antenna beamwidth. This research focuses on a thorough performance evaluation of the T-UE across diverse network configurations, encompassing LAP density, the transmission power of LAPs, and the critical signal-to-interference ratio threshold. The outcomes of this study distinctly underscore the substantial disruptive impact resulting from fluctuating beamwidth on the performance of the T-UE within the UAV-assisted cellular network. Additionally, this performance is further impacted by larger densities and transmission power of the LAP. Hence, it is imperative to take into account the influence of these fluctuations on network association, coverage, and SE whenever contemplating a UAV-assisted cellular network.

A Self‐Decoupled and High‐Resolution 2D Displacement Sensor Based on Triboelectric Nanogenerator for Manipulator Precision Positioning

Abstract2D displacement sensor (2DDS) is significant in precision manufacturing positioning, machine condition monitoring, robotic manipulation, and human–computer interaction system. However, the reported 2DDSs have severe limitations, such as low accuracy, poor decoupling, and lack of smart signal analysis. Herein, a self‐decoupled and high‐resolution 2DDS based on two freestanding triboelectric nanogenerators (F‐TENGs) is presented. This sensor achieves self‐decoupling by two orthogonal and independent 1D displacement sensors with low‐cost grating‐structured PCB. The test results of the sensor exhibit a high accuracy ≥99.06% (1D) and 98.94% (2D). Also, the experimental and simulation results demonstrate that this sensor has a high resolution of 200 µm and the function of initial position identification. A 2D displacement sensing system is developed that is capable of collecting F‐TENGs’ open‐circuit voltages in real‐time and performing 2D trajectory synthesis and display. This work expands the potential application of the TENG‐based 2DDS in robotic precision machining.

A three-dimensional Magnetic Resonance Spin Tomography in Time-domain protocol for high-resolution multiparametric quantitative magnetic resonance imaging.

Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) is a multiparametric quantitative MR framework, which allows for simultaneously acquiring quantitative tissue parameters such as T1, T2, and proton density from one single short scan. A typical two-dimensional (2D) MR-STAT acquisition uses a gradient-spoiled, gradient-echo sequence with a slowly varying RF flip-angle train and Cartesian readouts, and the quantitative tissue maps are reconstructed by an iterative, model-based optimization algorithm. In this work, we design a three-dimensional (3D) MR-STAT framework based on previous 2D work, in order to achieve better image signal-to-noise ratio, higher though-plane resolution, and better tissue characterization. Specifically, we design a 7-min, high-resolution 3D MR-STAT sequence, and the corresponding two-step reconstruction algorithm for the large-scale dataset. To reduce the long acquisition time, Cartesian undersampling strategies such as SENSE are adopted in our transient-state quantitative framework. To reduce the computational burden, a data-splitting scheme is designed for decoupling the 3D reconstruction problem into independent 2D reconstructions. The proposed 3D framework is validated by numerical simulations, phantom experiments, and in vivo experiments. High-quality knee quantitative maps with 0.8 × 0.8 × 1.5 mm3 resolution and bilateral lower leg maps with 1.6 mm isotropic resolution can be acquired using the proposed 7-min acquisition sequence and the 3-min-per-slice decoupled reconstruction algorithm. The proposed 3D MR-STAT framework could have wide clinical applications in the future.

From IMU Measurement Sequence to Velocity Estimate Sequence: An Effective and Efficient Data-Driven Inertial Odometry Approach

We propose a novel deep learning-based inertial odometry framework to improve both localization accuracy and efficiency. The measured 3D accelerations and angular velocities are constructed as two independent channel 2D features. In each channel, the 3 axes of accelerations or angular velocities constitute the height dimension, whereas the temporal axis represents the width dimension. In the neural network module, we first employ 2D convolutional operations to convert the input features to 1D series with 4-fold downsampling along the temporal axis. We then apply the convolutional attention module to refine the generated 1D feature map and long short-term memory (LSTM) layers to generate the hidden state for each refined time index, which is followed by two separate 2-layer multilayer perceptrons (MLPs) to regress the velocity and the uncertainty. During backpropagation, we use the negative log-likelihood loss to optimize the velocity and the uncertainty objectives. Benefiting from the velocity sequence objective in a single network forward propagation procedure, we also add the average squared distance between the ground-truth relative position and that of the prediction to minimize the location error. Extensive experiments on both the RoNIN dataset and the real scenario collected data in the CUHK campus show higher localization accuracy with 10 times higher efficiency of the proposed framework than the comparing methods.

Analytical Method in the Linear Three-Dimensional Aerodynamics of a Thin Rectangular Wing

The paper develops an analytical method in the classical problem on a flow around a thin rectangular plate of large span. It is shown that with a specific expansion on an orthogonal system of functions with a weight defined by qualitative properties of the solution, the initial 2d integral equation is asymptotically equivalent to a set of independent 1d integral equations. For them, we construct an asymptotic method allied to a boundary layer method, which permits development of analytical representations for basic aerodynamic characteristics. Comparison with the numerical method of discrete vortices shows that precision of the obtained solution is good not only for large but also for medium span of the wing.

Subtype-specific kinase dependency regulates growth and metastasis of poor-prognosis mesenchymal colorectal cancer

BackgroundColorectal cancer (CRC) can be divided into four consensus molecular subtypes (CMS), each with distinct biological features. CMS4 is associated with epithelial-mesenchymal transition and stromal infiltration (Guinney et al., Nat Med 21:1350–6, 2015; Linnekamp et al., Cell Death Differ 25:616–33, 2018), whereas clinically it is characterized by lower responses to adjuvant therapy, higher incidence of metastatic spreading and hence dismal prognosis (Buikhuisen et al., Oncogenesis 9:66, 2020).MethodsTo understand the biology of the mesenchymal subtype and unveil specific vulnerabilities, a large CRISPR-Cas9 drop-out screen was performed on 14 subtyped CRC cell lines to uncover essential kinases in all CMSs. Dependency of CMS4 cells on p21-activated kinase 2 (PAK2) was validated in independent 2D and 3D in vitro cultures and in vivo models assessing primary and metastatic outgrowth in liver and peritoneum. TIRF microscopy was used to uncover actin cytoskeleton dynamics and focal adhesion localization upon PAK2 loss. Subsequent functional assays were performed to determine altered growth and invasion patterns.ResultsPAK2 was identified as a key kinase uniquely required for growth of the mesenchymal subtype CMS4, both in vitro and in vivo. PAK2 plays an important role in cellular attachment and cytoskeletal rearrangements (Coniglio et al., Mol Cell Biol 28:4162–72, 2008; Grebenova et al., Sci Rep 9:17171, 2019). In agreement, deletion or inhibition of PAK2 impaired actin cytoskeleton dynamics in CMS4 cells and, as a consequence, significantly reduced invasive capacity, while it was dispensable for CMS2 cells. Clinical relevance of these findings was supported by the observation that deletion of PAK2 from CMS4 cells prevented metastatic spreading in vivo. Moreover, growth in a model for peritoneal metastasis was hampered when CMS4 tumor cells were deficient for PAK2.ConclusionOur data reveal a unique dependency of mesenchymal CRC and provide a rationale for PAK2 inhibition to target this aggressive subgroup of colorectal cancer.

Representing color as multiple independent scales: brightness versus saturation.

The concept of color space has served as a basis for vast scientific inquiries into the representation of color, including colorimetry, psychology, and neuroscience. However, the ideal color space that can model color appearance attributes and color difference as a uniform Euclidean space is still, to our best knowledge, not yet available. In this work, based on the alternative representation of independent 1D color scales, the brightness and saturation scales for five Munsell principal hues were collected via partition scaling, where the MacAdam optimal colors served as anchors. Furthermore, the interactions between brightness and saturation were evaluated using maximum likelihood conjoint measurement. For the average observer, saturation as constant chromaticity is independent of luminance changes, while brightness receives a small positive contribution from the physical saturation dimension. This work further supports the feasibility of representing color as multiple independent scales and provides the framework for further investigation of other color attributes.

The use of miniaturised seismic sources for reduced environmental impact

Reducing the widths of seismic cutlines helps to minimise the environmental impact of acquiring seismic surveys. This can be accomplished by utilising miniaturised seismic sources, but not all small sources provide sufficient energy for imaging deeper oil sands reservoirs at the required resolution. In the winter of 2020, two independent 2D field trials were conducted to test the use of miniaturised seismic sources for imaging both shallow and deep oil sands reservoirs in Canada. This case study examines the results for frequency content and potential reduction in environmental impact as compared to conventional oil sands explosive sources. The resolution and signal-to-noise ratio of the seismic records from miniaturized seismic sources were matching and equivalent to the conventional seismic source.

Deep 1D Landmark Representation Learning for Space Target Pose Estimation

Monocular vision-based pose estimation for known uncooperative space targets plays an increasingly important role in on-orbit operations. The existing state-of-the-art methods of space target pose estimation build the 2D-3D correspondences to recover the space target pose, where space target landmark regression is a key component of the methods. The 2D heatmap representation is the dominant descriptor in landmark regression. However, its quantization error grows dramatically under low-resolution input conditions, and extra post-processing is usually needed to compute the accurate 2D pixel coordinates of landmarks from heatmaps. To overcome the aforementioned problems, we propose a novel 1D landmark representation that encodes the horizontal and vertical pixel coordinates of a landmark as two independent 1D vectors. Furthermore, we also propose a space target landmark regression network to regress the locations of landmarks in the image using 1D landmark representations. Comprehensive experiments conducted on the SPEED dataset show that the proposed 1D landmark representation helps the proposed space target landmark regression network outperform existing state-of-the-art methods at various input resolutions, especially at low resolutions. Based on the 2D landmarks predicted by the proposed space target landmark regression network, the error of space target pose estimation is also smaller than existing state-of-the-art methods under all input resolution conditions.

Automated 3D Whole-Heart Mesh Reconstruction From 2D Cine MR Slices Using Statistical Shape Model.

Cardiac magnetic resonance (CMR) imaging is the one of the gold standard imaging modalities for the diagnosis and characterization of cardiovascular diseases. The clinical cine protocol of the CMR typically generates high-resolution 2D images of heart tissues in a finite number of separated and independent 2D planes, which are appropriate for the 3D reconstruction of biventricular heart surfaces. However, they are usually inadequate for the whole-heart reconstruction, specifically for both atria. In this regard, the paper presents a novel approach for automated patient-specific 3D whole-heart mesh reconstruction from limited number of 2D cine CMR slices with the help of a statistical shape model (SSM). After extracting the heart contours from 2D cine slices, the SSM is first optimally fitted over the sparse heart contours in 3D space to provide the initial representation of the 3D whole-heart mesh, which is further deformed to minimize the distance from the heart contours for generating the final reconstructed mesh. The reconstruction performance of the proposed approach is evaluated on a cohort of 30 subjects randomly selected from the UK Biobank study, demonstrating the generation of high-quality 3D whole-heart meshes with average contours to surface distance less than the underlying image resolution and the clinical metrics within acceptable ranges reported in previous literature. Clinical Relevance- Automated patient-specific 3D whole-heart mesh reconstruction has numerous applications in car-diac diagnosis and multimodal visualization, including treatment planning, virtual surgery, and biomedical simulations.

Planes approximation method for investigating the physical origins of deep, wide phononic bandgaps

The objective of this paper is to formulate a simple analytical model that describes Bragg and Mie scattering leading to the creation of bandgaps in phononic crystals (PnCs). The model is a homogenized, mean-field 1D model, dubbed the Planes Approximation Method (PAM). Bragg and Mie scattering are decoupled into independent 1D models and then recombined graphically to understand better and visualize bandgap creation. Due to the model's simplicity, it allows for rapid solutions compared to other computational methods. This paper argues that acoustic impedance mismatches yield bandgaps based on the formulation. PAM is demonstrated using a square lattice of microscale circular inclusions of tungsten with a lattice constant of 45 μm in a SiO2 matrix and then compared to results from finite difference time domain and plane wave expansion simulations.

A lightweight 3D-2D convolutional neural network for spectral-spatial classification of hyperspectral images

Hyperspectral Image (HSI) is usually composed of hundreds of capturing wavelength bands, which not only increase the size of the HSI rapidly but also impose various obstacles in classifying the objects accurately. Moreover, the traditional machine learning schemes utilize only the spectral features for HSI classification, which, therefore, neglect the spatial features that have a significant impact on the classification improvement. To address the aforementioned issues, in this paper, we propose to employ the principal component analysis (PCA), the baseline feature extraction method, and a thoughtfully designed stacked autoencoder, a deep learning-based feature extraction approach, for reducing the high dimensionality of the HSI and then propose a novel lightweight 3D-2D convolutional neural network (CNN) framework to concurrently exploit both spatial and spectral features from the dimensionality-reduced HSI for classification. In particular, PCA and stacked autoencoder are applied to reduce the high dimensionality of the original HSI and then the proposed 3D-2D CNN provides a combination of 3D and 2D convolution operations to extract the subtle spatial and spectral features for efficient classification. We well-adjust the proposed 3D-2D CNN architecture, and perform extensive experiments on three benchmark HSI datasets and compare our approach with the state-of-the-art classical and deep learning methods. Experimental results illustrate that we have achieved an overall accuracy of 99.73%, 99.90%, and 99.32% on Indian Pines, Pavia University, and Kennedy Space Center datasets, respectively, which outperform the classical machine learning and independent 2D and 3D CNN-based state-of-the-art methods.

A study of multidimensional fifth-order WENO method for genuinely two-dimensional Riemann solver

In this paper we conduct a research on the multidimensional fifth-order reconstruction method for Balsara's genuinely two-dimensional (2D) HLLE Riemann solver. This solver possesses the multidimensional effect successfully by solving the 2D Riemann problem at the cell vertexes. However, it is difficult to be applied to higher-order reconstruction technology. In order to meet the requirement of the reconstruction of the variables at the vertexes, we construct the general WENO reconstruction formula to complete the reconstruction procedure along different spatial directions in turns. The Gaussian quadrature is employed to achieve the fifth-order accuracy of the numerical flux integration at the interface. Typical Simpson rule in Balsara's 2D HLLE solver is no longer used, and each Gaussian point is regarded as an independent 2D Riemann problem to obtain the 2D Riemann flux directly. Due to the inevitable numerical overshoots in the process of the fifth-order reconstruction, an accuracy-preserving limiter is introduced to enhance the computational stability, which could maintain the numerical accuracy in the smooth regions and suppress the overshoots near discontinuities. Solution accuracy tests show that the multidimensional WENO method could achieve the fifth-order accuracy. Several solution quality tests are also presented, which indicate that the method proposed in this study performs well in all test cases and can capture discontinuities and complex flow structures accurately and efficiently.

Three-dimensional numerical modeling of direct current resistivity methods in mixed space-wavenumber domain

At present, in order to obtain highly accurate solution for complex (geo-electric) conditions, it is necessary to divide the model precisely in the numerical modeling of DC resistivity method, which results in greatly increases the amount of computation and memory. To solve this problem, we propose a 3D numerical simulation method in a mixed space-wavenumber domain. The method transforms the partial differential equation of DC abnormal potential into many independent 1D differential equations with different wavenumbers by performing a 2D Fourier transform along the horizontal direction. It decomposes a large-scale 3D numerical modeling problem into many 1D numerical modeling problems, which greatly reduces the computation and memory requirements of modeling, and each 1D differential equation is independent, so it has high parallelism. The vertical direction is reserved as the spatial domain, which is convenient for the subdivision, taking into account the calculation accuracy and efficiency. A finite-element algorithm combined with a chasing method is used to solve the 1D differential equations with different wavenumbers, and a contraction operator based on electromagnetic integral equation method is used to the iteration of the algorithm for the first time. Taking full advantage of the rapidity of Fourier transform, the high efficiency of solving 1D differential equations and the high parallelism of 1D problems with different wavenumbers, it can greatly reduce the amount of calculation and storage, thus greatly improves the efficiency of the algorithm. The accuracy and convergence of the algorithm are verified by using the model of a sphere in half space. Based on the model with five cubes in half space, the efficiency of the algorithm is verified by comparing with the academic Gimli package based on the finite element method. In addition, a parallel algorithm based on OpenMP is used to verify the parallelism of the proposed algorithm. The examples results show that, the 3D numerical modeling method of DC resistivity in the mixed space-wavenumber domain has the characteristics of high efficiency, high precision and highly parallel. This method provides a new way for 3D numerical simulation of DC resistivity method.