Two-dimensional Objects Research Articles

Peripheral straightness leads to shape diversification during formations of entire leaves.

The ways to read out positional information are essential to determine final shapes in developmental processes. Relative shaping to different sizes of positional information enables robust morphogenesis; however, the same difference sometimes causes diversity. Different responses to a positional information will enable such switching of identical/diverse shapes, though detail mechanisms remain unknown. In this paper, we describe growing forms by constructing the contour of a two-dimensional object using propagating points and segments connecting them. In plant morphogenesis that lacks almost cell movements, tissue growth accompanied by cell divisions is central. We focused on peripheral cell composition in leaf formation as a frame. The growth with or without cell division on the periphery was analyzed with simple algorithms. We calculated the shapes of entire leaves with different ovality using combined growth algorithms as a model. Responces of the respective algorithms to simple positional information were explored to seek the origin of the shape diversification. The algorithm for "growth with cell divisions" maintained identical shapes; however, diverse shapes were generated by the algorithm "growth without cell division" with gradients. The simplified model allowed us to interpret the oval shape diversity due to slants on edges. We concluded that peripheral straightness can generate shape diversity, at least in leaf morphogenesis.

What will be the Euclidean dimension of an Ising ferromagnetic cubic shell?

The equilibrium and nonequilibrium properties of an Ising ferromagnetic cubic shell have been extensively studied by Monte Carlo simulation using Metropolis single spin flip algorithm. Although, geometrically the Euclidean dimension of the cubical shell is three, interestingly, the Ising ferromagnetic cubic shell undergoes ferromagnetic phase transition at a temperature which is very close to that for two-dimensional Ising ferromagnet. Surprisingly, the Ising ferromagnetic cubic shell shows a strange (neither exponential nor stretched exponential) kind of relaxation behaviour, instead of exponential relaxation as usually observed in the two dimensional Ising ferromagnet. The metastable lifetime of a ferromagnetic Ising cubical shell is studied as a function of the applied magnetic field. Here also, the cubic shell behaves more likely a two-dimensional object as found from statistical analysis and comparison with Becker-Döring prediction of classical nucleation theory.

BUDAYA TELLASEN TOPAK: DARI KETUPAT KE IDE AKTIVITAS SPATIAL SKILLS DALAM PEMBELAJARAN MATEMATIKA

Tellasen topak, which is identical to ketupat, is a culture of choosing, weaving, and processing janur into a medium for ketupat food. This culture is full of spatial skills activities, because it turns two-dimensional objects into space objects (three-dimensional). The purpose of this study is to explore the idea of spatial skills activities from ketupat in mathematics learning. The research method is qualitative research. Data collection techniques are observation, documentation, and internet search. Data analysis is in the form of data reduction, data presentation, and conclusion. The results concluded that in choosing, weaving, and processing janur into a medium for ketupat food, there are spatial skills activity ideas in the form of spatial visualization, spatial orientation and spatial rotation. In math learning, these ideas play a role in explaining the concepts of geometry, measurement and number. The results of this study contribute to mathematics learning, namely in developing culture-based teaching modules.

Temporally extended successor feature neural episodic control

One of the long-term goals of reinforcement learning is to build intelligent agents capable of rapidly learning and flexibly transferring skills, similar to humans and animals. In this paper, we introduce an episodic control framework based on the temporal expansion of subsequent features to achieve these goals, which we refer to as Temporally Extended Successor Feature Neural Episodic Control (TESFNEC). This method has shown impressive results in significantly improving sample efficiency and elegantly reusing previously learned strategies. Crucially, this model enhances agent training by incorporating episodic memory, significantly reducing the number of iterations required to learn the optimal policy. Furthermore, we adopt the temporal expansion of successor features a technique to capture the expected state transition dynamics of actions. This form of temporal abstraction does not entail learning a top-down hierarchy of task structures but focuses on the bottom-up combination of actions and action repetitions. Thus, our approach directly considers the temporal scope of sequences of temporally extended actions without requiring predefined or domain-specific options. Experimental results in the two-dimensional object collection environment demonstrate that the method proposed in this paper optimizes learning policies faster than baseline reinforcement learning approaches, leading to higher average returns.

High-resolution, highly monochromatic, time-framed, x-ray-backlit-imaging diagnostics with dual-channel toroidal crystals

Here, we developed a high-resolution, dual-channel toroidal-crystal x-ray imager for time-framed x-ray backlit imaging diagnostics using the 4.727 keV helium-like Ti line. We also presented a method for adjusting the dual-channel imager through the self-imaging of a two-dimensional periodic object. Offline x-ray experiments achieved a spatial resolution of ∼5.0 μm in the center and better than 8.0 μm within a field of view (FOV) of ∼2 mm. At the ShenGuang-III prototype laser facility, we obtained imaging results with a spatial resolution of better than 5 μm within an FOV of ±40 µm. This imager thus provides a way of observing with high spatial, temporal, and spectral resolutions to diagnose the behavior of laser-produced plasma.

An optimal control strategy to design passive thermal cloaks of arbitrary shape

In this paper, we describe a numerical framework for achieving passive thermal cloaking of arbitrary shapes in both static and transient regimes. The design strategy is cast as the solution of an optimal control problem (OCP) for the heat equation where the coefficients of the thermal diffusivity matrix take the role of control functions, and the distance between the uncloaked and the cloaked field is minimized in a suitable observation domain. The control actions enter bilinearly in the heat equation, thus making the resulting OCP nonlinear, and its analysis non-trivial. We show that optimal diffusivity coefficients exist both for the static and the transient case; we derive a system of first-order necessary optimality conditions; finally, we carry out their numerical approximation using the finite-element method. A series of numerical test cases assess the capability of our strategy to tackle passive thermal cloaking of arbitrarily complex two-dimensional objects.

Data augmentation based on highlight image models of underwater maneuvering target

With the development of underwater acoustic countermeasure technology, deep learning is applied to recognize echo geometry features of underwater targets, but it faces the problem of sample scarcity. In this paper, we improved the underwater target highlight model, and established the target echo information equation of active sonar. By changing the spatial positions of target and sonar regularly, we performed the highlight image models of underwater maneuvering targets. Taking an underwater vehicle as an example, the model construction process was introduced in detail, and highlight image models of four typical acoustic scale decoys were also established, and five multi-space state highlight image data samples were generated. The eHasNet-5 convolutional classification network was designed, and the network was trained, verified and tested with the generated data. Finally, the experimental data test shows that the target highlight image generation models provide a new data augmentation method for the application of deep learning in active sonar target recognition, and the trained network by generated data has the ability to classify two-dimensional objects.

Phase retrieval in inverse ghost diffraction using Sagnac interferometer

Ghost diffraction (GD) involves the use of non-local spatial correlations to image objects with light, which has not interacted with them. Here, we propose and experimentally demonstrate a new technique for first-order correlation measurement and retrieval of two-dimensional phase objects in the GD from inversion of the experimentally measured two-point complex correlation function in a first order interferometer. The GD scheme is experimentally implemented by a specially designed experimental setup wherein one of the orthogonal polarization components of the transversely polarized light interacts with the object and the other polarization component of the light remains intact and directly reaches the detector. The Fourier spectrum of the object is encoded into the two-point spatial correlation of these two orthogonal polarization components which is experimentally detected in an interferometer with a radial shearing in the Sagnac geometry. We experimentally demonstrated imaging of spatially varying phase objects and results are presented for three different cases.

Structural deformation and clamping force monitoring of reconfigurable tooling motivated by strain data in aircraft assembly

The quality of aircraft assembly is mainly guaranteed by toolings which are vital to the geometrical accuracy and service performance of aviation products. In this research, a real-time monitoring system that determines the structural deformation and clamping force of reconfigurable toolings using strain data is developed to perceive the service state of the toolings. By laying fiber Bragg gratings on positioning beam and baseplate of the reconfigurable tooling, strain data of the tooling structures are gauged and transferred to curvatures. The beam and baseplate are modeled as one and two-dimensional objects respectively and shape reconstruction algorithms are established to obtain their deflection curve and surface using curvature information. Distribution of fibers is optimized to minimize the conversion error from strain to curvature. An estimation that reveals the mathematical relationship between the shape reconstruction error and measurement interval is implemented, and a mapping model from strains to clamping force of the beam is established. These algorithms are integrated into the self-developed monitoring software and undergo simulating and experimental tests. The maximum relative errors of deformation and force are 4.53% and 4.12% respectively in simulation, and 9.21% and 7.29% individually in experiment, which validates the efficiencies of the method. Tests of the monitoring system suggest that it can provide a timely and accurate sensing of the deformation and force of the tooling.

Automatic Object Detection in Radargrams of Multi-Antenna GPR Systems Based on Simulation Data for Railway Infrastructure Analysis

Ground-penetrating radar (GPR) is a non-invasive technology that uses electromagnetic pulses for subsurface exploration. In the railroad sector, it is crucial to assessing soil layers and infrastructure, offering insights into soil stratification and geological features and aiding in identifying subsurface hazards. However, the automation of radargram analysis is impeded by the lack of ground truth—accurate real-world data used to validate machine learning models—thus affecting the deployment of advanced algorithms. This study focuses on generating high-quality simulated data to address the shortage of real-world data in the context of object detection along railroad tracks and presents a fully automated pipeline that includes data generation, algorithm training, and validation using real-world data. By doing so, it paves the way for significantly easing the future task of object detection algorithms in the railway sector. A simulation environment, including the digital twin of a GPR antenna, was developed for artificial data generation. The process involves pre- and post-processing techniques to transform the three-dimensional data from the multichannel GPR system into two-dimensional datasets. This ensures minimal information loss and suitability for established two-dimensional object detection algorithms like the well-known YOLO (You Only Look Once) framework. Validation involved real-world measurements on a track with predefined buried objects. The entire pipeline, encompassing data generation, processing, training, and application, was automated for efficient algorithm testing and implementation. Artificial data show promise for better performance with increased training. Future AI and sensor advancements will enhance subsurface exploration, contributing to safer and more reliable railroad operations.

Development of Augmented Reality Application as An Educational Media for Visitors to Museum Pusaka Keraton Kasepuhan Cirebon Using Object Tracking Method and Fast Corner Detection Algorithm Based on Android

Augmented Reality is a technology that combines two-dimensional or three-dimensional virtual objects and projects these virtual objects in real time. One implementation of Augmented Reality in the tourism sector is to educate museum visitors. Museum Pusaka Keraton Kasepuhan Cirebon does not yet have a touch of technology to attract visitors, and the public paradigm is that visiting the museum only sees heirloom objects, nothing interesting or unique. The aim of the research carried out by the author is to apply Augmented Reality with the Object Tracking method and the FAST Corner Detection algorithm to educate museum visitors, so that it can attract visitors' attention. By utilizing these methods and algorithms, it can be easier for visitors to explore heirloom objects to obtain the desired information. So the results obtained from the research conducted by the author are that the response time for objects appearing using the Tracking Object method and the FAST Corner Detection algorithm in environments that use glass is an average of 1.52 seconds to 2.40 seconds and that does not use glass, namely 2.84 seconds to 4.71 seconds with a level of confidence at the 95% level.

Data and physics-driven modeling for fluid flow with a physics-informed graph convolutional neural network

A physics-informed graph convolutional network (PINN-GCN) data-driven model was proposed to solve steady-state fluid flows with various geometries. A GCN guarantees a data-driven model that adapts to irregularly structured or unstructured mesh data. In addition, the mean squared residuals of the governing equations in partial differential forms and boundary conditions are introduced into the loss function of the network model, the Physics-Informed Neural Network (PINN) increases the knowledge of the physical rules of the model, enhances the model accuracy even with sparse training data. With nine training cases containing approximately 2000 spatial point data individually, PINN-GCN could accurately predict the physical flow field over a two-dimensional object of variable size, achieving above 98.6% for mean accuracy. With five training data cases, the model could predict flow over a cylinder with variable position, achieving above 98.2% for mean accuracy. Moreover, the PINN-GCN framework can produce high-resolution predictions, even with low-resolution field training data. Furthermore, the PINN-GCN prediction speed was approximately 1000 times faster than that of the numerical simulations. Thus, the proposed model can rapidly predict high-resolution flow fields using a physics-informed, grid- and geometry-adaptive, data-driven model.

A New Class of Irregular Packing Problems Reducible to Sphere Packing in Arbitrary Norms

Packing irregular objects composed by generalized spheres is considered. A generalized sphere is defined by an arbitrary norm. For three classes of packing problems, balance, homothetic and sparse packing, the corresponding new (generalized) models are formulated. Non-overlapping and containment conditions for irregular objects composed by generalized spheres are presented. It is demonstrated that these formulations can be stated for any norm. Different geometrical shapes can be treated in the same way by simply selecting a suitable norm. The approach is applied to generalized spheres defined by Lp norms and their compositions. Numerical solutions of small problem instances obtained by the global solver BARON are provided for two-dimensional objects composed by spheres defined in Lp norms to demonstrate the potential of the approach for a wide range of engineering optimization problems.

Optical radiation force and torque of light-sheets on a cylindrical particle near an infinite boundary

This work aims to extend the previous studies on the optical radiation force and torque for a cylindrical dielectric particle to the case near an infinite boundary. Without loss of generality, the two-dimensional cylindrical object is assumed to have an arbitrary shape and be immersed in any light-sheet beam of arbitrary wave front. The partial-wave series expansion method in cylindrical coordinates and the method of images are utilized to derive the exact expressions for the axial and transverse radiation force and torque functions, which are dependent on the beam shape coefficients of the incident light-sheet and the scattering coefficients characterized by the boundary conditions at the surface. Numerical computations are performed for a circular cylindrical particle illuminated by a two-dimensional plane wave and a two-dimensional Gauss beam, respectively, with particular emphases on the size parameter, the refractive index, the particle-boundary distance, the beam waist and the offset shifts. The radiation force will increase in magnitude and reverse its direction under selected conditions. When the absorptive cylindrical particle is shifted off-axially with respect to the beam axis, it will be rotated counterclockwise or clockwise by the radiation torque. The results obtained in this work have potential applications in non-contact particle manipulation and transportation using optical tweezers.

3D object tracking using integral imaging with mutual information and Bayesian optimization

Integral imaging has proven useful for three-dimensional (3D) object visualization in adverse environmental conditions such as partial occlusion and low light. This paper considers the problem of 3D object tracking. Two-dimensional (2D) object tracking within a scene is an active research area. Several recent algorithms use object detection methods to obtain 2D bounding boxes around objects of interest in each frame. Then, one bounding box can be selected out of many for each object of interest using motion prediction algorithms. Many of these algorithms rely on images obtained using traditional 2D imaging systems. A growing literature demonstrates the advantage of using 3D integral imaging instead of traditional 2D imaging for object detection and visualization in adverse environmental conditions. Integral imaging’s depth sectioning ability has also proven beneficial for object detection and visualization. Integral imaging captures an object’s depth in addition to its 2D spatial position in each frame. A recent study uses integral imaging for the 3D reconstruction of the scene for object classification and utilizes the mutual information between the object’s bounding box in this 3D reconstructed scene and the 2D central perspective to achieve passive depth estimation. We build over this method by using Bayesian optimization to track the object’s depth in as few 3D reconstructions as possible. We study the performance of our approach on laboratory scenes with occluded objects moving in 3D and show that the proposed approach outperforms 2D object tracking. In our experimental setup, mutual information-based depth estimation with Bayesian optimization achieves depth tracking with as few as two 3D reconstructions per frame which corresponds to the theoretical minimum number of 3D reconstructions required for depth estimation. To the best of our knowledge, this is the first report on 3D object tracking using the proposed approach.

Numerical analysis of a baryon and its dilatation modes in holographic QCD

We investigate a baryon and its dilatation modes in holographic QCD based on the Sakai-Sugimoto model, which is expressed as a 1+4 dimensional U(Nf) gauge theory in the flavor space. For spatially rotational symmetric systems, we apply a generalized version of the Witten Ansatz, and reduce 1+4 dimensional holographic QCD into a 1+2 dimensional Abelian Higgs theory in a curved space. In the reduced theory, the holographic baryon is described as a two-dimensional topological object of an Abrikosov vortex. We numerically calculate the baryon solution of holographic QCD using a fine and large lattice with spacing of 0.04 fm and size of 10 fm. Using the relation between the baryon size and the zero-point location of the Higgs field in the description with the Witten Ansatz, we investigate a various-size baryon through this vortex description. As time-dependent size-oscillation modes (dilatation modes) of a baryon, we numerically obtain the lowest excitation energy of 577 MeV and deduce the dilatational excitation of a nucleon to be the Roper resonance N*(1440). Published by the American Physical Society 2024

Bloch Point Quadrupole Constituting Hybrid Topological Strings Revealed with Electron Holographic Vector Field Tomography.

Topological magnetic (anti)skyrmions are robust string-like objects heralded as potential components in next-generation topological spintronics devices due to their low-energy manipulability via stimuli such as magnetic fields, heat, and electric/thermal current. While these 2D topological objects are widely studied, intrinsically 3D electron-spin real-space topology remains less explored despite its prevalence in bulky magnets. 2D-imaging studies reveal peculiar vortex-like contrast in the core regions of spin textures present in antiskyrmion-hosting thin plate magnets with S4 crystal symmetry, suggesting a more complex 3D real-space structure than the 2D model suggests. Here, holographic vector field electron tomography captures the 3D structure of antiskyrmions in a single-crystal, precision-doped (Fe0.63Ni0.3Pd0.07)3P (FNPP) lamellae at room temperature and zero field. These measurements reveal hybrid string-like solitons composed of skyrmions with topological number W = -1 on the lamellae's surfaces and an antiskyrmion (W = + 1) connecting them. High-resolution images uncover a Bloch point quadrupole (four magnetic (anti)monopoles that are undetectable in 2D imaging) which enables the observed lengthwise topological transitions. Numerical calculations corroborate the stability of hybrid strings over their conventional (anti)skyrmion counterparts. Hybrid strings result in topological tuning, a tunable topological Hall effect, and the suppression of skyrmion Hall motion, disrupting existing paradigms within spintronics.

Singular and Multimodal Techniques of 3D Object Detection: Constraints, Advancements and Research Direction

Two-dimensional object detection techniques can detect multiscale objects in images. However, they lack depth information. Three-dimensional object detection provides the location of the object in the image along with depth information. To provide depth information, 3D object detection involves the application of depth-perceiving sensors such as LiDAR, stereo cameras, RGB-D, RADAR, etc. The existing review articles on 3D object detection techniques are found to be focusing on either a singular modality (e.g., only LiDAR point cloud-based) or a singular application field (e.g., autonomous vehicle navigation). However, to the best of our knowledge, there is no review paper that discusses the applicability of 3D object detection techniques in other fields such as agriculture, robot vision or human activity detection. This study analyzes both singular and multimodal techniques of 3D object detection techniques applied in different fields. A critical analysis comprising strengths and weaknesses of the 3D object detection techniques is presented. The aim of this study is to facilitate future researchers and practitioners to provide a holistic view of 3D object detection techniques. The critical analysis of the singular and multimodal techniques is expected to help the practitioners find the appropriate techniques based on their requirement.

Application of Augmented Reality Technology in a Custom Car Ride Selection Application (Case Study: Impala Auto Fashion)

The use of augmented reality technology as an auxiliary medium in the wheel selection process is proof of the very rapid development of technology. An effective way to make work easier is by utilizing augmented reality technology, namely combining two-dimensional objects or three-dimensional objects that are applied to the real world. In other words, augmented reality is a combination of two or three-dimensional objects with the real world. A lot of time and energy is simply spent during the process of replacing car rims because the car owner does not feel comfortable with how to disassemble the rims, therefore the augmented reality application is a breakthrough to simplify and speed up the process of selecting rims, the method used is concept, design, assembly, testing, and distribution. The result of the research is an augmented reality application to simplify the process of selecting and replacing car rims on Impala auto fashion.

Detection of Fittings Based on the Dynamic Graph CNN and U-Net Embedded with Bi-Level Routing Attention

Accurate detection of power fittings is crucial for identifying defects or faults in these components, which is essential for assessing the safety and stability of the power system. However, the accuracy of fittings detection is affected by a complex background, small target sizes, and overlapping fittings in the images. To address these challenges, a fittings detection method based on the dynamic graph convolutional neural network (DGCNN) and U-shaped network (U-Net) is proposed, which combines three-dimensional detection with two-dimensional object detection. Firstly, the bi-level routing attention mechanism is incorporated into the lightweight U-Net network to enhance feature extraction for detecting the fittings boundary. Secondly, pseudo-point cloud data are synthesized by transforming the depth map generated by the Lite-Mono algorithm and its corresponding RGB fittings image. The DGCNN algorithm is then employed to extract obscured fittings features, contributing to the final refinement of the results. This process helps alleviate the issue of occlusions among targets and further enhances the precision of fittings detection. Finally, the proposed method is evaluated using a custom dataset of fittings, and comparative studies are conducted. The experimental results illustrate the promising potential of the proposed approach in enhancing features and extracting information from fittings images.