Asymmetric Objects Research Articles

AIPT: Adaptive information perception for online multi-object tracking

Information perception is crucial in MOT tasks. Recent approaches use positional, motion, and appearance information to model object states. However, in scenes involving camera motion, tracking tasks suffer from image distortion, trajectory loss, and mismatching issues. In this paper, we propose Adaptive Information Perception for Online Multi-Object Tracking, abbreviated as AIPT. AIPT consists of an Adaptive Motion Perception Module (AMPM) and an Asymmetric Information Suppression Module (AISM). In AMPM, we design an Adaptive Image Distortion Recovery Module (AIDRM) to perceive distortions in unknown scenes, allowing the tracker to autonomously recover distorted images as the scene changes. By designing the Information-Guided Trajectory Restoration Module (IGTRM), the tracker learns object motion states from prior information and constructs accurate reconstruction information during trajectory loss. Furthermore, our AISM module utilizes masking information to suppress potential relationships between asymmetric objects, thereby enhancing the ability of tracker to handle mismatches. Both AMPM and AISM exhibit excellent scalability, seamlessly integrating with most advanced tracking methods. Ultimately, our AIPT achieves leading performance on multiple benchmark platforms, including MOT17, MOT20, and KITTI.

Memory-induced alignment of colloidal dumbbells

When a colloidal probe is forced through a viscoelastic fluid which is characterized by a long stress-relaxation time, the fluid is excited out of equilibrium. This is leading to a number of interesting effects including a non-trivial recoil of the probe when the driving force is removed. Here, we experimentally and theoretically investigate the transient recoil dynamics of non-spherical particles, i.e., colloidal dumbbells. In addition to a translational recoil of the dumbbells, we also find a pronounced angular reorientation which results from the relaxation of the surrounding fluid. Our findings are in good agreement with a Langevin description based on the symmetries of a director (dumbbell) as well as a microscopic bath-rod model. Remarkably, we find an instability with amplified fluctuations when the dumbbell is oriented perpendicular to the direction of driving. Our results demonstrate the complex behavior of non-spherical objects within a relaxing environment which are of immediate interest for the motion of externally but also self-driven asymmetric objects in viscoelastic fluids.

Optimal path planning of spacecraft fleet to asteroid detumbling utilizing deep neural networks and genetic algorithm

Following space discoveries, asteroids were found as rich sources of minerals and organic matter that can be exploited. In this paper, we present a precise, fast, and robust energy-optimal soft landing with a combined GA-Collocation method for a spacecraft fleet aimed at an irregular asteroid detumbling mission. It is assumed that the spacecraft fleet carried to the asteroid's equilibrium points introduced as the start locations of the mission. Here, the 433 Eros asteroid has been considered as the stony target known for its irregular elongated shape and nearly large dimension. We modeled the gravitational potential field by polyhedron as the most accurate one for such asymmetric objects. Hence, to decline the high burden computational of related equations in gravitational acceleration calculation, a Deep Neural Network with seven hidden layers and a relatively large dataset covering 6000 pairs has been developed. We utilized a Genetic Algorithm to guess systematically more optimal and reliable initial costates of some key parameters to raise the speed and accuracy of estimation. The acceptable efficiency and accuracy in optimal path prediction of the presented approach for a fleet of spacecraft approved throughout precise simulations. Eventually, the feasibility of the proposed approach is demonstrated through the corresponding results.

Symmetry-based decomposition for optimised parallelisation in 3D printing processes

Current research in 3D printing focuses on improving printing performance through various techniques, including decomposition, but targets only single printers. With improved hardware costs increasing printer availability, more situations can arise involving a multitude of printers, which offers substantially more throughput in combination that may not be best utilised by current decomposition approaches. A novel approach to 3D printing is introduced that attempts to exploit this as a means of significantly increasing the speed of printing models. This was approached as a problem akin to the parallel delegation of computation tasks in a multi-core environment, where optimal performance involves computation load being distributed as evenly as possible. To achieve this, a decomposition framework was designed that combines recursive symmetric slicing with a hybrid tree-based analytical and greedy strategy to optimally minimise the maximum volume of subparts assigned to the set of printers. Experimental evaluation of the algorithm was performed to compare our approach to printing models normally (“in serial”) as a control. The algorithm was subjected to a range of models and a varying quantity of printers in parallel, with printer parameters held constant, and yielded mixed results. Larger, simpler, and more symmetric objects exhibited more significant and reliable improvements in fabrication duration at larger amounts of parallelisation than smaller, more complex, or more asymmetric objects.

Analysis of air bearing torsion pendulum moment of inertia measurements including nonlinear oscillation and damping.

For precise measurement of the moment of inertia (MOI) of asymmetric objects, using an air bearing torsional pendulum, the nonlinearity of the torsion bar and the damping effect caused by the interaction between air and sample need to be included in the analysis. The instantaneous undamped natural frequency and instantaneous damping coefficient of the motion are obtained from the Hilbert transform of the angular displacement signal and are used for data fitting to calculate the MOI. Measurements for algorithm verification were made using a special sample with large windvanes to increase the damping effect. The nonlinear analysis of the MOI experimental results indicated a relative error of less than 0.1% compared with 0.3% for classical linear analysis of the data.

The Nature of Metal Artifacts in X-ray Computed Tomography and Their Reduction by Optimization of Tomography Systems Parameters

A significant gap in the known methods for assessing the levels of metal artifacts in X-ray computed tomography and approaches to their reduction is an almost complete disregard for the physical nature of this artifact—the proximity to zero of radioscopic transparency. The proposed work fills this gap. A mathematical model has been developed for evaluating metal artifacts in X-ray computed tomography as applied to the geometry of a parallel beam. The simulation model was transformed into an algorithm, and a Mathcad program was designed to simulate images of the internal structure of the test objects. The algorithm for estimating the studied artifact includes the stages of generating sinograms and estimating the distributions of the linear coefficient over the sections of the object based on the back projection method with filtering. The efficiency of the metal artifacts simulation algorithm is demonstrated in the example of symmetric and asymmetric objects with low- and high-density inclusions and inclusions from materials with high atomic number values. The possibility of reducing metal artifacts with the help of a rational choice of the maximum energy of X-ray radiation and the ADC bit depth is illustrated. For example, for an aluminum cylinder 200 mm in diameter with a central cylindrical cavity 80 mm in diameter, cylindrical inserts 12 mm in diameter with material densities from 1.5 g/cm3 to 10 g/cm3, and effective atomic numbers of materials from 13 to 47, the numerical simulation method proved the following: it is practically unattainable to significantly reduce the level of metal artifacts by increasing the ADC capacity to the maximum X-ray energy of 160 keV; the desired effect is achieved by simultaneously increasing the maximum X-ray energy to 225 keV and the ADC capacity to 24 or 32; increasing the maximum X-ray energy from 160 keV to 225 keV leads to an increase in the energy absorbed in the material of the test object by 26%. The results of this research can be used at the design stage of X-ray computed tomography systems designed to control objects with fragments of low radiation transparency.

Ambiguity-Aware Multi-Object Pose Optimization for Visually-Assisted Robot Manipulation

6D object pose estimation aims to infer the relative pose between the object and the camera using a single image or multiple images. Most works have focused on predicting the object pose without associated uncertainty under occlusion and structural ambiguity (symmetricity). However, these works demand prior information about shape attributes, and this condition is hardly satisfied in reality; even asymmetric objects may be symmetric under the viewpoint change. In addition, acquiring and fusing diverse sensor data is challenging when extending them to robotics applications. Tackling these limitations, we present an ambiguity-aware 6D object pose estimation network, PrimA6D++, as a generic uncertainty prediction method. The major challenges in pose estimation, such as occlusion and symmetry, can be handled in a generic manner based on the measured ambiguity of the prediction. Specifically, we devise a network to reconstruct the three rotation axis primitive images of a target object and predict the underlying uncertainty along each primitive axis. Leveraging the estimated uncertainty, we then optimize multi-object poses using visual measurements and camera poses by treating it as an object SLAM problem. The proposed method shows a significant performance improvement in T-LESS and YCB-Video datasets. We further demonstrate real-time scene recognition capability for visually-assisted robot manipulation.

Automatic data collection for object detection and grasp-position estimation with mobile robots and invisible markers

ABSTRACT Deep convolutional networks have dominated advances in object detection and grasp-position estimation using computer vision. The data-collection process for these networks is, however, time-consuming and expensive. We propose an automatic data-collection method for object detection and grasp-position estimation using mobile robots and invisible markers. Our method offers clear advantages over manual data annotation and synthetic data generation in terms of time consumption, cost, consistency, and similarity to real-world data. We compared data generated with our method against synthetically generated data to show how it can affect the robustness of the deep learning model when inferred under real-world conditions. We also conducted a comparison between our method and manual data-collection and synthetic-data-generation methods and demonstrated how our method could be used for data collection of asymmetric objects and key-point estimation tasks.

DOPNet: Achieving Accurate and Efficient Point Cloud Registration Based on Deep Learning and Multi-Level Features.

Point cloud registration aims to find a rigid spatial transformation to align two given point clouds; it is widely deployed in many areas of computer vision, such as target detection, 3D localization, and so on. In order to achieve the desired results, registration error, robustness, and efficiency should be comprehensively considered. We propose a deep learning-based point cloud registration method, called DOPNet. DOPNet extracts global features of point clouds with a dynamic graph convolutional neural network (DGCNN) and cascading offset-attention modules, and the transformation is predicted by a multilayer perceptron (MLP). To enhance the information interaction between the two branches, the feature interaction module is inserted into the feature extraction pipeline to implement early data association. We compared DOPNet with the traditional method of using the iterative closest point (ICP) algorithm along with four learning-based registration methods on the Modelnet40 data set. In the experiments, the source and target point clouds were generated by sampling the original point cloud twice independently; we also conducted additional experiments with asymmetric objects. Further evaluation experiments were conducted with point cloud models from Stanford University. The results demonstrated that our DOPNet method outperforms these comparative methods in general, achieving more accurate and efficient point cloud registration.

Passive objects in confined active fluids: A localization transition

We study how walls confining active fluids interact with asymmetric passive objects placed in their bulk. We show that the objects experience nonconservative long-ranged forces mediated by the active bath. To leading order, these forces can be computed using a generalized image theorem. The walls repel asymmetric objects, irrespective of their microscopic properties or their orientations. For circular cavities, we demonstrate how this may lead to the localization of asymmetric objects in the center of the cavity, something impossible for symmetric ones.

A Normal Distributed Dwarf Mongoose Optimization Algorithm for Global Optimization and Data Clustering Applications

As data volumes have increased and difficulty in tackling vast and complicated problems has emerged, the need for innovative and intelligent solutions to handle these difficulties has become essential. Data clustering is a data mining approach that clusters a huge amount of data into a number of clusters; in other words, it finds symmetric and asymmetric objects. In this study, we developed a novel strategy that uses intelligent optimization algorithms to tackle a group of issues requiring sophisticated methods to solve. Three primary components are employed in the suggested technique, named GNDDMOA: Dwarf Mongoose Optimization Algorithm (DMOA), Generalized Normal Distribution (GNF), and Opposition-based Learning Strategy (OBL). These parts are used to organize the executions of the proposed method during the optimization process based on a unique transition mechanism to address the critical limitations of the original methods. Twenty-three test functions and eight data clustering tasks were utilized to evaluate the performance of the suggested method. The suggested method’s findings were compared to other well-known approaches. In all of the benchmark functions examined, the suggested GNDDMOA approach produced the best results. It performed very well in data clustering applications showing promising performance.

Acoustic Radiation Force and Torque Acting on Asymmetric Objects in Acoustic Bessel Beam of Zeroth Order Within Rayleigh Scattering Limit

Acoustic momentum exchange between objects and the surrounding fluid can be quantified in terms of acoustic radiation force and torque, and depends on several factors including the objects’ geometries. For a one-dimensional plane wave type, the induced torque on the objects with arbitrary shape becomes a function of both, direct polarization and Willis coupling, as a result of shape asymmetry, and has only in-plane components. Here, we investigate, in the Rayleigh scattering limit, the momentum transfer to objects in the non-planar pressure field of an acoustic Bessel beam with axisymmetric wave front. This type of beam is selected since it can be practically realized by an array of transducers that are cylindrically arranged and tilted at the cone angle β which is a proportionality index of the momentum distribution in the transverse and axial propagation directions. The analytical expressions of the radiation force and torque are derived for both symmetric and asymmetric objects. We show the dependence of radiation force and torque on the characteristic parameters β and radial distance from the beam axis. By comparing against the case of a plane travelling plane wave, zero β angle, we demonstrated that the non-planar wavefront of a zeroth order Bessel beam causes an additional radial force and axial torque. We also show that, due to Willis coupling, an asymmetric object experiences greater torques in the θ direction, by minimum of one order of magnitude compared to a plane travelling wave. Further, the components of the partial torques owing to direct polarization and Willis coupling act in the same direction, except for a certain range of cone angle β. Our findings show that a non-planar wavefront, which is quantified by β in the case of a zeroth-order Bessel beam, can be used to control the magnitude and direction of the acoustic radiation force and torque acting on arbitrarily shaped objects, implying that the wavefront should be adjusted according to the object’s shape to impart acoustic momentum in all directions and achieve a desired acoustophoretic response.

Hybrid Reptile Search Algorithm and Remora Optimization Algorithm for Optimization Tasks and Data Clustering

Data clustering is a complex data mining problem that clusters a massive amount of data objects into a predefined number of clusters; in other words, it finds symmetric and asymmetric objects. Various optimization methods have been used to solve different machine learning problems. They usually suffer from local optimal problems and unbalance between the search mechanisms. This paper proposes a novel hybrid optimization method for solving various optimization problems. The proposed method is called HRSA, which combines the original Reptile Search Algorithm (RSA) and Remora Optimization Algorithm (ROA) and handles these mechanisms’ search processes by a novel transition method. The proposed HRSA method aims to avoid the main weaknesses raised by the original methods and find better solutions. The proposed HRSA is tested on solving various complicated optimization problems—twenty-three benchmark test functions and eight data clustering problems. The obtained results illustrate that the proposed HRSA method performs significantly better than the original and comparative state-of-the-art methods. The proposed method overwhelmed all the comparative methods according to the mathematical problems. It obtained promising results in solving the clustering problems. Thus, HRSA has a remarkable efficacy when employed for various clustering problems.

Bilateral Symmetry Has No Effect on Stereoscopic Shape Judgments.

A single experiment is reported that measured the apparent stereoscopic shapes of symmetric and asymmetric objects at different viewing distances. The symmetric stimuli were specifically designed to satisfy the minimal conditions for computing veridical shape from symmetry. That is to say, they depicted complex, bilaterally symmetric, plane-faced polyhedra whose symmetry planes were oriented at an angle of 45° relative to the line of sight. The asymmetric stimuli were distorted versions of the symmetric ones in which the 3D position of each vertex was randomly displaced. Prior theoretical analyses have shown that it is mathematically possible to compute the 3D shapes of symmetric stimuli under these conditions, but those algorithms are useless for asymmetric objects. The results revealed that the apparent shapes of both types of objects were expanded or compressed in depth as a function of viewing distance, in exactly the same way as has been reported in many other studies, and that the presence or absence of symmetry had no detectable effect on performance.

Spiral features in the early Universe

Galaxies The early assembly of galaxies is thought to have produced disturbed and asymmetric objects. Morphological features seen in nearby galaxies, such as stellar disks, bulges, and spiral arms, require time to form and would be disturbed by the frequent galaxy mergers that occurred at early times. Tsukui and Iguchi identified a distant galaxy containing a disk of gas with a spiral morphology. The galaxy also has a compact central mass concentration due to a combination of a supermassive black hole and a possible stellar bulge. These features must have formed within 1.4 billion years after the Big Bang. Science , abe9680, this issue p. [1201][1] [1]: /lookup/doi/10.1126/science.abe9680

Spiral morphology in an intensely star-forming disk galaxy more than 12 billion years ago.

Spiral galaxies have distinct internal structures, including a stellar bulge, a disk, and spiral arms. It is unknown when in cosmic history these structures formed. In this study, we analyzed observations of BRI1335-0417, an intensely star-forming galaxy in the distant Universe, at a redshift of 4.41. The [C ii] gas kinematics shows a steep velocity rise near the galaxy center and has a two-armed spiral morphology, which extends from about 2 to 5 kiloparsecs in radius. We interpret these features as due to a central compact structure such as a bulge, a rotating gas disk, and either spiral arms or tidal tails. These features had formed within 1.4 billion years after the Big Bang, long before the peak of cosmic star formation.

Correlated Optical-Electrochemical Measurements Reveal Bidirectional Current Steps for Graphene Nanoplatelet Collisions at Ultramicroelectrodes.

Single-entity electrochemistry has emerged as a powerful tool to study the adsorption behavior of single nanoscale entities one-at-a-time on an ultramicroelectrode surface. Classical single-entity collision studies have focused on the behavior of spherical nanoparticles or entities where the orientation of the colliding entity does not impact the electrochemical response. Here, we report a detailed study of the collision of asymmetric single graphene nanoplatelets onto ultramicroelectrodes. The collision of conductive graphene nanoplatelets on biased ultramicroelectrode surfaces can be observed in an amperometric i-t trace, revealing a variety of current transients (both positive and negative steps). To elucidate the dynamics of nanoplatelet adsorption processes and probe response heterogeneity, we correlated the collision events with optical microscopy. We show that positive steps are due to nanoplatelets coming into contact with the ultramicroelectrode, making an electrical connection, and adsorbing partly on the glass surrounding the ultramicroelectrode. Negative steps occur when nanoplatelets adsorb onto the glass without an electrical connection, effectively blocking flux of ferrocenemethanol to the ultramicroelectrode surface. These measurements allow rigorous quantification of current transients and detailed insights into the adsorption dynamics of asymmetric objects at the nanoscale.

Design of robotic manipulator for space applications

The paper illustrates the various design parameters implemented on our robotic manipulator that enables its usage in an alien environment and for space exploration. The robotic manipulator features a light weight variable configuration arm design, high precision actuating parts, a customized power distribution grids, tele-motion control system and an unorthodox approach for obtaining inverse kinematic values. The high configurability of this arm enables it to perform different tasks efficiently under a single platform. The 6 DOF freedom arm is custom designed to specific configurations in order to provide considerable advantage over conventional manipulators used in space applications. The arm is equipped with 2 types of end effectors in an adaptive manner such that it can be utilized in grasping both symmetric to asymmetric objects.

Self-assembly of plasmonic chiral superstructures with intense chiroptical activity

Chiral nanostructures are asymmetric nanoarchitectures that cannot be superimposed with their mirrored-symmetric counterparts, which have attracted considerable attention due to their special photophysical properties and potential applications in plasmonics, spectroscopy and nanosensors. In particular, Self-Assembly of chiral nanostructures with symmetric or asymmetric objects might exhibit exceptional optical activity because those chiral superstructures can manipulate chiral states of light that leads to circular dichroism (CD) effect. This review highlights recent advances on the self-assembly of plasmonic chiral superstructures from simpler dimeric, and trimeric chiral nanoassemblies to complicated chiral nanoarchitectures, especially emphasizes the resulted superior optical activity and the corresponding principles.

Free Induction Decay and Spin Echo Signals from Spin Triplet States of Axially Asymmetric Objects in Single Crystals in Zero Constant Magnetic Field:

Anisotropic dynamics of the spin triplet states (STSs) in single crystals with the zero field splitting (ZFS) of their levels by the axially asymmetric Hamiltonian is investigated in zero constant magnetic field (ZF) under the action of the canonically oriented varying magnetic fields. The equations of motion for single transition operators (STOs) corresponding to the definite transition of ZFS are derived. The obtained equations written in terms of one averaged equation for STO vector appeared to be a particular case (for STS) of the universal equation of Feynman et al, which is valid for any kind of perturbation affecting only two levels of any quantum mechanical system. As well as that, our equation is analogous to the Bloch equation without decay for the usual magnetization components of the Zeeman system in a constant magnetic field and a transverse to it varying field. This statement is valid, if the population probabilities of the corresponding levels of STS are not artificially equalized. At that, the motion of the observable macroscopic sample magnetization, which follows from our equations, has quite different character. Here, in terms of this magnetization the signals of the free induction decay and of the two-pulse spin echo are calculated in ZF.