Rotation Of Object Research Articles

Mental rotation of sequentially presented 3D figures: sex and sex hormones related differences in behavioural and ERP measures

Mental rotation of 3D objects demonstrates one of the largest sex differences. We investigated sex and sex hormones-related differences in behaviour and event related potentials (ERP) using a modified Shepard and Metzler task composed of sequentially presented 3D figures in 29 men and 32 women. We demonstrated a significant increase in response time and decrease in both accuracy and positivity of the parietal ERP with increasing angular disparity between the figures. Higher angular disparity evoked an increase of global field power (GFP) from 270 to 460 ms and different activation topographies from 470 to 583 ms with lower parietal, but higher left frontal positivity. Flatter slopes in higher angular disparity condition suggest distinct strategies being implemented depending on the difficulty of the rotation. Men performed the task more accurately than women. Performance accuracy in women tended to be negatively related to estradiol while the response time tended to increase with increasing progesterone. There were no associations with testosterone. Women demonstrated higher GFP and an increased positivity over the parietal scalp area, while men showed higher activation in the left frontal cortex. Together these findings indicate dynamic angular disparity- and sex-related differences in brain activity during mental rotation of 3D figures.

Алгоритмы функционирования автономной системы замедления вращательного движения некооперированного объекта орбитального сервиса

Алгоритмы функционирования автономной системы замедления вращательного движения некооперированного объекта орбитального сервиса

Sustained Self-Starting Orbital Motion of a Glass-Fiber “Nanoengine” Driven by Photophoretic Forces

Controllable optically driven rotation of microscopic objects is desirable in many applications, but is difficult to achieve. Here we report a sustained self-starting orbital motion of a clamped elongated nanostructure, a glass-fiber nanospike, when a CW laser beam is focused axially onto its tip. Analysis shows that photophoretic antitrapping forces, acting on the nanospike with a delayed response, introduce optomechanical gain into the mechanical motion, overcoming the intrinsic mechanical dissipation and resulting in growth from noise of oscillations at the resonant frequency of the nanospike. These photophoretic forces further enable phase-locking of the orthogonal fast and slow vibrations of the nanospike (induced by slight mechanical anisotropy), giving rise to a self-sustained orbital motion. The locked phase of orbital motion can be changed by tuning the gas pressure and adjusting the geometrical asymmetry of the system. This light-driven nanoengine opens up a new degree of freedom for controlling the rotational motion of elongated nano-objects.

Spin state evolution of asteroid (367943) Duende during its 2013 earth flyby

On February 15, 2013 asteroid (367943) Duende, provisionally named 2012 DA14, experienced an extremely close earth encounter, passing within 27,700 km altitude. An observation campaign was made possible by one year's notice of the flyby. This campaign, discussed in detail in the companion paper by Moskovitz et al. (2019), yielded visible-wavelength photometry before and after closest approach. Post-flyby Goldstone Doppler-delay radar data were also obtained. These data indicated a roughly 40 m × 20 m object in non-principal axis rotation. Leveraging light curve frequency analysis from the Moskovitz et al. companion paper, dynamical and inertia constraints from the rough radar-derived elongations, and simulated photometry, only two post-flyby states were deemed viable. These were a long axis mode (LAM) with long axis convention periods Pϕ¯=6.36 h and Pψ = 8.73 h and a short axis mode (SAM) with Pϕ¯=8.71 h and Pψ = 23.7 h (each with nominal long-intermediate and long-short ellipsoid axis ratios of 1.7:1 and 2.3:1 respectively). The SAM solution was more consistent with the post-flyby photometry and radar data. Nevertheless, there were differences between the amplitude and phase of the post-flyby and best-fit simulated SAM light curves at some epochs. These discrepancies may be due to a non-ellipsoidal shape, non-uniform albedo, and/or incorrect spin state solution (attitude, inertias, tumbling periods, or rotation mode).Analysis of the sparse pre-flyby photometry by Moskovitz et al. yielded poor Fourier series solutions for all principal axis states and different peaks in the pre and post-flyby WindowCLEAN power spectra. This suggests the asteroid was tumbling before the encounter and that its spin state changed. Propagating the best-fitting LAM and SAM solutions backwards through the flyby with terrestrial tidal torques resulted in significant dispersion of the rotation states. The pre-flyby SAM states coincided with the notable Moskovitz et al. Fourier solution P1 = 8.37 h and P2 = 24.2 h. Overall, we propose Duende was tumbling before the 2013 flyby and that its spin state may have changed during the flyby. Furthermore, our analysis suggests Duende is currently in SAM with Pϕ¯=8.71 h and Pψ = 23.7 h with pole J2000 ecliptic longitude and obliquity of roughly 70° and 95° or 245° and 90° respectively. The spin state characterization approach outlined in this paper could be used for future analyses with inertia, dynamical, and observational constraints.

Object Detection In Cluttered Background using Color Clusters

Object detection in presence of complex background and illumination variation is important image analysis problem with many applications. Most of the object detection algorithms use local image descriptors which are computed from interest points based on luminance information and neglect precious color information of an object. If appearances of the object to be detected contain multiple colors in non-homogeneous distributions then it makes it difficult to detect these objects using shape features. In this context, we propose a robust algorithm designed to detect a class of objects using a descriptor which is computed from color information of an object. Clusters are formed in Hue and Saturation (HS) color space of an object using k-means clustering and cluster analysis based on number of pixels belong to each cluster, object detection is performed. Use of clustering algorithm in color space of an object to form descriptor reduces the large dimensionality of the histogram bins in the computation. The performance of the algorithm is demonstrated by experimentation carried out on standard dataset GroZi-120. Experimental results shows that the proposed algorithm is insensitive to scaling, object rotation, illumination variations and capable of handling cluttered background effectively. Finally results shows that proposed algorithm outperforms closely related algorithm by a decisive margin.

Quantum remote sensing of the angular rotation of structured objects

Based on two-photon entanglement, quantum remote sensing enables the measurement and detection to be done non-locally and remotely. However, little attention has been paid to implement a noncontact way to sense a real objects angular rotation, which is a key step towards the practical applications of precise measurements with entangled twisted photons. Here, we use photon pairs entangled in orbital angular momentum (OAM) to show that a real object's angular rotation can be measured non-locally. Our experiment reveals that the angular sensitivity of the object encoded with idler photons is proportional to the measured OAM values of signal photons. It suggests potential applications in developing a noncontact way for angle remote sensing of an object with customized measurement resolution. Moreover, this feature may provide potential application in sensing of some light-sensitive specimens when the entangled photon pairs, which have significantly different wavelengths, are used, such as one photon is infrared but the other one is visible.

Latent transformations neural network for object view synthesis

We propose a fully convolutional conditional generative neural network, the latent transformation neural network, capable of rigid and non-rigid object view synthesis using a lightweight architecture suited for real-time applications and embedded systems. In contrast to existing object view synthesis methods which incorporate conditioning information via concatenation, we introduce a dedicated network component, the conditional transformation unit. This unit is designed to learn the latent space transformations corresponding to specified target views. In addition, a consistency loss term is defined to guide the network toward learning the desired latent space mappings, a task-divided decoder is constructed to refine the quality of generated views of objects, and an adaptive discriminator is introduced to improve the adversarial training process. The generalizability of the proposed methodology is demonstrated on a collection of three diverse tasks: multi-view synthesis on real hand depth images, view synthesis of real and synthetic faces, and the rotation of rigid objects. The proposed model is shown to be comparable with the state-of-the-art methods in structural similarity index measure and $$L_{1}$$ metrics while simultaneously achieving a 24% reduction in the compute time for inference of novel images.

An interactive and intuitive visualisation method for X-ray computed tomography data of biological samples in 3D Portable Document Format

3D imaging approaches based on X-ray microcomputed tomography (microCT) have become increasingly accessible with advancements in methods, instruments and expertise. The synergy of material and life sciences has impacted biomedical research by proposing new tools for investigation. However, data sharing remains challenging as microCT files are usually in the range of gigabytes and require specific and expensive software for rendering and interpretation. Here, we provide an advanced method for visualisation and interpretation of microCT data with small file formats, readable on all operating systems, using freely available Portable Document Format (PDF) software. Our method is based on the conversion of volumetric data into interactive 3D PDF, allowing rotation, movement, magnification and setting modifications of objects, thus providing an intuitive approach to analyse structures in a 3D context. We describe the complete pipeline from data acquisition, data processing and compression, to 3D PDF formatting on an example of craniofacial anatomical morphology in the mouse embryo. Our procedure is widely applicable in biological research and can be used as a framework to analyse volumetric data from any research field relying on 3D rendering and CT-biomedical imaging.

Dexterous Manipulation of Unknown Objects Using Virtual Contact Points

The manipulation of unknown objects is a problem of special interest in robotics since it is not always possible to have exact models of the objects with which the robot interacts. This paper presents a simple strategy to manipulate unknown objects using a robotic hand equipped with tactile sensors. The hand configurations that allow the rotation of an unknown object are computed using only tactile and kinematic information, obtained during the manipulation process and reasoning about the desired and real positions of the fingertips during the manipulation. This is done taking into account that the desired positions of the fingertips are not physically reachable since they are located in the interior of the manipulated object and therefore they are virtual positions with associated virtual contact points. The proposed approach was satisfactorily validated using three fingers of an anthropomorphic robotic hand (Allegro Hand), with the original fingertips replaced by tactile sensors (WTS-FT). In the experimental validation, several everyday objects with different shapes were successfully manipulated, rotating them without the need of knowing their shape or any other physical property.

An ESG-based Sensor for Measuring Three Angles of a Moving Object Rotation: Some Issues of the Theory and Mathematical Simulation of its Operation

The main features of the design and circuitry of an ESG-based sensor for measuring three angles of a moving object rotation are discussed. The physical basis of the proposed engineering solution is generation of rotational and single- or two-component translational motion of an electrostatically suspended rotor with the use of a torquer and force transducer. The theoretical justification, algorithms and the results of comparative mathematical simulation are given. It is shown that in the case of single-component rotor oscillations in the equatorial plane, the error in determining the azimuth angle is three times smaller than that in the case of two-component oscillations.

Model-free pose estimation using point cloud data

A model-free pose (relative attitude and position) estimation process using point cloud data is developed to support envisioned autonomous satellite servicing missions. Unlike model-based pose estimation that typically requires a three-dimensional geometric model of a satellite to be serviced, the model-free pose estimation presented in this paper employs object features detected in situ using point cloud data. Its overall process is similar to existing model-free pose estimation algorithms in that it requires edge detection, object boundary identification, edge vector determination to form an orthogonal triad for attitude estimation, and object area and centroid calculation for position estimation with respect to the sensor. However, significant improvements have been made toward fully autonomous, model-free pose estimation by employing the Hough transform that allows robust line fitting of jagged edges resulting from object rotations and the discrete nature of point cloud data. In addition, a plane fitting algorithm using the homogeneous transformation was developed to compute plane normal vectors to help improve attitude estimation accuracy. Using both measured and simulated point cloud data of test objects, the performance of the developed model-free pose estimation process was examined. It was able to produce accurate position and attitude of the objects. Future effort will be needed to include more general geometric features such as circles, ellipses, and arcs to provide capabilities needed toward fully autonomous model-free pose estimation process.

Which brain areas are responsible for which aspects of grasping?

Most neuroimaging studies of both grasping and object recognition have focused on simple objects made from homogenous materials. However, in behavioral experiments with complex objects, we have shown that participants can use visual object shape and material distribution to select optimal grasps based on a combination of factors: natural grasp axis, grasp aperture, the object’s overall weight, and mass distribution. Given that visual perception of object shape and material distribution are putatively a ventral-stream function; whereas, grasping is putatively a dorsal-stream function, we used functional magnetic resonance imaging (fMRI) to characterize the role of brain regions in both streams during grasping of multi-material objects. We recorded brain activation while participants grasped and lifted objects made of wooden and brass cubes at preselected grasp points. To tease apart the different components of grasp selection, grasps were optimized for either torque, grasp aperture, grasp axis, or a combination of these. For example, some grasps were optimal with respect to minimizing torque, but suboptimal with respect to the aperture. Within visual and visuomotor regions of interest, we examined activation levels and patterns (with representational similarity analysis) to infer neural coding of grasp attributes. When participants grasped objects in configurations with high torque – that is when their grips had to counteract object rotation due to gravity – versus low torque, activation was observed in the grasp-selective anterior intraparietal sulcus, aIPS (in the lateral dorsal stream), the reach-selective superior parieto-occipital sulcus, SPOC (in the medial dorsal stream), and the object-selective lateral occipital cortex, LOC (in the ventral stream). These results suggest that when materials and their distribution are relevant for grasping, both visual streams are recruited.

Categorizing Three-Dimensional Symmetry Using Reflection, Rotoinversion, and Translation Symmetry

Symmetry is a property that has been widely examined clinically as a measurement of health and aesthetic appeal. Many current techniques that assess geometric symmetry rely on interpretation from a trained operator or produce two-dimensional measurements that cannot express the three-dimensional character of an object. In this article, we propose a comprehensive markerless method that describes an object’s symmetry using three types of fundamental symmetry, reflection, rotoinversion a combination of reflection and rotation and translation a process of reflection and rigid movement. This is done by mirroring an object over an arbitrary plane and aligning the mirrored image with the original object in a position that minimizes deviation between both objects. Each object’s symmetry can be displayed in two ways, numerically, with a best plane of symmetry or “Psym”, a fixed point and the mirrored objects rotation and magnitude of translation in relation to the original object, and visually, through a 3D deviation contour map. Three examples were made: Model 1 showed reflection symmetry and resulted in a standard deviation of 0.002 mm, Model 2 expressed rotoinversion symmetry and produced a standard deviation of 0.003 mm and Model 3 expressed translational symmetry which resulted in a translation magnitude difference of 0.015% with respect to model height. This simple procedure accurately recognizes reflection, rotoinversion and translation symmetry, takes minimal time and expertise and has the ability to expand previous case specific methods to a global application of symmetry analysis.

Real-time interactive holographic 3D display with a 360° horizontal viewing zone.

To realize a real-time interactive holographic three-dimensional (3D) display system, we synthesize a set of 24 full high-definition (HD) binary computer-generated holograms (CGHs) based on a 3D fast-Fourier-transform-based approach. These 24 CGHs are streamed into a digital micromirror device (DMD) as a single 24-bit image at 60 Hz: 1440 CGHs are synthesized in less than a second. Continual updates of the CGHs displayed on the DMD and synchronization with a rotating mirror enlarges the horizontal viewing zone to 360° using a time-division approach. We successfully demonstrate interactive manipulation, such as object rotation, rendering mode switching, and threshold value alteration, for a medical dataset of a human head obtained by X-ray computed tomography.

IterGANs: Iterative GANs to learn and control 3D object transformation

We are interested in learning visual representations which allow for 3D manipulations of visual objects based on a single 2D image. We cast this into an image-to-image transformation task, and propose Iterative Generative Adversarial Networks (IterGANs) which iteratively transform an input image into an output image. Our models learn a visual representation that can be used for objects seen in training, but also for never seen objects. Since object manipulation requires a full understanding of the geometry and appearance of the object, our IterGANs learn an implicit 3D model and a full appearance model of the object, which are both inferred from a single (test) image. Two advantages of IterGANs are that the intermediate generated images can be used for an additional supervision signal, even in an unsupervised fashion, and that the number of iterations can be used as a control signal to steer the transformation. Experiments on rotated objects and scenes show how IterGANs help with the generation process.

Virial clouds explaining the observed rotational asymmetry in the galactic halos

Rotation of galactic objects has been seen in the cosmic microwave background (CMB) that could be ascribed to molecular hydrogen clouds with, or without, dust contamination and contamination from other sources. We model the clouds using the canonical ensemble for pure molecular hydrogen, a mixture of hydrogen helium and/or dust, in order to constrain the physical parameters of these clouds. Since the clouds are cold, we justify the use of the canonical ensemble by explicitly calculating the interaction between the hydrogen molecules and the CMB photons and determining the time required for thermal equilibrium to be reached and show that there is enough time for equilibrium to be attained.

Rotated object detection with forward-looking sonar in underwater applications

Autonomous underwater vehicles (AUVs) are often used to inspect the condition of submerged structures in oil and gas fields. Because the use of global positioning systems to aid AUV navigation is not feasible, object detection is an alternative method of supporting underwater inspection missions by detecting landmarks. Objects are detected not only to plan the trajectory of the AUVs, but their inspection can be the ultimate goal of the mission. In both cases, detecting an object’s distance and orientation with respect to the AUV provides clues for the vehicle’s navigation. Accordingly, we introduce a novel multi-object detection system that outputs object position and rotation from sonar images to support AUV navigation. To achieve this aim, two novel convolutional neural network-based architectures are proposed to detect and estimate rotated bounding boxes: an end-to-end network (RBoxNet), and a pipeline comprised of two networks (YOLOv2+RBoxDNet). Both proposed networks are structured from one of three novel representations of rotated bounding boxes regressed deep inside. Experimental analyses were performed by comparing several configurations of our proposed methods (by varying the backbone, regression representation, and architecture) with state-of-the-art methods using real sonar images. Results showed that RBoxNet presents the optimum trade-off between accuracy and speed, reaching an averaged mAP@[.5,.95] of 90.3% at 8.58 frames per second (FPS), while YOLOv2+RBoxDNet is the fastest solution, running at 16.19 FPS but with a lower averaged mAP@[.5,.95] of 77.5%. Both proposed methods are robust to additive Gaussian noise variations, and can detect objects even when the noise level is up to 0.10.

Packaging optimization using the dynamic vector fields method

SummaryIn this paper, a novel packaging optimization method for convex objects is presented. This method solves the packaging optimization problem through dynamic simulation of object positions and rotations over time. Object positions and orientations are determined by dynamic vector fields, which accelerate objects according to optimization objectives or physical effects between objects or their environment. Using these vector fields, any number of objectives or effects can be accounted for, and this scalability allows the method to potentially be employed to solve a wide variety of engineering packaging optimization problems. The current implementation, as presented in this paper, represents the foundation of the method that future improvements will build upon and is currently limited to the analysis of convex objects. Three basic vector fields are presented to solve packing density maximization problems: the first maximizes packing density, the second prevents collisions between objects, and the third optimally orients objects relative to each other. Collisions between objects are relaxed in this method, allowing objects to pass through each other, which provides the potential for reduced initial condition dependence and has shown promising results thus far. Several test problems are presented and solved, demonstrating the method and its ability to generate optimal solutions.

Py_convrot: rotation conventions, to understand and to apply

Rotation is a core crystallographic operation. Two sets of Cartesian coordinates of each point of a rotated object, those before and after rotation, are linearly related, and the coefficients of these linear combinations can be represented in matrix form. This 3 × 3 matrix is unique for all points and thus describes unambiguously a particular rotation. However, its nine elements are mutually dependent and are not interpretable in a straightforward way. To describe rotations by independent and comprehensible parameters, crystallographic software usually refers to Euler or to polar angles. In crystallography and cryo-electron microscopy, there exists a large choice of conventions, making direct comparison of rotation parameters difficult and sometimes confusing. The program py_convrot, written in Python, is a converter of parameters describing rotations. In particular, it deals with all possible choices of polar angles and with all kinds of Euler angles, including all choices of rotation axes and rotation directions. Using a menu, a user can build their own rotation parameterization; its action can be viewed with an interactive graphical tool, Demo. The tables in this article and the extended help pages of the program describe details of these parameterizations and the decomposition of rotation matrices into all types of parameters. The program allows orthogonalization conventions and symmetry operations to be taken into account. This makes the program and its supporting materials both an illustrative teaching material, especially for non-specialists in mathematics and computing, and a tool for practical use.

Design and Implementation of Intelligent Robot Walking Control System Based on DSP

In recent years, the aging of the population and the acceleration of urbanization have led to a decrease in labor force, an increase in production costs and a decrease in labor productivity. In order to solve these problems, the degree of mechanical automation is bound to increase. This paper has completed the design and development of the automatic walking robot control system, mainly to realize the functions of visual navigation and walking control of the control system. The experiments prove that the vision navigation system of the automatic walking robot can provide the walking control system with direction identification and identification information of the ridge and furrow extension paths between fields. The designed walking control system can call the motor driver to realize the forward, backward, left forward, right forward, left backward and right backward rotation of the control object according to the path information. After experimental debugging, the automatic walking robot can move according to the direction of the identification image, which greatly improves the real-time and intelligent level of the automatic walking robot.