Unknown Orientation Research Articles

Precision Weak Gravitational Lensing Using Velocity Fields: Fisher Matrix Analysis

Abstract Weak gravitational lensing measurements based on photometry are limited by shape noise: the variance in the unknown unlensed orientations of the source galaxies. If the source is a disk galaxy with a well-ordered velocity field, however, velocity field data can support simultaneous inference of the shear, inclination, and position angle, virtually eliminating shape noise. We use the Fisher information matrix formalism to forecast the precision of this method in the idealized case of a perfectly ordered velocity field defined on an infinitesimally thin disk. For nearly face-on targets, one shear component, γ ×, can be constrained to , where I 0 is the signal-to-noise ratio of the central intensity pixel and n pix is the number of pixels across a diameter enclosing 80% of the light. This precision degrades with inclination angle by a factor of 3 by i = 50°. The uncertainty on the other shear component, γ +, is about 1.5 (7) times larger than the γ × uncertainty for targets at i = 10° (50°). For an arbitrary galaxy position angle on the sky, these forecasts apply not to γ + and γ × as defined on the sky but to two eigenvectors in (γ +, γ ×, μ) space, where μ is the magnification. We also forecast the potential of less expensive partial observations of the velocity field, such as slit spectroscopy. We conclude by outlining some ways in which real galaxies depart from our idealized model and thus create random or systematic uncertainties not captured here. In particular, our forecast γ × precision is currently limited only by the data quality rather than scatter in galaxy properties because the relevant type of scatter has yet to be measured.

Square-shaped sensor clusters for acoustic source localization in anisotropic plates by wave front shape-based approach

Various techniques have emerged in the past few years for localizing the acoustic source in an anisotropic plate. The wave front shape-based approach, one of the recent additions to this field of research, has the advantage of circumventing the unrealistic assumption of a straight line wave propagation path through an anisotropic medium. In their most recent versions, the two major wave front shape-based techniques (i.e., the ellipse- and the parametric curve-based techniques) are applicable to the situations with an unknown orientation of the axes of symmetry of an anisotropic plate. However, this approach still relies on estimating the angle of wave-incidence at a given location of the plate via a single L-shaped sensor cluster. The incidence angle so obtained may deviate significantly from the true angle of wave arrival. To improve the estimation accuracy of the incidence angle, in the present study a square-shaped cluster composed of four densely-spaced sensors forming the four vertices of a square is proposed to be installed at the location of interest. Essentially, a square-shaped sensor cluster contains four L-shaped clusters oriented in different directions. A formulation to estimate the angle of incidence from the signal data acquired by a squared-shaped cluster is presented. The wave front shape-based approach can then be applied to estimate the acoustic source location. A numerical study is conducted to illustrate the proposed methodology. Performance comparisons between square- and L-shaped clusters reveal that in general the square-shaped clusters lead to more accurate source location estimates than the L-shaped clusters.

5G Synchronization, Positioning, and Mapping From Diffuse Multipath

5G mmWave communication systems have the potential to jointly estimate the positions of user equipment (UE) and mapping their propagation environments using a single base station. But such potential depends on the characteristics of the reflecting surfaces, such as a deterministic specular nature, a stochastic diffuse/scattering nature, or a combination of both. In this letter, we proposed a 5G positioning and mapping algorithm with unknown orientation and clock bias for single-bounce diffuse multipath channel models. The method is able to accurately localize, calibrate and synchronize the UE, even in the absence of line-of-sight and specular components. This enables robust positioning and mapping using only diffuse multipath.

CryoGAN: A New Reconstruction Paradigm for Single-Particle Cryo-EM Via Deep Adversarial Learning

We present CryoGAN, a new paradigm for single-particle cryo-electron microscopy (cryo-EM) reconstruction based on unsupervised deep adversarial learning. In single-particle cryo-EM, the structure of a biomolecule needs to be reconstructed from a large set of noisy tomographic projections with unknown orientations. Current reconstruction techniques are based on a marginalized maximum-likelihood formulation that requires calculations over the set of all possible poses for each projection image, a computationally demanding procedure. Our approach is to seek a 3D structure that has simulated projections that match the real data in a distributional sense, thereby sidestepping pose estimation or marginalization. We prove that, in an idealized mathematical model of cryo-EM, this approach results in recovery of the correct structure. Motivated by distribution matching, we propose CryoGAN, a specialized GAN that consists of a 3D structure, a cryo-EM physics simulator, and a discriminator neural network. During reconstruction, the 3D structure is optimized so that its projections obtained through the simulator resemble real data (to the discriminator). Simultaneously, the discriminator is trained to distinguish real projections from simulated projections. CryoGAN takes as input only real projection images and the distribution of the cryo-EM imaging parameters. It involves neither prior training nor an initial estimation of the 3D structure. CryoGAN currently achieves a 10.8 Å resolution on a realistic synthetic dataset. Preliminary results on experimental β-galactosidase and 80S ribosome data demonstrate the ability of CryoGAN to exploit data statistics under standard experimental imaging conditions. We believe that this paradigm opens the door to a family of novel likelihood-free algorithms for cryo-EM reconstruction.

Principled Design and Implementation of Steerable Detectors.

We provide a complete pipeline for the detection of patterns of interest in an image. In our approach, the patterns are assumed to be adequately modeled by a known template, and are located at unknown positions and orientations that we aim at retrieving. We propose a continuous-domain additive image model, where the analyzed image is the sum of the patterns to localize and a background with self-similar isotropic power-spectrum. We are then able to compute the optimal filter fulfilling the SNR criterion based on one single template and background pair: it strongly responds to the template while being optimally decoupled from the background model. In addition, we constrain our filter to be steerable, which allows for a fast template detection together with orientation estimation. In practice, the implementation requires to discretize a continuous-domain formulation on polar grids, which is performed using quadratic radial B-splines. We demonstrate the practical usefulness of our method on a variety of template approximation and pattern detection experiments. We show that the detection performance drastically improves when we exploit the statistics of the background via its power-spectrum decay, which we refer to as spectral-shaping. The proposed scheme outperforms state-of-the-art steerable methods by up to 50% of absolute detection performance.

High-Power Lensless THz Imaging of Hidden Objects

The potential of pulsed THz radiation for time-of-flight imaging applications is well recognized. However, advances in this field are currently severely limited by the low average power of ultrafast THz sources. Typically, this results in impractically long acquisition times and a loss in resolution and contrast. These limitations make imaging of the objects in real-life scenarios impossible. Here, conclusively, the potential of state-of-the-art high-average power THz time-domain spectrometer (TDS), driven by a 100-W class, one-box ultrafast oscillator for imaging applications is shown by demonstrating lensless THz imaging in reflection mode of a dielectric sample with low reflectivity. Images obtained with our home-built 20-mW average power THz-TDS system show a significant contrast enhancement compared to a state-of-the-art commercial THz-TDS with less than $200~\mu \text{W}$ of average power. Our unique setup even allows us to obtain images of such an object with high-contrast hidden inside a medium-density fiberboard (MDF) box. This opens the door to THz time-of-flight imaging of concealed objects of unknown shape and orientation in various real-life scenarios which were so far impossible to realize.

Setup-Independent UHF RFID Sensing Technique Using Multidimensional Differential Measurement

Ultrahigh frequency radio-frequency identification (UHF RFID) technology using transducer-integrated antennas provides a cost-effective option for sensing. A novel setup-independent UHF RFID sensing system that can overcome challenges caused by the measurement setup with a priori unknown mutual distance and orientation between the tag and the read/write device (RWD) is proposed. The concept is based on using off-the-shelf radio-frequency identification (RFID) chips having self-tuning capability, which maximizes power transfer coefficient between RFID chip and RFID antenna. An ON-OFF differential (OOD) measurement is implemented to provide setup-independent sensing. The OOD parameters form a 3-D vector that can support more sensing states than prior art using single-dimensional information. Power transfer efficiency (PTE) and tag sensor efficiency (TSE) are defined, where PTE is the minimal power transfer coefficient in all sensing states and TSE is the minimal pairwise Euclidean distance of the OOD parameters. The RFID antenna layout is optimized by optimizing PTE and TSE simultaneously so as to offer sensing robustness without sacrificing communication performance in all sensing states. A water filling-level sensor has been designed and evaluated. Theoretical predications are favorably compared with experimental results under different measurement setups, having different relative distances and orientations between the tag and RWD.

ГЕОДЕЗІЯ, КАРТОГРАФІЯ І АЕРОФОТОЗНІМАННЯ

Aim. Determination of the elements of external spatial orientation of the surveying systems at the moment of image acquisition is the fundamental task in photogrammetry. Principally, this problem is solving in two ways. The first way is direct positioning and measuring of directions of camera optical axis in the geodetic space with the help of GNSS/INS equipment. The second way is the analytical solution of the problem using a set of reference information (often such information is a set of ground control points whose geodetic positions are known with sufficient accuracy and which are reliably recognised on aerial images of the photogrammetric block). The authors consider the task of providing reference and control information using the second approach, which has a number of advantages in terms of reliability and accuracy of determining the unknown image exterior orientation parameters. It is proposed to obtain additional images of ground control points by the method of their auxiliary aerial photography using an unmanned aerial vehicle (UAV) on a larger scale compared to the scale of the images of the photogrammetric block. The aim of the presented work is the implementation of the method of creating reference points and experimental confirmation of its effectiveness for photogrammetric processing. Methods and results. For the entire realization of the potential of the analytical way to determine the elements of external orientation of images, it is necessary to have a certain number of ground control points (GCP) and to keep the defined scheme of their location on the photogrammetric block. As the main source of input data authors use UAV aerial images of the terrain, which are obtained separately from the block of aerial survey, and have a better geometric resolution and which clearly depict the control reference points. Application of such auxiliary images gives the possibility of automated transferring of the position of ground control point into images of the main photogrammetric block. In our interpretation, these images of ground control points and their surroundings on the ground are called "control reference images". The basis of the work is to develop a method for obtaining the auxiliary control reference images and transferring of position of GCP depicted on them into aerial or space images of terrain by means of computer stereo matching. To achieve this goal, we have developed a processing method for the creation of control reference images of aerial image or a series of auxiliary multi-scale aerial images obtained by a drone from different heights above the reference point. The operator identifies and measures the GCP once on the auxiliary aerial image of the highest resolution. Then there is an automatic stereo matching of the control reference image in the whole series of auxiliary images in succession with a decrease in the resolution and, ultimately, directly with the aerial images of photogrammetric block. On this stage there are no recognition/cursor targeting by the human operator, and therefore there are no discrepancies, errors or mistakes related to it. In addition, if to apply fairly large size of control reference images, the proposed method can be used on a low-texture terrain, and therefore deal in many cases without the physical marking of points measured by GNSS method. And this is a way to simplify and reduce the cost of photogrammetric technology. The action of the developed method has been verified experimentally to provide the control reference information of the block of archival aerial images of the low-texture terrain. The results of the experimental approbation of the proposed method give grounds to assert that the method makes it possible to perform geodetic reference of photogrammetric projects more efficiently due to the refusal of the physical marking of the area before aerial survey. The proposed method can also be used to obtain the information for checking the quality of photogrammetric survey for provision of check points. The authors argue that the use of additional equipment - UAV of semi-professional class to obtain control reference images is economically feasible. Scientific novelty and practical relevance. The results of approbation of the "control reference image" method with obtaining stereo pairs of aerial images with vertical placement of the base are presented for the first time. There was implemented the study of the properties of such stereo pairs of aerial images to obtain images of reference points. The effectiveness of including reference images in the main block of the digital aerial triangulation network created on UAV’s images is shown.

Point cloud classification with deep normalized Reeb graph convolution

Recently, plenty of deep learning methods have been proposed to handle point clouds. Almost all of them input the entire point cloud and ignore the information redundancy lying in point clouds. This paper addresses this problem by extracting the Reeb graph from point clouds, which is a much more informative and compact representation of point clouds, and then filter the graph with deep graph convolution. To be able to classify or segment point clouds well, we propose (1) Graph Normalization to transform various graphs into a canonical graph space; (2) Normalized Similarity Distance to better identify the graph structure;(3) Reeb Graph Guided Node Pooling in order to aggregate high-level features from kNN graphs. Besides, our method naturally fits into the problem of classifying point clouds with unknown orientations. In the results, we show that our method gives a competitive performance to the state-of-the-art methods and outperforms previous methods by a large margin on handling point clouds with unknown orientations.

Extracting three-dimensional (3D) spatial information from sequential oblique unmanned aerial system (UAS) imagery for digital surface modeling

ABSTRACT The advent of unmanned aerial system (UAS) has prompted close-range imagery a prevalent source to diversify spatial applications. In addition to nadir scenes, UAS is able to take oblique imagery, which increases the opportunity to acquire sophisticated spatial information from different viewing angles. These images provide more possibilities to reconstruct the land surfaces more completely in three-dimensions (3D). However, dealing with UAS imagery for 3D modelling has been a challenging task for years due to unstable and/or unknown exterior orientation parameters (EOPs) measured by direct georeferencing. With sequential oblique UAS imagery with inadequate or missing EOPs, this paper attempts to extract 3D spatial information from these types of images to achieve digital surface reconstruction. A modular workflow integrating the recovery of camera EOPs and 3D reconstruction in a relative space is presented. These images are spatially related by a feature-based incremental structure-from-motion (fi-SfM) for localization, stereo pairs selection and modification. Digital surface reconstruction, thenceforth, is addressed through dense matching and space intersection upon the outcomes of fi-SfM. The experimental results show that the designed schema is coherent in estimating the camera EOPs and modifying the inappropriate image pairs for improved 3D reconstruction. Furthermore, the surface model generated by discrete stereo pairs can be merged automatically to present a complete digital surface model (DSM). The completeness assessment has verified that the majority of the land surface can be successfully obtained by more than 90%, and the accuracy less than 1 (m) indicates that the implemented workflow can be used to achieve 3D modelling effectively.

Directional source localization based on RSS-AOA combined measurements

Source localization plays an indispensable role in many applications. This paper addresses the directional source localization problem in a three-dimensional (3D) wireless sensor network using hybrid received-signal-strength (RSS) and angle-of-arrival (AOA) measurements. Both the position and transmission orientation of the source are to be estimated. In the considered positioning scenario, the angle and range measurements are respectively corresponding to the AOA model and RSS model that integrates the Gaussian-shaped radiation pattern. Given that the localization problem is non-convex and the unknown parameters therein are coupled together, this paper adopts the second-order cone relaxation and alternating optimization techniques in the proposed estimation algorithm. Moreover, to provide a performance benchmark for any localization method, the corresponding Cramer-Rao lower bounds (CRLB) of estimating the unknown position and transmission orientation of the source are derived. Numerical and simulation results demonstrate that the presented algorithm effectively resolves the problem, and its estimation performance is close to the CRLB for the localization with the hybrid measurements.

Definition of the NV-center orientations relative to the diamond crystal plane

We investigate the electron spin resonance of an ensemble of Nitrogen-Vacancy color centers in a bulk diamond crystal obtained by CVD-method. The diamond crystal had 1 ppm concentration of nitrogen. The four possible orientations of the NV-center in the crystal lattice lead to different dependences of a magnitude and an orientation of the external static magnetic field. We used the field of 35 Gauss with the vector in the plane of the crystal. We could rotate it to a certain angle within almost 360 degrees around the normal vector to the plane of the crystal. The goal was to define the unknown orientations of the four axes of the NV-centers relatively to the crystal plane. Experimental results obtained at room temperature with a continuous microwave excitation and presented in this paper are in a good agreement with our simulations.

Accuracy of collagen fibre estimation under noise using directional MR imaging

In tissues containing significant amounts of organised collagen, such as tendons, ligaments, menisci and articular cartilage, MR imaging exhibits a strong signal intensity variation caused by the angle between the collagen fibres and the magnetic field. By obtaining scans at different field orientations it is possible to determine the unknown fibre orientations and to deduce the underlying tissue microstructure. Our previous work demonstrated how this method can detect ligament injuries and maturity-related changes in collagen fibre structures. Practical application in human diagnostics will demand minimisation of scanning time and likely use of open low-field scanners that can allow re-orienting of the main field. This paper analyses the performance of collage fibre estimation for various image SNR values, and in relation to key parameters including number of scanning directions and parameters of the reconstruction algorithm. The analysis involved Monte Carlo simulation studies which provided benchmark performance measures, and studies using MR images of caprine knee samples with increasing levels of synthetic added noise. Tractography plots in the form of streamlines were performed, and an Alignment Index (AI) was employed as a measure of the detected orientation distribution. The results are highly encouraging, showing high accuracy and robustness even for low image SNR values.

Microscale approximation of the elastic mechanical properties of randomly oriented rock cuttings

This study examined a method for approximating the transversely isotropic (TI) elastic mechanical properties and bedding plane orientations of randomly oriented rock cuttings. Microindentation testing was conducted on multiple rock cuttings with unknown bedding orientations to obtain their experimental indentation moduli. The measured indentation moduli were assumed to be functions of both the bedding orientations and the intrinsic TI mechanical properties of the rock cuttings. This assumption holds due to the anisotropic stress history and preferred horizontal alignment of the rock fabric along the bedding direction, as well as the presence of plate-like clay particles with intrinsic TI mechanical properties. An anisotropic contact mechanics solution was then utilized to predict both the TI elastic mechanical properties and bedding plane orientations of the cuttings using a constrained inverse algorithm that minimizes the error between the predicted indentation modulus (a function of both the predicted elastic constants and the orientation of the cuttings) and the experimental indentation modulus. Several constraints were imposed on the inverse algorithm to mathematically bound the TI stiffness matrix and optimization results. A Monte Carlo simulation was also incorporated into the inverse algorithm to consider the effects of uncertainties in the experimental results. The results obtained from the proposed indentation–inverse algorithm approach show good agreement with the results obtained using non-invasive ultrasonic pulse velocity measurements on a 2.5 cm cube sample.

Angle-Displacement Rigidity Theory With Application to Distributed Network Localization

This article investigates the localization problem of a network in 2-D and 3-D spaces given the positions of anchor nodes in a global frame and internode relative measurements in local coordinate frames. It is assumed that the local frames of different nodes have different unknown orientations. First, an angle-displacement rigidity theory is developed, which can be used to localize all the free nodes by the known positions of the anchor nodes and local relative measurements (local relative position, distance, local relative bearing, angle, or ratio-of-distance measurements). Then, necessary and sufficient conditions for network localizability are given. Finally, a distributed network localization protocol is proposed, which can globally estimate the locations of all the free nodes of a network if the network is infinitesimally angle-displacement rigid. The proposed method unifies local-relative-position-based, distance-based, local-relative-bearing-based, angle-based, and ratio-of-distance-based distributed network localization approaches. The novelty of this article is that the proposed method can be applied in both generic and nongeneric configurations with an unknown global coordinate frame in both 2-D and 3-D spaces.

EM-detwin: A Program for Resolving Indexing Ambiguity in Serial Crystallography Using the Expectation-Maximization Algorithm

Serial crystallography (SX), first used as an application of X-ray free-electron lasers (XFELs), is becoming a useful method to determine atomic-resolution structures of proteins from micrometer-sized crystals with bright X-ray sources. Because of unknown orientations of crystals in SX, indexing ambiguity issue arises when the symmetry of Bravais lattice is higher than the space group symmetry, making some diffraction signals wrongly merged to the total intensity in twinned orientations. In this research, we developed a program within the CrystFEL framework, the EM-detwin, to resolve this indexing ambiguity problem based on the expectation-maximization algorithm. Testing results on the performance of the EM-detwin have demonstrated its usefulness in correctly indexing diffraction data as a valuable tool for SX data analysis.

Orientation control of ideal blue phase photonic crystals

Three-dimensional (3D) photonic crystals like Blue Phases, self-assemble in highly organized structures with a sub-micrometer range periodicity, producing selective Bragg reflections in narrow bands. Current fabrication techniques are emerging at a fast pace, however, manufacturing large 3D monocrystals still remains a challenge, and controlling the crystal orientation of large crystals has not yet been achieved. In this work, we prepared ideal 3D Blue Phase macrocrystals with a controlled crystal orientation. We designed a method to obtain large monocrystals at a desired orientation and lattice size (or reflection wavelength) by adjusting the precursor materials formulation and a simple surface treatment. Moreover, using the same method, it is possible to predict unknown lattice orientations of Blue Phases without resorting to Kossel analysis. Producing large 3D photonic crystals that are also functional tunable structures is likely to have a direct impact on new photonic applications, like microcavity lasers, displays, 3D lasers, or biosensors.

Distributed Formation Control of Mobile Agents via Global Orientation Estimation

This article proposes a distributed control approach via global orientation estimation for a range of coordination control tasks of multiple mobile agents. In particular, we first present a distributed orientation estimation protocol that determines the orientations of agents almost globally and exponentially up to an unknown constant orientation. Based on the orientation estimation, we then present distributed control laws for formation stabilization of and controlling the moving formations of holonomic double-integrator agents. Furthermore, a distributed control protocol is proposed and almost globally asymptotic convergence of the desired formation is established for nonholonomic mobile agents subject to velocity constraints. Finally, simulation results and experimental verification are also provided.

Single-Crystal 31P and 7Li NMR of the Ionic Conductor LiH2PO4

The electronic surroundings of phosphorus and lithium atoms in the ionic conductor lithium dihydrogen phosphate (LDP) have been studied by single-crystal nuclear magnetic resonance (NMR) spectroscopy at room temperature. From orientation-dependent NMR spectra of a large homegrown LDP single crystal, the full 31P chemical shift (CS) and 7Li quadrupole coupling (QC) tensor was determined, using a global fit over three rotation patterns. The resulting CS tensor is characterized by its three eigenvalues: δ 11 P A S = ( 67.0 ± 0.6 ) ppm, δ 22 P A S = ( 13.9 ± 1.5 ) ppm, and δ 33 P A S = ( − 78.7 ± 0.9 ) ppm. All eigenvalues have also been verified by magic-angle spinning NMR on a polycrystalline sample, using Herzfeld–Berger analysis of the rotational side band pattern. The resulting 7Li QC tensor is characterized by its quadrupolar coupling constant χ = Q 33 P A S = ( − 71 ± 1 ) kHz and the two eigenvalues Q 11 P A S = ( 22.3 ± 0.9 ) kHz, and Q 22 P A S = ( 48.4 ± 0.8 ) kHz. The initially unknown orientation of the mounted crystal, expressed by the orientation of the rotation axis in the orthorhombic crystal frame, was included in the global data fit as well, thus obtaining it from NMR data only.

Effect of dealloying rate on transformation behavior during liquid metal dealloying

Abstract Liquid metal dealloying is a new process that can be applied to synthesize non-noble porous materials by preventing oxidation in a utilized metallic melt. The effect of dealloying rate was investigated by electron backscattered diffraction at the reaction layer at various temperatures (600–800 °C) using (FeCo)xNi100−x precursors in a Mg melt. FeCo ligaments transformed via different mechanisms depending on dealloying rate, temperature, and concentration of Ni. At the initial stage near the precursor surface, random texture was dominant in all cases. When the dealloying rate slowed down at 600 °C and 800 °C, the parent face-centered cubic grain structure played a significant role. The second stage differed depending on the dealloying conditions, including formation of //normal direction fiber texture or an unknown specific orientation relationship. The orientation relationships also differed in the third stage. At 800 °C, the dealloying rate should be much slower than that at 600 °C to appear the third stage of the specific orientation relationship.