Unknown Orientation Research Articles

Orientation-error observer-based tracking control of nonholonomic mobile robots

In this paper, a novel trajectory tracking controller is proposed for mobile robots with unknown orientation angle by employing the orientation-error observer (OEO). In order to overcome the local stability resulted from linearization design methods, an asymptotically stable controller is designed using Lyapunov’s direct method. This method breaks down nonlinear systems into low-dimensional systems and simplifies the controller design using virtual auxiliary error function and partial Lyapunov functions. A state-feedback controller for the nonlinear error dynamics of the mobile robot is combined with an observer that estimates the orientation-error based on available trajectory information and measurement of the position coordinates. The stability of the system is easily proved via the Lyapunov theory. Abundant simulation and experiment results validate the effectiveness and superiority of the proposed control method.

Low frequency oscillating gradient spin-echo sequences improve sensitivity to axon diameter: An experimental study in viable nerve tissue

Mapping axon diameters within the central and peripheral nervous system could play an important role in our understanding of nerve pathways, and help diagnose and monitor an array of neurological disorders. Numerous diffusion MRI methods have been proposed for imaging axon diameters, most of which use conventional single diffusion encoding (SDE) spin echo sequences. However, a growing number of studies show that oscillating gradient spin echo (OGSE) sequences can provide additional advantages over conventional SDE sequences. Recent theoretical results suggest that this is especially the case in realistic scenarios, such as when fibres have unknown or dispersed orientation. In the present study, we adopt the ActiveAx approach to experimentally investigate the extent of these advantages by comparing the performances of SDE and trapezoidal OGSE in viable nerve tissue. We optimise SDE and OGSE ActiveAx protocols for a rat peripheral nerve tissue and test their performance using Monte Carlo simulations and a 800 mT/m gradient strength pre-clinical imaging experiment. The imaging experiment uses excised sciatic nerve from a rat's leg placed in a MRI compatible viable isolated tissue (VIT) maintenance chamber, which keeps the tissue in a viable physiological state that preserves the structural complexity of the nerve and enables lengthy scan times. We compare model estimates to histology, which we perform on the nerve post scanning. Optimisation produces a three-shell SDE and OGSE ActiveAx protocol, with the OGSE protocol consisting of one SDE sequence and two low-frequency oscillating gradient waveform sequences. Both simulation and imaging results show that the OGSE ActiveAx estimates of the axon diameter index have a higher accuracy and a higher precision compared to those from SDE. Histology estimates of the axon diameter index in our nerve tissue samples are 4–5.8 μm and these are excellently matched with the OGSE estimates 4.2–6.5 μm, while SDE overestimates at 5.2–8 μm for the same sample. We found OGSE estimates to be more precise with on average a 0.5 μm standard deviation compared to the SDE estimates which have a 2 μm standard deviation. When testing the robustness of the estimates when the number of the diffusion gradient directions reduces, we found that both OGSE and SDE estimates are affected, however OGSE is more robust to these changes than the SDE. Overall, these results suggest, quantitatively and in in vivo conditions, that low-frequency OGSE sequences may provide improved accuracy of axon diameter mapping compared to standard SDE sequences.

An Enhanced Viola-Jones Vehicle Detection Method From Unmanned Aerial Vehicles Imagery

This research develops an advanced vehicle detection method, which improves the original Viola-Jones (V-J) object detection scheme for better vehicle detections from low-altitude unmanned aerial vehicle (UAV) imagery. The original V-J method is sensitive to objects’ in-plane rotation, and therefore has difficulties in detecting vehicles with unknown orientations in UAV images. To address this issue, this research proposes a road orientation adjustment method, which rotates each UAV image once so that the roads and on-road vehicles on rotated images will be aligned with the horizontal direction and the V-J vehicle detector. Then, the original V-J can be directly applied to achieve better efficiency and accuracy. The enhanced V-J method is further applied for vehicle tracking. Testing results show that both vehicle detection and tracking methods are competitive compared with other existing methods. Future research will focus on expanding the current methods to detect other transport modes, such as buses, trucks, motorcycles, bicycles, and pedestrians.

Reconstruction from limited single-particle diffraction data via simultaneous determination of state, orientation, intensity, and phase

Free-electron lasers now have the ability to collect X-ray diffraction patterns from individual molecules; however, each sample is delivered at unknown orientation and may be in one of several conformational states, each with a different molecular structure. Hit rates are often low, typically around 0.1%, limiting the number of useful images that can be collected. Determining accurate structural information requires classifying and orienting each image, accurately assembling them into a 3D diffraction intensity function, and determining missing phase information. Additionally, single particles typically scatter very few photons, leading to high image noise levels. We develop a multitiered iterative phasing algorithm to reconstruct structural information from single-particle diffraction data by simultaneously determining the states, orientations, intensities, phases, and underlying structure in a single iterative procedure. We leverage real-space constraints on the structure to help guide optimization and reconstruct underlying structure from very few images with excellent global convergence properties. We show that this approach can determine structural resolution beyond what is suggested by standard Shannon sampling arguments for ideal images and is also robust to noise.

On Tomography with Unknown Orientation

We consider the two-dimensional parallel beam Tomography problem in which both the object being imaged and the projection directions are unknown. The angles of projections need not to be uniformly distributed. Our solution combines two known approaches: the Geometric Moment and the Graph Laplacian. After sorting the projections using the Graph Laplacian method we create a one to one moment function of the angles. We then solve for each angle uniquely.

Generalized dispersion analysis of arbitrarily cut monoclinic crystals

Dispersion analysis is applicable to arbitrarily cut monoclinic crystals of unknown orientation in order to find the symmetry axis. By this it is possible to differentiate between the transition moments oriented parallel and normal to the b-axis and to determine the dielectric tensor functions of those two principal directions. Dispersion analysis of arbitrarily cut monoclinic crystals is based on an extension of the evaluation scheme developed for arbitrarily cut orthorhombic crystals. We present dispersion analysis of monoclinic crystals exemplarily on spodumene (LiAl(SiO3)2) and yttrium orthosilicate (Y2SiO5).

Upscaling PV Power Considering Module Orientations

Only a small share of photovoltaic (PV) systems installed in Germany are equipped with near real-time measurement devices. To estimate the generated power of a large number of unmeasured photovoltaic systems (e.g., to determine the sum of all systems in a portfolio), existing methodologies typically incorporate an upscaling step based on measured reference units. The upscaling approach presented in this paper extends this methodology by additionally taking into account the module orientation of all reference and target systems. Unknown module orientations are determined in a parameterization step using available geographic information system (GIS) based data sources. The power of the reference units is then used to estimate the power generation of all target units by applying a power projection and spatial interpolation as described in the literature. This new approach leads to improvements up to $\sim5\%$ in terms of $\text{RMSE}_{\text{rel}}$ values by offering a high spatial resolution and covering individual differences of PV systems.

Determining Complex Structures using Docking Method with Single Particle Scattering Data.

Protein complexes are critical for many molecular functions. Due to intrinsic flexibility and dynamics of complexes, their structures are more difficult to determine using conventional experimental methods, in contrast to individual subunits. One of the major challenges is the crystallization of protein complexes. Using X-ray free electron lasers (XFELs), it is possible to collect scattering signals from non-crystalline protein complexes, but data interpretation is more difficult because of unknown orientations. Here, we propose a hybrid approach to determine protein complex structures by combining XFEL single particle scattering data with computational docking methods. Using simulations data, we demonstrate that a small set of single particle scattering data collected at random orientations can be used to distinguish the native complex structure from the decoys generated using docking algorithms. The results also indicate that a small set of single particle scattering data is superior to spherically averaged intensity profile in distinguishing complex structures. Given the fact that XFEL experimental data are difficult to acquire and at low abundance, this hybrid approach should find wide applications in data interpretations.

Adaptive Dipole Model Based Disturbance Compensation in Nonlinear Magnetic Position Systems

The nonlinear magnetic model of an oscillating ferromagnetic object can be used for accurate real-time estimation of its position. This is useful for piston position estimation in a number of automation and performance improvement applications involving hydraulic actuators, pneumatic cylinders, and internal combustion engines. A significant challenge to magnetic field based position estimation comes from disturbances due to unexpected ferromagnetic objects coming close to the sensors. This paper develops a new disturbance estimation method based on modeling the magnetic disturbance as a dipole with unknown location, magnitude, and orientation. A truncated interval unscented Kalman filter is used to estimate all the parameters of this unknown dipole, in addition to estimating piston position from nonlinear magnetic field models. Experimental data from a pneumatic actuator are used to verify the performance of the developed estimator. Experimental results show that the developed estimator is significantly superior to a linear magnetic field model based disturbance estimator. It can reliably estimate piston position and the unknown dipole parameters in the presence of a variety of unknown disturbances.

Three-dimensional profilometric reconstruction using flexible sensing integral imaging and occlusion removal.

We present a method for three-dimensional (3D) profilometric reconstruction using flexible sensing integral imaging with object recognition and automatic occlusion removal. Two-dimensional images, known as elemental images (EIs), of a scene containing an object behind occlusion are captured by flexible sensing integral imaging using a moving camera randomly placed on a non-planar surface with unknown camera position and orientation. After 3D image acquisition, the unknown camera poses are estimated using the EIs and 3D reconstruction is performed based on flexible sensing integral imaging. Object recognition using the 3D reconstructed images is conducted to detect the object behind occlusion and estimate the object depth and position. Occlusion removal is then performed on the 2D EIs for the occluded object by computing variance maps of the scene. For each EI, occluded object pixels with low variance are replaced by object pixels from other perspectives using multi-view geometry. The new set of elemental images may be used to visualize the 3D profile of the scene containing the object without occlusion. Experiments are performed to validate the feasibility of the proposed method. To the best of our knowledge, this is the first report of applying flexible sensing integral imaging to profilometric reconstruction with object recognition and occlusion removal.

Direction-sensitive smart monitoring of structures using heterogeneous smartphone sensor data and coordinate system transformation

Mobile, heterogeneous, and smart sensor networks produce pervasive structural health monitoring (SHM) information. With various embedded sensors, smartphones have emerged to innovate SHM by empowering citizens to serve as sensors. By default, smartphones meet the fundamental smart sensor criteria, thanks to the built-in processor, memory, wireless communication units and mobile operating system. SHM using smartphones, however, faces technical challenges due to citizen-induced uncertainties, undesired sensor-structure integration, and lack of control over the sensing platform. Previously, the authors presented successful applications of smartphone accelerometers for structural vibration measurement and proposed a monitoring framework under citizen-induced spatiotemporal uncertainties. This study aims at extending the capabilities of smartphone-based SHM with a special focus on the lack of control over the sensor (i.e., the phone) positioning by citizens resulting in unknown sensor orientations. Using smartphone gyroscope, accelerometer, and magnetometer; instantaneous sensor orientation can be obtained with respect to gravitational and magnetic north directions. Using these sensor data, mobile operating system frameworks return processed features such as attitude and heading that can be used to correct misaligned sensor signals. For this purpose, a coordinate transformation procedure is proposed and illustrated on a two-story laboratory structural model and real-scale bridges with various sensor positioning examples. The proposed method corrects the sensor signals by tracking their orientations and improves measurement accuracy. Moreover, knowing structure’s coordinate system a priori, even the data from arbitrarily positioned sensors can automatically be transformed to the structural coordinates. In addition, this paper also touches some secondary mobile and heterogeneous data issues including imperfect sampling and geolocation services. The coordinate system transformation methods proposed in this study can be implemented in other non-smartphone-based SHM systems as long as similar instrumentation is available.

Astronomy and Architecture in the Maya Lowlands

This article summarises recent systematic research into the use and significance of astronomical orientations in ancient Maya architecture, and shows how by applying a more rigorous methodology to a large number of orientations we were able to detect alignment patterns that shed light on the validity of former hypotheses and provide a basis for novel interpretations. Our measurements and analysis confirm that orientations to the Sun’s position on the horizon, which largely prevail, allowed the use of observational calendars composed of multiples of 13 and 20 days and were also intended to facilitate proper scheduling of agricultural activities. Further, while some buildings recorded Venus extremes, a previously unknown orientation group has been found to refer to major lunar standstill positions. Some important buildings, aside from exhibiting astronomical orientations, are aligned to prominent features of natural or cultural landscape, implying a deliberate selection of these places for their construction. Discussing some relevant contextual evidence, we argue that Maya architectural and urban planning was dictated by a complex set of rules, in which astronomical considerations and their practical uses were embedded in a broader framework of cosmological concepts.

Rapid Solution of the Cryo-EM Reconstruction Problem by Frequency Marching

Determining the three-dimensional (3D) structure of proteins and protein complexes at atomic resolution is a fundamental task in structural biology. Over the last decade, remarkable progress has been made using “single particle” cryo-electron microscopy (cryo-EM) for this purpose. In cryo-EM, hundreds of thousands of two-dimensional (2D) images are obtained of individual copies of the same particle, each held in a thin sheet of ice at some unknown orientation. Each image corresponds to the noisy projection of the particle's electron-scattering density. The reconstruction of a high-resolution image from this data is typically formulated as a nonlinear, nonconvex optimization problem for unknowns which encode the angular pose and lateral offset of each particle. Since there are hundreds of thousands of such parameters, this leads to a very CPU-intensive task---limiting both the number of particle images which can be processed and the number of independent reconstructions which can be carried out for the pur...

Extended PCJO for the Detection-Localization of Hypersignals and Hyposignals in CT Images

The anthropomorphic model observer (MO) plays an important role in the assessment and optimization of medical imaging systems. The MO is a task-based approach; while the abnormality can appear as a hypersignal or a hyposignal for different imaging modalities, sequences, or organs, no MO has been proposed for the hyposignals detection-localization task in the literature. To improve the clinical relevance of the existing MOs, we propose an anthropomorphic MO that can also deal with hyposignals in this paper. In a previous study, we reported a perceptually relevant channelized joint observer (PCJO) for detecting and localizing multiple signals with unknown amplitude, orientation, size, and location. Here, we extend it mathematically to hyposignals task. A free-response study (close to the real-diagnostic procedure) for both hypersignals and hyposignals in cerebral and abdominal CT images was conducted with four radiologists. The equally weighted alternative free-response operating characteristic was used as the figure of merit. Statistical analyses show that the extended PCJO approaches the experts’ performances with no significant difference in the studied tasks. The results demonstrate that the extended PCJO is an alternative to replace radiologists for the evaluation and comparison of different medical image processing algorithms. The PCJO has been originally proposed on magnetic resonance imaging but tested on computerized tomography (CT) here; the coherent results show that the PCJO can be generalized to another modality-CT. We also provide in this paper, the reference values of all the parameters in the PCJO to facilitate its future application on magnetic resonance (MR) or CT images.

Multi-directional adjacent wave subtraction and shifted time point mapping algorithms and their application to defect visualization in a space tank liner

We propose the multi-directional adjacent wave subtraction (MAWS), shifted time point (STP) mapping and variable time window amplitude (VTWA) mapping algorithms for crack visualization based on our finding that the processing direction of the adjacent wave subtraction with respect to crack orientation can dramatically affect crack visibility. The proposed MAWS, STP and VTWA mapping algorithms were implemented in a laser ultrasonic propagation imaging system and were applied in a nondestructive evaluation of an Al-alloy tank liner for space applications. A crack and void on the fusion zone of a weld were more effectively visualized using the proposed algorithms, and the crack could be visualized regardless of its orientation. The multi-directional processing results of the anomalous wave propagation imaging were visualized as video clips. The STP mapping algorithm, which maps the number of shifted time points to minimize the difference between adjacent waves, was developed to enhance the crack visualization capability of the system. Additionally, the multi-directional VTWA mapping algorithm performed data projection using the residuals of MAWS processing and provided stationary mapping results of crack-induced anomalous waves. The presented results demonstrate that the proposed visualization algorithms are promising techniques that do not overlook unknown crack orientations.

A Computationally-Efficient Inverse Approach to Probabilistic Strain-Based Damage Diagnosis

This work presents a computationally-efficient inverse approach to probabilistic damage diagnosis. Given strain data at a limited number of measurement locations, Bayesian inference and Markov Chain Monte Carlo (MCMC) sampling are used to estimate probability distributions of the unknown location, size, and orientation of damage. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. The approach is experimentally validated on cracked test specimens where full field strains are determined using digital image correlation (DIC). Access to full field DIC data allows for testing of different hypothetical sensor arrangements, facilitating the study of strain-based diagnosis effectiveness as the distance between damage and measurement locations increases. The ability of the framework to effectively perform both probabilistic damage localization and characterization in cracked plates is demonstrated and the impact of measurement location on uncertainty in the predictions is shown. Furthermore, the analysis time to produce these predictions is orders of magnitude less than a baseline Bayesian approach with the FE method by utilizing surrogate modeling and effective numerical sampling approaches.

Efficient Simulation of Short Fibre Reinforced Composites

AbstractIn this paper we present a way how short fibre reinforced composites, produced by injection moulding, can be simulated in a linear thermoelastic setting, with an instationary heat equation. The main problem is the unknown stochastic fibre orientation. The only information known about this orientation field are the first moments of its distribution. (© 2016 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Improving the climate for LGBTQ students at an Historically Black University

ABSTRACTAfrican American lesbian, gay, bisexual, transgender, and queer (LGBTQ) students attending an Historically Black College or University (HBCU) may experience bullying. Using snowball sampling, the authors recruited six LGBTQ students for a focus group to learn how they perceived their HBCU campus atmosphere toward LGBTQ students. Thirty faculty members, with unknown sexual orientation, were recruited through e-mail. All groups reported bullying but that the LGBTQ student activity group had improved the atmosphere during the past several years and that faculty needed sensitivity training and administration should take the lead. A curriculum is introduced to provide training to faculty and students to increase support of LGBTQ students.

Facilitating model reconstruction for single-particle scattering using small-angle X-ray scattering methods.

X-ray free-electron lasers generate intense femtosecond X-ray pulses, so that high-resolution structure determination becomes feasible from noncrystalline samples, such as single particles or single molecules. At the moment, the orientation of sample particles cannot be precisely controlled, and consequently the unknown orientation needs to be recovered using computational algorithms. This delays the model reconstruction until all the scattering patterns have been re-oriented, which often entails a long elapse of time and until the completion of the experiment. The scattering patterns from single particles or multiple particles can be summed to form a virtual powder diffraction pattern, and the low-resolution region, corresponding to the small-angle X-ray scattering (SAXS) regime, can be analysed using existing SAXS methods. This work presents a pipeline that converts single-particle data sets into SAXS data, from which real-time model reconstruction is achieved using the model retrieval approach implemented in the software package SASTBX [Liu, Hexemer & Zwart (2012). J. Appl. Cryst.45, 587-593]. To illustrate the applications, two case studies are presented with real experimental data sets collected at the Linac Coherent Light Source.

Uncertainty quantification of residual stress evaluation by the FIB–DIC ring-core method due to elastic anisotropy effects

• Compact matrix formulation is presented for residual stress calculation from residual strains using Voigt's 6-vector notation. • By performing the calculation for 8000 randomly selected crystal orientations, Uncertainty Quantification is obtained of residual stress evaluation for cubic materials with different anisotropy factors. Elastic anisotropy can have a significant effect on the reliability and precision of residual stress evaluation, due to the uncertainty in the elastic constants multiplied by the measured strains. For the focused ion beam – digital image correlation (FIB–DIC) ring-core method taken as an example, a Mathematica package was developed to evaluate the complete in-plane residual stress state from the measured strain relief values using known material orientation and anisotropic elastic properties for materials displaying cubic symmetry. However, in many practical situations the underlying material orientation is unknown, and nominal isotropic continuum elastic constants are used. This leads to a systematic error in the stress calculation. The present analysis focuses on the statistical evaluation of the uncertainty in stress evaluation due to the unknown material orientation as a function of its degree of anisotropy. We demonstrate an experimental application of this procedure to a real case of micron scale residual stress analysis in a nickel-base superalloy.