Real Point Research Articles

Per-pixel distortion correction method of light field camera with homography matrix and phase calculation

A light field camera is constructed by positioning a microlens array between the main lens and an imaging plane, allowing it to record depth information. One of the factors affecting the reconstruction accuracy is distortion. To address this, we propose a novel method for correcting distortion in light field cameras using a homography matrix and phase calculation of the fringe projection technique. In this method, the distortion characteristic of each pixel in light field cameras can be directly measured. Firstly, the phase values of the concentric circular target captured by the light field camera are acquired using phase calculation technique. Next, the homography matrix is obtained by extracting the center coordinates of the target and their corresponding phase values. The phase value of each ideal point is obtained using the homography matrix, whereas the phase value of the corresponding real point is obtained through phase calculation technique. Finally, the distortion mapping table is generated based on the per-pixel ideal and real point pairs. By comparing the full-field error of the images before and after correction, the results show an average improvement of 15.992 % using this method.

Tube inner wall defect detection method based on rotary scanning micro laser distance sensor

In this paper, an inspection device to detect the inner wall defects of oil tubes is proposed, mainly consisting of a micro laser distance sensor and a stepper motor. When the inspection device moves along the axial direction inside the tube, the stepper motor drives the micro laser distance sensor to rotate and scan the inner wall of the tube so that the coordinate data of the 3D point cloud can be obtained. In order to obtain the pose transformation matrix for the laser distance sensor coordinate system with respect to the device flange coordinate system, a particle swarm optimization algorithm is introduced. Finally, experiments are carried out on two real oil tubes and point cloud data are obtained which provide 3D information about the inner walls of the tubes. The measuring error of tube eccentric wear is within 0.2 mm, which verifies the effectiveness of this novel method.

Numerical Fréchet derivatives of the displacement tensor for 2.5‐D frequency‐domain seismic full‐waveform inversion in viscoelastic TTI media

AbstractDerivatives of the displacement tensor with respect to the independent model parameters of the subsurface, also called Fréchet derivatives (or sensitivity kernels), are a key ingredient for seismic full‐waveform inversion (FWI) with a local‐search optimization algorithm. They provide a quantitative measure of the expected changes in the seismograms due to perturbations of the subsurface model parameters for a given survey geometry. Because 2.5‐D wavefield modelling involves a real point source in a 2‐D geological model with 3‐D (spherical) wave properties, it yields synthetic data much closer to the actual practical field data than the commonly used 2‐D wave simulation does, which uses an unrealistic line‐source in which the waves spread cylindrically. Based on our recently developed general 2.5‐D wavefield modelling scheme, we apply the perturbation method to obtain explicit analytic expressions for the derivatives of the displacement tensor for 2.5‐D/2‐D frequency‐domain seismic FWI in general viscoelastic anisotropic media. We then demonstrate the numerical calculations of all these derivatives in two common cases: (1) viscoelastic isotropic; and (2) viscoelastic tilted transversely isotropic (TTI) solids. Examples of the differing sensitivity patterns for the various derivatives are investigated and compared for four different homogeneous models involving 2‐D and 2.5‐D modelling. Moreover, the numerical results are verified against the analytic solutions for homogeneous models. We further validate the numerical derivatives in a 2‐D heterogeneous viscoelastic TTI case by conducting a synthetic data experiment of frequency‐domain FWI to individually recover the 12 independent model parameters (density, dip angle, 5 elastic moduli and 5 corresponding Q‐factors) in a simple model comprising an anomalous square box target embedded in a uniform background. Another 2.5‐D multi‐target model experiment presenting impacts from four common seismic surveying geometries validates the Fréchet derivatives again.

Sub-Array-Based Millimeter Wave Massive MIMO Channel Estimation

Combination of millimeter wave (mmWave) and massive multiple input multiple output (MIMO) forms a promising technology for future sixth generation networks. As the number of antennas increases, propagation channel starts yielding spherical wavefronts, for which the traditional channel estimation algorithms are no longer applicable. To overcome this technical gap, a novel MIMO channel estimation scheme based on the assumption of spherical wavefront is proposed in this work. The large antenna array is first divided into several sub-arrays, and then the channel estimation is performed for each sub-array based on the orthogonal matching pursuit algorithm. In addition, the joint estimation of angle of arrivals and departures (AoAs and AoDs) of each uniform planar sub-array is transformed into separate estimation by dimensionality reduction. Next, an iterative estimation is performed using Taylor expansion to continuously approximate the real grid points to recover the AoAs/AoDs. Lastly, the superiority of the proposed algorithm is verified by numerical simulations, where the proposed algorithm exhibits the best NMSE performance, compared with the existing MIMO channel estimation methods.

Update: Diagnostics and treatment of pulmonary tuberculosis

Molecular diagnostic tools have changed the approach to the detection of Mycobacterium tuberculosis and associated drug-resistance substantially. PCR-based technologies allow a more rapid detection with higher diagnostic sensitivity in pulmonary and extrapulmonary specimens. However, a real point of care test, which needs minimal technical resources remains missing. Genome sequencing technologies are currently changing tuberculosis drug resistance testing, and for some questions are replacing phenotypic drug resistance testing, based on culture.New evidence on treatment for drug-sensitive tuberculosis allows shortening of treatment to 4 months, or in selected cases even to 2 months based on the use of fluoroquinolones, high dose rifamycins and newly developed TB medicines.Such developments will very likely simplify the management of tuberculosis, although prevention remains the most important pillar of any tuberculosis related public health strategy.

Methodological approaches to assessing digital maturity of logistics activities of pharmaceutical enterprises

The aim: development of methodological approaches to assessing the digital maturity of the logistics activity of a pharmaceutical enterprise and elaboration of the proposed methodology on the example of those domestic enterprises that have a developed logistics system and some experience in the implementation of digital technologies in logistics activities.
 Materials and methods: Research was conducted using databases on the Internet: the European Center for Information Systems Research, the European Medicines Agency, and scientific and metric databases - Scopus, Web of Science. Logical, systematic analysis and synthesis, graphic methods of research, and methods of mathematical and statistical analysis (expert, pair correlation and taxonomic) were used.
 The results. An analysis of the state of implementation of digital technologies was carried out on the example of domestic pharmaceutical enterprises, which have been the leaders of the pharmaceutical market in recent years. It is substantiated that the assessment of digital maturity is a mandatory prerequisite for the digital transformation of pharmaceutical market entities. The existing models of digital maturity assessment are analyzed, and their shortcomings are identified. Based on this, a methodology for assessing the digital maturity of the logistics activities of pharmaceutical enterprises is proposed. An algorithm for the practical implementation of the methodology for assessing the digital maturity of the logistics activity of pharmaceutical enterprises is proposed. Based on the application of the proposed methodological approaches for assessing digital maturity using the taxonomic method, the calculation of the integral indicator of the level of digital maturity of the logistics activity of domestic pharmaceutical enterprises was carried out. The priorities for the implementation of digital technologies in the logistics activities of pharmaceutical enterprises were determined depending on the degree of their digital maturity.
 Conclusions. It has been proven that the digital transformation of the logistics activities of pharmaceutical enterprises is a complex process that must consider many factors of the internal and external environment of the pharmaceutical enterprise. It is impossible to form a strategy and make decisions about investing in digital technologies without first assessing your real opportunities and weak points of the enterprise. That is why the assessment of the existing level of digital maturity is an important step on the way to the systematic digitalization of the logistics activities of a pharmaceutical enterprise. The proposed methodical approaches to assessing the digital maturity of the logistics activity of pharmaceutical enterprises will allow to form a holistic view of the real level of digital maturity of a certain enterprise and its deviation from the desired state, determine the priority directions for increasing the level of digital maturity, based on the specifics of the logistics activity of a particular pharmaceutical enterprise, and develop a sound digital strategy to improve the performance of both the individual business and the pharmaceutical supply chain

Expressive curves

We initiate the study of a class of real plane algebraic curves which we call expressive. These are the curves whose defining polynomial has the smallest number of critical points allowed by the topology of the set of real points of a curve. This concept can be viewed as a global version of the notion of a real morsification of an isolated plane curve singularity. We prove that a plane curve C C is expressive if (a) each irreducible component of C C can be parametrized by real polynomials (either ordinary or trigonometric), (b) all singular points of C C in the affine plane are ordinary hyperbolic nodes, and (c) the set of real points of C C in the affine plane is connected. Conversely, an expressive curve with real irreducible components must satisfy conditions (a)–(c), unless it exhibits some exotic behaviour at infinity. We describe several constructions that produce expressive curves, and discuss a large number of examples, including: arrangements of lines, parabolas, and circles; Chebyshev and Lissajous curves; hypotrochoids and epitrochoids; and much more.

Research on visual recognition and positioning of industrial robots based on big data technology

Abstract This paper proposes a fast recognition and positioning algorithm based on deep learning for the problems of slow recognition of complex workpieces, low accuracy, and inaccurate positioning of industrial robots. The image grayscale and parameter calibration are performed by building an industrial high-precision vision system on the target image information. The target image is then localized and segmented by boundary pixel detection, and the trained improved SSD algorithm is used to identify the target, obtain the coordinates of the target location and the category to which it belongs, and realize the industrial robot sorting. The results show that the target recognition algorithm based on the improved SSD algorithm has an error of less than 0.5 mm, the fastest recognition speed of 0.045 sec/each, and the recognition accuracy can be maintained above 98% in the experimental environment, and the distance error between the real point and the calculated point is 8.09 mm on average, indicating that the algorithm has good accuracy and stability. Building a prototype system based on the improved SSD algorithm for industrial robots with complex processes is expected to provide an automated robot identification and positioning solution for production lines.

Quality assurance for building components through point cloud segmentation leveraging synthetic data

Quality Assurance and Quality Control (QA/QC) play a crucial role in the building project life cycle, especially during construction, as discrepancies between as-built structures and as-designed models can lead to cost overruns and schedule delays. Ensuring building quality is of utmost importance, but traditional manual inspections suffer from errors, consume time, and incur significant expenses. This paper describes a deviation detection method for building components using synthetic point clouds and semantic segmentation models. The method entails training a three-dimensional semantic segmentation model using synthetic point clouds generated from BIM to label each point with an object class, resulting in a mean IoU of 41.1% in semantic segmentation. Subsequently, real point clouds collected onsite are segmented using the same model and then compared with the synthetic point cloud to assess the disparities between the building components of the as-designed and as-built structures. This approach can improve the efficiency of QA/QC by reducing the manual workload of field inspectors.

Lift path planning for tower cranes based on environmental point clouds

The planning of lift paths is a critical task in crane operations. Traditionally, crane operators and assistants perform operation tasks based on their observation and experience, this is a tedious and error-prone process. Previous studies mainly focus on the optimization of path length or planning time, but seldom consider the accessibility of information for planning and the execution ability of crane operators. As a result, existing solutions may not be suitable for lifting practices due to the ideal validation environment. To address the gap, this research proposes an automated lift path planning method for tower cranes based on environmental point clouds. The method utilizes an octree-based sampling strategy to generate a roadmap from point clouds in the configuration space and a novel collision checking method for point clouds. Furthermore, an optimized A* algorithm is employed to identify executable paths. The proposed method is validated using real point clouds from a construction site. The result shows that the method can automatically generate more executable paths with higher efficiency only using point cloud data. Specifically, the proposed method reduces searching time, path length and the number of inflection points by 57%, 50% and 40% respectively, when compared to the RRT algorithm.

Cascade Graph Neural Networks for Few-Shot Learning on Point Clouds

Point cloud data, a flexible 3D object representation, is critical for various applications such as autonomous driving, robotics and remote sensing. Despite the recent success of deep neural networks (DNNs) on supervised point cloud analysis tasks, they still rely on tedious manual annotation of point clouds and cannot make predictions for new classes. Unlike few-shot learning for 2D images with the advantages of large-scale datasets and high-quality deep pre-trained models like ResNet, for 3D few-shot learning, obtaining discriminative representations of unseen classes with high intra-class similarity and inter-class difference is very challenging. To address this issue, this work proposes a novel cascade graph neural network for few-shot learning on point clouds, termed as CGNN, in which two cascade GNNs are adopted to extract the intra-object topological information and learn the inter-object relations respectively. To further increase the discriminability of point cloud features, we first design a novel discriminative edge label to model the intra-class similarity and inter-class dissimilarity based on channel-wise feature variance and class consistency. Second, we propose a novel few-shot circle loss which classifies the nodes into two subsets, i.e., support to support pairs and support to query pairs, and optimizes the pair-wise similarity on two subsets independently. Extensive experiments on benchmark CAD and real LiDAR point cloud datasets have demonstrated that CGNN improves accuracy by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.98\%$</tex-math> </inline-formula> over the state-of-the-art GNN-based few-shot classification methods.

UPU-SNet: Siamese network for unsupervised point cloud upsampling based on spatial-aware transformers

In the majority of cases, the paired dataset is not easy to obtain, which makes it difficult to train a supervised point cloud upsampling network directly. Therefore, we propose a novel siamese network architecture for the unsupervised upsampling task, called UPU-SNet. This architecture contains two branches based on hierarchical spatial-aware transformers. Specifically, one branch includes a global-local transformer (GLT) module and a standard reconstruction module for coarse point generation. The other contains a shared GLT module, a transformer shuffle (TranShuffle) module and a shared reconstruction module for dense point generation. For multi-level feature extraction, the GLT module we propose aims to explore feature fusions of global-to-local and local-to-global, respectively, with the aid of the multi-head cross-attention mechanism of the transformers. Then, the TranShuffle module is constructed closely after the GLT module to further optimize and expand the extracted features. Moreover, taking full account of sparse points, coarse points and dense points, we design a joint loss function, so as to support the network to generate denser and more uniform point clouds without using ground-truth. Through extensive experiments, we show how our proposal not only outperforms existing unsupervised methods but also achieves competitive results against previous supervised and self-supervised methods. Also, we present various surface reconstruction results on both synthetic and real point clouds, and show our network can enable accurate 3D reconstructions.

From CAD Models to Soft Point Cloud Labels: An Automatic Annotation Pipeline for Cheaply Supervised 3D Semantic Segmentation

We propose a fully automatic annotation scheme that takes a raw 3D point cloud with a set of fitted CAD models as input and outputs convincing point-wise labels that can be used as cheap training data for point cloud segmentation. Compared with manual annotations, we show that our automatic labels are accurate while drastically reducing the annotation time and eliminating the need for manual intervention or dataset-specific parameters. Our labeling pipeline outputs semantic classes and soft point-wise object scores, which can either be binarized into standard one-hot-encoded labels, thresholded into weak labels with ambiguous points left unlabeled, or used directly as soft labels during training. We evaluate the label quality and segmentation performance of PointNet++ on a dataset of real industrial point clouds and Scan2CAD, a public dataset of indoor scenes. Our results indicate that reducing supervision in areas that are more difficult to label automatically is beneficial compared with the conventional approach of naively assigning a hard “best guess” label to every point.

Reversed tension characteristics in subsea mooring connectors for an FPSO SPM in shallow water

The reversed tension on subsea mooring connectors is studied to prove its appearance. The mooring line is chosen from the real single point mooring system of Floating Production Storage and Offloading Unit (FPSO) wording in the South China Sea. The forced motion excitations are applied on the top end of the mooring line with the data monitored by the GPS device on the FPSO. The simulations are carried out with the lumped mass model and the nonlinear finite element model to verify the accuracy of the whole process. In the results of the forced excitation case, the reversed tensions are found three times. The occurrence of the revered tensions is consistent with the minus peak of the vertical displacement in time history, and the vertical displacement is recognized as the key factor for the reason of reversed tension. The reversed tension will apply a compression force on the rigid subsea mooring connect after a generous vertical displacement change. It may lead to the failure of the connector, and then the mooring line. Therefore, the minimum tension design is suggested to be added to the rules of classification society to avoid the reversed tension and slack condition of the mooring line.

Elastography of the thyroid nodule, cut-off points between benign and malignant lesions for strain, 2D shear wave real time and point shear wave: a correlation with pathology, ACR TIRADS and Alpha Score.

A prospective cross-sectional investigation of 170 thyroid nodules (TN) between January 2020 and December 2021 at Alpha Imagen was conducted to determine cut-off points (C/O) for elastography measurements and their diagnostic accuracy. Nodules were categorized by ACR TI-RADS, Alpha Score (AS), and Bethesda; all were evaluated using 2D Shear Wave Real Time Elastography (RT-SWE), point Shear Wave (pSWE), and Strain Elastography (SE). Data was assessed with ROC curves, the Shapiro-Wilk test, T test, Chi-square test, and ANOVA. C/O were as follows: RTSWE Emax of 115kPa and 6.5 m/s, Emean of 47.5 kPa and 4.1 m/s, pSWE (average) of 52.4 kpa and 4.15 m/s; sensitivity of 81.2% and specificity of 57.6%, with a PPV of 72.4% and NPV of 70.0%. SE Value A had a C/O of 0.20%, with a sensitivity of 84%, specificity of 57%, PPV of 72.4% and NPP of 73.6%. The Strain Ratio nodule/tissue C/O was calculated as 2.69, with a sensitivity of 84%, specificity of 57%, PPV of 72.3%, and NPV of 73.5%. The RLBIndex quality control must be at least 92%; for pSWE, we suggest a mean interquartile ratio of ≤15.7% for kPa and 8.1% for m/s. The recommended depth is between 1.2 and 1.5 cm, and commonly used ROI boxes were 3x3 and 5x5mm. 2D-SWE and pSWE with Emax and Emean demonstrated C/O with excellent diagnostic accuracy. To maximize the correct classification of TN, we suggest combining ACR TI-RADS and AS with any of the elastography measurements assessed here.

Constraints on the transition redshift from the calibrated gamma-ray burst Ep–Eiso correlation

ABSTRACT We constrain the deceleration–acceleration epoch, namely the transition redshift ztr, adopting model-independent techniques that utilize a calibrated Ep–Eiso correlation for gamma-ray bursts (GRBs). To do so, in addition to real data points, we employ up to 1000 simulated observational Hubble data (OHD) points. We then calibrate the Ep–Eiso correlation by means of the well-consolidate Bézier polynomial technique, interpolating OHD up to the second order. Once GRB data have been calibrated, we consider two strategies of cosmographic expansions, i.e. first we take a direct Hubble rate expansion around ztr, and second the expansion of the deceleration parameter around the same redshift, but with a different order. Employing Type Ia supernovae, baryonic acoustic oscillations and GRB data sets, from Monte Carlo analyses we infer tight constraints on ztr and the jerk parameters at z = ztr, namely jtr. Our results are extremely compatible with previous outcomes and confirm the Lambda cold dark matter predictions, being slightly different in terms of the jerk parameter. In this respect, we conjecture which extensions of the concordance paradigm are possible and we compare our findings with expectations provided by generic dark energy models.

Enhancing a Three-Mass Vehicle Model with Wheel-Size Effect for Scanning Bridge Frequencies

An advanced miniature vehicle model is proposed by incorporating size of two wheels in a three-mass vehicle model to consider the offset of contact points due to the presence of pavement irregularity. In this proposed vehicle–bridge system, each wheel has its own dimension in addition to the mass related to the degree-of-freedom while it is assumed to be rigid. In the theoretical formulation, the real contact points and contact displacements turn to be unknown parameters. As such, a numerical framework is established by proposing a procedure in the time integration scheme to determine these parameters and deriving explicit coding structure to locate the two wheels on the bridge during simulation. The parametric study investigates the size effect of wheels, effect of vehicle damping, and pitching effect as a result of wheel size on the identification of frequencies. The results show that using larger wheels leads to smaller dynamic responses of the bridge and smaller vertical responses of the vehicle by the present vehicle model. Furthermore, higher accuracy is observed in the proposed vehicle model in comparison with the traditional three-mass vehicle model. The first three bridge frequencies can be identified distinctly under the most severe pavement irregularity.

Measuring small displacements of an optical point source with digital holography.

The image of an optical point source is blurred due to light diffraction so that estimating small displacements of the point source with direct imaging demands elaborate processing on the observation data of a camera. Using quantum parameter estimation, we show that for the imaging systems with a real point spread function, any measurement basis constituted by a complete set of real-valued spatial-mode functions is optimal for estimating the displacement. For small displacements, we can concentrate the information about the value of displacement to the measurement of a few spatial modes, which can be selected in terms of the Fisher information distribution. We use digital holography with a phase-only spatial light modulator to implement two simple estimation strategies that are mainly based on the projection measurement of two spatial modes and the readout of a single pixel of a camera.

Automatic generation of as-is BEM models of buildings

This paper shows a method with which to create a proprietary as-is 3D semantic thermal model of a building and transform it into standard BEM formats. Geometric and relational information regarding the essential structural elements of the building (i.e., zone, ceiling, floor, wall, door, etc.) is, together with their corresponding as-is temperatures, first extracted from thermal points clouds of the scene and then adapted to the standard geometric BEM requirements. Non-geometric properties concerning definitions and properties of envelopes, windows and doors are also defined as essential information. After all this information has been collected, a BEM file is eventually generated in order to be used in energy simulation software. One of the specific features of this work is the generation of as-is BIM formats of the interior of a building that are later adapted for energy simulation applications in EnergyPlus and TRNSYS software. An ASCII file containing the temperatures of the main structural elements of the building is also created. The method has been tested on real thermal point clouds of the ground floor of a building, yielding promising results.

Needle Navigation; a New Era in the Interventional Pain Practice: A Narrative Review

Background: The hazardous effects of x-ray based technologies and shortcomings of other imaging systems like ultrasound have raised attention to create a new guiding system for interventional practice. After the introduction of virtual reality, the next challenge was how to implement it in the medical guiding systems. Medical engineering used registration aligned features of one imaging modality with real anatomical points and merged them into one single hybrid model. This augmented reality made the foundation and provided a level surface for the construction of every other new level. The next level was how to track a device in this augmented reality, which was accomplished with trackers and registration. To overcome the shortcomings of each imaging modality, the solution was fusion imaging, the combination of more than one modality to overcome the limitations of each. Nowadays, this fusion imaging plus needle tracking is the most sophisticated of navigational systems that interventionists work with. Despite clinical improvements in other medical fields like neurosurgery and urology, most of the work in the interventional pain practice has been done on phantom models and human cadavers, and there is still low experience in clinical studies about this new guiding system. Methods: The electronic search for this review included full text English article between 1990-2021 in PubMed and Google Scholar databases. The terms that have been searched are: “virtual reality”, “augmented reality”, “needle navigation”, “needle tracking”, and “fusion imaging”. Results: The navigation imaging guided system can be a practical, safe, efficient, reproducible, feasible guiding option for interventional pain procedure. Conclusions: The navigation based interventional injection is a new era in the interventional pain practice and can omit X-ray based hazardous.