Sparse Scanning Research Articles

High-temperature structure, elasticity, and thermal expansion of ε-ZrH1.8

Zirconium hydride is a promising candidate material for nuclear microreactor applications as a solid-state moderator component, owing to its favorable neutronics properties and good thermal stability over other metal hydrides. In the present work, the crystal structure, thermal expansion, and elastic properties of the hydrogen-rich ε phase hydride were measured at elevated temperatures in the range 300–900 K. Samples were prepared by direct hydriding Zircaloy-4 metal – a nuclear-grade zirconium alloy. Room-temperature lattice parameters agree well with those reported from literature for unalloyed zirconium hydride and fall within an observed quadratic H-content dependence. The coefficients of thermal expansion, determined from lattice expansion and dilatometry, agree well within our work but were about 30 % lower than those reported by others for unalloyed hydrides. Density functional theory-based molecular dynamics simulations were used to compare with thermal expansion and elasticity measurements. Results showed lattice parameter temperature dependence and slope of thermal expansion align with those from measurements. Based on diffraction scans at select temperatures, ε phase remained stable in air up to at least 770 K. Likewise, dilatometry showed smooth thermal expansion up to the thermal decomposition temperature around 950 K. The precise decomposition temperature was not determined via diffraction due to sparse scanning. The complete elastic property measurements were gathered for ε-phase Ziracloy-4 hydride for the first time. Young's modulus was lower compared to the metal and δ hydride phases. High-temperature elasticity measurements were limited to <350 K due to acoustic dissipation effects.

A fast laser ultrasonic SAFT method with sparse scanning data and F-K based interpolation

ABSTRACT Laser ultrasonic SAFT can be applied for noncontact testing and imaging of defects in structures. However, due to the large demand for signal data by laser scanning, the time consumption of this method would be relatively long. To address this problem, this paper proposes a fast LU-SAFT method with sparse scanning data and F-K based interpolation. It aims to reduce the inspection time but without decreasing the imaging resolution. First, the method determines the minimum number of scanning sparse points using simulation. Then the F-K interpolation method is utilised to recover the full-field unscanned point signal from the sparse scan data. Finally, the reconstructed signal is used for SAFT image reconstruction of the defect. The experimental results show that, for the same scanning area, the proposed method in this paper reduces the number of scanning points by 87.5% and shortens the scanning time by 86.0% compared to conventional LU-SAFT point-by-point scanning. Additionally, the signal-to-noise ratio of the defective SAFT imaging decreases by only about 7.9%.

Event-Responsive Beam-Modulated STEM with Multi-Frame and Sparse Scanning

Event-Responsive Beam-Modulated STEM with Multi-Frame and Sparse Scanning

High-speed forward-viewing optical coherence tomography probe based on Lissajous sampling and sparse reconstruction.

We present a novel endoscopy probe using optical coherence tomography (OCT) that combines sparse Lissajous scanning and compressed sensing (CS) for faster data collection. This compact probe is only 4 mm in diameter and achieves a large field of view (FOV) of 2.25 mm2 and a 10 mm working distance. Unlike traditional OCT systems that use bulky raster scanning, our design features a dual-axis piezoelectric mechanism for efficient Lissajous pattern scanning. It employs compressive data reconstruction algorithms that minimize data collection requirements for efficient, high-speed imaging. This approach significantly enhances imaging speed by over 40%, substantially improving miniaturization and performance for endoscopic applications.

Accelerated Nano-Optical Imaging through Sparse Sampling.

The integration time and signal-to-noise ratio are inextricably linked when performing scanning probe microscopy based on raster scanning. This often yields a large lower bound on the measurement time, for example, in nano-optical imaging experiments performed using a scanning near-field optical microscope (SNOM). Here, we utilize sparse scanning augmented with Gaussian process regression to bypass the time constraint. We apply this approach to image charge-transfer polaritons in graphene residing on ruthenium trichloride (α-RuCl3) and obtain key features such as polariton damping and dispersion. Critically, nano-optical SNOM imaging data obtained via sparse sampling are in good agreement with those extracted from traditional raster scans but require 11 times fewer sampled points. As a result, Gaussian process-aided sparse spiral scans offer a major decrease in scanning time.

High-resolution non-line-of-sight imaging based on liquid crystal planar optical elements

Abstract Non-line-of-sight (NLOS) imaging aims at recovering hidden objects located beyond the traditional line of sight, with potential applications in areas such as security monitoring, search and rescue, and autonomous driving. Conventionally, NLOS imaging requires raster scanning of laser pulses and collecting the reflected photons from a relay wall. High-time-resolution detectors obtain the flight time of photons undergoing multiple scattering for image reconstruction. Expanding the scanning area while maintaining the sampling rate is an effective method to enhance the resolution of NLOS imaging, where an angle magnification system is commonly adopted. Compared to traditional optical components, planar optical elements such as liquid crystal, offer the advantages of high efficiency, lightweight, low cost, and ease of processing. By introducing liquid crystal with angle magnification capabilities into the NLOS imaging system, we successfully designed a large field-of-view high-resolution system for a wide scanning area and high-quality image reconstruction. Furthermore, in order to reduce the long data acquisition time, a sparse scanning method capitalizing on the correlation between measurement data to reduce the number of sampling points is thus proposed. Both the simulation and experiment results demonstrate a &gt;20 % reduction in data acquisition time while maintaining the exact resolution.

MFSCNN: APPENDING A MASKED BRANCH TO FAST-SCNN TO IMPROVE ROAD MARKING EXTRACTION ON SPARSE MLS POINT CLOUD-DERIVED IMAGES

Abstract. With the rise of self-driving cars, an increasing number of vehicles are equipped with low-cost light detection and ranging (LiDAR) sensors that could potentially serve as a massive mobile mapping resource, particularly for jobs that require multiple and frequent scanning, such as maintaining dynamic high-definition maps or digital twins. However, low-cost LiDAR sensors produce sparser point clouds during scanning which can make deep learning techniques for the automatic retrieval of features difficult like extracting road markings. In this work, we aim to improve the performance of a convolutional neural network (CNN) model for road marking extraction from sparse mobile LiDAR scanning (MLS) point cloud-derived images. We propose the modification of the Fast-SCNN model structure by adding a 2D convolution branch with masking in the feature fusion step: MFSCNN. To retain speed we only use MFSCNN to boost model training and still utilize Fast-SCNN for inference. Our results indicate potential, with a 4.6% increase in mean f1-score and an 8% decrease in uncertainty for the road marking class after multiple trials. Additionally, this research aims to support and increase research interest in lower-cost LiDARs for mobile mapping.

Advances in sparse dynamic scanning in spectromicroscopy through compressive sensing.

Scanning microscopies and spectroscopies like X-ray Fluorescence (XRF), Scanning Transmission X-ray Microscopy (STXM), and Ptychography are of very high scientific importance as they can be employed in several research fields. Methodology and technology advances aim at analysing larger samples at better resolutions, improved sensitivities and higher acquisition speeds. The frontiers of those advances are in detectors, radiation sources, motors, but also in acquisition and analysis software together with general methodology improvements. We have recently introduced and fully implemented an intelligent scanning methodology based on compressive sensing, on a soft X-ray microscopy beamline. This demonstrated sparse low energy XRF scanning of dynamically chosen regions of interest in combination with STXM, yielding spectroimaging data in the megapixel-range and in shorter timeframes than were previously not feasible. This research has been further developed and has been applied to scientific applications in biology. The developments are mostly in the dynamic triggering decisional mechanism in order to incorporate modern Machine Learning (ML) but also in the suitable integration of the method in the control system, making it available for other beamlines and imaging techniques. On the applications front, the method was previously successfully used on different samples, from lung and ovarian human tissues to plant root sections. This manuscript introduces the latest methodology advances and demonstrates their applications in life and environmental sciences. Lastly, it highlights the auxiliary development of a mobile application, designed to assist the user in the selection of specific regions of interest in an easy way.

High-speed mapping of surface charge dynamics using sparse scanning Kelvin probe force microscopy

Unraveling local dynamic charge processes is vital for progress in diverse fields, from microelectronics to energy storage. This relies on the ability to map charge carrier motion across multiple length- and timescales and understanding how these processes interact with the inherent material heterogeneities. Towards addressing this challenge, we introduce high-speed sparse scanning Kelvin probe force microscopy, which combines sparse scanning and image reconstruction. This approach is shown to enable sub-second imaging (>3 frames per second) of nanoscale charge dynamics, representing several orders of magnitude improvement over traditional Kelvin probe force microscopy imaging rates. Bridging this improved spatiotemporal resolution with macroscale device measurements, we successfully visualize electrochemically mediated diffusion of mobile surface ions on a LaAlO3/SrTiO3 planar device. Such processes are known to impact band-alignment and charge-transfer dynamics at these heterointerfaces. Furthermore, we monitor the diffusion of oxygen vacancies at the single grain level in polycrystalline TiO2. Through temperature-dependent measurements, we identify a charge diffusion activation energy of 0.18 eV, in good agreement with previously reported values and confirmed by DFT calculations. Together, these findings highlight the effectiveness and versatility of our method in understanding ionic charge carrier motion in microelectronics or nanoscale material systems.

Improved Boundary Identification of Stacked Objects with Sparse LiDAR Augmentation Scanning

Improved Boundary Identification of Stacked Objects with Sparse LiDAR Augmentation Scanning

Visualization and quantitative evaluation of delamination defects in GFRPs via sparse millimeter-wave imaging and image processing

Multilayer glass fiber reinforced polymer (GFRP) has broad applications, playing a pivotal role in various fields including aerospace and wind energy production. These structures may develop internal delamination defects during their manufacture or service, which can significantly impair the integrity and safety of the equipment. Thus, the inspection of such delamination defects during the service life of the equipment is crucial for assessing its operational performance and lifespan. Millimeter-wave (MMW) nondestructive testing (NDT) presents a promising method for detecting these defects in GFRPs, due to its high resolution and powerful penetration characteristics. This paper proposes a visual NDT and quantitative characterization method based on sparse imaging and image processing. This method encompasses the following three key points. (i) To facilitate rapid detection and visualization in monostatic mode, we proposed a detection approach utilizing sparse scanning and imaging. Initially, we developed a sparse sampling control matrix grounded to guide the probe for spatial automatic sparse sampling, thereby enhancing data acquisition efficiency and reducing data volume. Subsequently, we introduced a rapid sparse imaging method, integrating wave spectrum reconstruction and compressed sensing (CS). Beginning from the original data domain, we designed a fast imaging operator employing forward and inverse transformations of spectrum reconstruction imaging. (ii) In order to further refine the quality of the detection images, we proposed an adaptive singular value decomposition (SVD) approach to suppress the coupling signal and enhance the specimen signal. By adaptively adjusting the singular values, we generated a signal with the optimal target-to-background ratio (TBR). (iii) We ascertained the position and area parameters of the delamination defects using the proposed defect quantification method, combining image segmentation and image statistics. Experimental validation affirmed the effectiveness of the proposed method, revealing the following results: 1) The proposed method, despite utilizing a small amount of observational data, generates better visual images while maintaining efficient detection; 2) It can effectively identify all 0.5 mm-thick internal delamination defects in GFRPs, simultaneously retaining high quantitative characterization precision; 3) The positional quantification error remains less than 0.5 mm, and the relative area quantification error is constrained within 8%.

EXPLORING GROUND SEGMENTATION FROM LIDAR SCANNING-DERIVED IMAGES USING CONVOLUTIONAL NEURAL NETWORKS

Abstract. Recent works have attempted to extract features such as road markings from sparse mobile LiDAR scanning point cloud-derived images via convolutional neural networks (CNN). In this paper, the use of such methods for ground segmentation was explored. To begin, point clouds from each channel will be projected onto the y-z plane to generate the images that will be used for training and testing the CNN model. Then, for the main workflow, the following steps were performed for each channel: (1) point cloud-to-image conversion; (2) CNN classification; and (3) image-to-point cloud projection. Then utilizing multi-threading, each channel is processed in parallel to generate our ground-segmented sparse point cloud. Our findings have shown successful ground segmentation, achieving an f1-score of 98.9%. However, it performed 27.81% slower as compared to RANSAC. Overall, this initial investigation has demonstrated that ground segmentation from sparse point cloud-derived imagery is possible, and with further improvements to the CNN model, to make it faster, it has good potential to act as an alternative to conventional point cloud processing.

Automated piezoresponse force microscopy domain tracking during fast thermally stimulated phase transition in CuInP2S6 * *Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide

Real-time tracking of dynamic nanoscale processes such as phase transitions by scanning probe microscopy is a challenging task, typically requiring extensive and laborious human supervision. Smart strategies to track specific regions of interest (ROI) in the system during such transformations in a fast and automated manner are necessary to study the evolution of the microscopic changes in such dynamic systems. In this work, we realize automated ROI tracking in piezoresponse force microscopy during a fast (≈0.8 °C s−1) thermally stimulated ferroelectric-to-paraelectric phase transition in CuInP2S6. We use a combination of fast (1 frame per second) sparse scanning with compressed sensing image reconstruction and real-time offset correction via phase cross correlation. The applied methodology enables in situ fast and automated functional nanoscale characterization of a certain ROI during external stimulation that generates sample drift and changes local functionality.

Sparse Lissajous scanning reflectance confocal microscope with an adjustable field of view and fast iterative Fourier filtering reconstruction.

Medical imaging devices are becoming increasingly compact, necessitating optimization research into different methods of actuation. Actuation influences important parameters of the imaging device such as size, weight, frame rate, field of view (FOV), and image reconstruction for imaging devices point scanning techniques. Current literature around piezoelectric fiber cantilever actuators focuses on device optimization with a fixed FOV but neglects adjustability. In this paper, we introduce an adjustable FOV piezoelectric fiber cantilever microscope and provide a characterization and optimization procedure. To overcome calibration challenges, we utilize a position sensitive detector (PSD) and address trade-offs between FOV and sparsity with a novel inpainting technique. Our work demonstrates the potential for scanner operation when sparsity and distortion dominate the FOV, extending the usable FOV for this form of actuation and others that currently only operate under ideal imaging conditions.

Focal Combo Loss for Improved Road Marking Extraction of Sparse Mobile LiDAR Scanning Point Cloud-Derived Images Using Convolutional Neural Networks

Road markings are reflective features on roads that provide important information for safe and smooth driving. With the rise of autonomous vehicles (AV), it is necessary to represent them digitally, such as in high-definition (HD) maps generated by mobile mapping systems (MMSs). Unfortunately, MMSs are expensive, paving the way for the use of low-cost alternatives such as low-cost light detection and ranging (LiDAR) sensors. However, low-cost LiDAR sensors produce sparser point clouds than their survey-grade counterparts. This significantly reduces the capabilities of existing deep learning techniques in automatically extracting road markings, such as using convolutional neural networks (CNNs) to classify point cloud-derived imagery. A solution would be to provide a more suitable loss function to guide the CNN model during training to improve predictions. In this work, we propose a modified loss function—focal combo loss—that enhances the capability of a CNN to extract road markings from sparse point cloud-derived images in terms of accuracy, reliability, and versatility. Our results show that focal combo loss outperforms existing loss functions and CNN methods in road marking extractions in all three aspects, achieving the highest mean F1-score and the lowest uncertainty for the two distinct CNN models tested.

Sparse scanning Hartmann wavefront sensor

We propose a sparse scanning Hartmann wavefront sensor (SS-HWFS), which decouples the dynamic range and spatial resolution that used to be traded off in a conventional HWFS by removing its arrayed architecture. In addition, the SS-HWFS applies the compressed sensing (CS) reconstruction and is able to restore the wavefront with a sparsely-chosen acquisition. In the SS-HWFS, a collimated beam as the reference is incident on the sample, which is then sparsely scanned over by a pinhole. The wavefront slopes are measured by tracking the diffraction spots recorded by an imaging sensor behind the pinhole. The wavefront is then reconstructed by assuming sparsity in the Zernike domain, which is frequently used for aberration analysis. Compared with traditional Hartmann wavefront sensors (HWFS), the SS-HWFS could achieve high spatial resolution, extensive dynamic range, extended aperture, and compressed acquisition simultaneously, which is a great benefit for the non-null testing of static optical components. Our sparse scanning wavefront measurement scheme is applicable not only for the HWFS but also for the Shack–Hartmann wavefront sensor (SHWFS). Wavefront measurement of a free-form lens is performed to demonstrate the capability of our proposed method.

Optimizing sampling for surface localization in 3D-scanning microscopy

3D-scanning fluorescence imaging of living tissue is in demand for less phototoxic acquisition process. For the imaging of biological surfaces, adaptive and sparse scanning schemes have been proven to efficiently reduce the light dose by concentrating acquisitions around the surface. In this paper, we focus on optimizing the scanning scheme at a constant photon budget, when the problem is to estimate the position of a biological surface whose intensity profile is modeled as a Gaussian shape. We propose an approach based on the Cramér-Rao bound to optimize the positions and number of scanning points, assuming signal-dependant Gaussian noise. We show that, in the case of regular sampling, the optimization problem can be reduced to a few parameters, allowing us to define quasi-optimal acquisition strategies, first when no prior knowledge of the surface location is available and then when the user has a prior on this location.

Reconstruction of tomographic gamma scanning transmission image from sparse projections based on convolutional neural networks

Tomographic Gamma Scanning (TGS) is an important Non-Destructive Assay (NDA) method for radioactive waste, and transmission imaging is the basis of the TGS. Traditional algorithms reconstruct the transmission image require the provision of the same amount of projection data as the pixel value of the transmission image, which requires a long time to scan and severely limits the industrial application of TGS. Sparse angle scanning is an optimal approach to improve the efficiency of the TGS system, but the amount of data generated by sparse angle scanning is difficult to support traditional algorithms, and blurs and artifacts will appear in the reconstructed image. In this work, Algebraic Reconstruction Technique (ART) combined with the Residual Network(ResNet) to reconstruct TGS transmission images was proposed. The training data set was built with simulation data by the Monte Carlo method, and the self-developed TGS equipment was used for the experimental test. Experimental results show that ResNet can be used to reconstruct high-resolution TGS transmission images from sparse projection data. And compared with traditional algorithms, the proposed method has faster reconstruction speed and higher image quality.

First Demonstration of Dynamic High-Gain Beam Steering With a Scanning Lens Phased Array

We report on the first demonstration of dynamic beam steering using a scanning lens phased array. A scanning lens phased array relies on a combination of mechanical and electrical phase shifting to dynamically steer a high-gain beam beyond the grating-lobe free region using a sparse array. These two concepts have been demonstrated separately in the past, here we present, for the first time, a prototype demonstration where active mechanical and electrical phase shifting are combined. For this purpose, we have developed a sparse 4x1 scanning lens phased array at W-band (75-110 GHz) capable of beam steering a directive beam (>30 dBi) towards ± 20° with low grating lobe levels (around -10 dB). The lens array is fed by a waveguide-based leaky-wave feeding architecture that illuminates the lenses with high aperture efficiency over a wide bandwidth, which is required in the proposed scanning lens phased array architecture. The electrical phase shifting has been implemented using IQ-mixers around 15 GHz in combination with x6 multipliers to reach the W-band. The mechanical phase shifting relies on a piezo-electric motor, which is able to achieve displacements of the lens array of 6 mm with an accuracy of a few nanometers. The entire active array is calibrated over the air with an ad-hoc quasi-optical measurement setup. Resulting measurements show excellent agreement with the anticipated performance.

Lower Order Description and Reconstruction of Sparse Scanning Lidar Measurements of Wind Turbine Inflow Using Proper Orthogonal Decomposition

Preview measurements of the inflow by turbine-mounted lidar systems can be used to optimise wind turbine performance or alleviate structural loads. However, nacelle-mounted lidars suffer data losses due to unfavourable environmental conditions and laser beam obstruction by the rotating blades. Here, we apply proper orthogonal decomposition (POD) to the simulated line-of-sight wind speed measurements of a turbine-mounted scanning lidar obtained from two large eddy simulations. This work aimed at identifying the dominant POD modes that can be used to subsequently derive a reduced-order representation of the turbine inflow. Secondly, we reconstructed the data points lost due to blade passage by using Gappy-POD. We found that only a few modes are required to capture the dynamics of the wind field parameters commonly used for lidar-assisted wind turbine control, such as the effective wind speed, vertical shear and directional misalignment. By evaluating turbine-relevant metrics in the time and frequency domain, we found that a ten-mode reconstruction could accurately describe most spatio-temporal variations in the inflow. Furthermore, a modal interpretation is presented by direct comparison with these wind field parameters. We found that the Gappy-POD method performs substantially better than spatial interpolation techniques, accurately reconstructing up to even 50% of missing data. A POD-based wind field reconstruction offers a trade-off between wind field reconstruction techniques requiring flow assumptions and more complex physics-based representations, offers dimensional reduction and can overcome the blade passage limitation of nacelle-mounted lidar systems.