Direct Imaging Method Research Articles

Object Localization in Highly Cluttered Environments Using Neural Network Learning on Microwave Scattering Data

ABSTRACTBifocusing‐based microwave imaging is a promising direct imaging method for object localization in various applications. However, the method has the limitation that it only works well in environments with a homogenous background. In this paper, we present a direct microwave imaging method using a neural network (NN) model that can localize a small object even in highly cluttered environments with strong scatterers. The NN model is trained on some data sets obtained through multistatic measurements in a nonhomogeneous environment. To verify the approach, we prepared an experimental testbed equipped with a water tank containing 3 strong scatterers and 16 antennas to obtain 920 MHz multistatic scattering data. This experiment shows good localization performance, with an average localization error of approximately 4 mm (1/9 of a wavelength) over the entire experimental area, even in a strong scattering background.

Design and Characterization of Porous Electrodes for Redox Flow Batteries

The commercialization of redox flow batteries as large-scale energy storage systems depends on lowering the capital and operating costs which depend on achieving maximum power density. Optimizing electrode design parameters and flow field-electrode combinations is critical to minimize losses and maximize active material utilization. Modeling the current drawn from an electrode offers a means to interpret electrode structure based on the electrolyte distribution driven by internal design. However, direct imaging methods (such as in-situ fluorescence microscopy and X-ray tomography) and numerical methods for current to potential prediction (3D Lattice Boltzmann modeling) are specific to individual electrode structures and possess limited predictable value to obtain an ideal electrode design. In this work, we present an electrode impedance spectroscopy (EIS)-based approach for determining electrode design parameters and devise a general method for determination of effective diffusion layer thickness. Using COMSOL modeling we verified the development of slug flow behavior in highly randomized porous electrode systems, indicating that the mass transport properties are dictated by inter-fiber distance. An analytical model to predict current as a function of applied overpotential is presented which allows us to vary structural parameters of the porous electrodes to study the influence of surface area, inter-fiber distances and diffusion layer thickness on the RFB performance. This model is a highly flexible tool applicable to any electrochemical system using a porous electrode and identifies critical variables affecting electrode design to characterize optimal electrode structure to minimize mass transport losses and optimize operational parameters.

Hidden Impurities Generate False Positives in Single Atom Catalyst Imaging

AbstractSingle‐atom catalysts (SACs) are an emerging class of materials, leveraging maximum atom utilization and distinctive structural and electronic properties to bridge heterogeneous and homogeneous catalysis. Direct imaging methods, such as aberration‐corrected high‐angle annular dark‐field scanning transmission electron microscopy, are commonly applied to confirm the atomic dispersion of active sites. However, interpretations of data from these techniques can be challenging due to simultaneous contributions to intensity from impurities introduced during synthesis processes, as well as any variation in position relative to the focal plane of the electron beam. To address this matter, this paper presents a comprehensive study on two representative SACs containing isolated nickel or copper atoms. Spectroscopic techniques, including X‐ray absorption spectroscopy, were employed to prove the high metal dispersion of the catalytic atoms. Employing scanning transmission electron microscopy imaging combined with single‐atom‐sensitive electron energy loss spectroscopy, we scrutinized thin specimens of the catalysts to provide an unambiguous chemical identification of the observed single‐atom species and thereby distinguish impurities from active sites at the single‐atom level. Overall, the study underscores the complexity of SACs characterization and establishes the importance of the use of spectroscopy in tandem with imaging at atomic resolution to fully and reliably characterize single‐atom catalysts.

Hidden Impurities Generate False Positives in Single Atom Catalyst Imaging.

Single-atom catalysts (SACs) are an emerging class of materials, leveraging maximum atom utilization and distinctive structural and electronic properties to bridge heterogeneous and homogeneous catalysis. Direct imaging methods, such as aberration-corrected high-angle annular dark-field scanning transmission electron microscopy, are commonly applied to confirm the atomic dispersion of active sites. However, interpretations of data from these techniques can be challenging due to simultaneous contributions to intensity from impurities introduced during synthesis processes, as well as any variation in position relative to the focal plane of the electron beam. To address this matter, this paper presents a comprehensive study on two representative SACs containing isolated nickel or copper atoms. Spectroscopic techniques, including X-ray absorption spectroscopy, were employed to prove the high metal dispersion of the catalytic atoms. Employing scanning transmission electron microscopy imaging combined with single-atom-sensitive electron energy loss spectroscopy, we scrutinized thin specimens of the catalysts to provide an unambiguous chemical identification of the observed single-atom species and thereby distinguish impurities from active sites at the single-atom level. Overall, the study underscores the complexity of SACs characterization and establishes the importance of the use of spectroscopy in tandem with imaging at atomic resolution to fully and reliably characterize single-atom catalysts.

High-quality direct ghost imaging of random dynamic targets based on convolutional neural network

We propose a novel high-quality direct random dynamic target ghost imaging method, based on Convolutional Neural Networks (CNNs). This method requires input of one-dimensional bucket detector values collected during the motion of the target object, enabling direct reconstruction of the target image. The results demonstrate the method’s capability to achieve high-quality reconstructed images for moving targets across various sampling rates. Furthermore, the method effectively addresses the challenge of image quality degradation caused by target motion. This contributes to the potential practical application value of ghost imaging in fields such as remote sensing and radar.

Computational Imaging Encryption with a Steganographic and Holographic Authentication Strategy

AbstractRobust data security is of paramount importance, particularly for highly classified information. To achieve this, advanced encryption and decryption methods, along with stringent access control and physical security measures, are essential to mitigate the risk of unauthorized access and data exposure. Ghost imaging (GI), a computational imaging technique that operates indirectly, offers a distinct approach compared to traditional spatially resolved imaging techniques. By utilizing computational algorithms, GI enables indirect image reconstruction, making it well‐suited for encryption applications. This proposal introduces a new computational imaging encryption scheme that combines Fourier ghost imaging (FGI) and computational holography. The scheme utilizes an untrained‐tree‐network to generate two camouflaged computer‐generated holograms (CGHs). Integration of CGH‐based steganography and holographic authentication enhances the encoding and decoding processes of FGI encryption. The effectiveness and security of the proposed scheme are rigorously validated through numerical simulations and optical experiments. The results demonstrate promising potential for effectively merging direct and indirect imaging methods in security applications.

Direct imaging of inhomogeneities in a 3D shallow ocean waveguide with an icecap

We are concerned with identifying scattering inhomogeneities in a three-dimensional shallow ocean waveguide with an icecap, which is motivated by applications in oceanic acoustics. A direct imaging method (DIM) is proposed to identify the marine point sources and medium obstacles from the near-field data. The key ingredient of the DIM is a certain imaging functional whose indicating behaviors is quantitatively characterized. Moreover, as corroborated by extensive numerical experiments, the DIM is computationally efficient and highly robust against noises, and the DIM can identify multiple sources and the scatterers of different shapes and scales from a few incidences and receivers.

Transit and radial velocity method for exoplanet detection

This article introduces some basic methods that human usually uses nowadays to detect exoplanets, including transits method, radial velocity method, direct imaging method, gravitational microlensing method, and astrometry method. As we all know, none of these methods are perfect, each of them has its advantages: some of them are good at detecting planets with great mass, some are good at detecting planets with great radius, and some of the methods are good at detecting planets far away from their host star. But at the same time, each method has its own disadvantages. That is the reason why sometimes some of these methods are used together to get information about specific exoplanets. This chapter will introduce these methods by giving information on how these methods work, the equipment each of them requires, the advantages and limitations of these methods, and the history and development of these methods. Finally, there is a conclusion that states the characteristics of the planets each method is good at detecting.

Intelligent classification of online wear particle in lubricating oil using optical direct imaging method and convolutional neural network for rotating machinery

Different types of wear particles in lubricating oil are crucial information sources for describing wear properties of rotating machinery components. Solving the problem of particle classification using artificial intelligence models depends on sufficient wear samples, though the samples are difficult to obtain, especially in lubricants with real-time online monitoring. First, a high precision particle analyzer for online acquisition of multi-particle images in the circulating oil system based on the optical direct imaging method is developed. Then, a novel hybrid convolutional neural network (CNN) model based on the visual geometry group (VGG) network feature and residual block, called VRCNN, is proposed to classify nine types of metallic and non-metallic particles, including normal, sliding, fatigue, cutting, sphere, ceramic, bubble, plastic, and fiber, using their images. Finally, a dataset produced by a pin disc friction tribometer and bearing accelerated fatigue tester is used to demonstrate the approach. The proposed VRCNN model is also compared with the typical classifiers, including AlexNet, VGG16, RestNet50, SqueezeNet, MobileNetV2, and ShuffleNetV2 models in terms of classification accuracy, standard deviation of accuracy, F1-score, and detection time. The results show that the VRCNN model has a classification accuracy of more than 95%, and its response time is superior to that of the other networks. Furthermore, the operational process of the VRCNN classifier is analyzed visually using the Grad-CAM map. This work is beneficial for improving any type of rotating machinery’s wear mechanism analysis.

The Tianlin Mission: A 6 m UV/Opt/IR Space Telescope to Explore Habitable Worlds and the Universe

It is expected that ongoing and future space-borne planet survey missions including Transiting Exoplanet Survey Satellite (TESS), PLATO and Earth 2.0 will detect thousands of small to medium-sized planets via the transit technique, including over a hundred habitable terrestrial rocky planets. To conduct a detailed study of these terrestrial planets, particularly the cool ones with wide orbits, the exoplanet community has proposed various follow-up missions. The currently proposed European Space Agency mission Ariel is the first step for this purpose, and it is capable of characterization of planets down to warm super-Earths mainly using transmission spectroscopy. The NASA Large Ultraviolet/Optical/Infrared Surveyor mission proposed in the Astro2020 Decadal Survey white paper will endeavor to further identify habitable rocky planets, and is expected to launch around 2045. In the meanwhile, China is funding a concept study of a 6 m class space telescope named Tianlin (a UV/Opt/NIR large aperture space telescope) that aims to start its operation within the next 10–15 yr and last for 5+ yr. Tianlin will be primarily aimed at the discovery and characterization of rocky planets in the habitable zones around nearby stars and to search for potential biosignatures mainly using the direct imaging method. Transmission and emission spectroscopy at moderate to high resolution will be carried out as well on a population of exoplanets to strengthen the understanding of the formation and evolution of exoplanets. It will also be utilized to perform in-depth studies of the cosmic web and early galaxies, and constrain the nature of dark matter and dark energy. We describe briefly the primary scientific motivations and main technical considerations based on our preliminary simulation results. We find that a monolithic off-axis space telescope with primary mirror diameter larger than 6 m equipped with a high contrast coronagraph can identify water in the atmosphere of a habitable-zone Earth-like planet around a Sun-like star. More simulations for the detectability of other key biosignatures including O3, O2, CH4 and chlorophyll are coming.

Distinguishing artificial spin ice states using magnetoresistance effect for neuromorphic computing

Artificial spin ice (ASI) consisting patterned array of nano-magnets with frustrated dipolar interactions offers an excellent platform to study frustrated physics using direct imaging methods. Moreover, ASI often hosts a large number of nearly degenerated and non-volatile spin states that can be used for multi-bit data storage and neuromorphic computing. The realization of the device potential of ASI, however, critically relies on the capability of transport characterization of ASI, which has not been demonstrated so far. Using a tri-axial ASI system as the model system, we demonstrate that transport measurements can be used to distinguish the different spin states of the ASI system. Specifically, by fabricating a tri-layer structure consisting a permalloy base layer, a Cu spacer layer and the tri-axial ASI layer, we clearly resolve different spin states in the tri-axial ASI system using lateral transport measurements. We have further demonstrated that the tri-axial ASI system has all necessary required properties for reservoir computing, including rich spin configurations to store input signals, nonlinear response to input signals, and fading memory effect. The successful transport characterization of ASI opens up the prospect for novel device applications of ASI in multi-bit data storage and neuromorphic computing.

The Anatomy of Amorphous, Heterogeneous Catalyst Pellets.

This review focuses on disordered, or amorphous, porous heterogeneous catalysts, especially those in the forms of pellets and monoliths. It considers the structural characterisation and representation of the void space of these porous media. It discusses the latest developments in the determination of key void space descriptors, such as porosity, pore size, and tortuosity. In particular, it discusses the contributions that can be made by various imaging modalities in both direct and indirect characterisations and their limitations. The second part of the review considers the various types of representations of the void space of porous catalysts. It was found that these come in three main types, which are dependent on the level of idealisation of the representation and the final purpose of the model. It was found that the limitations on the resolution and field of view for direct imaging methods mean that hybrid methods, combined with indirect porosimetry methods that can bridge the many length scales of structural heterogeneity and provide more statistically representative parameters, deliver the best basis for model construction for understanding mass transport in highly heterogeneous media.

A Review of Molecular Models for Gas Adsorption in Shale Nanopores and Experimental Characterization of Shale Properties.

Shale gas, as a promising alternative energy source, has received considerable attention because of its broad resource base and wide distribution. The establishment of shale models that can accurately describe the composition and structure of shale is essential to perform molecular simulations of gas adsorption in shale reservoirs. This Review provides an overview of shale models, which include organic matter models, inorganic mineral models, and composite shale models. Molecular simulations of gas adsorption performed on these models are also reviewed to provide a more comprehensive understanding of the behaviors and mechanisms of gas adsorption on shales. To accurately understand the gas adsorption behaviors in shale reservoirs, it is necessary to be aware of the pore structure characteristics of shale reservoirs. Thus, we also present experimental studies on shale microstructure analysis, including direct imaging methods and indirect measurements. The advantages, disadvantages, and applications of these methods are also well summarized. This Review is useful for understanding molecular models of gas adsorption in shales and provides guidance for selecting experimental characterization of shale structure and composition.

Full-Field Super-Resolution Terahertz Imaging Based on Rotating Coherent Scattering Microscopy

For decades, terahertz (THz) microscopic imaging has been limited by the resolution of the system due to the larger wavelength, the power of the source, and the equivalent noise power of the detector, so a lot of research has focused on single-point scanning imaging. With the development of hardware, full-field THz imaging based on high-power continuous-wave THz sources have been developed such as the direct intensity imaging method and lensless coherent imaging. In particular, the THz direct intensity imaging method requires no complicated computational reconstruction, while the high resolution, as a key issue, still needs to be improved. In this paper, the rotating coherent scattering microscopy was applied to THz imaging for the first time. Here, we designed and fabricated a hemisphere lens with high-resistance silicon. The tilted hemisphere lens transformed the incident divergent beam into a plane wave, and the total internal reflection occurred in the planar surface within the hemispherical lens, and generated evanescent waves in the rare medium. At the same time, the sample was placed very close to the plane of the hemispherical lens, so that the sample was illuminated by the evanescent waves. The scattered waves carried high frequency information to the far field, and thus through an objective, the super-resolution imaging was achieved along a single direction. Then, the hemispherical lens was rotated to obtain coherent scattering microscopic images under different evanescent wave illumination angles. Finally, the full-field super-resolution imaging results were obtained through incoherent superposition.

Weighted sum of harmonic signals for direct imaging in magnetic particle imaging

Objective. Magnetic particle imaging (MPI) is a novel radiation-free medical imaging modality that can directly image superparamagnetic iron oxide tracers (SPIOs) with high sensitivity, temporal resolution, and good spatial resolution. The MPI reconstruction task can be formulated mathematically as a Fredholm integral problem, but the concrete inversion is not easily possible because of the particle dynamics or scanner issues. Measurement based system matrix inversion takes these factors into account, but prior measurement and calibration are time consuming. Approach. We proposed a direct imaging method based on the weighted sum of harmonic signals. The harmonic signals with spatial information are obtained by the short-time Fourier transform, and odd harmonic components are selected for recombination and then mapped to the sampling trajectory to image the concentration distribution of SPIOs. In addition, we adopt a normalized-weighted sum of harmonics to improve the resolution of the native image. Main results. The effectiveness of the proposed method is verified by simulation imaging experiments and our in-house scanner-based experiments. Quantitative evaluation results show that compared with traditional methods, the structural similarity improved by 48%, mean square error decreased by 88%, and signal-to-artifact ratio increased by 2.5 times. Significance. The proposed method can rapidly image the concentration distribution of nanoparticles without any prior calibration measurements and reduce the blur of MPI images without deconvolution, which has the potential to be implemented as a multi-patch imaging method in MPI.

A possible converter to denoise the images of exoplanet candidates through machine learning techniques

The method of direct imaging has detected many exoplanets and made important contribution to the field of planet formation. The standard method employs angular differential imaging (ADI) technique, and more ADI image frames could lead to the results with larger signal-to-noise-ratio (SNR). However, it would need precious observational time from large telescopes, which are always over-subscribed. We thus explore the possibility to generate a converter which can increase the SNR derived from a smaller number of ADI frames. The machine learning technique with two-dimension convolutional neural network (2D-CNN) is tested here. Several 2D-CNN models are trained and their performances of denoising are presented and compared. It is found that our proposed Modified five-layer Wide Inference Network with the Residual learning technique and Batch normalization (MWIN5-RB) can give the best result. We conclude that this MWIN5-RB can be employed as a converter for future observational data.

Achieving single nucleotide sensitivity in direct hybridization genome imaging

Direct visualization of point mutations in situ can be informative for studying genetic diseases and nuclear biology. We describe a direct hybridization genome imaging method with single-nucleotide sensitivity, single guide genome oligopaint via local denaturation fluorescence in situ hybridization (sgGOLDFISH), which leverages the high cleavage specificity of eSpCas9(1.1) variant combined with a rationally designed guide RNA to load a superhelicase and reveal probe binding sites through local denaturation. The guide RNA carries an intentionally introduced mismatch so that while wild-type target DNA sequence can be efficiently cleaved, a mutant sequence with an additional mismatch (e.g., caused by a point mutation) cannot be cleaved. Because sgGOLDFISH relies on genomic DNA being cleaved by Cas9 to reveal probe binding sites, the probes will only label the wild-type sequence but not the mutant sequence. Therefore, sgGOLDFISH has the sensitivity to differentiate the wild-type and mutant sequences differing by only a single base pair. Using sgGOLDFISH, we identify base-editor-modified and unmodified progeroid fibroblasts from a heterogeneous population, validate the identification through progerin immunofluorescence, and demonstrate accurate sub-nuclear localization of point mutations.

Imaging Atomic Structure, Strain, and Disorder By Atomic Electron Tomography

Knowledge of the atomic structure of materials is critical to understanding their functionality in many fields such as biology, microelectronics, condensed matter, and nanotechnology. Traditionally, x-ray crystallography has been the main method used to solve structures of all types. New techniques such as micro electron diffraction and single particle cryo electron microscopy are now utilizing electrons to study crystals that cannot be crystallized at the scales necessary for x-ray experiments. However, these techniques require ensembles of identical unit cells (like molecules or proteins) to solve the structure from a series of images or diffraction patterns. The need to average over many unit cells means that important material properties such as defects, dopants, and disorder are not captured. Investigations of unique arrangements of atoms requires a direct imaging method that does not rely on structural averaging or the assumption of crystallinity.Transmission electron microscopy (TEM) provides the ability to directly image atomic structure, and the development of powerful, stable aberration correctors in the last decade now routinely provide sub-Angstrom imaging resolution. Scanning TEM (STEM) has also become an effective method for measuring the atomic structure of inorganic materials based on so-called Z-contrast where the image intensities are proportional to the atomic number of the material being imaged. However, by their nature TEMs are limited to producing only two-dimensional projections of a structure. Electron tomography is a technique that can reconstruct the three-dimensional structure of unique nanoscale objects from a series of two-dimensional images acquired at different viewing angles. Tomography was traditionally limited to nanoscale resolution due to experimental difficulties and a lack of high-quality reconstruction algorithms.We have developed atomic electron tomography (AET) by utilizing the sub-Angstrom real-space resolution of STEM with advanced tomographic reconstruction algorithms to solve the structure of materials at the single atom level without averaging or the assumption of crystallinity. This talk will present recent progress and capabilities in the field of AET for reconstructing order and disorder in materials with 20 picometer precision. Our success in resolving chemical order/disorder in metallic FePt nanoparticles was extended to include the time domain by capturing nucleation and growth or ordered phases in the same nanoparticle. Also, the ability to reconstruct atomic structure without the need for averaging or crystallinity led to the achievement of directly imaging three-dimensional atomic arrangements in amorphous solids. We determined the chemical and structural disorder of an eight-element metallic glass to quantitatively characterize short- and medium-range order, and we determined the disordered atomic packing of monatomic amorphous materials.The field of electron microscopy is rapidly changing with the advent of improved detectors, aberration correctors, and monochromators. We are exploring the use of these new hardware to implement new imaging algorithms such as ptychography to allow AET to explore all new classes of materials especially lightly scattering materials like oxygen. We have also established the Materials Databank to share experimentally determined 3D atomic structures with other scientists. As more structures and classes of materials are solved, we expect AET to become an important technique in the field of atomic structural characterization.

Characterization of per- and polyfluoroalkyl substances on fire suppression system piping and optimization of removal methods

Fire suppression systems are known to be impacted with residual, entrained per- and polyfluoroalkyl substances (PFASs) because of historical use of aqueous film forming foam (AFFF) and fluoroprotein foam. Amphiphilic PFASs aggregate at liquid:solid interfaces creating a hydrophobic layer which reduces the effectiveness of water to remove PFAS from layered surfaces. When fire suppression systems are transitioned to fluorine free foam (F3) without appropriate cleaning, residual PFASs associated with the surfaces of the fire suppression system can contaminate the replacement F3. Release of residual PFASs from fire suppression systems into F3 has been documented; however, little is known about the residual PFASs associated with the surfaces of the fire suppression systems. More information is needed to develop methods to appropriately remove PFASs from fire suppression systems to prevent costly and inefficient foam transitioning and preserve the PFAS-free benefit of F3. The objective of this work was to evaluate the distribution and composition of PFASs on hangar piping exposed to PFAS-containing firefighting foam for a prolonged period. Two assessment methods were used: 1) extractions with methanol, water, and a proprietary aqueous organic solvent (Fluoro Fighter™); and 2) direct imaging methods of the surface. Extractions were analyzed with mass spectrometry and combustion ion chromatography. Results indicate pipe in contact with PFAS-containing firefighting foam can amass approximately 10 μg/cm2 of surface-associated PFAS residual following decades of exposure. Fluoro Fighter demonstrated higher PFAS removal per surface area of pipe than methanol (p = 0.007) or water extraction (p < 0.0001). Scanning electron microscope (SEM) images of the hangar piping reveal deposits suspected to be self-assembled PFAS layers, as evidenced by examination of pipe surfaces using X-ray photoelectron spectroscopy (XPS), which revealed atomic fluorine on the surface of the pipe.

Triangulation of pore structural characterisation of disordered mesoporous silica using novel hybrid methods involving dual-probe porosimetries

The key issue, in applying indirect pore structure characterisation methods to disordered materials, is that usually some physical assumptions are necessary to probe pore size, such as concerning the mode of the phase transition used, which determines meniscus geometry or kernel type. This issue often undermines the relative advantages of indirect methods, over direct methods like imaging, from both the wider range of pores sizes that can be probed in a single experiment and the much better statistical representativeness of the data, which are essential for highly heterogeneous disordered materials. Further, parameter calibrations provided using supposedly the same model ordered porous materials are often conflictual. However, this work introduces dual-liquid thermoporometry, and also dual-probe, serial water sorption and mercury intrusion, to complement multi-adsorbate, serial gas sorption, to overcome this issue by triangulating the data for three key probe molecules used in three different porosimetries, namely liquid intrusion, gas sorption, and thermoporometry. It has been found that augmenting one porosimetry, using just one probe molecule, to a dual-probe experiment removes the aforementioned, key drawback of indirect methods. The alternative, complementary dual-probe porosimetries can also cross-validate this approach for each other. This allows indirect methods to stand alone, without the need for direct imaging methods to inform or validate prior assumptions. Further, the dual-probe experiments allow additional information on pore structure to be obtained beyond that provided by single-probe experiments. For example, dual-liquid thermoporometry also allows the probing for the presence and nature of the advanced melting effect, and serial water adsorption and mercury porosimetry delivers information on network filling mechanisms during adsorption in disordered media.