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The de-oxygenation of CO2 was explored by using hydrogen, methane, carbon etc., over alumina supported catalysts. The alumina-supported ruthenium, rhodium, platinum, molybdenum, vanadium and magnesium catalysts were first reduced in hydrogen atmosphere and then used for the de-oxygenation of CO2. Furthermore, experimental variables for the de-oxygenation of CO2 were temperature (range 50 to 650 °C), H2/CO2 mole ratios (1.0 to 5), and catalyst loading (0.5 to 10 wt %). During the de-oxygenation of CO2 with H2 or CH2 or carbon, conversion of CO2, selectivity to CO and CH2 were estimated. Moreover, 25.4 % conversion of CO2 by hydrogen was observed over 1 wt% Pt/Al2O3 catalyst at 650 °C with 33.8 % selectivity to CH2. However, 8.1 to 13.9 % conversion of CO2 was observed over 1 wt% Pt/Al2O2 catalyst at 550 °C in the presence of both H2 and CH2. Moreover, 42.8 to 79.4 % CH2 was converted with 9 to 23.1 % selectivity to CO. It was observed that the de-oxygenation of CO2 by hydrogen, carbon and methane produced carbon, CO and CH4. [ABSTRACT FROM AUTHOR]

Distribution and administration strategy are critical to successful population immunization efforts. Agent-based modeling (ABM) can reflect the complexity of real-world populations and can experimentally evaluate vaccine strategy and policy. However, ABMs historically have been limited in their time-to-development, long runtime, and difficulty calibrating. Our team had several technical advances in the development of our GradABMs: a novel class of scalable, fast and differentiable simulations. GradABMs can simulate million-size populations in a few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous sources. This allows for rapid and real-world sensitivity analyses. Our first epidemiological GradABM (EpiABMv1) enabled simulation interventions over real million-scale populations and was used in vaccine strategy and policy during the COVID-19 pandemic. Literature suggests decisions aided by evidence from these models saved thousands of lives. Our most recent model (EpiABMv2) extends EpiABMv1 to allow improved regional calibration using deep neural networks to incorporate local population data, and in some cases different policy recommendations versus our prior models. This is an important advance for our model to be more effective at vaccine strategy and policy decisions at the local public health level., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

We propose methods that use specular, multibounce LiDAR returns to detect and map specular surfaces that might be invisible to conventional LiDAR systems that rely on direct, single-scatter returns. We derive expressions that relate the time- and angle-of-arrival of these multibounce returns to scattering points on the specular surface, and then use these expressions to formulate techniques for retrieving specular surface geometry when the scene is scanned by a single beam or illuminated with a multi-beam flash. We also consider the special case of transparent specular surfaces, for which surface reflections can be mixed together with light that scatters off of objects lying behind the surface.

Objective: To model and compare effect of digital contact tracing versus shelter-in-place on severe acute respiratory syndrome - coronavirus 2 (SARS-CoV-2) spread., Methods: Using a classical epidemiologic framework and parameters estimated from literature published between February 1, 2020, and May 25, 2020, we modeled two non-pharmacologic interventions - shelter-in-place and digital contact tracing - to curb spread of SARS-CoV-2. For contact tracing, we assumed an advanced automated contact tracing (AACT) application that sends alerts to individuals advising self-isolation based on individual exposure profile. Model parameters included percentage population ordered to shelter-in-place, adoption rate of AACT, and percentage individuals who appropriately follow recommendations. Under influence of these variables, the number of individuals infected, exposed, and isolated were estimated., Results: Without any intervention, a high rate of infection (>10 million) with early peak is predicted. Shelter-in-place results in rapid decline in infection rate at the expense of impacting a large population segment. The AACT model achieves reduction in infected and exposed individuals similar to shelter-in-place without impacting a large number of individuals. For example, a 50% AACT adoption rate mimics a shelter-in-place order for 40% of the population and results in a greater than 90% decrease in peak number of infections. However, as compared to shelter-in-place, with AACT significantly fewer individuals would be isolated., Conclusion: Wide adoption of digital contact tracing can mitigate infection spread similar to universal shelter-in-place, but with considerably fewer individuals isolated., (Copyright © 2020 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.)

The analysis and identification of different attributes of produce such as taxonomy, vendor, and organic nature is vital to verifying product authenticity in a distribution network. Though a variety of analysis techniques have been studied in the past, we present a novel data-centric approach to classifying produce attributes. We employed visible and near infrared (NIR) spectroscopy on over 75,000 samples across several fruit and vegetable varieties. This yielded 0.90-0.98 and 0.98-0.99 classification accuracies for taxonomy and farmer classes, respectively. The most significant factors in the visible spectrum were variations in the produce color due to chlorophyll and anthocyanins. In the infrared spectrum, we observed that the varying water and sugar content levels were critical to obtaining high classification accuracies. High quality spectral data along with an optimal tuning of hyperparameters in the support vector machine (SVM) was also key to achieving high classification accuracies. In addition to demonstrating exceptional accuracies on test data, we explored insights behind the classifications, and identified the highest performing approaches using cross validation. We presented data collection guidelines, experimental design parameters, and machine learning optimization parameters for the replication of studies involving large sample sizes.

We use terahertz transmission through limestone sedimentary rock samples to assess the macro and micro porosity. We exploit the notable water absorption in the terahertz spectrum to interact with the pores that are two orders of magnitude smaller (<1μm) than the terahertz wavelength. Terahertz water sensitivity provides us with the dehydration profile of the rock samples. The results show that there is a linear correlation between such a profile and the ratio of micro to macro porosity of the rock. Furthermore, this study estimates the absolute value of total porosity based on optical diffusion theory. We compare our results with that of mercury injection capillary pressure as a benchmark to confirm our analytic framework. The porosimetry method presented here sets a foundation for a new generation of less invasive porosimetry methods with higher penetration depth based on lower frequency (f<10THz) scattering and absorption. The technique has applications in geological studies and in other industries without the need for hazardous mercury or ionizing radiation.

We demonstrate an imaging technique that allows identification and classification of objects hidden behind scattering media and is invariant to changes in calibration parameters within a training range. Traditional techniques to image through scattering solve an inverse problem and are limited by the need to tune a forward model with multiple calibration parameters (like camera field of view, illumination position etc.). Instead of tuning a forward model and directly inverting the optical scattering, we use a data driven approach and leverage convolutional neural networks (CNN) to learn a model that is invariant to calibration parameters variations within the training range and nearly invariant beyond that. This effectively allows robust imaging through scattering conditions that is not sensitive to calibration. The CNN is trained with a large synthetic dataset generated with a Monte Carlo (MC) model that contains random realizations of major calibration parameters. The method is evaluated with a time-resolved camera and multiple experimental results are provided including pose estimation of a mannequin hidden behind a paper sheet with 23 correct classifications out of 30 tests in three poses (76.6% accuracy on real-world measurements). This approach paves the way towards real-time practical non line of sight (NLOS) imaging applications.

Which neighborhoods experience physical improvements? In this paper, we introduce a computer vision method to measure changes in the physical appearances of neighborhoods from time-series street-level imagery. We connect changes in the physical appearance of five US cities with economic and demographic data and find three factors that predict neighborhood improvement. First, neighborhoods that are densely populated by college-educated adults are more likely to experience physical improvements-an observation that is compatible with the economic literature linking human capital and local success. Second, neighborhoods with better initial appearances experience, on average, larger positive improvements-an observation that is consistent with "tipping" theories of urban change. Third, neighborhood improvement correlates positively with physical proximity to the central business district and to other physically attractive neighborhoods-an observation that is consistent with the "invasion" theories of urban sociology. Together, our results provide support for three classical theories of urban change and illustrate the value of using computer vision methods and street-level imagery to understand the physical dynamics of cities., Competing Interests: The authors declare no conflict of interest.

We demonstrate a fast and cost-effective technique to perform three dimensional (3D) scanning and replication of large paleontological specimens, in this case the entire skull of a Tyrannosaurus rex (T.rex) with a volume in the range of 2 m3. The technique involves time-of-flight (TOF) depth sensing using the Kinect scanning module commonly used in gesture recognition in gaming. Raw data from the Kinect sensor was captured using open source software and the reconstruction was done rapidly making this a viable method that can be adopted by museums and researchers in paleontology. The current method has the advantage of being low-cost as compared to industrial scanners and photogrammetric methods but also of accurately scanning a substantial volume range which is well suited for large specimens. The depth resolution from the Kinect sensor was measured to be around 0.6 mm which is ideal for scanning large specimens with reasonable structural detail. We demonstrate the efficacy of this method on the skull of FMNH PR 2081, also known as SUE, a near complete T.rex at the Field Museum of Natural History.

Imaging through thick highly scattering media (sample thickness ≫ mean free path) can realize broad applications in biomedical and industrial imaging as well as remote sensing. Here we propose a computational "All Photons Imaging" (API) framework that utilizes time-resolved measurement for imaging through thick volumetric scattering by using both early arrived (non-scattered) and diffused photons. As opposed to other methods which aim to lock on specific photons (coherent, ballistic, acoustically modulated, etc.), this framework aims to use all of the optical signal. Compared to conventional early photon measurements for imaging through a 15 mm tissue phantom, our method shows a two fold improvement in spatial resolution (4db increase in Peak SNR). This all optical, calibration-free framework enables widefield imaging through thick turbid media, and opens new avenues in non-invasive testing, analysis, and diagnosis.

Spatial resolution, spectral contrast and occlusion are three major bottlenecks for non-invasive inspection of complex samples with current imaging technologies. We exploit the sub-picosecond time resolution along with spectral resolution provided by terahertz time-domain spectroscopy to computationally extract occluding content from layers whose thicknesses are wavelength comparable. The method uses the statistics of the reflected terahertz electric field at subwavelength gaps to lock into each layer position and then uses a time-gated spectral kurtosis to tune to highest spectral contrast of the content on that specific layer. To demonstrate, occluding textual content was successfully extracted from a packed stack of paper pages down to nine pages without human supervision. The method provides over an order of magnitude enhancement in the signal contrast and can impact inspection of structural defects in wooden objects, plastic components, composites, drugs and especially cultural artefacts with subwavelength or wavelength comparable layers.

We demonstrate a smartphone based spectrometer design that is standalone and supported on a wireless platform. The device is inherently low-cost and the power consumption is minimal making it portable to carry out a range of studies in the field. All essential components of the device like the light source, spectrometer, filters, microcontroller and wireless circuits have been assembled in a housing of dimensions 88 mm × 37 mm × 22 mm and the entire device weighs 48 g. The resolution of the spectrometer is 15 nm, delivering accurate and repeatable measurements. The device has a dedicated app interface on the smartphone to communicate, receive, plot and analyze spectral data. The performance of the smartphone spectrometer is comparable to existing bench-top spectrometers in terms of stability and wavelength resolution. Validations of the device were carried out by demonstrating non-destructive ripeness testing in fruit samples. Ultra-Violet (UV) fluorescence from Chlorophyll present in the skin was measured across various apple varieties during the ripening process and correlated with destructive firmness tests. A satisfactory agreement was observed between ripeness and fluorescence signals. This demonstration is a step towards possible consumer, bio-sensing and diagnostic applications that can be carried out in a rapid manner.

Asthma is the most common chronic illness among children. The skills required to diagnose it make it an even greater concern. In this work, we present a child-friendly wearable device, which allows in-home diagnosis of asthma. The device acquires simultaneous measurements from multiple stethoscopes. The recordings are then sent to a specialist who uses assistive diagnosis algorithms that enable auscultation (listening to lung sounds with a stethoscope) at any location in the lungs volume by sound refocusing. The specialist is also presented with a sound "heat map" which shows the location of sound sources in the lungs. We present design considerations of our device, as well as the algorithms for assistive diagnosis and their analysis which demonstrate reduction of ambient and measurement noise by over 10dB.

Three-dimensional (3D) visualization of oral cavity and oropharyngeal anatomy may play an important role in the evaluation for obstructive sleep apnea (OSA). Although computed tomography (CT) and magnetic resonance (MRI) imaging are capable of providing 3D anatomical descriptions, this type of technology is not readily available in a clinic setting. Current imaging of the oropharynx is performed using a light source and tongue depressors. For better assessment of the inferior pole of the tonsils and tongue base flexible laryngoscopes are required which only provide a two dimensional (2D) rendering. As a result, clinical diagnosis is generally subjective in tonsillar hypertrophy where current physical examination has limitations. In this report, we designed a hand held portable oral camera with 3D imaging capability to reconstruct the anatomy of the oropharynx in tonsillar hypertrophy where the tonsils get enlarged and can lead to increased airway resistance. We were able to precisely reconstruct the 3D shape of the tonsils and from that estimate airway obstruction percentage and volume of the tonsils in 3D printed realistic models. Our results correlate well with Brodsky's classification of tonsillar hypertrophy as well as intraoperative volume estimations.

The combination of computational techniques and ultrafast imaging have enabled sensing through unconventional settings such as around corners, and through diffusive media. We exploit time of flight (ToF) measurements to enable a flexible interface for imaging through permuted set of fibers. The fibers are randomly distributed in the scene and are packed on the camera end, thus making a brush-like structure. The scene is illuminated by two off-axis optical pulses. Temporal signatures of fiber tips in the scene are used to localize each fiber. Finally, by combining the position and measured intensity of each fiber, the original input is reconstructed. Unlike conventional fiber bundles with packed set of fibers that are limited by a narrow field of view (FOV), lack of flexibility, and extended coaxial precalibration, the proposed optical brush is flexible and uses off-axis calibration method based on ToF. The enabled brush form can couple to other types of ToF imaging systems. This can impact probe-based applications such as, endoscopy, tomography, and industrial imaging and sensing.

Microwaves can penetrate many obstructions that are opaque at visible wavelengths, however microwave imaging is challenging due to resolution limits associated with relatively small apertures and unrecoverable "stealth" regions due to the specularity of most objects at microwave frequencies. We demonstrate a multispectral time-of-flight microwave imaging system which overcomes these challenges with a large passive aperture to improve lateral resolution, multiple illumination points with a data fusion method to reduce stealth regions, and a frequency modulated continuous wave (FMCW) receiver to achieve depth resolution. The camera captures images with a resolution of 1.5 degrees, multispectral images across the X frequency band (8 GHz-12 GHz), and a time resolution of 200 ps (6 cm optical path in free space). Images are taken of objects in free space as well as behind drywall and plywood. This architecture allows "camera-like" behavior from a microwave imaging system and is practical for imaging everyday objects in the microwave spectrum.

Amplitude modulated continuous wave time-of-flight range cameras suffer from an inherent depth measurement error due to aliasing of the emitted signal vs reference signal correlation function. This is due to higher harmonics present in both signals which are not accounted for in the model or measurements. This "wiggling" error is generally corrected by employing a correction function based on frequency and depth dependent calibration data. This problem is shown to be equivalent to a multi-path interference problem. Casting the problem into the multi-path interference domain and utilizing multiple modulation frequencies provides tools for dealing with the depth error without calibration in a frequency independent way.

The use of fluorescent probes and the recovery of their lifetimes allow for significant advances in many imaging systems, in particular, medical imaging systems. Here we propose and experimentally demonstrate reconstructing the locations and lifetimes of fluorescent markers hidden behind a turbid layer. This opens the door to various applications for non-invasive diagnosis, analysis, flowmetry and inspection. The method is based on a time-resolved measurement that captures information about both fluorescence lifetime and spatial position of the probes. To reconstruct the scene, the method relies on a sparse optimization framework to invert time-resolved measurements. This wide-angle technique does not rely on coherence, and does not require the probes to be directly in line of sight of the camera, making it potentially suitable for long-range imaging.

The ability to record images with extreme temporal resolution enables a diverse range of applications, such as fluorescence lifetime imaging, time-of-flight depth imaging and characterization of ultrafast processes. Recently, ultrafast imaging schemes have emerged, which require either long acquisition times or raster scanning and have a requirement for sufficient signal that can only be achieved when light is reflected off an object or diffused by a strongly scattering medium. Here we present a demonstration of the potential of single-photon detector arrays for visualization and rapid characterization of events evolving on picosecond time scales. The single-photon sensitivity, temporal resolution and full-field imaging capability enables the observation of light-in-flight in air, as well as the measurement of laser-induced plasma formation and dynamics in its natural environment. The extreme sensitivity and short acquisition times pave the way for real-time imaging of ultrafast processes or visualization and tracking of objects hidden from view.

Medical data presents a number of challenges. It tends to be unstructured, noisy and protected. To train algorithms to understand medical images, doctors can label the condition associated with a particular image, but obtaining enough labels can be difficult. We propose an annotation approach which starts with a small pool of expertly annotated images and uses their expertise to rate the performance of crowd-sourced annotations. In this paper we demonstrate how to apply our approach for annotation of large-scale datasets of retinal images. We introduce a novel data validation procedure which is designed to cope with noisy ground-truth data and with non-consistent input from both experts and crowd-workers.

We propose a new design of complex self-evolving structures that vary over time due to environmental interaction. In conventional 3D printing systems, materials are meant to be stable rather than active and fabricated models are designed and printed as static objects. Here, we introduce a novel approach for simulating and fabricating self-evolving structures that transform into a predetermined shape, changing property and function after fabrication. The new locally coordinated bending primitives combine into a single system, allowing for a global deformation which can stretch, fold and bend given environmental stimulus.

We present a novel approach for evaluation of position and orientation of geometric shapes from scattered time-resolved data. Traditionally, imaging systems treat scattering as unwanted and are designed to mitigate the effects. Instead, we show here that scattering can be exploited by implementing a system based on a femtosecond laser and a streak camera. The result is accurate estimation of object pose, which is a fundamental tool in analysis of complex scenarios and plays an important role in our understanding of physical phenomena. Here, we experimentally show that for a given geometry, a single incident illumination point yields enough information for pose estimation and tracking after multiple scattering events. Our technique can be used for single-shot imaging behind walls or through turbid media.

In fluorescence measurements, light is often absorbed and scattered by a sample both for excitation and emission, resulting in the measured spectra to be distorted. Conventional linear unmixing methods computationally separate overlapping spectra but do not account for these effects. We propose a new algorithm for fluorescence unmixing that accounts for the attenuation-related distortion effect on fluorescence spectra. Using a matrix representation, we derive forward measurement formation and a corresponding inverse method; the unmixing algorithm is based on nonnegative matrix factorization. We also demonstrate how this method can be extended to a higher-dimensional tensor form, which is useful for unmixing overlapping spectra observed under the attenuation effect in spectral imaging microscopy. We evaluate the proposed methods in simulation and experiments and show that it outperforms a conventional, linear unmixing method when absorption and scattering contributes to the measured signals, as in deep tissue imaging.

Compressive displays are an emerging technology exploring the co-design of new optical device configurations and compressive computation. Previously, research has shown how to improve the dynamic range of displays and facilitate high-quality light field or glasses-free 3D image synthesis. In this paper, we introduce a new multi-mode compressive display architecture that supports switching between 3D and high dynamic range (HDR) modes as well as a new super-resolution mode. The proposed hardware consists of readily-available components and is driven by a novel splitting algorithm that computes the pixel states from a target high-resolution image. In effect, the display pixels present a compressed representation of the target image that is perceived as a single, high resolution image.

Ultrafast sources and detectors have been used to record the time-resolved scattering of light propagating through macroscopic scenes. In the context of computational imaging, decomposition of this transient light transport (TLT) is useful for applications, such as characterizing materials, imaging through diffuser layers, and relighting scenes dynamically. Here, we demonstrate a method of convolutional sparse coding to decompose TLT into direct reflections, inter-reflections, and subsurface scattering. The method relies on the sparsity composition of the time-resolved kernel. We show that it is robust and accurate to noise during the acquisition process.

Imaging through complex media is a well-known challenge, as scattering distorts a signal and invalidates imaging equations. For coherent imaging, the input field can be reconstructed using phase conjugation or knowledge of the complex transmission matrix. However, for incoherent light, wave interference methods are limited to small viewing angles. On the other hand, time-resolved methods do not rely on signal or object phase correlations, making them suitable for reconstructing wide-angle, larger-scale objects. Previously, a time-resolved technique was demonstrated for uniformly reflecting objects. Here, we generalize the technique to reconstruct the spatially varying reflectance of shapes hidden by angle-dependent diffuse layers. The technique is a noninvasive method of imaging three-dimensional objects without relying on coherence. For a given diffuser, ultrafast measurements are used in a convex optimization program to reconstruct a wide-angle, three-dimensional reflectance function. The method has potential use for biological imaging and material characterization.

Time-of-flight (ToF) cameras calculate depth maps by reconstructing phase shifts of amplitude-modulated signals. For broad illumination of transparent objects, reflections from multiple scene points can illuminate a given pixel, giving rise to an erroneous depth map. We report here a sparsity-regularized solution that separates K interfering components using multiple modulation frequency measurements. The method maps ToF imaging to the general framework of spectral estimation theory and has applications in improving depth profiles and exploiting multiple scattering.

We analyze multi-bounce propagation of light in an unknown hidden volume and demonstrate that the reflected light contains sufficient information to recover the 3D structure of the hidden scene. We formulate the forward and inverse theory of secondary scattering using ideas from energy front propagation and tomography. We show that using Fresnel approximation greatly simplifies this problem and the inversion can be achieved via a backpropagation process. We study the invertibility, uniqueness and choices of space-time-angle dimensions using synthetic examples. We show that a 2D streak camera can be used to discover and reconstruct hidden geometry. Using a 1D high speed time of flight camera, we show that our method can be used recover 3D shapes of objects "around the corner".

The recovery of objects obscured by scattering is an important goal in imaging and has been approached by exploiting, for example, coherence properties, ballistic photons or penetrating wavelengths. Common methods use scattered light transmitted through an occluding material, although these fail if the occluder is opaque. Light is scattered not only by transmission through objects, but also by multiple reflection from diffuse surfaces in a scene. This reflected light contains information about the scene that becomes mixed by the diffuse reflections before reaching the image sensor. This mixing is difficult to decode using traditional cameras. Here we report the combination of a time-of-flight technique and computational reconstruction algorithms to untangle image information mixed by diffuse reflection. We demonstrate a three-dimensional range camera able to look around a corner using diffusely reflected light that achieves sub-millimetre depth precision and centimetre lateral precision over 40 cm×40 cm×40 cm of hidden space.