Quantum Image Pixel Library (QPIXL++) v0.1.0
QPIXL++ is a software package to compile quantum circuits for compressed representation of images on quantum hardware.
Camps, Daan↗
SEARCH · Engineering Papers
Results for “image compression”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Compressed sensing for scanning tunnel microscopy imaging of defects and disorder
Not Available
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
SPRUCE Experimental Plot Top View Aerial Images beginning June 2019
This data set provides a record of the aerial images of the SPRUCE experimental plots obtained periodically using an unmanned aerial vehicle (UAV) with attached camera from 2019-05-05- to 2022-08-25. New images of the plots will be added periodically. The initial RGB images may be used for studies of greenness and snow coverage and compared with ground-based measurements and chamber-based tree and shrub Phenocam images (e.g. Schädel et al., 2022). Also included are NDVI (Normalized Difference Vegetation Index) images showing calculated difference between visible near-infrared (NIR) light reflectance from vegetation, as well as infrared (IR) and multispectral images. Files are presented in JPEG (*.jpg) or PDF (*.pdf) format inside of 28 compressed (*.zip) files. Images were collected for the 10 plots with enclosures, the 7 unchambered plots, and the environmental monitoring instrument cluster area. Images are slightly off-plot center at 12 to 15 meters above ground level or approximately 2 meters above the top of an experimental chamber.
54 ENVIRONMENTAL SCIENCES↗
SnowPac : a multiscale cubic B-spline wavelet compressor for astronomical images
ABSTRACT As more advanced and complex survey telescopes are developed, the size and scale of data being captured grows at increasing rates. Across various domains, data compression through wavelets has enabled the reduction of data size and increase in computation efficiency. In this paper, we provide qualitative and quantitative tests of a new wavelet-based image compression method compared against the current standard for astronomical images. The analysis is improved by making use of state-of-the-art object detection systems to accurately measure the impact of the compression. We find that a combination of lossy wavelet-based methods, efficient quantization, and lossless dictionary compressors can preserve up to 98 per cent of astronomical objects at a 10:1 compression ratio. This significant reduction in file size also preserves astronomical object properties better than existing methods. These methods help further reduce future workloads for image-heavy processing pipelines.
Pulido, Jesus↗
Vegetation Warming Experiment: Landscape-scale digital camera imagery for vegetation phenology, Utqiagvik (Barrow), Alaska, 2018
Images captured using a StarDot NetCam SC phenocamera looking east from the top of the Barrow Environmental Observatory (BEO) Sled Shed, Utqiagvik, Alaska. The camera was installed to remotely monitor plant phenology and operation of the BNL TEST group's ZPW (Zero Power Warming) chambers during the growing season of 2018. Images were captured from early spring (13 April) through to mid fall (18 October). Snowmelt, vegetation growth and senescence, and snow accumulation were captured. Images were uploaded to the BNL FTP server every hour until 16 June, and then every 10 minutes. Files were renamed with the date and time of the image. jpg images have been compressed in *.tar.gz format (7.9 GB). Different fields of view (northeasterly) were also captured using 3 Wingscapes TimelapseCam cameras mounted on a mast on the sled shed. Images were recorded from 21 June to 24 September, 2018, at 30 minute intervals, from 11:00 - 14:30, Alaska daylight time (AKDT, UTC-8). Movie files (.mp4) of each Wingscapes camera dataset. Additional metadata included in *.pdf and *.csv files. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
54 ENVIRONMENTAL SCIENCES↗
Scalable Hybrid Learning Techniques for Scientific Data Compression
Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific discovery. Unlike image and video compression algorithms that limit errors to primary data (PD), scientists require compression techniques that accurately preserve derived quantities of interest (QoIs). Here, this article presents a physics-informed compression technique implemented as an end-to-end, scalable, GPU-based pipeline for data compression that addresses this requirement. Our hybrid compression technique combines machine learning techniques and standard compression methods. Specifically, we combine an autoencoder, an error-bounded lossy compressor to provide guarantees on raw data error, and a constraint satisfaction post-processing step to preserve the QoIs within a minimal error (generally less than floating point error). The effectiveness of the data compression pipeline is demonstrated by compressing nuclear fusion simulation data generated by a large-scale fusion code, XGC, which produces hundreds of terabytes of data in a single day. Our approach works within the ADIOS framework and results in compression by a factor of more than 150 while requiring only a few percent of the computational resources necessary for generating the data, making the overall approach highly effective for practical scenarios.
ITER↗
A lightweight, user-configurable detector ASIC digital architecture with on-chip data compression for MHz X-ray coherent diffraction imaging
Today, most X-ray pixel detectors used at light sources transmit raw pixel data off the detector ASIC. With the availability of more advanced ASIC technology nodes for scientific application, more digital functionalities from the computing domains (e.g., compression) can be integrated directly into a detector ASIC to increase data velocity. In this paper, we describe a lightweight, user-configurable detector ASIC digital architecture with on-chip compression which can be implemented in 130 nm technologies in a reasonable area on the ASIC periphery. In addition, we present a design to efficiently handle the variable data from the stream of parallel compressors. The architecture includes user-selectable lossy and lossless compression blocks. The impact of lossy compression algorithms is evaluated on simulated and experimental X-ray ptychography datasets. This architecture is a practical approach to increase pixel detector frame rates towards the continuous 1 MHz regime for not only coherent imaging techniques such as ptychography, but also for other diffraction techniques at X-ray light sources.
47 OTHER INSTRUMENTATION↗
Cognition at the Point of Sensing
Over the last 15 years, compressive sensing techniques have been developed which have the potential to greatly reduce the amount of data collected by systems while preserving the amount of information obtained. A cost of this efficiency is that a computationally-intensive optimization routine must be used to put the sensed data into a form that a person can interpret. At the same time, machine learning techniques have experienced tremendous growth as well. Machines have demonstrated the ability learn how to effectively perform tasks such as detection and classification at speeds much faster than humanly possible. Our goal in this project was to study the feasibility of using compressive sensing systems "at the edge." That is, how can compressive sensing sensors be deployed such that information is created at the remote sensor rather than sending raw data to a central processing location? Studies were performed to analyze whether machine learning could be done on the compressively sensed data in its raw form. If a machine is performing the task, is it possible to do so without putting the data into a human interpretable form? We show that this is possible for some systems, in particular a compressive sensing snapshot imaging spectrometer. Machine learning tasks were demonstrated to be more effective and more robust to noise when the machine learning algorithm worked on data in its raw form. This system is shown to outperform a traditional spectrometer. Techniques for reducing the complexity of the reconstruction routine were also analyzed. Techniques for such as data regularization, deep neural networks, and matrix completion were studied and shown to have benefits over traditional reconstruction techniques. In this project we showed that compressive sensing sensors are indeed feasible at the edge. As always, sensors and algorithms must be carefully tuned to work in the constrained environment. In this project we developed tools and techniques to enable those analyses.
47 OTHER INSTRUMENTATION↗
Additively manufactured β-Ti5553 with laser powder bed fusion: Microstructures and mechanical properties of bulk and lattice parts
Ti5553 (Ti-5Al-5Mo-5V-3Cr wt%) is a titanium alloy widely used for its high strength-to-weight ratio and good formability at elevated temperatures. Unlike Ti-6Al-4V, Ti5553 does not undergo martensitic transformation, preventing cracking of brittle martensite upon rapid cooling. This makes it a strong candidate for additive manufacturing (AM), particularly laser powder bed fusion (L-PBF). L-PBF offers the unique opportunity to make fine lattice structures to reduce component weight. Despite the growing field of AM, there have been limited studies on L-PBF Ti5553 lattices and how their properties differ from the bulk. Here the present work addresses this knowledge gap by investigating microstructures and properties of L-PBF bulk and lattice parts and the effect of post L-PBF heat treatments. Electron microscopy and mechanical testing show that the high dislocation density formed during L-PBF increases bulk part’s yield strength by approximately 100 MPa compared to the conventional alloy. Digital image correlation during compression testing of octet truss lattices reveals a layer-by-layer failure mode. Compared to the bulk, the lattice contains copious ω nanoprecipitation, weaker <001> texture, smaller average grain sizes, and larger content of high-angle grain boundaries. These features elicit differences in Taylor factor distributions for the lattice depending on load direction, underlining challenges in predicting lattice mechanical response based on bulk properties. By examining the processing-structure-property relationships in the bulk and lattice, the present results delineate their microstructural and mechanical differences and establish a benchmark for the future design applications of L-PBF Ti5553.
36 MATERIALS SCIENCE↗
Multiphoton and Harmonic Imaging of Microarchitected Materials
Microadditive manufacturing has revolutionized the production of complex, nano- to microscale components across various fields. This work investigates two-photon (2P) and three-photon (3P) fluorescence imaging, as well as third-harmonic generation (THG) microscopy, to examine periodic microarchitected lattice structures fabricated using multiphoton lithography (MPL). By immersing the structures in refractive index matching fluids, we demonstrate high-fidelity 3D reconstructions of both fluorescent structures using 2P and 3P microscopy as well as low-fluorescence structures using THG microscopy. These results show that multiphoton fluorescence (MPF) imaging offers reduced signal decay with respect to depth compared to single-photon techniques in the examined structures. We further demonstrate the ability to nondestructively identify intentional internal modifications of the structure that are not immediately visible with scanning electron microscope (SEM) images and compression-induced fractures, highlighting the potential of these techniques for quality control and defect detection in microadditively manufactured components.
36 MATERIALS SCIENCE↗
The effects of carbonaceous inclusions and their distributions on dynamic failure processes in boron carbide ceramics
Commercially available boron carbide ceramics typically have heterogeneous microstructures that contain distributions of processing-induced inclusions. The inclusions that are rich in carbon (i.e., carbonaceous) govern the underlying mechanisms of brittle fracture through wing crack formation, and thus dictate the mechanical response of the ceramic. In this study, we investigate the dynamic failure of five boron carbide ceramic materials with different inclusion populations. All of the materials were prepared by hot-pressing; four of these boron carbides contained different sizes and concentrations of carbonaceous inclusions, while one contained no carbonaceous inclusions. The heterogeneity distributions were characterized in some detail for statistical analysis using scanning electron microscopy and quantitative image analysis. A modified compression Kolsky bar setup with in situ ultra-high-speed microscopic imaging (10 million frames per second) was then used to study the influence of the inclusion distributions on the dynamic failure processes in these materials, at nominal high strain rates of 10 2 -10 3 s -1 . The in situ ultra-high-speed microscopy highlighted the link between micro and macroscale failure processes and demonstrated that the carbonaceous inclusions are indeed the preferential sites for nucleation of wing cracks, as previously hypothesized based on post-mortem observations. The relative orientation of an inclusion with respect to the compression axis was shown to affect the likelihood that it would participate in crack nucleation. In conclusion, all of the ceramics were also found to have orientation-dependent peak compressive stress, regardless of the presence of carbonaceous inclusions, suggesting that grain orientation distributions are also important.
36 MATERIALS SCIENCE↗
Optimising the processing and storage of visibilities using lossy compression
The next-generation radio astronomy instruments are providing a massive increase in sensitivity and coverage, largely through increasing the number of stations in the array and the frequency span sampled. The two primary problems encountered when processing the resultant avalanche of data are the need for abundant storage and the constraints imposed by I/O, as I/O bandwidths drop significantly on cold storage. An example of this is the data deluge expected from the SKA Telescopes of more than 60 PB per day, all to be stored on the buffer filesystem. While compressing the data is an obvious solution, the impacts on the final data products are hard to predict. In this paper, we chose an error-controlled compressor – MGARD – and applied it to simulated SKA-Mid and real pathfinder visibility data, in noise-free and noise-dominated regimes. As the data have an implicit error level in the system temperature, using an error bound in compression provides a natural metric for compression. MGARD ensures the compression incurred errors adhere to the user-prescribed tolerance. To measure the degradation of images reconstructed using the lossy compressed data, we proposed a list of diagnostic measures, exploring the trade-off between these error bounds and the corresponding compression ratios, as well as the impact on science quality derived from the lossy compressed data products through a series of experiments. We studied the global and local impacts on the output images for continuum and spectral line examples. We found relative error bounds of as much as 10%, which provide compression ratios of about 20, have a limited impact on the continuum imaging as the increased noise is less than the image RMS, whereas a 1% error bound (compression ratio of 8) introduces an increase in noise of about an order of magnitude less than the image RMS. For extremely sensitive observations and for very precious data, we would recommend a 0.1% error bound with compression ratios of about 4. These have noise impacts two orders of magnitude less than the image RMS levels. At these levels, the limits are due to instabilities in the deconvolution methods. We compared the results to the alternative compression tool DYSCO, in both the impacts on the images and in the relative flexibility. MGARD provides better compression for similar error bounds and has a host of potentially powerful additional features.
Techniques: interferometric↗
Vegetation Warming Experiment: Landscape-scale digital camera imagery for vegetation phenology, Utqiagvik (Barrow), Alaska, 2017
Images captured using a StarDot NetCam SC phenocamera looking east from the top of the Barrow Environmental Observatory (BEO) Sled Shed, Barrow (now Utqiagvik), Alaska. The camera was installed to remotely monitor plant phenology and operation of the BNL TEST group's ZPW (Zero Power Warming) chambers during the growing season of 2017. Images were captured from early spring (24 April) through to late fall (15 November). Snowmelt, vegetation growth and senescence, and snow accumulation were captured. Images were uploaded to the BNL FTP server every 10 minutes. jpg images have been compressed in *.tar.gz format (12.1 GB) and additional information in *.pdf and *.txt files. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
54 ENVIRONMENTAL SCIENCES↗
Ultrasonic imaging of microscale processes in quartz gouge during compression and shearing
Not Available
42 ENGINEERING↗
In situ compression artifact removal in scientific data using deep transfer learning and experience replay
The massive amount of data produced during simulation on high-performance computers has grown exponentially over the past decade, exacerbating the need for streaming compression and decompression methods for efficient storage and transfer of this data---key to realizing the full potential of large-scale computational science. Lossy compression approaches such as JPEG when applied to scientific simulation data realized as a stream of images can achieve good compression rates but at the cost of introducing compression artifacts and loss of information. This paper develops a unified framework for in situ compression artifact removal in which the fully convolutional neural network architectures are combined with scalable training, transfer learning, and experience replay to achieve superior accuracy and efficiency while significantly decreasing the storage footprint as compared with the traditional optimization-based approaches. We demonstrate the proposed approach and compare it with compressed sensing postprocessing and other baseline deep learning models using climate simulations and nuclear reactor simulations, both of which are driven by hyperbolic partial differential equations. Our approach when applied to remove the compression artifacts on the JPEG-compressed nuclear reactor simulation data (using a transfer-trained model that was pretrained on the climate simulation data and updated incrementally as the nuclear reactor simulation progressed), achieved a significant improvement---mean peak signal-to-noise ratio of 42.438 as compared with 27.725 obtained with the compressed sensing approach.
97 MATHEMATICS AND COMPUTING↗
Biologically Inspired Robust Perception Approximating Sparse Coding [Slides]
Sparse coding has a variety of applications, such as image denoising and data compression. Recent work has shown the how sparse coding and biologically inspired architectures can mitigate adversarial attacks on images.
97 MATHEMATICS AND COMPUTING↗
DETERMINATION OF STRUT QUALITY FACTORS IN ADDITIVELY MANUFACTURED LATTICES USING IN-SITU COMPRESSION TESTING µ-CT
In response to the need for an automated, commercial method to qualify additively manufactured (AM) lattice components, an experiment was conducted to evaluate the effects of defective lattice struts on the structural compression strength. Lattice samples with known defective or missing struts were compressed using a Deben CT5000RT and imaged using xray µ-CT. The compressive force and x-ray computed tomography results were compared to defect free standards to evaluate the impact of each defect type on the overall structure’s compressive strength. This analysis will allow for simplifications to Finite Element Analysis (FEA) on AM parts without sacrificing model fidelity. Understanding the contribution of each defect type and severity will also better inform non-destructive evaluation (NDE) personnel of the inspection parameters necessary to detect the smallest feature of importance.
Dinova, Vincent A.↗
A holey cavity for single-transducer 3D ultrasound imaging with physical optimization
Within the compressive sensing (CS) framework, one effective way to increase the likelihood of successful signal reconstruction is to employ random processes in the construction of the sensing matrix. This work presents a 3D holey cavity, with diverse frequency modes, to spectrally code, that is, randomize, the ultrasound wave fields. The simulated results show that the use of such a cavity enables imaging simple or complex targets, such as spheres or the letter E, by only a single transceiver—something that is not possible without the use of a coding structure like the cavity. The effect of noise on imaging results and the size of the targets on the first-order Born approximation (BA) are also investigated. Moreover, this study attempts to optimize the cavity, based on a single numerical metric, such as the sum of singular values (SSV) or mutual coherence (MC). Yet, it will be shown that neither of these metrics can consistently compare the norm-one imaging performance between two cavities of different materials or hole sizes. This leaves finding a quantitative metric for these kinds of optimizations an open problem.
42 ENGINEERING↗