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At least 1,153 records · Page 64

Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.

Signal Recognition↗

Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.

Signal Classification↗

Evaluate ERTS imagery for mapping and detection of changes of snowcover on land and on glaciers

The author has identified the following significant results. Snowlines on a small drainage basin were accurately identified on bulk ERTS-1 images without use of digital processing, and results checked with high altitude and ground-based photography. The area and approximate shape of snow patches as small as 20,000 sq m could be correctly identified with a magnifying scanning densitometer. The resolution of ERTS is more than ample for most snow mapping needs. Mount Baker, Washington, has a large crater south of the summit and an area north of the summit which emit considerable geothermal heat in the form of fumaroles and hot ground. Temperatures are being monitored using an ERTS DCS. Debris flows are occassionally released from the crater due to water saturation at the base of a heavy snowpack lying on hydrothermally altered hot ground. These debris flows present a possible hazard to life and property, as they are discharged down the Boulder Glacier toward Baker Lake, the upper of two major hydroelectric power reservoirs which are situated above the populated Skagit River Valley. ERTS-1 images show that the most recent debris flow (20-21 August 1973) can be clearly discerned and mapped. ERTS images provide another important tool for monitoring this potential hazard.

Meier, M. F.↗

ThermoPore: Predicting part porosity based on thermal images using deep learning

Part qualification is often a critical and labor-intensive process in additive manufacturing, particularly in the detection of defects such as porosity, which stands to benefit significantly from advancements in machine learning. We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time porosity map of parts based on thermal images acquired during the build. The quantification task builds upon the established Convolutional Neural Network model architecture to predict pore count and the localization task leverages the spatial and temporal attention mechanisms of the novel Video Vision Transformer model to indicate areas of expected porosity. Our model for porosity quantification achieved a R 2 score of 0.57 and our model for porosity localization produced an average Intersection over Union (IoU) score of 0.32 and a maximum of 1.0. This work is setting the foundations of part porosity “Digital Twins” based on additive manufacturing monitoring data and can be applied downstream to reduce time-intensive post-inspection and testing activities during part qualification and certification. In addition, we seek to accelerate the acquisition of crucial insights normally only available through ex-situ part evaluation by means of machine learning analysis of in-situ process monitoring data.

Deep learning↗

Digital Rocks Portal (Digital Porous Media): Connecting data, simulation and community

Digital Rocks Portal (DRP, https://www.digitalrocksportal.org) organizes and preserves imaged datasets and experimental measurements of porous materials in subsurface, and beyond, with the mission to connect them to simulation and analysis, as well as educate the research community. We have over 150 projects represented in more than 200 publications, and an active community that reuses the data, most recently in multiple machine learning applications for automating image analysis as well as the prediction of transport. Such automation is crucial for performing formation evaluation tasks in near-real time. We present benchmark datasets that have played a role in recent machine learning prediction successes in the field. We further discuss the vision for further research advances, educational materials, as well as growth and sustainability plan of this digital rock physics community resource. In particular, we are in the process of expanding into a broader repository of engineered porous materials, specifically those for energy storage and the portal will transition to Digital Porous Media (DPM) in near future.

58 GEOSCIENCES↗

Accuracy of Length of Virtual Stents in Treatment of Intracranial Wide-Necked Aneurysms

Background and Purpose: Precise stent deployment is important for successful treatment of intracranial aneurysms by stent-assisted coiling (SAC). We evaluated the accuracy of virtual stents generated using commercial stent planning software by comparing the length of virtual and actually deployed intracranial laser cut stents on three-dimensional digital subtraction angiography (3D-DSA) images. Methods: We retrospectively analyzed the data of 75 consecutive cases of intracranial wide-necked aneurysms treated with the SAC technique using laser cut stents. Based on 3D-DSA images acquired by C-arm CT, stent sizing and placement were intraoperatively simulated by a commercial software application. The difference in length of the stents was estimated by measuring proximal discrepancies between the end points of the virtual and actually deployed stents on fused pre-procedural and post-procedural 3D-DSA images. Discrepancies between distal stent end points were manually minimized. The Kruskal–Wallis test was applied to test whether stent location, type, and length had an effect on difference in length between virtual and real stent. Results: The median difference in length between virtual and real stents was 1.58 mm with interquartile range 1.12–2.12 mm. There was no evidence for an effect of stent location (p = 0.23), stent type (p = 0.33), or stent length (p = 0.53) on difference in length between virtual and real stents. Conclusions: Stent planning software allows 3D simulation of laser cut stents overlain on 3D-DSA images of vessels and may thus be useful for stent selection and deployment of laser cut stents during stent-assisted coiling of intracranial aneurysms.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Study of CMOS strip sensor for future silicon tracker

Monolithic silicon sensors developed with High-Voltage CMOS (HV-CMOS) processes have become highly attractive for charged particle tracking. Compared with the standard CMOS sensors, HV-CMOS sensors can provide larger and deeper depletion regions that lead to larger signals and faster charge collection. They can provide high position resolution, low material budget, high radiation hardness and low cost that are desirable for high performance tracking in harsh collision environment. Various studies have been conducted to explore the technology feasibility for the large-area tracking systems at future collider experiments. CHESS (CMOS HV/HR Evaluation for Strip Sensor) sensor series have been developed as an alternative solution to the conventional silicon micro-strip detectors for the ATLAS inner tracker upgrade. The first prototype (named CHESS1) was to evaluate the diode geometry and the in-pixel analog electronics. Obtained test results were used to optimize the second prototype (named CHESS2). CHESS2 was implemented with a full digital readout architecture and realized as a full reticle sized monolithic sensor. Here, the basic characteristics of the CHESS2 prototype sensors and their performance in response to different input signals are presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Multiplexor and sensor system tradeoff study

The influence of certain sensor parameters on the accuracy of information extracted from satellite image data is studied, using a digital processing flow to simulate the effects of the sensor parameters. The method employed was to select representative images; scan and digitize them; process the digital data to simulate the effects of presampling filters, instantaneous field of view (IFOV) size and shape, sampling rates, noise, and word length; record the processed data on film; evaluate the film images; and draw conclusions from the evaluations. The study showed that presampling filters are not required to suppress aliasing, that the choice of square and circular IFOV shapes is not important, and that increased horizontal and vertical sampling rates produce an increase in apparent resolution. Work with low-contrast targets revealed a definite maximum in the tradeoff between IFOV size and SNR.

Ferneyhough, D. G., Jr.↗

Theory on data processing and instrumentation

A selection of NASA Earth observations programs are reviewed, emphasizing hardware capabilities. Sampling theory, noise and detection considerations, and image evaluation are discussed for remote sensor imagery. Vision and perception are considered, leading to numerical image processing. The use of multispectral scanners and of multispectral data processing systems, including digital image processing, is depicted. Multispectral sensing and analysis in application with land use and geographical data systems are also covered.

Billingsley, F. C.↗

LANDSAT 4 image data quality analysis

A comparative analysis of TM and MSS data was completed and the results indicate that there are half as many separable spectral classes in the MSS data than in TM. In addition, the minimum separability between classes was also much less in MSS data. Radiometric data quality was also investigated for the TM by computing power spectrum estimates for dark-level data from Lake Michigan. Two significant coherent noise frequencies were observed, one with a wavelength of 3.12 pixels and the other with a 17 pixel wavelength. The amplitude was small (nominally .6 digital count standard deviation) and the noise appears primarily in Bands 3 and 4. No significant levels were observed in other bands. Scan angle dependent brightness effects were also evaluated.

Anuta, P. E.↗

Global data bases on distribution, characteristics and methane emission of natural wetlands: Documentation of archived data tape

Global digital data bases on the distribution and environmental characteristics of natural wetlands, compiled by Matthews and Fung (1987), were archived for public use. These data bases were developed to evaluate the role of wetlands in the annual emission of methane from terrestrial sources. Five global 1 deg latitude by 1 deg longitude arrays are included on the archived tape. The arrays are: (1) wetland data source, (2) wetland type, (3) fractional inundation, (4) vegetation type, and (5) soil type. The first three data bases on wetland locations were published by Matthews and Fung (1987). The last two arrays contain ancillary information about these wetland locations: vegetation type is from the data of Matthews (1983) and soil type from the data of Zobler (1986). Users should consult original publications for complete discussion of the data bases. This short paper is designed only to document the tape, and briefly explain the data sets and their initial application to estimating the annual emission of methane from natural wetlands. Included is information about array characteristics such as dimensions, read formats, record lengths, blocksizes and value ranges, and descriptions and translation tables for the individual data bases.

Matthews, Elaine↗

Digital-flutter-suppression-system investigations for the active flexible wing wind-tunnel model

Active flutter suppression control laws were designed, implemented, and tested on an aeroelastically-scaled wind tunnel model in the NASA Langley Transonic Dynamics Tunnel. One of the control laws was successful in stabilizing the model while the dynamic pressure was increased to 24 percent greater than the measured open-loop flutter boundary. Other accomplishments included the design, implementation, and successful operation of a one-of-a-kind digital controller, the design and use of two simulation methods to support the project, and the development and successful use of a methodology for on-line controller performance evaluation.

Perry, Boyd, III↗

Rocket engine plume diagnostics using video digitization and image processing - Analysis of start-up

Video digitization techniques have been developed to analyze the exhaust plume of the Space Shuttle Main Engine. Temporal averaging and a frame-by-frame analysis provide data used to evaluate the capabilities of image processing techniques for use as measurement tools. Capabilities include the determination of the necessary time requirement for the Mach disk to obtain a fully-developed state. Other results show the Mach disk tracks the nozzle for short time intervals, and that dominate frequencies exist for the nozzle and Mach disk movement.

Disimile, P. J.↗

Programs Help Create And Evaluate Markov Models

Pade Approximation With Scaling (PAWS) and Scaled Taylor Exponential Matrix (STEM) computer programs provide flexible, user-friendly, language-based interface for creation and evaluation of Markov models describing behaviors of fault-tolerant reconfigurable computer systems. Produce exact solution for probabilities of system failures and provide conservative estimates of numbers of significant digits in solutions. Also offer as part of bundled package with SURE and ASSIST, two other reliable analysis programs developed by Systems Validation Methods group at Langley Research Center.

Butler, Ricky W.↗

Interface Electronic Circuitry for an Electronic Tongue

Electronic circuitry has been developed to serve as an interface between an electronic tongue and digital input/output boards in a laptop computer that is used to control the tongue and process its readings. Electronic tongues can be used for a variety of purposes, including evaluating water quality, analyzing biochemicals, analyzing biofilms, and measuring electrical conductivities of soils.

Keymeulen, Didier↗

GPS/Optical/Inertial Integration for 3D Navigation Using Multi-Copter Platforms

In concert with the continued advancement of a UAS traffic management system (UTM), the proposed uses of autonomous unmanned aerial systems (UAS) have become more prevalent in both the public and private sectors. To facilitate this anticipated growth, a reliable three-dimensional (3D) positioning, navigation, and mapping (PNM) capability will be required to enable operation of these platforms in challenging environments where global navigation satellite systems (GNSS) may not be available continuously. Especially, when the platform's mission requires maneuvering through different and difficult environments like outdoor opensky, outdoor under foliage, outdoor-urban and indoor, and may include transitions between these environments. There may not be a single method to solve the PNM problem for all environments. The research presented in this paper is a subset of a broader research effort, described in [1]. The research is focused on combining data from dissimilar sensor technologies to create an integrated navigation and mapping method that can enable reliable operation in both an outdoor and structured indoor environment. The integrated navigation and mapping design is utilizes a Global Positioning System (GPS) receiver, an Inertial Measurement Unit (IMU), a monocular digital camera, and three short to medium range laser scanners. This paper describes specifically the techniques necessary to effectively integrate the monocular camera data within the established mechanization. To evaluate the developed algorithms a hexacopter was built, equipped with the discussed sensors, and both hand-carried and flown through representative environments. This paper highlights the effect that the monocular camera has on the aforementioned sensor integration scheme's reliability, accuracy and availability.

Dill, Evan T.↗

Advancement of Deep Learning and Geometric Methods for Active Terrain Relative Navigation

To enhance NASA’s precision landing capabilities, in conjunction with the development of a novel active terrain relative navigation (ATRN) and terrain mapping system, denoted SHERIF, this work performed a comparative analysis between both deep-learning (DL) based and geometric approaches to hazard detection (HD) and safe-site-identification (SSI) through hardware-in-the loop testing on the Six degree-of-freedom Tendon Actuated Robot (STAR). The Standalone Hazard Evaluation and Refinement using Instrument Findings (SHERIF) system is capable of ingesting sensor data at an asynchronous rate, stitching successive terrain scans together to yield a high-resolution digital elevation map (DEM), performing absolute and relative localization using novel 3D feature extraction and matching methods, and HD/SSI activities. The DL-based HD/SSI algorithm provides a modular alternative to classical geometric approaches which have performance times that scale with map resolution. As the adoption of AI solutions become more prevalent for autonomous system decision making, it is prudent to explore the utility of such solutions in applications where they traditionally excel, such as image classification. Along with the development of a DL-based HD system, this work performed the first comparative analysis between DL and geometric approaches to HD/SSI using real sensor data from real-time testing in a relevant environment.

Davis Adams↗

Tuscaloosa Marine Shale Laboratory

The Tuscaloosa Marine Shale (TMS) in Louisiana and Mississippi is an Upper Cretaceous source rock formation sandwiched between the sands of the upper and lower Tuscaloosa sections. The TMS is believed to be the source rock for underlying prolific Tuscaloosa sand formation. The TMS has an unproven estimate of 7,000,000,000 bbls of recoverable oil while its current total average production is about 3,000 bbls of oil per day in 2017. In 2013 and 2014, more than 80 wells were drilled horizontally into the TMS that were fractured using multi-stage fracturing technology. The results from this have been mixed, but recent production for several wells show an appealing initial oil production rate of more than 1000 bbl/day. The preliminary core analysis by industry partners and a few literature studies shows that the TMS is one of the most clay-rich and sensitive shales to water. Due to these and other technical problems, there is high risk for the economic development of TMS compared to other shale plays. The experiences of major industrial players in the TMS show the necessity of open and collaborative efforts to better understand the critical gaps in the development of this challenging and potentially highly economic shale play to enable more cost-efficient and environmentally-sound recovery from this unconventional liquid-rich shale play. The overall objective of this project is to form a consortium of science and industry partners to address the following six major objectives using scientific and technical approaches: 1. To improve wellbore integrity by better understanding the sources of the wellbore instability issues, proposing innovative mud and cement design for the TMS. 2. To improve formation evaluation using laboratory techniques for the evaluation of mineralogical composition, organic content, and produced-water chemistry as well as well log and geophysical analysis. 3. To determine the role of geologic discontinuities on fracture growth and shale creep behavior using digital image correlation technique. 4. To investigate the application of stable CO 2 foam and super-hydrophobic proppants for improved reservoir stimulation. 5. To better understand the nature of water/hydrocarbon/CO 2 flow in clay and organic-rich formation and the role of water/fluid interaction on recovery. 6. To prepare better socio-economic environment for TMS development by community engagement. Subsequently, the TMS virtual laboratory conducted testing and analysis of various properties of rock and formation fluids from the TMS, including but not limited to the following: Analyzing reports and logs to better understand the source of wellbore instability in TMS wells; Experiments to design a customized cement based on TMS requirements; Experiments to obtain the mineralogical and geochemical composition of TMS samples; Seismic analysis of TMS geophysical data to better predict total organic carbon (TOC) content and brittleness in TMS; Well log analysis to better estimate the TOC and geo-mechanical properties of TMS; Experiments on formation water to understand the chemistry of produced water; Experiments to determine the role of lamination and natural fractures on fracture propagation or rock deformation using digital image correlation technique in in-direct tensile tests, semi-circular bend test and creep tests Experiments to determine the stability and rheological properties of nanoparticle-stabilized CO 2 foam in TMS rock samples; Experiments to determine fluid dynamics in un-propped TMS fractures and the role of nano-coating of proppants on fluid dynamics in fractures with proppants; Micro-fluidics experiments to enhance the understanding of fluid dynamics in tight liquidrich pores with high clay content; Socio-economic studies to better engage communities in TMS development.

58 GEOSCIENCES↗