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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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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Precise orbit determination of high-earth elliptical orbiters using differenced Doppler and range measurements

Recent advances in Deep Space Network station calibration methods have led to renewed interest in the use of differenced Doppler and range data types for interplanetary navigation. Described here is an orbit determination error analysis of the performance of these differenced data types when used with conventional two-way Doppler for precise navigation of High-Earth Orbiters. Three highly elliptical Earth orbits are investigated, with apogee heights on the order of 20,000 km, 70,000 km, and 156,000 km. Results indicate that the most significant navigational accuracy improvements, relative to the performance obtained from two way Doppler alone, are achieved for the lowest altitude orbit by using differenced Doppler measurements with two way Doppler (assuming that spacecraft onboard downlink antennas have no ground footprint limitation in the near-apogee regime). In the case of the two higher altitude orbits, accuracy improvements over Doppler-only performance, although less dramatic, are also achieved when differenced range measurements are combined with two-way Doppler.

Estefan, J. A.↗
NASA NTRS1992

Precise orbit determination of high-earth elliptical orbiters using differenced Doppler and ranging measurements

Recent advances in Deep Space Network station calibration methods have led to renewed interest in the use of differenced Doppler and range data types for interplanetary navigation. Described here is an orbit determination error analysis of the performance of these differenced data types when used with conventional two-way Doppler for precise navigation of High-Earth Orbiters. Three highly elliptical earth orbits are investigated, with apogee heights on the order of 20,000 km, 70,000 km, and 156,000 km. Results indicate that the most significant navigational accuracy improvements, relative to the performance obtained from two way Doppler alone, are achieved for the lowest altitude orbit by using differenced Doppler measurements with two way Doppler (assuming that spacecraft onboard downlink antennas have no ground footprint limitation in the near-apogee regime). In the case of the two higher altitude orbit accuracy improvements over Doppler-only performance, although less dramatic, are also achieved when differenced range measurements are combined with two-way Doppler.

Estefan, Jeff A.↗
DOE OSTI2022

Non-Blind Deblurring for Fluorescence: A Deformable Latent Space Approach with Kernel Parameterization

We report N\non-blind deblurring (NBD) is a modeling method of the image deblurring problem in computer vision, where the blurring kernel is known or can be externally estimated. In this paper, we attempt to solve a parametric NBD problem, inspired by the simultaneous acquisition of ptychography and fluorescent imaging (FI). Ptychography is an imaging method that favors larger probes, i.e. convolutional kernels, while FI relies on a small probe for high resolution. Also, the kernel can be solved during ptychographic reconstruction. With Ptycho-FI using the same larger kernel, we can perform NBD on the blurred fluorescent images to achieve high-resolution FI, and thus speed up the experiments. To this end, we design a deep latent space deformation network that is directly parameterized by the kernel. The network consists of three components: encoder, deformer, and decoder, where the deformer is specifically meant to rectify the latent space representations of blurred images to a standard latent space, regardless of the kernel. The deformation network is trained with a two-stage training scheme. We conduct extensive experiments to confirm that our parametric model can adapt to drastically different blurring kernels and perform robust deblurring.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS2011

The Impact of Traffic Prioritization on Deep Space Network Mission Traffic

A select number of missions supported by NASA's Deep Space Network (DSN) are demanding very high data rates. For example, the Kepler Mission was launched March 7, 2009 and at that time required the highest data rate of any NASA mission, with maximum rates of 4.33 Mb/s being provided via Ka band downlinks. The James Webb Space Telescope will require a maximum 28 Mb/s science downlink data rate also using Ka band links; as of this writing the launch is scheduled for a June 2014 launch. The Lunar Reconnaissance Orbiter, launched June 18, 2009, has demonstrated data rates at 100 Mb/s at lunar-Earth distances using NASA's Near Earth Network (NEN) and K-band. As further advances are made in high data rate space telecommunications, particularly with emerging optical systems, it is expected that large surges in demand on the supporting ground systems will ensue. A performance analysis of the impact of high variance in demand has been conducted using our Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) simulation tool. A comparison is made regarding the incorporation of Quality of Service (QoS) mechanisms and the resulting ground-to-ground Wide Area Network (WAN) bandwidth necessary to meet latency requirements across different user missions. It is shown that substantial reduction in WAN bandwidth may be realized through QoS techniques when low data rate users with low-latency needs are mixed with high data rate users having delay-tolerant traffic.

prioritization↗
NASA NTRS2011

The Impact of Traffic Prioritization on Deep Space Network Mission Traffic

A select number of missions supported by NASA's Deep Space Network (DSN) are demanding very high data rates. For example, the Kepler Mission was launched March 7, 2009 and at that time required the highest data rate of any NASA mission, with maximum rates of 4.33 Mb/s being provided via Ka band downlinks. The James Webb Space Telescope will require a maximum 28 Mb/s science downlink data rate also using Ka band links; as of this writing the launch is scheduled for a June 2014 launch. The Lunar Reconnaissance Orbiter, launched June 18, 2009, has demonstrated data rates at 100 Mb/s at lunar-Earth distances using NASA's Near Earth Network (NEN) and K-band. As further advances are made in high data rate space telecommunications, particularly with emerging optical systems, it is expected that large surges in demand on the supporting ground systems will ensue. A performance analysis of the impact of high variance in demand has been conducted using our Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) simulation tool. A comparison is made regarding the incorporation of Quality of Service (QoS) mechanisms and the resulting ground-to-ground Wide Area Network (WAN) bandwidth necessary to meet latency requirements across different user missions. It is shown that substantial reduction in WAN bandwidth may be realized through QoS techniques when low data rate users with low-latency needs are mixed with high data rate users having delay-tolerant traffic.

quality of service↗
DOE OSTI2023

Semi-supervised graph neural networks for pileup noise removal

Abstract The high instantaneous luminosity of the CERN Large Hadron Collider leads to multiple proton–proton interactions in the same or nearby bunch crossings (pileup). Advanced pileup mitigation algorithms are designed to remove this noise from pileup particles and improve the performance of crucial physics observables. This study implements a semi-supervised graph neural network for particle-level pileup noise removal, by identifying individual particles produced from pileup. The graph neural network is firstly trained on charged particles with known labels, which can be obtained from detector measurements on data or simulation, and then inferred on neutral particles for which such labels are missing. This semi-supervised approach does not depend on the neutral particle pileup label information from simulation, and thus allows us to perform training directly on experimental data. The performance of this approach is found to be consistently better than widely-used domain algorithms and comparable to the fully-supervised training using simulation truth information. The study serves as the first attempt at applying semi-supervised learning techniques to pileup mitigation, and opens up a new direction of fully data-driven machine learning pileup mitigation studies.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Protein–Protein Interaction Networks Derived from Classical and Machine Learning-Based Natural Language Processing Tools

The study of protein-protein interactions (PPIs) provides insight into various biological mechanisms, including the binding of antibodies to antigens, enzymes to inhibitors or promoters, and receptors to ligands. Recent studies of PPIs have led to significant biological breakthroughs. For example, the study of PPIs involved in the human:SARS-CoV-2 viral infection mechanism aided in the development of the SARS-CoV-2 vaccines. Though several databases exist for the manual curation of PPI networks, text mining methods have been routinely demonstrated as useful alternatives for newly studied or understudied species where databases are incomplete. Here, the relationship extraction (RE) performance of several open-source classical text processing, machine learning (ML)-based natural language processing (NLP), and large language model (LLM)-based NLP tools were compared. Overall, our results indicated that networks derived from classical methods tend to have high true positive rates at the expense of having overconnected-networks, ML-based NLP methods have lower true positive rates but networks with the closest structures to the target network, and LLM-based NLP methods tend to exist in-between the two other approaches, with variable performances. Finally, the selection of a specific NLP approach should be tied to the needs of a study and text availability, as models varied in performance due to the amount of text provided.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

High Throughput Laser Processing for Enhanced Battery Performance and Manufacturing

For widespread adoption of electric vehicles, lithium-ion batteries (LiBs) need to achieve energy densities of >275 Wh/kg, cell costs less than $100/kWh, and charge to more than 80% capacity within 15 minutes. Recently, it has been shown that introducing a secondary pore network in thick battery electrodes enables higher extracted capacity without a severe reduction in charge/discharge rate performance. Secondary pore networks consisting of ordered arrays of microstructures reduce tortuosity of Li+-ion transport deep into the thick electrodes, diminishing detrimental concentration gradients within the cell. Ultrafast (femtosecond)-pulsed laser ablation is a promising method to introduce such micro pores or channels in thick battery electrodes as it allows for precise control of pattern geometries, results in minimal damage to the electrode, and can be introduced into existing roll-to-roll electrode manufacturing lines. We have explored laser patterning to create secondary pore networks in a variety of battery electrode materials for both anode and cathode applications and characterized the laser-material interaction parameters. In addition, advanced materials characterization techniques (SEM-EDS, XRD) were used to explore whether ultrafast laser ablation affects the remaining electrode materials' morphology and structure. Furthermore, we have translated lab-scale laser ablation to high-throughput roll-to-roll processing at industrially relevant scales. Finally, we worked with Argonne National Laboratory, developers of the battery cost-analysis package BatPac, to estimate the additional manufacturing costs (CapEx and OpEx) for incorporation of laser-ablation processing to current battery electrode manufacturing. We estimate that ultrafast laser processing will add only -$1kWh, which for considerable improvements in cell performance, creates a compelling case for industry adoption.

battery↗
DOE OSTI2023

Neural Networks-Based Inverter Control: Modeling and Adaptive Optimization for Smart Distribution Networks

The optimal voltage control of inverter-based resources, especially under the high penetration of solar photovoltaics, is critical to the stability of the distribution power system. However, the computational complexity as well as the coordinated operation performance of the voltage control optimization in the distribution power system limits the real-time applications. To mitigate this issue, a model-free based adaptive optimal control scheme for the smart inverter is proposed to maximize the active power generation, minimize the power loss, and maintain the bus voltages in smart distribution networks. An inverter-based optimization model for coordinated operation is first established, considering the uncertainties of renewable power generation. Subsequently, by collecting the data and control strategies, the neural networks (NNs) based algorithm is proposed to efficiently predict the best possible control strategy. The main objective of this scheme is to accurately predict candidate optimal solutions with near-negligible feasibility and optimization gaps, with the advantage of avoiding complicated iteration-based numerical algorithms. Thereafter, the co-simulation among OpenDSS, MATLAB, and Python is set up to fully take advantage of the three individual software. Experiments are conducted based on different control parameter characteristics and structures of NNs. Finally, the results reveal that an average mean squared error of 0.013 and 1 ms response time are achieved, which is lower than some state-of-the-art methods.

42 ENGINEERING↗
DOE OSTI2015

Highly compressible 3D periodic graphene aerogel microlattices

Graphene is a two-dimensional material that offers a unique combination of low density, exceptional mechanical properties, large surface area and excellent electrical conductivity. Recent progress has produced bulk 3D assemblies of graphene, such as graphene aerogels, but they possess purely stochastic porous networks, which limit their performance compared with the potential of an engineered architecture. Here we report the fabrication of periodic graphene aerogel microlattices, possessing an engineered architecture via a 3D printing technique known as direct ink writing. The 3D printed graphene aerogels are lightweight, highly conductive and exhibit supercompressibility (up to 90% compressive strain). Moreover, the Young’s moduli of the 3D printed graphene aerogels show an order of magnitude improvement over bulk graphene materials with comparable geometric density and possess large surface areas. Ultimately, adapting the 3D printing technique to graphene aerogels realizes the possibility of fabricating a myriad of complex aerogel architectures for a broad range of applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Power and Communications Hardware-in-the-Loop CPS Architecture and Platform for DER Monitoring and Control Applications: Preprint

The rapid growth of distributed energy resources (DERs) has prompted increasing interest in the monitoring and control of DERs through hybrid smart grid communications. The deployment of communications and computation has transformed the traditional physical power grid into a smart cyber-physical system (CPS). To fully understand the interdependency between physical grid and cyber netowrks, this study designed a power and communications hardware-in-the-loop (PCommHIL) CPS architecture, which enables the flexible verification of DER monitoring and control with hybrid communications architectures and Internet protocols. Design, development and case study of a PCommHIL testbed for the DER coordination are discussed in detail, and the proposed platform integrates DER devices, Advanced Metering Infrastructures (AMIs), and a suite of hybrid communications networks for distribution automation applications. Case study on DER situational awareness and Volt-Var control validates the efficacy of this proposed PCommHIL platform with hybrid communications designs. Results show that the HAN communication technologies play a critical role in hybrid designs and it is the bottleneck for DER applications. High performance communication technologies are highly recommended to be applied in the HAN for enhanced monitoring and real-time control of DERs.

AMIs↗
DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗
NASA NTRS1996

Achieving High Throughput for Data Transfer over ATM Networks

File-transfer rates for ftp are often reported to be relatively slow, compared to the raw bandwidth available in emerging gigabit networks. While a major bottleneck is disk I/O, protocol issues impact performance as well. Ftp was developed and optimized for use over the TCP/IP protocol stack of the Internet. However, TCP has been shown to run inefficiently over ATM. In an effort to maximize network throughput, data-transfer protocols can be developed to run over UDP or directly over IP, rather than over TCP. If error-free transmission is required, techniques for achieving reliable transmission can be included as part of the transfer protocol. However, selected image-processing applications can tolerate a low level of errors in images that are transmitted over a network. In this paper we report on experimental work to develop a high-throughput protocol for unreliable data transfer over ATM networks. We attempt to maximize throughput by keeping the communications pipe full, but still keep packet loss under five percent. We use the Bay Area Gigabit Network Testbed as our experimental platform.

Johnson, Marjory J.↗
DOE OSTI2021

Gas transport characteristics of supramolecular networks of metal-coordinated highly branched Poly(ethylene oxide)

Model systems are developed and investigated to better understand the effect of polyether-metal ion interactions on gas separation characteristics. These systems help answer current questions raised by the substantial body of research on metal-organic frameworks (MOFs) dispersed in polyethers to improve gas separation performance, where favorable interactions between the metal centers and polyethers are preferred to improve interfacial compatibility. Specifically, we investigate CO 2 /gas transport properties of supramolecular networks comprising cross-linked poly(ethylene oxide) (XLPEO) and dissociable salts, including LiClO 4 , Ni(BF 4 ) 2 , and Cu(BF 4 ) 2 . Increasing the salt content increases the glass transition temperature (T g ) and generally decreases gas diffusivity and permeability, which can be successfully described using a T g -integrated free volume model with an expression similar to the Vogel-Tammann-Fulcher (VTF) equation. Surprisingly, low loadings of LiClO 4 and Cu(BF 4 ) 2 (2 mass% or less) can increase gas permeability by 30%–70% without affecting the CO 2 /gas selectivity. This increase correlates with polyether-metal ion dynamics as measured by dielectric spectroscopy. Understanding how interaction-mediated dynamics affect gas transport will be instrumental to designing MOF-based mixed matrix materials for gas separations.

36 MATERIALS SCIENCE↗
DOE OSTI2018

PIPER: Performance Insights for Programmers and Exascale Runtimes

During the lifetime of the project, the PIPER at the University of Utah worked in collaboration with the team from the partner institutions to establish a set of software tools for analyzing and improving the performance parallel computing tools. These activities complement and complete the work performed by the other X-Stack 2 projects with the definition of a common data models and computing infrastructures, which have been a central element in the targeted performance analysis pipeline and new programming models. The University of Utah team have focused mainly on developing data analysis and visualization systems to facilitate exploration, comparison and evaluation of the behavior and performance of complex algorithms in modern HPC infrastructures. This is involved several demonstrations of the performance achieved and presentation of the results in a way that facilitates the users understand bottlenecks and ways to overcome them. One major focus of the University of Utah activities has been the analysis of the performance characteristics of Fat-Tree and Dragonfly topology networks. The work involved the development of practical visualization systems especially in collaboration with the team at LLNL, and published several papers and gave invited talks at prestigious venues such as the Salishan Conference on High-Speed Computing or the International Conference on High Performance Computing, Data, and Analytics. In addition, the university of Utah team has advanced the core activities on analyzing the performance characteristics of I/O and other data movement systems and their practical use for scaling Uintah and other simulation codes poised for use in the exascale computing environments of the future.

42 ENGINEERING↗
NASA NTRS2005

System for Processing Coded OFDM Under Doppler and Fading

An advanced communication system has been proposed for transmitting and receiving coded digital data conveyed as a form of quadrature amplitude modulation (QAM) on orthogonal frequency-division multiplexing (OFDM) signals in the presence of such adverse propagation-channel effects as large dynamic Doppler shifts and frequency-selective multipath fading. Such adverse channel effects are typical of data communications between mobile units or between mobile and stationary units (e.g., telemetric transmissions from aircraft to ground stations). The proposed system incorporates novel signal processing techniques intended to reduce the losses associated with adverse channel effects while maintaining compatibility with the high-speed physical layer specifications defined for wireless local area networks (LANs) as the standard 802.11a of the Institute of Electrical and Electronics Engineers (IEEE 802.11a). OFDM is a multi-carrier modulation technique that is widely used for wireless transmission of data in LANs and in metropolitan area networks (MANs). OFDM has been adopted in IEEE 802.11a and some other industry standards because it affords robust performance under frequency-selective fading. However, its intrinsic frequency-diversity feature is highly sensitive to synchronization errors; this sensitivity poses a challenge to preserve coherence between the component subcarriers of an OFDM system in order to avoid intercarrier interference in the presence of large dynamic Doppler shifts as well as frequency-selective fading. As a result, heretofore, the use of OFDM has been limited primarily to applications involving small or zero Doppler shifts. The proposed system includes a digital coherent OFDM communication system that would utilize enhanced 802.1la-compatible signal-processing algorithms to overcome effects of frequency-selective fading and large dynamic Doppler shifts. The overall transceiver design would implement a two-frequency-channel architecture (see figure) that would afford frequency diversity for reducing the adverse effects of multipath fading. By using parallel concatenated convolutional codes (also known as Turbo codes) across the dual-channel and advanced OFDM signal processing within each channel, the proposed system is intended to achieve at least an order of magnitude improvement in received signal-to-noise ratio under adverse channel effects while preserving spectral efficiency.

Tsou, Haiping↗
DOE OSTI2016

Quantifying element incorporation in multispecies biofilms using nanoscale secondary ion mass spectrometry image analysis

Elucidating nutrient exchange in microbial communities is an important step in understanding the relationships between microbial systems and global biogeochemical cycles, but these communities are complex and the interspecies interactions that occur within them are not well understood. Phototrophic consortia are useful and relevant experimental systems to investigate such interactions as they are not only prevalent in the environment, but some are cultivable in vitro and amenable to controlled scientific experimentation. Nanoscale secondary ion mass spectrometry (NanoSIMS) is a powerful, high spatial resolution tool capable of visualizing the metabolic activities of single cells within a biofilm, but quantitative analysis of the resulting data has typically been a manual process, resulting in a task that is both laborious and susceptible to human error. In this work, the authors describe the creation and application of a semiautomated image-processing pipeline that can analyze NanoSIMS-generated data, applied to phototrophic biofilms as an example. The tool employs an image analysis process, which includes both elemental and morphological segmentation, producing a final segmented image that allows for discrimination between autotrophic and heterotrophic biomass, the detection of individual cyanobacterial filaments and heterotrophic cells, the quantification of isotopic incorporation of individual heterotrophic cells, and calculation of relevant population statistics. The authors demonstrate the functionality of the tool by using it to analyze the uptake of 15 N provided as either nitrate or ammonium through the unicyanobacterial consortium UCC-O and imaged via NanoSIMS. The authors found that the degree of 15N incorporation by individual cells was highly variable when labeled with 15 NH 4 + , but much more even when biofilms were labeled with 15 NO 3 – . In the 15 NH 4 + -amended biofilms, the heterotrophic distribution of 15 N incorporation was highly skewed, with a large population showing moderate 15 N incorporation and a small number of organisms displaying very high 15 N uptake. The results showed that analysis of NanoSIMS data can be performed in a way that allows for quantitation of the elemental uptake of individual cells, a technique necessary for advancing research into the metabolic networks that exist within biofilms with statistical analyses that are supported by automated, user-friendly processes.

59 BASIC BIOLOGICAL SCIENCES↗