Goddard Space Flight Center Awarded Contracts - Machine Learning | Federal Compass

Goddard Space Flight Center Awarded Contracts - Machine Learning

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NNG16PX51P - SOFTWARE MAINTENANCE AND SUPPORT MATLAB MAINTENANCE (SUBML) SIMULINK MAINTENANCE (SUBSL) AEROSPACE TOOLBOX MAINTENANCE CONTROL SYSTEM TOOLBOX MAINTENANCE OPTIMIZATION TOOLBOX MAINTENANCE STATISTICS AND MACHINE LEARNING
Purchase Order - 511210 Software Publishers
Contractor
MathWorks, Inc. (THE MATHWORKS INC)
Contracting Agency/Office
National Aeronautics and Space Administration»Goddard Space Flight Center
Effective date
06/15/2016
Obligated Amount
$15.5k
NNX12AL49G - ADVANCES IN OPTICAL REMOTE SENSING TECHNOLOGY OVER THE PAST TWO DECADES HAVE ENABLED THE DRAMATIC INCREASE IN SPATIAL, SPECTRAL, AND TEMPORAL DATA NOW AVAILABLE TO SUPPORT EARTH SCIENCE RESEARCH AND APPLICATIONS, NECESSITATING EQUIVALENT DEVELOPMENTS IN SIGNAL PROCESSING AND DATA EXPLOITATION ALGORITHMS. ALTHOUGH THIS INCREASE IN THE QUALITY AND QUANTITY OF DIVERSE MULTI-SOURCE DATA CAN POTENTIALLY FACILITATE IMPROVED UNDERSTANDING OF FUNDAMENTAL SCIENTIFIC QUESTIONS, CONVENTIONAL DATA PROCESSING AND ANALYSIS ALGORITHMS THAT ARE CURRENTLY AVAILABLE TO SCIENTISTS ARE DESIGNED FOR SINGLE-SENSOR, LOW DIMENSIONAL DATA. THESE METHODS CANNOT FULLY EXPLOIT EXISTING AND FUTURE EARTH OBSERVATION DATA. CAPABILITY TO EXTRACT INFORMATION FROM HIGH DIMENSIONAL DATA SETS ACQUIRED BY HYPERSPECTRAL SENSORS, TEXTURAL FEATURES DERIVED FROM MULTISPECTRAL AND POLARIMETRIC SAR DATA, AND VERTICAL INFORMATION REPRESENTED IN FULL WAVEFORM LIDAR IS PARTICULARLY LACKING. NEW ALGORITHMS ARE SPECIFICALLY NEEDED FOR ANALYSIS OF EXISTING DATA FROM THE NASA AIRBORNE (E.G. AVIRIS, LVIS, AND G-LIHT) AND EO-1 HYPERION AND ALI INSTRUMENTS, AND FUTURE DATA ACQUIRED BY THE UPCOMING LANDSAT DATA CONTINUITY MISSION, ICESAT-II, AND HYSPIRI. NEW PARADIGMS ARE ALSO REQUIRED TO EFFECTIVELY EXTRACT USEFUL INFORMATION FROM THE HIGH DIMENSIONAL FEATURE SPACE RESULTING FROM MULTI-SOURCE, MULTI-SENSOR, AND MULTI-TEMPORAL DATA. CLASSIFICATION OF REMOTELY SENSED DATA IS AN INTEGRAL PART OF THE ANALYSIS STREAM FOR LAND COVER MAPPING IN DIVERSE APPLICATIONS. HIGH DIMENSIONAL DATA PROVIDE CAPABILITY TO MAKE SIGNIFICANT IMPROVEMENTS IN CLASSIFICATION RESULTS, PARTICULARLY IN ENVIRONMENTS WITH COMPLEX SPECTRAL SIGNATURES WHICH MAY OVERLAP OR WHERE TEXTURAL FEATURES PROVIDE DISCRIMINATIVE INFORMATION. IN THIS PROJECT, A NEW CLASSIFICATION FRAMEWORK WILL BE DEVELOPED AND IMPLEMENTED FOR ROBUST ANALYSIS OF HIGH DIMENSIONAL, MULTI-SENSOR/MULTI-SOURCE DATA IN SMALL TRAINING SAMPLE SIZE CONDITIONS (LIMITED IN-SITU/GROUND REFERENCE DATA). THE FRAMEWORK WILL EMPLOY A MULTI-KERNEL SUPPORT VECTOR MACHINE, AN ENSEMBLE-CLASSIFICATION, AND DECISION FUSION SYSTEM FOR EFFECTIVELY EXPLOITING A DIVERSE COLLECTION OF POTENTIALLY DISPARATE FEATURES DERIVED EITHER FROM THE SAME SENSOR (E.G. SPATIAL-SPECTRAL ANALYSIS TASKS) OR FROM DIFFERENT SENSORS (E.G. LIDAR AND HYPERSPECTRAL DATA). THE SYSTEM WILL SIGNIFICANTLY ADVANCE RELIABILITY OF ACCURATE MAPPING OF REMOTELY SENSED DATA, PARTICULARLY FOR SCENARIOS WITH VERY LITTLE REFERENCE DATA TO TRAIN THE CLASSIFICATION MODEL. AN ACTIVE LEARNING (AL) COMPONENT WILL BE INTEGRATED IN THE MULTI-SOURCE/MULTI-SENSOR ENVIRONMENT TO MITIGATE THE IMPACT OF LIMITED TRAINING DATA, EFFECTIVELY CLOSING THE LOOP BETWEEN IMAGE ANALYSIS AND FIELD-COLLECTION TO ACQUIRE THE MOST INFORMATIVE SAMPLES FOR THE CLASSIFICATION TASK. THE PROPOSED PROJECT WILL HAVE AN ENTRY LEVEL OF TRL-2. DURING YEAR 1, THE TEAM WILL INCORPORATE THE MULTI-KERNEL SVM INTO THE CLASSIFICATION SYSTEM FOR HYPERSPECTRAL AND MULTI-SOURCE HIGH DIMENSIONAL DATA. MULTI-VIEW ACTIVE LEARNING METHODS PREVIOUSLY DEVELOPED BY THE PI WILL ALSO BE IMPLEMENTED IN THE MULTI-SOURCE ENVIRONMENT. EFFORTS IN YEAR 2 WILL FOCUS ON EXTENDING THE SPATIAL-SPECTRAL FEATURE EXTRACTION CAPABILITY, INCORPORATING MULTI-SENSOR CLASSIFICATION VIA THE MULTI-KERNEL SVM ENSEMBLE MODEL, AND INTEGRATING ACTIVE LEARNING IN THE MULTI-SENSOR ENVIRONMENT. APPLICATION OF THE METHODS WILL BE INITIATED USING A TEST-BED OF MULTISPECTRAL, HYPERSPECTRAL, AND LIDAR DATA. IN YEAR 3, INTEGRATION OF THE PROPOSED FRAMEWORK WILL BE COMPLETED, AND EXTENSIVE TESTING AND VALIDATION WILL BE CONDUCTED USING MULTIPLE RELEVANCY SCENARIOS. THE PROTOTYPE SYSTEM WILL BE IMPLEMENTED ON THE PURDUE HUB COMPUTATIONAL PLATFORM TO ENABLE A TRL-4 SYSTEM AT THE END OF THE STUDY.
Grant for Research
Contractor
Purdue University (PURDUE UNIVERSITY)
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
07/24/2012
Obligated Amount
$497.5k
NNX12CE33P - DIAMOND TURNING IS PROVEN TO BE ABLE TO QUICKLY PRODUCE HIGHLY ASPHERIC GRAZING INCIDENCE OPTICAL CONTOURS TO VISIBLE WAVELENGTH TOLERANCES WITH EXTREMELY SMOOTH SURFACES. SUPER ALLOYS WITH EXCEPTIONAL DIMENSIONAL STABILITY AND STRENGTH UNDER CYCLIC HIGH TEMPERATURES HAVE BEEN DEVELOPED FOR GAS TURBINE ENGINES. THE THERMAL EXPANSION CAN BE IN THE RANGE OF THE EXPANSION OF THE BOROSILICATE GLASSES USED FOR X-RAY MIRRORS. THIS PROPOSAL UTILIZES AN EXISTING MANUFACTURING LEARNING CURVE TO DEVELOP A RELIABLE MATERIAL AND MANUFACTURING PROCESS FOR GLASS SLUMPING MANDRELS. THIS DEVELOPMENT PROCESS WILL INVOLVE THE FOLLOWING INVESTIGATIONS AND DEVELOPMENT GOALS : -DEVELOP ELECTROLESS NICKEL PLATING PROCESSES FOR SUPER ALLOYS, -ULTRA PRECISION MACHINING AND POLISHABILITY OF SUPER ALLOYS. -DIAMOND TURNING OF ELECTROLESS NICKEL BEFORE AND AFTER THE SLUMPING HEAT CYCLE. -HEAT TREATMENT FOR DIMENSIONAL STABILITY UNDER THERMAL CYCLING. -EVALUATION OF OXIDATION OF DIRECTLY POLISHED SUPER ALLOYS. -EVALUATION OF OXIDATION OF POLISHED ELECTROLESS NICKEL. HEAT TREATMENT AND PLATING PROCESSES WILL BE EVALUATED BY PRODUCING A NUMBER OF FLAT TEST MIRROR SAMPLES WHICH WILL BE MEASURED OPTICALLY BEFORE AND AFTER THE GLASS SLUMPING PROCESS TO EVALUATE CONTOUR DISTORTION, OXIDATION RESISTANCE, INCREASE IN SURFACE ROUGHNESS, AND DIAMOND MACHINEABILITY OF THE ELECTROLESS NICKEL PLATING. A TEST SLUMPING MANDREL WILL BE DESIGNED FOR FABRICATION IN A PHASE II SBIR.
Purchase Order - 541712 Research and Development in the Physical, Engineering, and Life Sciences (except Biotechnology)
Contractor
DALLAS OPTICAL SYSTEMS INC
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
02/23/2012
Obligated Amount
$124.2k
NNX12AE49A - ASTROPHYSICS, PARTICLE PHYSICS, AND PHOTON SCIENCE ARE ENTERING AN ERA WHERE THEY WILL HAVE COMMON ISSUES IN DATA ANALYSIS INVOLVING VERY COMPLEX AND VERY LARGE DATA SETS - PETABYTES IN SIZE- FOR WHICH MACHINE LEARNING, DATA MINING, AND OTHER AUTOMATED ANALYSIS TECLMIQUES WILL BE NEEDED TO EXPAND CURRENT KNOWLEDGE AS WELL AS TO UNCOVER NEW PHENOMENA. DEEP STATISTICAL ISSUES INVOLVING COMPUTATIONAL EFFICIENCY, ACCOUNTING FOR FALSE POSITIVES, AUTOMATED OBJECTIVE ANALYSIS PROCEDURES, LOOM HIGH ON THE LIST OF CHALLENGES IN THIS NEW ERA. FOR EXAMPLE, X-RAY FREE ELECTRON LASER (FEL) FACILITIES SUCH AS THE LINAC COHERENT LIGHT SOURCE PROVIDE A NEW SOURCE OF LARGE AND FERTILE DATA SETS ASSOCIATED WITH PHOTON SCIENCE. IN THE CASE OF SERIAL FEMTOSECOND XRAY DIFFRACTION EXPERIMENTS USED TO STUDY BIOLOGICAL SAMPLES, ENGINEERED NANOMATERIALS AND AEROSOLS ARE PRODUCING UP TO 10 TB OF DATA PER 12 HOUR SHIFT WITH LCLS OPERATED AT 120HZ. FUTURE X-RAY FEL FACILITIES SCHEDULED TO BE OPERATIONAL BEFORE 2020 HAVE PULSE DELIVERY RATES EXCEEDING 1 00 KHZ . EXTRAPOLATING EXISTING EXPERIMENTS TO FUTURE FACILITY CAPABILITIES INDICATE THAT DATA RATES OF>2 PETABYTES PER DAY CAN BE EXPECTED. THIS PATTERN OF VERY LARGE AND RICH DATA SETS IS REPEATED FOR THE LHC AND LSST, AND THERE ARE IMPORTANT PARALLELS THAT MIGHT BE EXPLOITED IN COMMON STATISTICAL APPROACHES TO DATA ANALYSIS. THIS CONFERENCE WILL BRING TOGETHER PRACTITIONERS FROM ASTROPHYSICS, PARTICLE PHYSICS AND PHOTON SCIENCE AS WELL AS STATISTICIANS TO PRESENT THE CURRENT STATE OF THE ART AND EXAMINE SOME OF THE OPTIONS FOR THE FUTURE. EXISTING DATA SETS WILL BE SHOWCASED, STATISTICS AND COMPUTER SCIENCE ISSUES IN MACHINE LEARNING AND DATA MINING EFFORTS WILL BE HIGHLIGHTED, AND ONLINE DATABASES FOR OPEN ACCESS TO DATA SETS WILL BE DISCUSSED.
Cooperative Agreement
Contractor
Stanford University (LELAND STANFORD JUNIOR UNIVERSITY, THE)
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
01/26/2012
Obligated Amount
$0k
NNX11AL18G - THE PRIMARY OBJECTIVE OF THE PROPOSED WORK IS TO CONDUCT ALGORITHM DEVELOPMENT TO INTRODUCE NEW MODIS AND SEAWIFS DATA PRODUCTS FOR COLORED DISSOLVED ORGANIC MATTER (CDOM) ABSORPTION COEFFICIENT (A_CDOM), CDOM SPECTRAL SLOPE (S_CDOM) AND DISSOLVED ORGANIC CARBON (DOC) THAT WILL YIELD NEW ALGORITHM THEORETICAL BASIS DOCUMENTS (ATBD) FOR PEER REVIEW. FOR THE A_CDOM AND S_CDOM ALGORITHMS, WE WILL EXPAND OUR EXISTING EMPIRICAL BAND RATIO ALGORITHMS VALIDATED FOR THE COASTAL OCEAN ALONG THE NORTHEASTERN U.S. (ET AL. 2008) TO THE GLOBAL OCEAN BY APPLYING OUR OWN DATASETS AND MEASUREMENTS FROM OTHER SOURCES SUCH AS THE NOMAD SEABASS DATABASE. WE WILL ALSO APPLY SEVERAL MACHINE LEARNING APPROACHES, INCLUDING NEURAL NETWORK ANALYSIS, SUPPORT VECTOR MACHINES AND GAUSSIAN PROCESS MODELS, TO DEVELOP GLOBAL ALGORITHMS FOR DOC, A_CDOM AND S_CDOM USING OUR DATASETS, THE HANSELL/CARLSON DOM DATA COLLECTION, THE NOMAD SEABASS DATABASE AND OTHER DATA SOURCES. THE MACHINE LEARNING APPROACHES WILL BE TRAINED USING CURRENTLY AVAILABLE FIELD DATA AND COINCIDENT MODIS AND SEAWIFS REMOTE SENSING REFLECTANCES (RRS) AND MODIS SEA-SURFACE TEMPERATURES. RIGOROUS QUANTITATIVE ASSESSMENT OF THE MACHINE LEARNING AND BAND-RATIO ALGORITHMS WILL BE APPLIED TO DETERMINE THE BEST PERFORMING ALGORITHMS FOR OUR STUDY. THE GLOBAL MODIS-AQUA, -TERRA AND SEAWIFS OCEAN COLOR TIME SERIES FOR EACH PRODUCT WILL BE COMPUTED AT MONTHLY INTERVALS FROM 1997-2012 TO EXAMINE REGIONAL AND GLOBAL LONG-TERM TRENDS IN DOC, A_CDOM, AND S_CDOM. CONDUCT ALGORITHM DEVELOPMENT AND EVALUATION (STATISTICAL ANALYSES, TAYLOR DIAGRAMS, ETC.) FOR MULTIPLE OCEAN COLOR SATELLITE PRODUCTS (DISSOLVED ORGANIC CARBON, CDOM ABSORPTION COEFFICIENT, CDOM SPECTRAL SLOPE) USING VARIOUS MACHINE LEARNING APPROACHES, INCLUDING NEURAL NETWORK ANALYSIS, SUPPORT VECTOR MACHINES AND GAUSSIAN PROCESS MODELS. IF THESE APPROACHES DO NOT YIELD GOOD RESULTS, THEN WE COULD HAVE YOU APPLY SOME OTHER APPROACHES (SEMI-ANALYTICAL APPROACHES BASED ON ITERATIVE ESTIMATION OF THE ABSORPTION AND BACKSCATTER CONSTITUENTS OF THE OCEAN). THE CODE FOR THE MACHINE LEARNING TECHNIQUE THAT YIELDS THE BEST RESULTS WOULD BE TRANSFERRED TO MY PROGRAMMER FOR SATELLITE DATA ANALYSIS OF MODIS AQUA AND SEAWIFS.
Grant for Research
Contractor
University of Texas System (UNIVERSITY OF TEXAS AT DALLAS)
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
07/21/2011
Obligated Amount
$233.6k
NNX11CF15P - WE DESIGN AND DEMONSTRATE THE FEASIBILITY OF EXTENDING THE OPEN SOURCE ECLIPSE INTEGRATED DEVELOPMENT ENVIRONMENT (IDE) TO SUPPORT THE FULL RANGE OF CAPABILITIES NOW AVAILABLE TO JAVA DEVELOPERS BUT FOR FORTRAN. WE HAVE EXPERIENCE IN THIS PROCESS FROM HAVING DONE ANALOGOUS SBIR WORK FOR THE ADA LANGUAGE WITH THE NAVY AND MISSILE DEFENSE AGENCY. AS WAS THE CASE FOR ADA, THERE IS AN EXISTING BUT INSUFFICIENT PLUG IN FOR FORTRAN NOW AVAILABLE FOR ECLIPSE, NAMELY PHOTRAN. WE WILL LEVERAGE HARMONIA'S PAST WORK ON RISE PLUG-IN FOR ADA, C, C++ TO CREATE PRODUCT THAT BUILDS ON ECLIPSE PHOTRAN AS WELL AS PFUNIT AND FUNIT. FACILITIES WE WILL IMPLEMENT INCLUDE ANNOTATING SOURCE CODE AS AN OVERLAY TO THE EXISTING CODE, SO THAT DEVELOPERS LEARNING ABOUT THE CODE ARE FREE TO MARK IT UP WITHOUT AFFECTING THE ORIGINAL CODE, AND CREATING VARIOUS VIEWS TRACING EXECUTION, SHOWING POTENTIAL CONCURRENCY, ETC. WE ALSO INTEGRATE UNIT TESTING, GRAPHICAL EDITING OF WORKFLOWS TO CREATE SCRIPTS THAT ARE USED WITH FORTRAN, SUPPORT THE ANALYSIS AND PORTING OF FORTRAN CODE TO DIFFERENT TARGET ARCHITECTURES, AND PROVIDE A WEB SERVICE LINK TO ACCOMMODATE CASES WHERE THE FORTRAN COMPILER RUNS ON A DIFFERENT MACHINE FROM THE IDE. OUR GOAL IS A 25% PRODUCTIVITY IMPROVEMENT.
Purchase Order - 541712 Research and Development in the Physical, Engineering, and Life Sciences (except Biotechnology)
Contractor
Harmonia (HARMONIA HOLDINGS GROUP LLC)
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
02/17/2011
Obligated Amount
$100k
NNX09CD71P - DATA INTENSIVE SCIENTIFIC COMPUTING ON PETABYTE SCALABLE INFRASTRUCTURE THE INFRASTRUCTURE AND PROGRAMMING PARADIGM FOR PETABYTE-LEVEL DATA PROCESSING PERFORMED AT COMPANIES LIKE GOOGLE AND YAHOO SHED SOME PROMISING LIGHTS ON THE DATA-INTENSIVE SCIENTIFIC COMPUTING. OPEN SOURCE SOFTWARE AND INEXPENSIVE COMMODITY HARDWARE MAKE PROPRIETARY TECHNOLOGIES WITHIN THE GRASP OF ACADEMIC COMMUNITIES. BY LEVERAGING THESE COMMERCIALLY PROVEN AND PUBLICLY AVAILABLE TECHNOLOGIES, WE ARE GOING TO DEVELOP A SUITE OF NOVEL DATA MANAGEMENT AND ANALYSIS LIBRARIES, AS AN EXTENSION TO EXISTING PRIMITIVE ALGORITHMS ORIGINALLY DESIGNED FOR WEB SEARCH. THESE LIBRARIES TAKE ADVANTAGE OF THE UNDERLYING PETABYTE-SCALABLE DATA INFRASTRUCTURE, PARALLELIZE COMPUTATION TRANSPARENTLY AND ALLOW SCIENTISTS AND FUTURE COMMERCIAL USERS TO PERFORM RATHER COMPLEX TASKS (DATA MINING, DATA VISUALIZATION AND MACHINE LEARNING) IN A DATA INTENSIVE ENVIRONMENT.
Purchase Order - 541712 Research and Development in the Physical, Engineering, and Life Sciences (except Biotechnology)
Contractor
OPEN RESEARCH
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
01/19/2009
Obligated Amount
$100k

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