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48,453 grants matching “machine learning”
Collaborative Research: SCH: Machine-Learning-Enhanced Computational Models of Cardiac Pathophysiology
$760,046Jonathan F Wenk · University Of Kentucky Research Foundation · · FY2024 · CSE
Molecular Modeling of the DR domain of an HIV restriction factor PSGL-1
$760,038Yuntao Wu · George Mason University · R56 · FY2024 · AI
Emotion Processing: Risk for Psychopathology in Children
$760,017Seth David Pollak · University Of Wisconsin-Madison · R01 · FY2025 · MH
** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** BLUE CRABS AND THEIR MEAT ARE AN ICONIC CULTURAL EXPORT FOR THE ATLANTIC AND GULF MEXICO COASTAL REGIONS. HOWEVER, PROCESSING ENOUGH MEAT TO SATISFY THE MARKET DEMAND IS CHALLENGING; EVEN ON THE LARGEST SCALES, COMMERCIAL MEAT IS STILL HAND-PICKED. THE PROCESS DEMANDS A HIGHLY SKILLED AND ATTENTIVE WORKER UTILIZING A KNIFE TO SEPARATE THE LEGS AND JOINTS, AS WELL AS BREAK THE INNER CARTILAGE TO REVEAL THE PRECIOUS AND FRAGILE LUMP MEAT INSIDE THE MEAT COMPARTMENTS. THE PROCESS IS ARDUOUS AND DANGEROUS; AND THE MOST SKILLED OF PICKERS CAN PROCESS AT MOST TWO CRABS PER MINUTE. ALTHOUGH VARIOUS AUTOMATED SYSTEMS HAVE BEEN ATTEMPTED FOR THE PAST 50 YEARS, NONE HAVE DECONSTRUCTED THE CRAB AND REMOVED THE MEAT WITHOUT DEGRADING THE MEAT QUALITY AND VALUE.UNDER THE SUPPORT OF NSF/USDA NRI 2.0 PROGRAM, WE HAVE ACHIEVED THE GOAL OF DEVELOPING A TWO-STAGE VISION-ROBOTIC SYSTEM EQUIPPED WITH HIGH-PRESSURE WATERJET KNIVES FOR AUTOMATED DISASSEMBLY OF COMPLEX CRAB COMPARTMENTS. AFTER THE CRAB HAS BEEN CUT INTO EASILY ACCESSIBLE SECTIONS, HUMANS CAN USE THEIR DIGITAL DEXTERITY TO GENTLY REMOVE THE MEAT FROM EACH COMPARTMENT. THIS IS A SEMI-AUTOMATED OPERATION.TO ADVANCE OUR RESULTS TO A FULLY AUTOMATED SYSTEM, TWO ADDITIONAL TASKS WILL BE MADE: CRAB MEAT EXTRACTION AND CRAB LOADING. IN THE CRABMEAT HARVESTING PROCESS, THE TOUGHEST HURDLE TO OVERCOME IS THE EXTRACTION OF THE COVETED LUMP MEAT. THIS TASK REQUIRES A SKILLFUL APPLICATION OF FORCE TO MANEUVER THROUGH THE COMPLEX EXO-SKELETON AND INNER STRUCTURES OF A COOKED CRAB. PRECISION CUTS MUST BE MADE ALONG COMPLEX CURVES TO OPEN ACCESS TO THE MEAT CHAMBERS TO PRESERVE MEAT WHOLESOMENESS AND MAXIMAL VALUES.THIS PROJECT IS TO ADVANCE THE MULTISTAGE ROBOTIC PROCESSES, MOVING BEYOND A SEMI-AUTOMATIC SYSTEM TO A FULLY AUTOMATED ONE. MACHINE-VISION GUIDED ROBOTICS, MACHINE DEEP LEARNING, AND INTEGRATED SYSTEM WILL BE CREATED TOWARD THE ON-LINE AUTOMATED SMART FOOD PROCESSING SYSTEM TECHNOLOGY. THE RESULT OF THIS PROJECT CANHAVEA BROADER IMPACT IN ROBOT-ASSISTED SCALABLE TECHNOLOGY TO MEET THE USDA MISSIONS THAT BENEFIT CONSUMERS AND COASTAL RURAL COMMUNITIES. IT WILL INCREASE EFFICIENCY AND PRODUCTIVITY WITH REDUCED RISK AND SUSTAINABLE FOOD AGRICULTURE.
$760,000University Of Maryland, College Park · · FY2023 · National Institute of Food and Agriculture
NRI: Collaborative Research: Experiential Learning for Robots: From Physics to Actions to Tasks
$760,000Dieter Fox · University Of Washington · · FY2016 · CSE
Collaborative Research: DMREF: High-Throughput Screening of Electrolytes for the Next Generation of Rechargeable Batteries
$760,000Tao Li · Northern Illinois University · · FY2023 · ENG
AeroSpec: An Adaptive Spectrum Framework for Autonomous Aerial Systems: Optimization, Decentralized Markets, and Deployment
$760,000Morteza Hashemi · University Of Kansas Center For Research Inc · · FY2025 · CSE
SBIR Phase II: Enabling Techologies for Energy-Centric Mobile App Design to Extend Mobile Device Battery Life
$759,998Abhilash Jindal · Mobile Enerlytics Llc · · FY2017 · TIP
SBIR Phase II: A Security, Privacy and Governance Policy Enforcement Framework for Big Data
$759,993Fahad Shaon · Data Security Technologies Llc · · FY2018 · TIP
Research Resource for Complex Physiologic Signals
$759,918Ary L Goldberger · Beth Israel Deaconess Medical Center · R01 · FY2020 · EB
NINDS Quantitative MRI Core Facility
$759,886Govind Nair · National Institute Of Neurological Disorders And Stroke · ZIC · FY2023 · NS
Development of tools for site-directed analysis of gene function
$759,853Jeffrey J Essner · Iowa State University · R24 · FY2016 · OD
Clinical applications of urine proteomics to lupus nephritis
$759,737Andrea Fava · Johns Hopkins University · R01 · FY2025 · DK
CDI -Type II: Collaborative Research: Bibliographic Knowledge Network
$759,656John B Conrey · American Institute Of Mathematics · · FY2008 · MPS
Harnessing smartphones for real-time detection of affective disturbance and future depression risk in adolescents
$759,581Christian Anthony Webb · Mclean Hospital · R01 · FY2025 · MH
Prospective sudden cardiac death risk stratification using CMR and echocardiography machine learning in mitral valve prolapse
$759,534Francesca N Delling · University Of California, San Francisco · R01 · FY2024 · HL
Neural mechanisms of risk and resilience in early childhood irritability
$759,490Jillian Lee Wiggins · San Diego State University · R01 · FY2020 · MH
Social-Affective Vulnerability to Suicidality among LGBTQ Young Adults: Proximal and Distal Factors
$759,447Erika E Forbes · University Of Pittsburgh At Pittsburgh · R01 · FY2024 · MH
Development and clinical interpretation of machine learning emergency department suicide prediction algorithms using electronic health records and claims
$759,440Steven C Marcus · University Of Pennsylvania · R01 · FY2024 · MH
Impact of early-life perturbations on pediatric microbiome maturation
$759,369Gautam Dantas · Washington University · R01 · FY2025 · AI
Multilingualism as a factor of resilience to Alzheimer's disease and related dementias in India
$759,363Jinkook Lee · University Of Southern California · R01 · FY2025 · AG
Optimized Affective Computing Measures of Social Processes and Negative Valence in Youth Psychopathology
$759,322John David Herrington · Children'S Hosp Of Philadelphia · R01 · FY2023 · MH
High-throughput Phenotyping of iPSC-derived Airway Epithelium by Multiscale Machine Learning Microscopy
$759,296Kwonmoo Lee · Boston Children'S Hospital · R01 · FY2024 · HL
Harnessing Diverse BioInformatic Approaches to Repurpose Drugs for Alzheimers Disease
$759,291Mark W Albers · Massachusetts General Hospital · R01 · FY2020 · AG
** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** THE CROP PROGRESS DATASET (SOWING DATE, EMERGENCE, CONDITION RATINGS, ETC.) IS A VERY IMPORTANT SOURCE FOR CROP MODELLING, YIELD FORECASTING, AND CROP GROWTH ASSESSMENT. CURRENTLY, CROP PROGRESS AND GROWTH CONDITION FOR DIFFERENT CROP TYPES IN THE USA ARE RELEASED WEEKLY AT STATE OR ADMINISTRATIVE DISTRICT LEVELS BY THE USDA (UNITED STATES DEPARTMENT OF AGRICULTURE) NASS (NATIONAL AGRICULTURAL STATISTICS SERVICE). THESE DATA ARE SUMMARIZED FROM MORE THAN 3,600 RESPONDENTS WHO MAKE VISUAL OBSERVATIONS AND CONTACT WITH FARMERS IN THEIR COUNTIES. HOWEVER, COLLECTING AND DISSEMINATING CROP DATA THROUGH FIELD SURVEYING AND REPORTING ARE TIME-CONSUMING, LABOR-INTENSIVE, HIGHLY SUBJECTIVE, AND ERROR PRONE. MOREOVER, THE REPORT AT A STATE OR ADMINISTRATIVE DISTRICT LEVEL IS TOO COARSE SPATIALLY FOR THE OPERATIONAL CROP MANAGEMENT AND YIELD ESTIMATION EFFORTS.SATELLITE-BASED MONITORING OF CROP PROGRESS AND CONDITION HAS SIGNIFICANT ADVANTAGES IN TERMS OF SPATIAL AND TEMPORAL RESOLUTIONS AND HAS RECEIVED TREMENDOUS ATTENTIONS OVER THE LAST THREE DECADES. IN CURRENTLY AVAILABLE SYSTEMS, HOWEVER, THE MAIN ISSUES ARE: (1) LACK OF SPATIAL DISTRIBUTION INFORMATION OF CROP TYPES WHEN IMPLEMENTING A NEAR REAL TIME (NRT) MONITORING, (2) COARSE SPATIAL RESOLUTION (>500 M) RESULTING IN GROWTH INFORMATION FROM THE MIXTURE OF MULTIPLE CROP TYPES OR CROP AND NATURAL VEGETATION, AND (3) INSUFFICIENT TIMELY AVAILABLE HIGH QUALITY SATELLITE OBSERVATIONS TO ACCURATELY TRACK CROP GROWTH DUE TO THE FREQUENT CLOUD CONTAMINATIONS. MOREOVER, ALTHOUGH CROP TYPE MAPPING IN A FIXED TIME DURING THE GROWING SEASONS HAS BEEN IMPROVED, THE ALGORITHMS ARE UNABLE TO BE APPLIED FOR OPERATIONALLY CLASSIFYING CROP TYPES DUE TO THE COMPLEXITY OF CLASSIFICATION MODELS AND THE LACK OF TIMELY AVAILABILITY OF CLOUD-FREE SATELLITE OBSERVATIONS.THIS PROPOSAL IS TO DEVELOP AN ENHANCED GEOSPATIAL TOOL FOR NRT MONITORING OF SPECIES-SPECIFIC CROP GROWTH USING THE FUSION OF MULTIPLE NEW GENERATION SATELLITE OBSERVATIONS. THE FUSION WILL BE PERFORMED BY EMPLOYING THE TIME SERIES OF LOW TEMPORAL BUT HIGH SPATIAL RESOLUTION DATA FROM THE NASA HARMONIZED LANDSAT-8/9 AND SENTINEL-2 (HLS) PRODUCT (3-DAY, 30-M) AND THE TEMPORAL SHAPE OF HIGH TEMPORAL (5-10 MIN) BUT LOW SPATIAL RESOLUTION (500 M) DATA FROM THE ADVANCED BASELINE IMAGER (ABI) ONBOARD GEOSTATIONARY OPERATIONAL ENVIRONMENTAL SATELLITE R SERIES (GOES-R). IMPLEMENTING THE TOOL OF THE NRT MONITORING WILL BE ABLE TO REPORT CROP PROGRESS AND CONDITION IN THE FIRST DAY OF THE GIVEN IMPLEMENTATION WEEK (A LATENCY LESS THAN ONE WEEK) AND PREDICT THE CROP GROWTH FOR THE FOLLOWING WEEK. TO DEVELOP THIS ENHANCED GEOSPATIAL TOOL, THE SPECIFIC OBJECTIVES WE PROPOSE ARE TO: 1) DEVELOP A NOVEL ALGORITHM FOR GENERATING SYNTHETIC TIME SERIES OF CROP GREENNESS BY FUSING HLS AND ABI TIME SERIES; 2) INVESTIGATE MACHINE LEARNING MODELS FOR CLASSIFYING CROP TYPES WEEKLY WITHIN SEASON FROM THE SYNTHETIC TIME SERIES; 3) EXPLORE THE DETECTION OF SPECIES-SPECIFIC CROP,PROGRESS AND GROWTH CONDITION WEEKLY FROM TIMELY AVAILABLE HLS AND ABI OBSERVATIONS AND CLIMATOLOGICAL TIME SERIES; AND 4) PREPARE COMPUTER CODES FOR THE GEOSPATIAL TOOL AND DELIVER TO USDA NASS STAKEHOLDER.THE EXPECTED RESULT FROM THIS PROJECT IS A GEOSPATIAL TOOL FOR NRT MONITORING OF SPECIES-SPECIFIC CROP PROGRESS AND CONDITION AT A CROP FIELD. IT WILL PRODUCE THE FOLLOWING OUTPUTS: 1) ALGORITHMS AND COMPUTER CODES (WRITTEN IN C) TO PROCESS TIME SERIES SATELLITE DATA FROM HLS AND ABI OBSERVATIONS; 2) ALGORITHMS AND COMPUTER CODES TO FUSE HLS AND ABI TIME SERIES; 3) ALGORITHMS AND COMPUTER CODES FOR CROP PHENOLOGY DETECTIONS; 4) MACHINE LEARNING MODELS FOR CLASSIFYING CROP TYPES WITHIN CROP GROWING SEASON; 5) ALGORITHMS AND COMPUTER CODES FOR DETERMINING CROP GROWTH CONDITION; 6) RESULTS AND VALIDATION DATASETS FROM PHENOCAM OBSERVATIONS, UNMANNED AERIAL VEHICLES (UAV) OBSERVATIONS, AND USDA NASS CROP PROGRESS; 7) WEEKLY CROP PROGRESS AND GROWTH CONDITION AT A 30M FIELD FROM 2022-2026 ACROSS NINE STATES OF MIDWESTERN US; 8) SIX PEER-REVIEWED PAPERS TO BE PUBLISHED AT HIGH RANKED JOURNALS AND ~6 CONFERENCE PAPERS TO BE PRESENTED AT INTERNATIONAL CONFERENCES; 9) ONE PHD STUDENT AND ONE-POSTDOC TO BE TRAINED, AND 10) A GEOSPATIAL TOOL TO BE DELIVERED TO USDA NASS STAKEHOLDERS.THE OUTPUTS FROM THIS PROJECT WILL HAVE A BROAD IMPACT. THE PROPOSED NEW GEOSPATIAL TOOL IS ABLE TO PROVIDE NRT MONITORING OF CROP GROWTH CONDITION AT 30-M FIELD SCALES IN A WEEKLY BASIS. THE TIMELY MONITORING OF CROP GROWTH IN AN OPERATIONAL WAY WILL SERVE PRECISION AGRICULTURAL MANAGEMENT AND SUPPORT AGRICULTURAL FINANCE, SUCH AS FOR FARMERS TO MAKE DECISIONS OF MANAGEMENT PRACTICES AND INSURANCE COMPANIES AND POLICYMAKERS TO AVOID HUMAN AND LIVESTOCK FAMINE. WITH THE DELIVERY TO THE USDA NASS STAKEHOLDER FOR IMPLEMENTATION OF THIS TOOL, THE RESULT IS EXPECTED TO SIGNIFICANTLY IMPROVE NASS'S CROP PROGRESS REPORTS FOR SUPPORTING PRECISION CROP MANAGEMENT AND FINANCE AND INSURANCE DECISION MAKING. MOREOVER, THIS RESEARCH OUTPUT WILL IMPROVE OUR UNDERSTANDING OF SATELLITE CAPABILITY TO MONITOR CROP PROGRESS AND CONDITION AT THE FIELD SCALE. WITH THE PUBLICATIONS IN PEER-REVIEWED JOURNALS AND PRESENTATIONS AT INTERNAL CONFERENCES, THE RESULT WILL DELIVER THE SCIENCE-BASED KNOWLEDGE TO BROAD AUDIENCES.
$759,272South Dakota State University · · FY2023 · National Institute of Food and Agriculture