Sort
48,453 grants matching “machine learning”
CCF: Medium: Learning From Classical and Quantum Data: a Fourier Perspective
$1,199,996Wojciech Szpankowski · Purdue University · · FY2022 · CSE
RI: Medium: Learning to Search: Provable Guarantees and Applications
$1,199,995Maria-Florina Balcan · Carnegie Mellon University · · FY2019 · CSE
SCH: In-Vivo Real-Time Assessment of Thrombus in Stroke Treatment via Fiber Raman Spectroscopy and Multi-Physics Neural-Operator Learning
$1,199,991Yihao Zheng · Worcester Polytechnic Institute · · FY2024 · CSE
SCC-IRG: TREE-CARE: Treefall Risk Evaluation and Empowerment for Community Assessment and Resilience Enhancement
$1,199,990Aikaterini P Kyprioti · University Of Oklahoma Norman Campus · · FY2025 · CSE
CIRCLE: Catalyzing and Integrating Research, Collaboration, and Learning in Computing, Mathematics, and their Applications
$1,199,934David M Pennock · Rutgers University New Brunswick · · FY2014 · CSE
PHILMS: COLLABORATORY ON MATHEMATICS AND PHYSICS INFORMED LEARNING MACHINES FOR MULTISCALE AND MULTIPHYSICS PROBLEMS
$1,199,932University Of California, Santa Barbara · · FY2018 · Department of Energy
SCH: MS-ADAPT: Multi-Sensor Adaptive Data Analytics for Physical Therapy
$1,199,930Emilia Farcas · University Of California-San Diego · · FY2022 · CSE
SBIR Phase II: An artificial intelligence system for autonomous numerical control programming for advanced manufacturing
$1,199,927Tanmay Aggarwal · Lambda Function, Inc. · · FY2023 · TIP
SitS: Spatial and Temporal Patterns of Soil N and P Cycles Quantified by a Sensor-Model Fusion Framework: Implications for Sustainable Nutrient Management
$1,199,919Licheng Liu · University Of Minnesota-Twin Cities · · FY2021 · ENG
RI: Medium: Assessment of Machine Learning Algorithms in the Wild
$1,199,898Padhraic Smyth · University Of California-Irvine · · FY2019 · CSE
National Resource for Network Biology (NRNB)
$1,199,890Trey Ideker · University Of California, San Diego · P41 · FY2023 · GM
PHILMS: COLLABORATORY ON MATHEMATICS AND PHYSICS INFORMED LEARNING MACHINES FOR MULTISCALE AND MULTIPHYSICS PROBLEMS
$1,199,862Brown University · · FY2018 · Department of Energy
SHF: Medium: Training Sparse Neural Networks with Co-Designed Hardware Accelerators: Enabling Model Optimization and Scientific Exploration
$1,199,849Keith M Chugg · University Of Southern California · · FY2018 · CSE
SCH: Neonatal Facial Coding for Pain Recognition Monitoring System (PRAMS)
$1,199,832Renee C Manworren · Ann & Robert H. Lurie Children'S Hospital Of Chicago · · FY2023 · CSE
III: Medium: Hardware/Software Accelerated Data Mining for Real-Time Monitoring of Streaming Pediatric ICU Data
$1,199,822Eamonn Keogh · University Of California-Riverside · · FY2012 · CSE
SBIR Phase II: Radar-based Building Automation
$1,199,809Mustafa Homsi · Rivieh, Inc. · · FY2024 · TIP
RI: HCC: Medium: Equity, Justice, and Incentives in Societal Resource Allocation
$1,199,808Sanmay Das · George Mason University · · FY2024 · CSE
National Resource for Network Biology (NRNB)
$1,199,754Trey Ideker · University Of California, San Diego · P41 · FY2024 · GM
SCH: INT: Distributed Analytics for Enhancing Fertility in Families
$1,199,750Ioannis Paschalidis · Trustees Of Boston University · · FY2019 · CSE
Strategies: Water SCIENCE: Supporting Collaborative Inquiry, Engineering, and Career Exploration with Water
$1,199,608Carolyn J Staudt · Concord Consortium · · FY2014 · EDU
FISH FARMING IS AN IMPORTANT AGRICULTURE SECTOR AND PLAYS A CRITICAL ROLE IN SECURING FOOD SAFETY IN THE UNITED STATES AND AROUND THE WORLD. HOWEVER, FOR FISH FARMING TO TAKE OFF, DRASTIC IMPROVEMENTS ARE REQUIRED TO CURRENT LABOR-INTENSIVE AND RESOURCE-INEFFICIENT OPERATIONS.THE MAIN OBJECTIVE OF THE PROPOSED PROJECT IS TO DESIGN, DEVELOP AND FIELD-TEST THE HYBRID AERIAL/UNDERWATER ROBOTIC SYSTEM (HAUCS) FOR AQUACULTURE FISH FARM WATER QUALITY MONITORING. HAUCS WILL BE CAPABLE OF COLLABORATIVE MONITORING AND DECISION-MAKING ON FARMS OF VARYING SCALES. HAUCS WILL CONDUCT AUTOMATED SAMPLING AT FREQUENCIES RELEVANT FOR ACCURATE PREDICTION OF WATER QUALITY VARIATION (E.G., HOURLY DIEL READINGS), PROVIDING SIGNIFICANT ADVANTAGES IN SPEED, COST, RESOURCE EFFICIENCY, ADAPTABILITY, AND CONTROLLABILITY OVER THE TRADITIONAL MANUAL AND TRUCK-MOUNTED WATER QUALITY MEASUREMENT SYSTEMS ON THE FISH FARMS. HAUCS HAS THE POTENTIAL TO BRING TRANSFORMATIVE CHANGES TO HOW THE AQUACULTURE FISH FARMS OPERATE.HAUCS IS AN END-TO-END FRAMEWORK CONSISTS OF THREE ESSENTIAL SUBSYSTEMS: A TEAM OF COLLABORATIVE AMPHIBIOUS ROBOTIC SENSING PLATFORMS INTEGRATED WITH UNDERWATER SENSORS; A LAND-BASED HOME STATION THAT CAN PROVIDE AUTOMATED CHARGING AND SENSOR CLEANING; AND THE BACKEND PROCESSING CENTER CONSISTS OF A MACHINE LEARNING BASED WATER QUALITY PREDICTION MODEL AND THE FARM CONTROL CENTER.EACH HAUCS PLATFORM COVERS A SUBSET OF PONDS, AUTOMATICALLY ACQUIRES SENSOR DATA IN EACH POND AT A REGULAR INTERVAL. THE AMPHIBIOUS DESIGN ENABLES THE PLATFORM TO MOVE OVER THE LEVEE SEPARATING THE PONDS AND TO BETTER COPE WITH THE SEVERE WEATHER SUCH AS HIGH WIND. IN COMBINATION WITH THE AUTOMATIC CLEANING AT THE LAND-BASED HOME STATION, THE RISK OF SENSOR BIOFOULING WILL BE AVOIDED. THE BRAIN IN THE BACKEND PROCESSING CENTER PROVIDES SEVERAL-STEPS-AHEAD PREDICTION OF THE POND WATER QUALITY AND CAN MITIGATE A POND IN DISTRESS SUCH AS PREDICTED DISSOLVED OXYGEN DEPLETION, EITHER AUTOMATICALLY OR IN CLOSE COLLABORATION WITH THE HUMAN SITE MANAGERS AND OPERATORS.THE PROPOSED HAUCS FRAMEWORK IS A DISRUPTIVE TECHNOLOGY THAT HAS THE POTENTIAL TO MARKEDLY INCREASE ADOPTION OF ROBOTIC TECHNOLOGY IN THE FIELD OF AQUACULTURE FARMING, A SECTOR OF AGRICULTURE THAT HAS SEEN MINIMAL ROBOTICS DEVELOPMENT. WHILE THE PROJECT AIMS AT OVERCOMING ONE CRITICAL FACTOR PLAGUING THE AQUACULTURE FARMING - THE HIGH-COST AND UNRELIABILITY OF THE WATER QUALITY CONTROLS (IN PARTICULAR, DISSOLVED OXYGEN DEPLETION), THE UNDERLYING METHODOLOGY OF BUILDING AN INTERNET OF THINGS FRAMEWORK ON THE AQUACULTURE FARM CAN BE ENHANCED TO HANDED OTHER TASKS ON THE AQUACULTURE FARMS. THIS TECHNOLOGY, THEREFORE, HAS SIGNIFICANT SOCIAL, ENVIRONMENTAL, AND ECONOMIC BENEFITS AND CAN FUNDAMENTALLY TRANSFORM HOW AQUACULTURE FARMING IS CONDUCTED IN THE UNITED STATES AND AROUND THE WORLD.A MAJOR REASON FOR THE LOW ADOPTION OF ROBOTIC TECHNOLOGY IN AQUACULTURE FISH FARMING IS THE LACK OF TECHNOLOGY AWARENESS AMONG THE FISH FARMERS. ONE OUTCOME OF THIS PROJECT CAN BE THE ESTABLISHMENT OF AN EXEMPLARY HAUCS AQUACULTURE FISH FARM AT HBOI/FAU AS A DEMONSTRATION SITE, THROUGH THE COLLABORATION BETWEEN THE RESEARCH TEAM AND USDA. SUCH SITE WILL BE HIGHLY BENEFICIAL IN EDUCATING THE FARMERS WITH THE GOAL TO SIGNIFICANTLY IMPROVE THE PENETRATION OF THE ROBOTIC TECHNOLOGY IN THE FISH FARMING INDUSTRY. THIS PROJECT ALSO ADDRESSES THE BROADER IMPACTS OF IMPROVING UNDERGRADUATE STEM EDUCATION, INCREASING PARTICIPATION OF UNDER-REPRESENTED STEM POPULATIONS, AND PROMOTING PUBLIC SCIENTIFIC LITERACY. INTEGRATING RESEARCH, EDUCATION, AND COMMUNITY OUTREACH, INQUIRY-BASED AND SERVICELEARNING COMPONENTS ARE PROPOSED AS EVIDENCED-BASED PRACTICES TO PROMOTE STUDENT OUTCOMES AND WORKPLACE SKILLS. EVALUATION COMPONENTS INCLUDE A KNOWLEDGE INVENTORY FOR MIDDLE-SCHOOL STUDENTS AND MEASURES OF UNDERGRADUATE ENGAGEMENT.
$1,199,362Florida Atlantic University · · FY2019 · National Institute of Food and Agriculture
INSPIRE: Computer Learning of Dynamical Systems to Investigate Cognitive and Motivational Effects of Social Media Use on Political Participation
$1,199,319John T Jost · New York University · · FY2012 · SBE
CCF: Medium: Inference with dynamic deep probabilistic models
$1,199,286Petar M Djuric · Suny At Stony Brook · · FY2022 · CSE
CPS: Medium: Safety-Critical Cyber-Physical Systems: From Validation & Verification to Test & Evaluation
$1,199,209Aaron D Ames · California Institute Of Technology · · FY2019 · CSE
CNS Core: Medium:Model-driven Resource Management for Avoiding Performance Pitfalls in Edge Computing
$1,199,134Prashant Shenoy · University Of Massachusetts Amherst · · FY2022 · CSE