Agriculture relies heavily on irrigation, but this essential practice consumes a significant portion of our increasingly scarce freshwater resources. Climate change and urban expansion exacerbate this challenge, making efficient irrigation management a critical factor in global sustainability. Additionally, excessive irrigation harms the environment by polluting waterways, depleting water sources, and salinizing soil. This webinar presents a groundbreaking data-driven intelligent irrigation scheduling model that optimizes water use efficiency and promotes agricultural sustainability. By leveraging readily available soil moisture and evapotranspiration data from the High-Resolution Land Data Assimilation System (HRLDAS), the model provides precise irrigation recommendations tailored to specific field conditions. Key benefits of the decision model include: - 20-40% water savings: Achieve significant water conservation while ensuring optimal crop growth. - Increased crop yield: Maximize production by providing the right amount of water at the right time. - Reduced environmental impact: Minimize water pollution and soil salinization for a healthier environment. - Cost-effective and easy to implement: No expensive sensors or data subscriptions required, making it accessible to all farmers. During this webinar, you will learn the decision support system in depth: - How the data-driven model works and its key features - The benefits of ET-Water Balance and soil-moisture based irrigation scheduling methods. - How deep reinforcement learning further optimizes irrigation decisions. - Real-world examples and case studies showcasing the model's effectiveness. - How to access and utilize the model through the free WaterSmart Data Information Portal. Speaker(s): Haoteng Zhao, Virtual: https://events.vtools.ieee.org/m/389291

The proliferation of edge devices and the gigantic amount of data they generate are distributed everywhere. Such distributed data fuel the intelligence at the edge where data reside. In this talk, I will present our research on how to enable intelligence on large-scale edge devices by leveraging the power of federated learning. In particular, I present our recent works on jointly optimizing the communication and computation cost of federated learning, personalization of federated learning, and automated hyperparameter optimization in federated learning. Additionally, I will also discuss the challenges and opportunities of federated learning in the era of large Al models. Room: 4105, Bldg: Brendan Iribe Center for Computer Science and Engineering, 8125 Paint Branch Dr, College Park, Maryland, United States, 20740, Virtual: https://events.vtools.ieee.org/m/391114

The proliferation of edge devices and the gigantic amount of data they generate are distributed everywhere. Such distributed data fuel the intelligence at the edge where data reside. In this talk, I will present our research on how to enable intelligence on large-scale edge devices by leveraging the power of federated learning. In particular, I present our recent works on jointly optimizing the communication and computation cost of federated learning, personalization of federated learning, and automated hyperparameter optimization in federated learning. Additionally, I will also discuss the challenges and opportunities of federated learning in the era of large Al models. Room: 4105, Bldg: Brendan Iribe Center for Computer Science and Engineering, 8125 Paint Branch Dr, College Park, Maryland, United States, 20740, Virtual: https://events.vtools.ieee.org/m/391114

The IEEE-Robotics & Automation Society (RAS) Jt. Washington D.C. and Northern Virginia Chapter cordially invite you to attend a Distinguish Lecture (DL) seminar “Robot-assistance Guidance Regulation”. IEEE accredits this presentation for continuing education/professional development. Attendees are eligible to receive Continuing Education Units /Professional Development Hours (CEU/PDH) by attending the full duration of the presentation, completing, and submitting forms at the close of the program. Speaker(s): , Yi Guo Agenda: 15:00 – 15:05 Welcome remarks, RAS Chapter Chair 15:05 – 16:00 DL Speaker, Yi Guo, Ph.D “Robot Assistance Guidance Regulation” 16:00 – 16:10 Questions and Answers 16:10 – Closing Remarks / Adjourn Virtual: https://events.vtools.ieee.org/m/388002

The IEEE-Robotics & Automation Society (RAS) Jt. Washington D.C. and Northern Virginia Chapter cordially invite you to attend a Distinguish Lecture (DL) seminar “Robot-assistance Guidance Regulation”. Attendees are eligible to receive Continuing Education Units /Professional Development Hours (CEU/PDH) by attending the full duration of the presentation, completing, and submitting forms at the close of the program. Speaker(s): , Yi Guo Agenda: 15:00 – 15:05 Welcome remarks, RAS Chapter Chair 15:05 – 16:00 DL Speaker, Yi Guo, Ph.D “Robot Assistance Guidance Regulation” 16:00 – 16:10 Questions and Answers 16:10 – Closing Remarks / Adjourn Virtual: https://events.vtools.ieee.org/m/388168

Come have a talk with Dr. Tom Simpson on phase slips in locked laser systems 4459 Cedar Park Dr,, Dayton, Ohio, United States, 45440

Advancing Large-area Crop Mapping with Satellite Data Satellite remote sensing is transforming global agricultural monitoring. Crop maps from satellite data are essential for crop monitoring systems and crop modeling projects. Generating high-resolution crop maps is a research area of significant potential. The Landsat and Sentinel series of satellites, with 10-30 m spatial resolution, sub-weekly revisit frequency, free data policy, and standardized pre-processing, represent the best available datasets for crop mapping over large areas. This talk presents a multi-scale mapping approach with satellite Analysis Ready Data (ARD) generation, machine learning, probability sampling and in situ data collection as critical components. The approach can simultaneously produce crop area estimates with uncertainty estimates and crop classification maps with validation accuracy. Crop mapping results at national and continental scales over the United States, Canada, China and South America are illustrated. Speaker(s): Xiaopeng Song, Virtual: https://events.vtools.ieee.org/m/391822

Advancing Large-area Crop Mapping with Satellite Data Satellite remote sensing is transforming global agricultural monitoring. Crop maps from satellite data are essential for crop monitoring systems and crop modeling projects. Generating high-resolution crop maps is a research area of significant potential. The Landsat and Sentinel series of satellites, with 10-30 m spatial resolution, sub-weekly revisit frequency, free data policy, and standardized pre-processing, represent the best available datasets for crop mapping over large areas. This talk presents a multi-scale mapping approach with satellite Analysis Ready Data (ARD) generation, machine learning, probability sampling and in situ data collection as critical components. The approach can simultaneously produce crop area estimates with uncertainty estimates and crop classification maps with validation accuracy. Crop mapping results at national and continental scales over the United States, Canada, China and South America are illustrated. Speaker(s): Xiaopeng Song, Virtual: https://events.vtools.ieee.org/m/391822