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  • Category: Applied Computing

    Article by Sidike Paheding in Elsevier’s Remote Sensing of Environment


    An article by Dr. Sidike Paheding, Applied Computing, has been accepted for publication in the Elsevier journal, Remote Sensing of Environment, a top journal with an impact factor of 9.085. The journal is ranked #1 in the field of remote sensing, according to Google Scholar.

    The paper, “Estimation of root zone soil moisture from ground and remotely sensed soil information with multisensor data fusion and automated machine learning,” will be published in Volume 260, July 2021 of the journal. Read and download the article here.

    Highlights

    • A machine learning approach to estimation of root zone soil moisture is introduced.
    • Remotely sensed optical reflectance is fused with physical soil properties.
    • The machine learning models well capture in situ measured root zone soil moisture.
    • Model estimates improve when measured near-surface soil moisture is used as input.

    Paheding’s co-authors are:

    • Ebrahim Babaeian, Assistant Research Professor, Environmental Science, University of Arizona, Tucson
    • Vijay K. Devabhaktuni, Professor of Electrical Engineering, Department Chair, Purdue University Northwest, Hammond, IN
    • Nahian Siddique, Graduate Student, Purdue University Northwest
    • Markus Tuller, Professor, Environmental Science, University of Arizona

    Abstract

    Root zone soil moisture (RZSM) estimation and monitoring based on high spatial resolution remote sensing information such as obtained with an Unmanned Aerial System (UAS) is of significant interest for field-scale precision irrigation management, particularly in water-limited regions of the world. To date, there is no accurate and widely accepted model that relies on UAS optical surface reflectance observations for RZSM estimation at high spatial resolution. This study is aimed at the development of a new approach for RZSM estimation based on the fusion of high spatial resolution optical reflectance UAS observations with physical and hydraulic soil information integrated into Automated Machine Learning (AutoML). The H2O AutoML platform includes a number of advanced machine learning algorithms that efficiently perform feature selection and automatically identify complex relationships between inputs and outputs. Twelve models combining UAS optical observations with various soil properties were developed in a hierarchical manner and fed into AutoML to estimate surface, near-surface, and root zone soil moisture. The addition of independently measured surface and near-surface soil moisture information to the hierarchical models to improve RZSM estimation was investigated. The accuracy of soil moisture estimates was evaluated based on a comparison with Time Domain Reflectometry (TDR) sensors that were deployed to monitor surface, near-surface and root zone soil moisture dynamics. The obtained results indicate that the consideration of physical and hydraulic soil properties together with UAS optical observations improves soil moisture estimation, especially for the root zone with a RMSE of about 0.04 cm3 cm−3. Accurate RZSM estimates were obtained when measured surface and near-surface soil moisture data was added to the hierarchical models, yielding RMSE values below 0.02 cm3 cm−3 and R and NSE values above 0.90. The generated high spatial resolution RZSM maps clearly capture the spatial variability of soil moisture at the field scale. The presented framework can aid farm scale precision irrigation management via improving the crop water use efficiency and reducing the risk of groundwater contamination.


    Remote Sensing of Environment (RSE) serves the Earth observation community with the publication of results on the theory, science, applications, and technology of remote sensing studies. Thoroughly interdisciplinary, RSE publishes on terrestrial, oceanic and atmospheric sensing. The emphasis of the journal is on biophysical and quantitative approaches to remote sensing at local to global scales.


    GenCyber Teacher Camp Is July 19-23, 2021


    An NSA/NSF GenCyber Cybersecurity Teacher Camp for K-12 teachers will take place at Michigan Tech the week of July 19 – 23, 2021. The residential camp is offered at no cost to all participants.

    Topics include fundamental security knowledge, cyber hygiene, and other topics such as email phishing, password management, and cyber ethics. Participants will also learn how to develop lesson plans to teach cybersecurity in K-12.

    Room and board are included. Each teacher participant will receive a stipend of $500 for attending and completing camp activities. Commuting is also possible. Camp activities will count for 25 State Continuing Education Clock Hours (SCECH).

    Find complete details and apply here.  The application deadline is May 1, 2021.

    Funding of the camp is provided jointly by the National Security Agency (NSA) and the National Science Foundation (NSF) through a grant award led by Professor Yu Cai and Tim Van Wagner, both from the College of Computing Department of Applied Computing.

    Watch a video from the 2019 GenCyber Teacher Camp below.

    Gencyber Teacher Camp @ Michigan Tech 2019


    Assistants, Helpers Needed for Cybersecurity Teacher Camp, July 19-23


    Dr. Yu Cai, Applied Computing, is seeking motivated students to help with this summer’s GenCyber Teacher Camp, which takes place on campus July 19-23, 2021.

    1. Twenty K-12 teachers attending the camp.
    2. Students will work as teaching assistants and camp helpers. They will set up the lab, help during hands-on activities and games, manage the website, and help the assessment. Students will be paid for 3 weeks of work during July.
    3. Contact Dr. Yu Cai (cai@mtu.edu) for details and to apply.

    Weihua Zhou, CC, to Present Lecture April 8

    by Mechanical Engineering-Engineering Mechanics

    The net virtual graduate Seminar Speaker will be held at 4 p.m. tomorrow (April 8) via Zoom.

    Weihua Zhou (CC) will present “Artificial intelligence for medical image analysis: our approaches. “

    Zhou, is an assistant professor of applied computing at Michigan Tech. He has been doing research on medical imaging and informatics since 2008. Attend virtually.

    View the University Events Calendar, which includes a registration link and additional information about Dr. Zhou and his research.


    Our Stories: Dr. Nathir Rawashdeh

    This is part of a series of short introductions about College students, faculty, and staff. Would you like to be featured? Send a photo and some background info about yourself to computing@mtu.edu.

    Dr. Nathir Rawashdeh, Assistant Professor, Applied Computing

    • Affiliated Assistant Professor, Dept. of Electrical and Computer Engineering
    • Years teaching at Michigan Tech: 2
    • Years teaching overall: 12
    • Member, Data Sciences research group, Institute of Computing and Cybersystems (ICC)
    • Ph.D., Electrical Engineering, University of Kentucky, 2007
    • MS, Electrical and Computer Engineering, University of Massachusetts, Amherst, 2003
    • Faculty Profile

    Classes Dr. Rawashdeh Teaches

    • Programmable Logic Control (PLC)
    • Digital Electronics
    • Analog Electronics
    • Image Processing
    • Automatic Control Systems
    • Instrumentation and Measurement

    The “coolest” class you teach, and why:

    Programmable Logic Controllers (PLCs), because every factory in the world is controlled by PLCs.

    The importance of your class topics to the overall understanding of Computing and your discipline: 

    Computing is the way of the future. And in all disciplines we rely more and more on sophisticated design, modeling, and control software. The Digital Electronics course is key to the overall understanding of computer systems. We discuss the building blocks of computers, and programmable logic controllers apply computing solutions for automation programming and industrial communication.

    Your teaching philosophy: 

    • I believe in the social connection between teacher and student because it enables them to learn from each other, and more than just technical material and information.
    • In today’s changing world, courses and delivery methods must be constantly updated to maximize learning in a wide sense. When teaching online, I always turn on my camera and teach from the classroom.
    • I interact actively with students, and when I see that they need a break I tell them a story from my professional or personal experience. In the labs, I am almost always engaged with students, helping them solve problems.

    Labs you direct and their general focus:

    • In the Programmable Logic Controllers labs (for introductory and advanced level courses), students learn how to program industrial controllers and interface with sensors and actuators.
    • In the Digital Electrics lab, students learn the building blocks of computers and program FPGA boards, which is the fastest programmable hardware possible.

    Research projects in which students are assisting: 

    • An ECE PhD student is working on sensor fusion for autonomous driving in the snow.
    • I plan to hire a graduate student this summer to implement indoor simultaneous location and mapping of a mobile robot.
    • Recently, an undergraduate EET student helped me build a virus sterilizing mobile robot that uses ultraviolet light. Read a news article, view photos and a YouTube video here.
    • In personal research, I also work on image analysis and industrial inspection research.

    Other cool things your students are doing:

    • Recent senior design projects include a gesture controlled robotic arm and a PID control system based on a levitating ball.
    • See more projects on my lab website: https://www.morolab.mtu.edu/students.

    Interests beyond teaching and research:

    • I am married and have four children. The eldest is studying Environmental Engineering at Tech.
    • I like cars and ground robots, painting, swimming, and playing soccer.
    • I speak three languages and have lived in four countries, in each for over a decade.

    1010 with … Dr. Alex Sergeyev, Applied Computing


    Are you a high school student, current undergraduate student, or a recent BS graduate? Are you are interested in robotics, automation, and controls?

    You are invited to spend one-zero-one-zero—that is, ten—minutes with Dr. Aleksandr Sergeyev on Thursday, April 15, from 4:30 to 4:40 p.m. EST.

    “If you’d like to learn more about the Mechatronics and the BS and MS programs at Michigan Tech, please join this 1010 conversation,” Professor Sergeyev urges.

    Join the Zoom meeting here.


    Dr. Sergeyev is a professor in the Applied Computing department and director of the Mechatronics graduate program. He also directs the FANUC Certified Industrial Robotics Training Center at Michigan Tech.

    He will discuss his research, the Applied Computing department, and the Mechatronics BS and MS programs. He will answer questions following his presentation.

    Michigan Tech is a pioneer in Mechatronics education, having introduced a graduate degree program in 20xx, and a bachelor’s program in Fall 2019.

    “Mechatronics is an industry buzzword synonymous with robotics, controls, automation, and electromechanical engineering,” Sergeyev says.

    In his presentation, he will discuss Mechatronics in general, explain what the degree has to offer, job opportunities in Mechatronics, and some of the research he is conducting in this field.

    In Spring 2021, a Mechatronics Playground was opened on campus. The hands-on learning lab and industry-grade equipment was funded by alumnus Mark Gauthier of Donald Engineering, Grand Rapids, MI, and other major companies.

    A common degree in Europe, China, Japan, Russia, and India, advanced study in Mechatronics is an underdeveloped academic discipline in the United States, even though the industrial demand for these professionals is enormous, and continues to grow.

    Sergeyev’s areas of expertise are in electrical and computer engineering, physics, and adaptive optics, and his professional interests include robotics. He is principal investigator for research grants totaling more that $1 million. He received both his MS and PhD degrees at Michigan Tech, in physics and electrical and computer engineering, respectively.

    We look forward to spending 1010 minutes with you!

    A Mechatronics student operates a robotics arm.