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    Grad Students Take 6th Place in Navy’s AI Tracks at Sea Challenge

    by Karen S. Johnson, Communications Director, College of Computing


    The Challenge

    Four Michigan Tech graduate students recently took 6th place in the U.S. Navy’s Artificial Intelligence (AI) Tracks at Sea Challenge, receiving a $6,000 prize.

    The Challenge solicited software solutions to automatically generate georeferenced tracks of maritime vessel traffic based on data recorded from a single electro-optical camera imaging the traffic from a moving platform.

    Each Challenge team was presented with a dataset of recorded camera imagery of vessel traffic, along with the recorded GPS track of a vessel of interest that is seen in the imagery.

    Graduate students involved in the challenge were Zach DeKraker and Nicholas Hamilton, both Computer Science majors advised by Tim Havens; Evan Lucas, Electrical Engineering, advised by Zhaohui Wang; and Steven Whitaker, Electrical Engineering.

    Submitted solutions were evaluated against additional camera data not included in the competition testing set in order to verify generalization of the solutions. Judging was based on track accuracy (70%) and overall processing time (30%).

    “We never got our final score, but we were the “first runner up” team,” says Lucas. “Based on our testing before sending it, we think it worked well most of the time and occasionally tracked a seagull or the wrong boat.”

    The total $200,000 prize was distributed among five winning teams, which submitted full working solutions, and three runners-up, which submitted partial working solutions.

    The Challenge was sponsored by the Naval Information Warfare Center (NIWC) Pacific and the Naval Science, Technology, Engineering, and Mathematics (STEM) Coordination Office, and managed by the Office of Naval Research. Its goal was to engage with the workforce of tomorrow on challenging and relevant naval problems, with the immediate need to augment unmanned surface vehicles’ (USVs’) maritime contact tracking capability.

    The Problem

    “The problem presented was to find a particular boat in a video taken of a harbor, and track its GPS coordinates.,” says Zach DeKraker. “We were provided with samples of other videos along with the target boat’s GPS coordinates for that video, which we were able to use to come up with a mapping from pixels to GPS coordinates.”

    “Basically, we wanted to track boats with a video camera,” adds ECE graduate student Steven Whitaker. “Our team used machine learning and computer vision to do this. At weekly meetings we brainstormed approaches to tackling the problem, and at regular work sessions, together we programmed it all and produced a white paper with the technical details.”

    Whitaker says the competition tied in pretty closely to work the students have already done. “We had a good majority of the code already written. We just needed to fit everything together and add in a few more details and specialize it for the AI Tracks at Sea research,” he explains.

    Competitions like this one often connect directly or indirectly with a student’s academic and career goals.

    “It’s good to not be pigeon-holed, and to use our knowledge in a different scenario,” Steven Whitaker says of these opportunities. “This helps us remember that there are other things in the world other than our small section of research.”

    Dividing Responsibilities

    The team knew that there were two primary issues at hand. First, how can the pixel coordinates be translated into GPS coordinates? And second, how can the boat be located so that GPS pixel coordinates can be determined?

    “Once we broke it down into these two subproblems, it became pretty clear how to solve each half,” DeKraker says. “Steven had already done a significant amount of work mapping pixel coordinates into GPS coordinates, so we had a pretty quick answer to subproblem one.”

    AI Tracks at Sea Flowchart

    The team met weekly to discuss their ideas for the project and compare and contrast how effective they would be as solutions to the problem at hand. Then, they got together on Fridays or during the weekends to work together on the project.

    “Dr. Havens would come in to our weekly meetings and nudge us in the right direction or give tips on what we should do and what we should avoid,” Whitaker adds.

    For subproblem two, after some discussion the group decided it was probably best to use a machine learning approach, as that promised the most significant gains for the least amount of effort, which was important given the tight schedule.

    “We tried some different sub-projects independently and then worked together to combine the parts we thought worked best,” Evan Lucas says.

    The Solution

    To identify the boat and track its movement, the team used a simple neural network and a computer vision technique called optical flow, which made the analysis much faster and cleaner. They used a pre-built algorithm, adding a bit of optical flow so that the boat’s position didn’t have to be verified every time.

    AI Tracks at Sea Neural Net Summary

    “These two tools allowed us to find the pixel coordinates of the boat and turn them into GPS coordinates,” DeKraker says, whose primary role in the project was integrating the two tools and packaging it for testing.

    “Part of my PhD is to map out a snowmobile’s GPS coordinates with a camera,” Whitaker says. “This is extremely similar to mapping out a boat’s GPS coordinates. I could even say that it was exactly the same. I don’t believe I’ll add anything new, but I’ve tweaked it to work for my research.”

    Whitaker sums up the team’s division of responsibilities like this: “Evan detects all the boats in the picture; Nik detects which of those boats is our boat; Steven takes our boat position and converts it to GPS coordinates, Zach glued all of our pieces together.”

    DeKraker says, “One of the things the judges stressed was the ease of implementing the solution. Since that falls under what I would consider user experience (UX) or user interface (UI), it was pretty natural for me to take these tasks on, having studied software engineering for my undergrad,” DeKraker says.

    A primary focus was speed. “Using machine learning for object detection tends to be slow, so to mitigate that we used the boat detector only once every 5 seconds,” DeKraker explains.

    “Most of the tracking was done using a very fast technique called optical flow, which looks at the difference between two consecutive frames of a video to track motion,” DeKraker says. “It tended to drift from the target though, so we decided on running the boat detector every 5 seconds to keep optical flow on target. “

    “The end result is that our solution could run nearly in real-time,” he says. “The accuracy wasn’t the best, but given a little bit more time and more training data, the neural network could be significantly improved.”

    AI Tracks at Sea Homography Transform

    Zach DeKraker

    DeKraker’s graduate studies focus heavily on various machine learning techniques, He says that this opportunity to integrate machine learning into our solution was a fantastic experience.

    “First, it sounded like an interesting challenge. I don’t get to do a lot of software design these days, and this challenge sounded like a great opportunity to do just that,” he explains.

    “Second, it looked like a great opportunity to build up my resume a little bit. Saying that you won thousands of dollars for your university in a nationwide competition sounds really good. And finally, I really wanted the chance to see a practical application of machine learning in action.”

    DeKraker completed a BS in Software Engineering at Michigan Tech in 2018. He returned to Michigan Tech the next year to complete his master’s degree. He says the biggest reason he did so was to learn more about machine learning.

    “Before embarking on this journey, I really didn’t know anything about it,” he says of machine learning. “Having this chance to actually solve a problem, to integrate a neural network into a fully realized boat tracker using nothing but a video helped me see how machine learning can be used practically, rather than merely understanding how it works.”

    And although it was a fascinating exploration into the practical side of machine learning and computer vision, DeKraker says it’s rather tangential to his main research focus right now, which is on comparing different network architectures to evaluate which one performs best given particular data and the problem being solved.

    DeKraker believes that the culture is the most magnetizing thing about Tech. “Everybody here is cut from the same cloth. We’re all nerds and proud of it,” he explains. “You can have a half-hour conversation with a complete stranger about singularities, the economics of fielding a fleet of star destroyers, or how Sting was forged.”

    And the most appealing thing about Michigan Tech was its size. DeKraker says. “When I looked at a ranking of the top universities in Michigan, Tech was number 3, but still extremely small. It was a perfect blend of being a small but very good school.”

    And he says the second-best thing about Tech is the location. “The Keweenaw is one of the most beautiful places on earth.”

    DeKraker has many ideas about where he’d like to take his career. For instance, he’d love the chance to work for DARPA, Los Alamos National Laboratory, or NASIC. He also intends to commission into the Air Force in the next couple of years, “if they have a place for programmers like me.”

    Evan Lucas

    Evan Lucas is a PhD candidate in the Electrical Engineering department., advised by Zhaohui Wang. Lucas completed both a bachelor’s and master’s in Mechanical Engineering at Tech in 2012 and 2014,

    Lucas, whose research interests are in applying machine learning methods to underwater acoustic communication systems, worked on developing a classifier to separate the boat of interest from the many other boats in the image. Although the subject of the competition is tangential to Lucas’s graduate studies, as computer vision isn’t his area, there was some overlap in general machine learning concepts. respectively.

    “It sounded like a fun challenge to put together an entry and learn more about computer vision,” Lucas says. “Working with the rest of the team was a really good opportunity to learn from people who have experience making software that is used by other people.”

    Following completion of his doctoral degree, hopefully in spring 2023, Lucas plans to return to industry in a research focused role that applies some of the work he did in his PhD.


    Steven Whitaker

    Steven Whitaker’s research interests are in machine learning and acoustics. He tracks and locates the position of on-ice vehicles, like snowmobiles, based on acoustics. He says he has used some of the results from this competition project in his PhD research.

    Whitaker’s machine learning research is experiment-based., and that’s why he chose Michigan Tech. “There aren’t many opportunities in academia to do experiment-based research,” he says. “Most machine learning is very software-focused using pre-made datasets. I love doing the experiments myself. Research is fun. I enjoy getting paid to do what I normally would do in my free time.”

    In 2019, Whitaker completed his BS in Electrical Engineering at Michigan Tech. He expects to complete his master’s degree in Electrical Engineering at the end of the summer 2021 semester, and his PhD in summer 2022. His advisors are Tim Havens and Andrew Barnard.

    Whitaker would love to be a university professor one day, but first he wants to work in industry.


    Background Info

    Timothy Havens is associate dean for research, College of Computing; the William and Gloria Jackson Associate Professor of Computer Systems; and director of the Institute of Computing and Cybersystems (ICC). His research interests are in pattern recognition and machine learning, signal and image processing, sensor and data fusion, heterogeneous data mining, and explosive hazard detection.

    Michael Roggeman is a professor in the Electrical and Computer Engineering department. His research interests include optics, image reconstruction and processing, pattern recognition, and adaptive and atmospheric optics.

    Zhaohui Wang is an associate professor in the Electrical and Computer Engineering department. Her research interests are in communications, signal processing, communication networks, and network security, with an emphasis on underwater acoustic applications.

    The Naval Information Warfare Center (NIWC) Pacific and the Naval Science, Technology, Engineering, and Mathematics (STEM) Coordination Office, managed by the Office of Naval Research are conducting the Artificial Intelligence (AI) Tracks at Sea challenge.

    View more details about the Challenge competition here: https://www.challenge.gov/challenge/AI-tracks-at-sea/

    Watch a Navy webinar about the Challenge here: https://www.youtube.com/watch?v=MjZwvCX4Tx0.

    Challenge.gov is a web platform that assists federal agencies with inviting ideas and solutions directly from the public, or “crowd.” This is called crowdsourcing, and it’s a tenet of the Challenge.gov program. The website enables the U.S. government to engage citizen-solvers in prize competitions for top ideas and concepts as well as breakthrough software, scientific and technology solutions that help achieve their agency missions.

    This site also provides a comprehensive toolkit, a robust repository of considerations, best practices, and case studies on running public-sector prize competitions as developed with insights from prize experts across government.


    Volunteers Needed for Augmented Reality Study

    by Department of Computer Science

    We are looking for volunteers to take part in a study exploring how people may interact with future Augmented Reality (AR) interfaces. During the study, you will record videos of yourself tapping on a printed keyboard. The study takes approximately one hour, and you will be paid $15 for your time. You will complete the study at your home.

    To participate you must meet the following requirements:

    • You must have access to an Android mobile phone
    • You must have access to a printer
    • You must be a fluent speaker of English
    • You must be 18 years of age or older
    • You must live in the United States

    If you would like to take part, please contact rhabibi@mtu.edu


    AI, Mobile Security Grad-level Research Assistant Needed

    Dr. Xiaoyong (Brian) Yuan and Dr. Bo Chen are seeking an hourly paid graduate research assistant to work in the areas of artificial intelligence and mobile security. The project is expected to begin Summer 2021 (5/10/2021).

    Preferred Qualifications:
    1.     Passion for research in artificial intelligence and mobile security.
    1.     Familiar with Android OS and Android app development.
    2.     Basic knowledge of machine learning and deep learning.
    3.     Solid programming skills in Java, Python, or related programming languages. 
    4.     Experience with popular deep learning frameworks, such as Pytorch and Tensorflow is a plus.

    To Apply: Please send a resume and a transcript to Dr. Yuan (xyyuan@mtu.edu).


    Graduate Research Colloquium 2021

    by Graduate Student Government

    This year’s Graduate Research Colloquium organized by the Graduate Student Government was hosted virtually due to COVID restrictions. There were in total 48 presentations — 17 poster presenters and 31 oral presenters.

    Poster presentations took place in a pre-recorded video style and the oral sessions were hosted live via Zoom. You can watch all the poster videos and recordings for the oral sessions here. Each presentation was scored by two judges from the same field of research.

    Participants were able to gain valuable feedback from these judges before presenting their research at an actual conference. It was stiff competition amongst all presenters. Following are the winners for each of these sessions.

    Of the many presentations were the following by two graduate students affiliated with the College of Computing.

    Simulating the Spread of Infectious Diseases
    Meara Pellar-Kosbar, Data Science

    This simulation is designed to show how a fictional viral illness could spread among people in a virtual room. Over the course of the virtual simulation, a number of automatic simulated people called subjects will move about an adjustable virtual grid. During this time, subjects will come into contact with each other and with item cells in the virtual room. Subjects will be exposed to this fictional virus via contact with other subjects, items, and via the air when within a certain distance of a contagious subject. The viral counts of each subject will be tracked and shown as the simulation runs, showing how the actions of the subjects’ affects their viral counts.

    Cultural Competence Effects of Repeated Implicit Bias Training
    Karen Colbert, Social Sciences

    Karen Colbert is a PhD student in the Computational Sciences and Engineering department.

    Abstract: Diversity training literature suggests that mandatory and recurrent sessions should maximize training efficacy, but research has primarily focused on single, brief training sessions that are often voluntary. Michigan Tech is one of few universities to implement required and repeated diversity training for all faculty who serve on search, tenure, and promotion committees. The goal of this study is to evaluate the training’s effectiveness, as well as to fill the gap in research on mandatory recurring diversity training. To do this, we anonymously surveyed faculty members on their knowledge, attitudes, and skills related to content from the Diversity Literacy program and scored responses to create a single composite score for each participant. We hypothesized that composite Cultural Competency Score (CCS) would be higher for faculty who 1) have taken more refresher trainings, and 2) completed training more recently. This study included 130 total respondents (large sample), 69 of whom provided their Diversity Literacy completion information anonymously through Human Resources (small sample). Composite CCS did not differ significantly by frequency of training, H(2)=3.78, p=.151. CCS did differ significantly by years since last training, F(2,63)=4.436, p=.016. Results from both large and small groups showed no statistical significant relationship between CCS and faculty committee service. CCS was negatively correlated with years employed at Tech in both the large (r=-0.363, p=0.002) and small (r = -0.258, p=0.01) samples. This relationship between low CCS and longer employment at Tech may additionally be related to the Diversity Literacy program’s implementation in 2010. Qualitative responses were also collected regarding training material that faculty found most memorable (N=102) and most confident to put into practice (N=93).

    View all the Research Colloquium abstracts here.


    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.


    Get to Know Dr. Sangyoon Han, Biomedical Engineering


    Dr. Sangyoon Han is an assistant professor in Michigan Tech’s Biomedical Engineering department, and an affiliated assistant professor in the Mechanical Engineering-Engineering Mechanics department. He is also advisor to the Korean Students Association. He has been with Michigan Tech since 2017.

    Han recently joined the Institute of Computing and Cybersystems and its Data Sciences research group. His primary research interests are in mechanobiology, cell migration, and image data modeling. His research goals include applying computer vision to microscopic images to capture meaningful information, and he’s looking for collaborators.

    “Anyone with a good machine learning background is encouraged to contact me to discuss potential research,” he says. “Also, students who learned assignment problems or particle tracking are encouraged to contact me to discuss potential tracking-related projects.”

    Teaching and Mentoring

    Han’s teaching interests include computer vision for microscopic images, fluid mechanics, cell biomechanics and mechanobiology, and soft tissue mechanics. This academic year, he instructed Computer Vision for Microscopic Images in the Fall semester, and Fluid Mechanics this Spring.

    Han enjoys teaching and interacting with students, “and feel their energy, too.” He says he makes a deliberate effort in his classes to pause from time to time so that his students can ask questions.

    Han advises two Biomedical Engineering Ph.D. students, Nikhil Mittal and Mohanish Chandurkar.

    “Nik is working on finding myosin-independent mechanosensitivity mechanism for stiffness sensing, and Mohanish works on the project finding mechano-transmission for fluid shear stress sensing by endothelial cells,” he says.

    Research Aspirations

    Han’s Mechanobiology Lab is interested in finding fundamental mechanisms governing mechanotransduction, and how cells sense mechanical forces and convert them into biochemical signals.

    “We image cells and associated forces using high-resolution live imaging, which we analyze to obtain statistically meaningful quantity of data,” Han explains. “We apply force-measuring and molecular-imaging/analysis technologies for stiffness sensing, shear flow sensing, adhesion assembly, and cancer mechanobiology.”

    Han is working to gain a thorough understanding of the mechano-chemical interaction between cancer cells and their microenvironment, and develop a an effective mechano-therapeutic strategy to stop the progression of cancer, and breast cancer in particular. Ultimately, he wants to apply that knowledge to cancer mechanobiology

    Han is principal investigator of a three-year NIH/NIGMS research project, “Nascent Adhesion-Based Mechano-transmission for Extracellular Matrix Stiffness Sensing.” The research aims to determine whether newly-born adhesions can sense tissue stiffness through the accurate measurement of the mechanical force and molecular recruitment of early adhesion proteins.

    Some Background

    In 2012, Han received his Ph.D. in Mechanical Engineering from the University of Washington in the areas of cell mechanics, multiphysics modeling, and bioMEMS.

    For his postdoctoral training, he joined the Computational Cell Biology lab led by Dr. Gaudenz Danuser in the Cell Biology department of Harvard Medical School. In 2014, he joined the UT Southwestern (University of Texas) Department of Cell Biology and Bioinformatics. Han received his B.S and M.S. degrees in mechanical engineering at Seoul National University, Korea, in 2002 and 2004, respectively.

    Han holds several patents and in 2015, he developed an open-source TFM (Traction Force Microscopy) Package, which is shared via his lab’s website: hanlab.biomed.mtu.edu/software.

    Beyond Research and Teaching

    Han loves science and discovering something new in his research investigations. Beyond his work as a professor and scientist, he describes himself as a husband to Sunny, and a dad to his son, Caleb.

    “I am just a normal Korean who likes singing and dancing,” he says. “Unfortunately, my voice is still recovering from surgery, but I hope to get back to it soon. I also like to listen to all kinds of music, including hip-hop, classics, and pop.”

    He appreciates a good sense of humor, but he says that being humorous in American English is something he continues to learn.

    Han says he tries to be “normal” and not too nerd-like when he’s not pursuing his research, but “there are times when I am making my own hypothesis about some phenomena I observe in my daily life.”

    Han enjoys life at Michigan Tech and in the Cooper Country. He likes getting to know his energetic students and he finds Michigan Tech faculty members very strong and collegial. He also enjoys the snow, hockey, and the mountains.

    “I really like the snow here. I am already sad that the weather is becoming too mild!” he confirms. “It’s also a safe environment to raise kids, which is a big plus.”

    And he likes his academic department. “Everyone is so nice in the Biomedical Engineering program, they have been so welcoming and appreciative my research,” Han says. “It’s a family-like environment.”


    Active Research

    1R15GM135806-01 (09/16/2019 – 08/31/2022)

    Funding Agency: NIH/NIGMS

    Nascent Adhesion-Based Mechano-transmission for Extracellular Matrix Stiffness Sensing

    Project Goals: To determine whether newly-born adhesions can sense tissue stiffness by accurate measurement of mechanical force and of molecular recruitment of early adhesion proteins using traction force microscopy and computer vision techniques.
    Role: Principal Investigator


    Additional Information

    The Mechanobiology Lab studies mechanobiology, particularly how adherent cells can sense and respond to mechanical stiffness of the extracellular matrix. To investigate this, the lab has established experimental and computational frameworks for force measurement and adhesion dynamics quantification. Researchers apply these frameworks, with cutting edge computer vision technique, on live-cell microscope images to investigate the fundamental mechanism underlying mechanosensation in normal cells, and the biomechanical signature of the diseased cells whose signaling has gone awry.

    The Institute of Computing and Cybersystems (ICC) creates and supports an arena in which faculty and students work collaboratively across organizational boundaries in an environment that mirrors contemporary technological innovation. The ICC’s 60+ members, in six research centers, represent more than 20 academic disciplines at Michigan Tech. https://www.mtu.edu/icc/

    The ICC Center for Data Sciences (DataS) focuses on the research of data sciences education, algorithms, mathematics, and applications. https://www.mtu.edu/icc/centers/data-sciences/

    The National Institutes of Health (NIH), a part of the U.S. Department of Health and Human Services, is the nation’s medical research agency — making important discoveries that improve health and save lives. https://www.nih.gov/

    The National Institute of General Medical Sciences (NIGMS) supports basic research that increases understanding of biological processes and lays the foundation for advances in disease diagnosis, treatment, and prevention. https://www.nigms.nih.gov/


    Recent Publications

    • Han, S. J.; Azarova, E. V.; Whitewood, A. J.; Bachir, A.; Guttierrez, E.; Groisman, A.; Horwitz, A. R.; Goult, B. T.; Dean, K. M.; Danuser, G. Pre-Complexation of Talin and Vinculin without Tension Is Required for Efficient Nascent Adhesion Maturation. eLife 2021, 10, e66151. https://doi.org/10.7554/eLife.66151.
    • Schäfer, C., Ju, Y., Tak, Y., Han, S.J., Tan, E., Shay, J.W., Danuser, G., Holmqvist, M., Bubley, G. (2020) TRA-1-60-positive cells found in the peripheral blood of prostate cancer patients correlate with metastatic disease. Heliyon 6(1), e03263.
    • Isogai, T., Dean, K.M., Roudot, P., Shao, Q., Cillay, J.D., Welf, E.S., Driscoll, M.K., Royer, S.P., Mittal, N., Chang, B., Han, S.J., Fiolka, R., Danuser, G., Direct Arp2/3-vinculin binding is essential for cell spreading, but only on compliant substrates and in 3D, BioRxiv, 2019
    • Mohan, A.S., Dean, K.M., Isogai, T., Kasitinon, S.Y., Murali, V.S., Roudot, P., Groisman, A., Reed, D.K., Welf, E.S., Han, S.J., Noh, J., and Danuser, G. (2019). Enhanced Dendritic Actin Network Formation in Extended Lamellipodia Drives Proliferation in Growth-Challenged Rac1P29S Melanoma Cells. Developmental Cell, 49(3), pp.444-460.
    • Manifacier I., Milan, J., Beussman, K., Han, S.J., Sniadecki, N.J., About, I (2019) The consequence of large-scale rigidity on actin network tension. In press. Comp Meth Biomech Biomed Eng, 2019 Oct;22(13):1073-1082.
    • Costigliola, N., Ding, L., Burckhardt, C.J., Han, S.J., Gutierrez, E., Mota, A., Groisman, A., Mitchison, T.J., and Danuser, G. (2017) Vimentin directs traction stress. PNAS2017 114 (20) 5195-5200.
    • Han, S.J., Rodriguez M.L., Al-Rekabi, Z., Sniadecki, N.J. (2016) Spatial and Temporal Coordination of Traction Forces in One-Dimensional Cell Migration, Cell Adhesion & Migration. 10(5): 529-539.
    • Oudin, M.J., Barbier, L., Schäfer, C, Kosciuk, T., Miller, M.A., Han, S.J., Jonas, O., Lauffenburger, D.A., Gertler, F.B. (2016) Mena confers resistance to Paclitaxel in triple-negative breast cancer. Mol Cancer Ther.DOI: 10.1158/1535-7163. MCT-16-0413. 
    • Milan,J., Manifacier, I., Beussman, K.M., Han, S.J., Sniadecki, N.J., About, I., Chabrand, P. (2016) In silico CDM model sheds light on force transmission in cell from focal adhesions to nucleus. J Biomechanics. 49(13):2625-2634. 
    • Lomakin. A.J., Lee, K.C., Han, S.J., Bui, A., Davidson, M., Mogilner, A., Danuser G. (2015) Competition for molecular resources among two structurally distinct actin networks defines a bistable switch for cell polarization, Nature Cell Biology. 17, 1435–1445
    • Han, S.J., Oak, Y., Groisman, A., Danuser, G. (2015) Traction Microscopy to Identify Force Modulation in Sub-resolution Adhesions, Nature Methods. 12(7): 653–656


    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?

    “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 Alex Sergeyev urges.

    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.


    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.

    Dr. Sergeyev 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!


    Call for Manuscripts: Fault Tolerance in Cloud/Edge/Fog Computing

    Call for Manuscripts:

    Special Issue on Fault Tolerance in Cloud/Edge/Fog Computing in Future Internet, an international peer-reviewed open access monthly journal published by MDPI.

    Informational Flyer

    https://blogs.mtu.edu/icc/files/2021/04/ali-ebnenasir-call-for-papers-032521-sm.pdf

    Deadline

    April 20, 2021

    Author Notification

    June 10, 2021

    Website

    mdpi.com/journal/futureinternet/special_issues/FT_CEFC

    Collection Editors

    Keywords

    • Fault tolerance
    • Cloud computing
    • Edge computing
    • Resource-constrained devices
    • Distributed protocols
    • State replication

    Topics

    Including, but not limited to:

    • Faults and failures in cloud and edge computing.
    • State replication on edge devices under the scarcity of resources.
    • Fault tolerance mechanism on the edge and in the cloud.
    • Models for the predication of service latency and costs in distributed fault-tolerant protocols on the edge and in the cloud.
    • Fault-tolerant distributed protocols for resource management of edge devices.
    • Fault-tolerant edge/cloud computing.
    • Fault-tolerant computing on low-end devices.
    • Load balancing (on the edge and in the cloud) in the presence of failures.
    • Fault-tolerant data intensive applications on the edge and the cloud.
    • Metrics and benchmarks for the evaluation of fault tolerance mechanisms in cloud/edge computing.

    Background

    The Internet of Things (IoT) has brought a new era of computing that permeates in almost every aspect of our lives. Low-end IoT devices (e.g., smart sensors) are almost everywhere, monitoring and controlling the private and public infrastructure (e.g., home appliances, urban transportation, water management system) of our modern life. Low-end IoT devices communicate enormous amount of data to the cloud computing centers through intermediate devices, a.k.a. edge devices, that benefit from stronger computational resources (e.g., memory, processing power).

    To enhance the throughput and resiliency of such a three-tier architecture (i.e., low-end devices, edge devices and the cloud), it is desirable to perform some tasks (e.g., storing shared objects) on edge devices instead of delegating everything to the cloud. Moreover, any sort of failure in this three-tier architecture would undermine the quality of service and the reliability of services provided to the end users.

    Scope

    Theoretical and experimental methods that incorporate fault tolerance in cloud and edge computing, which have the potential to improve the overall robustness of services in three-tier architectures.

    Manuscript Submission Information

    Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website (https://www.mdpi.com/user/login/). Once you are registered, click here to go to the submission form (https://susy.mdpi.com/user/manuscripts/upload/?journal=futureinternet).

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    Sangyoon Han Publishes Paper in eLife

    eLife, a prestigious journal in cell biology, has published a paper co-written by Sangyoon Han, “Pre-complexation of talin and vinculin without tension is required for efficient nascent adhesion maturation.”

    Dr. Han is an assistant professor in the Biomedical Engineering department, and a member of the Data Sciences research group of the Institute of Computing and Cybersystems (ICC).

    View the paper here.

    eLife is a non-profit organization created by funders and led by researchers. Their mission is to accelerate discovery by operating a platform for research communication that encourages and recognizes the most responsible behaviors.