Day: September 5, 2019

EET Alumnus Aaron Zarembski Named Emerging Leader

Aaron Zarembski

EET alumnus Aaron Zarembski was recently named one of 20 emerging leaders by Production Manufacturing magazine. The magazine’s annual list recognizes professionals under the age of 40 who are making a difference in the precision machined parts industry. Selections are based on nominations received by readers of the publication. The emphasis is on leadership and potential leadership, whether in the nominee’s company or their involvement in the industry.

Aaron works as a controls engineer for Ecoclean Group, a global company that supplies machinery for industrial parts cleaning and surface treatment applications. He was cited in particular for his strengths in customer service and technical knowledge.  Aaron’s career history also includes working with robotic waterjet cutting and plastic welding.

“Aaron is an outstanding problem solver and has been an invaluable asset for Ecoclean in every role he has taken on,” said his nominator, Peter Feamster, product management director for Jomesa North America Inc. “Aaron uses patience and efficient/effective solutions in order to satisfy customers while managing enormous pressure to keep production lines operating. He has an ability to apply PLC programming knowledge to a challenging automation process,” adding that Aaron is always looking at problems from a unique perspective, is great at thinking outside the box, is a natural innovator, and he is reliable, reachable and personable, as well.

View the magazine’s blog post here: https://www.productionmachining.com/blog/post/2019-emerging-leader-aaron-zarembski

Read more about Aaron and the other 19 emerging leaders here: https://www.productionmachining.com/production-machinings-2019-emerging-leaders/

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Weihua Zhou to Present Invited Talk at 2019 American Society of Nuclear Cardiology Conference

Weihua Zhou

Weihua Zhou, assistant professor of health informatics, will present an invited talk and give a poster presentation at the 2019 American Society of Nuclear Cardiology conference (ASNC), September 12-15, in Chicago, IL.

His talk, “Machine Learning for Automatic LV Segmentation and Volume Quantification,” will discuss the results of his recent research for the American Heart Association, “A new image-guided approach for cardiac resynchronization therapy.” (Project Number: 17AIREA33700016, PI: Weihua Zhou).

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Keith Vertanen and Scott Kuhl Awarded $499K NSF Grant

Scott Kuhl
Scott Kuhl
Keith Vertanen
Keith Vertanen

Keith Vertanen, assistant professor of computer science (HCC), and Scott Kuhl (HCC), associate professor of computer science, are principal investigators of a recently funded three-year National Science Foundation grant for their project, “CHS: Small: Rich Surface Interaction for Augmented Environments.” The expected funding over three years is $499,552.00.

Vertanen and Kuhl are members of Michigan Tech’s Institute of Computing and Cybersystems (ICC) Center for Human-Centered Computing. A 2018 ICC research seed grant funded by ECE Alumnus Paul Williams was used to produce some of the preliminary results in the successful proposal. More info about the Williams Seed Grant can be found here: https://blogs.mtu.edu/icc/2019/07/16/appropriating-everyday-surfaces-for-tap-interaction/.

A related video can be found here: https://youtu.be/sF7aeXMfsIQ.

Abstract: Virtual Reality (VR) and Augmented Reality (AR) head-mounted displays are increasingly being used in different computing related activities such as data visualization, education, and training. Currently, VR and AR devices lack efficient and ergonomic ways to perform common desktop interactions such as pointing-and-clicking and entering text. The goal of this project is to transform flat, everyday surfaces into a rich interactive surface. For example, a desk or a wall could be transformed into a virtual keyboard. Flat surfaces afford not only haptic feedback, but also provide ergonomic advantages by providing a place to rest your arms. This project will develop a system where microphones are placed on surfaces to enable the sensing of when and where a tap has occurred. Further, the system aims to differentiate different types of touch interactions such as tapping with a fingernail, tapping with a finger pad, or making short swipe gestures.

This project will investigate different machine learning algorithms for producing a continuous coordinate for taps on a surface along with associated error bars. Using the confidence of sensed taps, the project will investigate ways to intelligently inform aspects of the user interface, e.g. guiding the autocorrection algorithm of a virtual keyboard decoder. Initially, the project will investigate sensing via an array of surface-mounted microphones and design “surface algorithms” to determine and compare the location accuracy of the finger taps on the virtual keyboard. These algorithms will experiment with different models including existing time-of-flight model, a new model based on Gaussian Process Regression, and a baseline of classification using support vector machines. For all models, the project will investigate the impact of the amount of training data from other users, and varying the amount of adaptation data from the target user. The project will compare surface microphones with approaches utilizing cameras and wrist-based inertial sensors. The project will generate human-factors results on the accuracy, user preference, and ergonomics of interacting midair versus on a rigid surface. By examining different sensors, input surfaces, and interface designs, the project will map the design space for future AR and VR interactive systems. The project will disseminate software and data allowing others to outfit tables or walls with microphones to enable rich interactive experiences.

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