December ECN Meeting: Several people presented their interests and “tech toys” for the meeting. Jack Kissingler, a new member of IEEE (welcome, Jack!) showed a PIC-microcomputer based device that mimicked a cat’s ability to follow a sound. The device used a pair of microphones and a fast ADC to correlate the direction of sound arrival and drive a servo to move the cat’s head. Scott Olsen brought in an InfraRed camera and explained how he uses it to find wasted energy in facilities. He mentioned that the cameras used to cost many tens of thousands of dollars, but are now in the range of a consumer pocketbook as attachments to cell phones. Also shown was a weather sensor network complete with a 3D printed sensor housing that looked like a bird house, a Turtlebot 3 robot, and an add-on Matrix Creator Raspberry Pi hat.
December Social Event: Twenty-five people attended an evening meeting/election event at Brasserie V on Monroe Street. After a brief election, the evening turned to a social event, honoring the IEEE-Madison Volunteers. Many thanks to our volunteers. For 2017, they were Tom Kaminski – Chair,/ECN Chair/Newsletter Editor, Scott Olsen – Vice Chair, Charles Gervasi – Treasurer, Steve Schultheis – Secretary, Nate Toth – Webmaster, Dennis Bahr – Engineering in Medicine and Biology Chapter Chair, Chuck Kime – Life Member Affinity Group Chair, Charles Cowie – Life Member Affinity Group Vice Chair, David Jensen – Life Member Affinity Group Secretary, Members at Large: Clark Johnson, Craig Heilman, Dennis Bahr, Sandy Rotter.
January, 2018 Life Member Affiliate Group Meeting: Professor Barmish gave an excellent presentation on his research into algorithmic stock trading and showed some of the results that he and his graduate students have attained. His primary interest is in applying control theory to stock trading using simple techniques based on the current value and past history of the stocks to be traded. First, he motivated his interest in not using predictive models, because models do not do well under extreme changes. Second, he used illustrative examples of historic stock data to see how different algorithms behaved. Using Apple, Inc. stock, he showed how day traders would have performed poorly using daily trades and not holding the stocks. The best strategy would have been to buy-and-hold the stocks for the period discussed. A simple control algorithm would have fared better, but not as well as the buy-and-hold for the period. Dr. Barmish also discussed some of the practical issues, such as where you get historic data and how you can manage to defer trading fees with a big enough investment. He encouraged everyone to try it and join in with his research group if you are interested.