Humber IEEE WIE Competition

Room: J233A, Bldg: J, Humber College, 205 Humber College Blvd, Etobicoke, Ontario, Canada, M9W 5L7

Decided on "Smart Pillbox" project, Olga Leontiuk will be in charge of project with Raji. LED will flash correct pill to consume in dispense box. Project is due at the end of March. More students will join. Men are encouraged to assist with their WIE project. Meetings are in J233A on Thursdays from 2-3 pm. Room: J233A, Bldg: J, Humber College, 205 Humber College Blvd, Etobicoke, Ontario, Canada, M9W 5L7

Humber IEEE Smart City meeting

Room: J233A, Bldg: J233A, 205 Humber College Blvd, Etobicoke, Ontario, Canada, M9W5L7

Meeting organizing smart city project plan, meeting new volunteers and delegating tasks. The current Smart city project is "Smart City Planter" It is a Raspberry Pi Pico W based BLE Mesh device that monitors the soil condition, controls watering and communicates with other plants. It has an RGB LED output to light up the planters and tamper detection. We need people of all stages of the design process. The original design is complete. We need to design the schematic, the circuit board, the 3D case, the software and the BLE stack, debugging/testing. This is a multi-year project and I hope that there will be many people interested in assisting with it. Once it is completed, we can take it to the next level with IEEE and hopefully get a contract to build them. Room: J233A, Bldg: J233A, 205 Humber College Blvd, Etobicoke, Ontario, Canada, M9W5L7

The Use of Gartner’s Analytics Ascendancy Model to Enhance System Reliability: A Convergence of Data Analytics and Knowledge Management Techniques

2000 Simcoe Street North, Oshawa, Ontario, Canada

The proliferation of data acquisition and sensor technology has made enormous amounts of data available to System Engineers (SE) which can facilitate real-time monitoring and enable the creation of reliability models improve operational and maintenance decisions. Gartner’s Analytics Ascendancy Model (AAM) identifies four types of statistical tools in an increasing order. Starting from descriptive statistics, it progresses to diagnostic methods to predictive methods and finally prescriptive analytics. Implementing Knowledge Management (KM) programs can help the SE convert the information from AAM into knowledge and use it to improve system performance. This may lead to a sustainable Machine Learning (ML) environment and autonomous system reliability adjustments. Speaker(s): Dr. Ashraf Sadek, 2000 Simcoe Street North, Oshawa, Ontario, Canada