The William D. (Bill) Mensch, Jr. Best Use of Embedded Intelligence Innovation Award
Given at The UArizona College of Engineering on Design Day
Description: The Mensch Prize for Best Use of Embedded Intelligence recognizes the engineering innovation team that best integrates embedded intelligence into a potential commercial product that was developed in a senior design project. Specifically, the award will be granted to a student team that has built a smart connected prototype that may have a commercial market. Embedded Intelligence is characterized as the ability of a product to sense, process, communicate, and actuate (SPCA) based upon information gained from an understanding of both itself and others and for the benefit of many. Preference will be given to designs with SPCA capabilities that can demonstrably surpass human abilities to perform the same function.
Background: Embedded Intelligence (EI) is defined and used here to include the combination of software and micro-processing hardware used in Industry 4.0 and beyond Embedded Systems, Intelligent Systems and/or characterized as having Artificial Intelligence. Application-specific embedded intelligence is used in a wide range of solutions in products like microwave ovens, refrigerators, pacemakers, smartphones, automobiles, robots, drones, and airplanes. The most common feature of these products, which fall into the category of “Internet of Things (IoT),” is that they communicate with each other (increasingly through what is described as “cloud services,” which constitutes a higher level of intelligence). Visit The Bill and Dianne Mensch Foundation, Inc. website at TheMenschFoundation.org to gain a better understanding of the concepts associated with embedded intelligence.
Self-Nomination Requirements: Provide justification in the space below for how your project deserves to win this award (two pages max). Provide details on the embedded system used and how it was integrated with the rest of the system. The justification should include a brief description of what was sensed, processed, communicated, and actuated in the system designed in the project. In addition, in keeping with entrepreneurial aspects of the Mensch Prize, the document must include a statement on the innovative value of the designed system to mankind.
A 1-2 page self-nomination document must be submitted to the College of Engineering. Nomination document(s) may be shared with The Bill and Dianne Mensch Foundation, Inc. in a nonvoting capacity, subject to the Federal Education Rights and Privacy Act (FERPA) and confidentiality considerations.
Prize: Prize amount is $1,000. The amount may be reevaluated based on the available Payout from the endowment.
2021 Award Winner
Team #21014 Advanced Hospital Bed System
Project Description: This project resolves the formation of pressure ulcers (bed sores) which are expensive to treat. Bedsores are a recurring problem for immobile patients and often lead to infection, necrosis, and other complications which are resource and cost-intensive to address.
2020 Award Winner
Team #19094 Biosphere 2 Controlled Systems Monitors
Project Description: To design and build real-time, low-cost, high precision, and high accuracy environmental monitoring systems for two controlled environments at The University of Arizona Biosphere 2 which demonstrate both aquatic and terrestrial applications of embedded sensor systems.
2019 Award Winner
Team #18055 Unmanned Aircraft Ground Control System for Automated Date Pollinator
Project Description: The system, designed in collaboration, controls an unmanned aircraft as it flies through a date palm plantation and pollinates the palms. The computational system mounted on the aircraft uses computer vision and artificial intelligence to observe, model and act on its environment to efficiently and safely complete the pollination process. This includes the identification of the palm trees, the command to release pollen, and the detection and avoidance of obstacles. The system provides the date farmers with a modular product capable of pollinating a field of date palm trees in an industrial-scale farming environment. This allows for significant savings by reducing labor costs and the risk of injury during manual pollination.