Winners of Digi-Key/Cypress Design Challenge Announced

August 21, 2019

Scottsdale, Arizona – August 21, 2019 – Embedded Computing Design, which fosters the largest embedded engineering community across digital, print, and live event platforms, recently launched a design challenge in partnership with Digi-Key Electronics and Cypress Semiconductor. In the two-phase challenge, engineers were asked to come up with a design based on one of two Cypress development kits, the CYW20819 Bluetooth 5 Arduino Eval Board or the EZ-BT™ Cypress Bluetooth Mesh Development Kit.

From those entries, a series of semi-finalists were chosen, each of whom received the requested kit. They were then asked to build their design. From those semi-finalists, three winners were chosen. They will receive prizes totaling over $3000US.

“We loved the enthusiasm shown by the community in this challenge,” said Rich Nass, Executive Vice President of Embedded Computing Design. “It’s clear they did their homework and came up with some very innovative designs.”

The winners are:

TIRE PRESSURE MONITORING SYSTEM (TPMS) BLUETOOTH MESH

Ashok R. submitted a demonstration of a tire pressure monitoring system (TPMS) using both the Cypress CYW20819 Bluetooth Mesh Eval Kit and Arduino Eval Kit. This simulated demo employs a temperature sensor in his car. The Bill of Materials include: EZ-BT Mesh Evaluation Kit (CYBT-213043-MESH) - x4 boards, CYW920819EVB-02 Evaluation Kit - 1 board, EPD Display – 2.13-inch (waveshare).

AUTOMATED FALL DETECTION

Submitted by Ryan Dreifuerst, this project provides a fast and early response to medical emergencies, ensuring recovery from hazardous situations. Wrist-Rescue is a solution that leverages machine learning for classifying movements and detecting falls. A classic supervised machine learning concept, a random forest, comprises the heart of the software. This software is running on a Cypress Arduino Eval Kit (CYW920819EVB-02), which has onboard the CYW20819 Bluetooth 5.0 MCU and 9-axis accelerometer.

PREDICTIVE MAINTENANCE WITH BLUETOOTH MESH

Submitted by Nekhil R., this project sets out to discover how to implement predictive maintenance in a machine with the Cypress Bluetooth Mesh Evaluation Kit. The mesh boards were fixed to different points of a machine. They continuously collected sensor data from the machine that was streamed to a customized version of the Cypress Android Mesh mobile app. This data could be used to predict the possible machine failure.

Congratulations to the three talented winners!

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