Embedded Deep Learning, ECE, Carnegie Mellon University
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    Projects Spring 2023


    ECE, Carnegie Mellon University
    • WHERE--Wrist-based HAR (Human Activity Recognition) with Embedded Real-time Equipment

      Omkar Savkur, Nicholas Toldalagi, and Kevin Xie

      Posted on May, 2023

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      With the rise of wearable personal electronics such as the smartwatch, more and more users have a capable edge computing device and sensing suite at their fingertips. Despite this, few projects have investigated how to perform continuous, power-efficient Human Activity... [Read More]
    • Sound-Based Trash Categorization (SBTrashCat)

      Jacky Wang and Gordonson Yan

      Posted on May, 2023

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      The key feature of SBTrashCat is to use the acoustic fingerprint of containers made with different materials to determine their categorization. For our baseline, SBTrashCat can identify containers including aluminum cans, plastic bottles, and tetra-pak. To ensure the robustness of... [Read More]
    • Multinode Embedded Deep Learning

      Jong-Ik Park, Chad Taylor, and Anudeep Bolimera

      Posted on May, 2023

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      The rapid expansion of the Internet of Things (IoT) and the growing number of edge devices have significantly increased the amount of data generated and processed. This shift has led to a rising demand for efficient and cost-effective embedded systems... [Read More]
    • Motor Care - Condition Based Monitoring

      Abhishek Basrithaya and Yuyang Xu

      Posted on May, 2023

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      An electric motor is a rudimentary electro-mechanical device that is ubiquitous from wheels of an automobile, fans, automatic windows shades to magnetic disks for memory storage etc. Efficient operation of these fundamental devices is imperative for the smooth operation of... [Read More]
    • Inventory Management on Edge (Empty Shelf Percentage Estimator)

      Netra Trivedi, Rishi Pachipulusu, and Cathy Tungyun

      Posted on May, 2023

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      In a warehouse or a retail setup: Given a frame of reference, our model indicates the percentage of empty spaces with reference to a shelf full of boxes (either rectangular or square) for new inventory. [Read More]
    • Feel the Vibe (Emotion Recognition)

      Samuel Leong and Alex Strasser

      Posted on May, 2023

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      Wearables are getting increasingly popular and feasible as computing power becomes smaller and more powerful. Indeed, the advent of advanced sensors and intelligence being packed into smaller packages opens up new opportunities for novel applications. In “Feel the Vibe”, we... [Read More]
    • Dog Fitbit

      Ajith Potluri, Eion Tyacke, and Parker Crain

      Posted on May, 2023

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      This project investigates on-device deep learning models paired with an embedded system that can be used to track the movement of a dog for its owner. This valuable insight may help an owner to better understand the activities of the... [Read More]
    • Crosswalk Aid

      James Li, Pingyuan Liang, and Xuanye Li

      Posted on May, 2023

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      The visually impaired encounter a number of difficulties when crossing streets. Without auditory information from parallel traffic, blind pedestrians can easily veer away from crosswalks. Complicated road junctions may also confuse a blind pedestrian’s ability to accurately determine the location... [Read More]
    • Bird Feeder

      Eric Wu, Harry Rosmann, and Blaine Huey

      Posted on May, 2023

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      The idea originated from an animal status monitor and evolved into a bird feeder monitoring system with squirrel detection. We are running two models on the Arduino Nano 33 BLE. The first model detects whether the approaching animal is a... [Read More]
    • Athletech

      Adeesh Bhargava, Varun Jain, and Rohan Paranjape

      Posted on May, 2023

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      The goal of our project is to use cutting-edge computer vision techniques in combination with embedded devices to help people improve their exercise form while also prioritizing their health and safety. While modern computer vision algorithms are able to detect... [Read More]
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