Difference between revisions of "Adaptively Controlled Hysteresis Curve Tracer For Polymer Ultrasonic Transducers (1 S/B)"
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==Short Description== | ==Short Description== | ||
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===Status: Available === | ===Status: Available === | ||
− | + | : Looking for 1-2 Semester/Bachelor students | |
− | : Looking for 1-2 Semester/ | ||
: Contact: [[:User:Cleitne | Christoph Leitner]], [mailto:marco.giordano@pbl.ee.ethz.ch Marco Giordano (PBL)] | : Contact: [[:User:Cleitne | Christoph Leitner]], [mailto:marco.giordano@pbl.ee.ethz.ch Marco Giordano (PBL)] | ||
===Prerequisites=== | ===Prerequisites=== | ||
− | : | + | : Analog Mixed Signal Design |
− | : | + | : PCB Design |
+ | : Microcontrollers | ||
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===Status: Completed === | ===Status: Completed === | ||
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===Character=== | ===Character=== | ||
− | : | + | : 20% Literature research |
− | : | + | : 40% PCB Design |
− | : 30% | + | : 30% Microcontroller programming |
+ | : 10% Testing | ||
===Professor=== | ===Professor=== |
Revision as of 00:11, 6 February 2023
Contents
Short Description
Status: Available
- Looking for 1-2 Semester/Bachelor students
- Contact: Christoph Leitner, Marco Giordano (PBL)
Prerequisites
- Analog Mixed Signal Design
- PCB Design
- Microcontrollers
Character
- 20% Literature research
- 40% PCB Design
- 30% Microcontroller programming
- 10% Testing
Professor
Detailed Task Description
This research aims to transfer an existing machine learning model and an accompanying post-processing algorithm to an ML accelerator, e.g., a Google Coral device. The accelerator will be paired with a portable IoT camera, and the algorithm's predictions will be sent via, e.g., an USB cable to a sub-computer. The model will be tested under lab conditions using data from walking or running subjects.
Goals
- Research of available ML accelerators and cameras
- Familiarization with ML accelerator
- Model quantization from tensorflow to tensorflow lite
- Deployment of the model on the accelerator and testing
- Build interfaces between IoT camera (2d video capture), the accelerator (prediction) and the sub-computer (display)
- Testing of the implemented pipeline on real data
Practical Details