Difference between revisions of "High-throughput Embedded System For Neurotechnology in collaboration with INI"
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Latest revision as of 14:48, 10 November 2020
The Neurotechnology group is developing advanced technologies, such as 1000+ electrodes implantable chips to study the brain and study cognitive behavior. Exploiting an active collaboration with the IIS group, the project will develop an acquisition embedded system that focus in transfer data from the implantable chips to a remote host. The student project will deal with the design and the implementation of the embedded system that can acquire, process, and transfer the data wirelessly. Due to the amount of data to acquire and the costarring of power consumption (below 1-200mW) a low power FPGA is planned to be used to acquire and "compress/process" the data. The figure shows a preliminary block diagram, which involves both the programming of a Low power FPGA and a System on Chip with ARM cortex-M4F and Bluetooth low energy 5.0. The project is quite challenging and the main goal is to build a whole working prototype tha will be tested in-field.
Depending on the applicant's profile and project type, his tasks may involve some of the following:
(not all need to be met by the single candidate)
- Experience using the laboratory instrumentation - signal generators, oscilloscopes, DAQ cards, Matlab etc..
- Knowledge of microcontroller and FPGA programming and PC programming (C/C++, preferably embedded C)
- basic knowledge or interests on power converters, wireless communication, and circuit design at a components level (IC design is NOT involved)
- Motivation to build and test a real system
- PCB Desing or willing to learn it
- Machine learning and deep learning on PC and microcontroller/FPGA (or the motivation/interest to learn it)
Detailed Task Description A detailed task description will be worked out right before the project, taking the student's interests and capabilities into account.
- Looking for Bachelor, Semester and Master Project Students
- Supervisors: Michele Magno
- 35% Theory
- 45% Implementation
- 20% Testing