Sina Faraji Alamouti

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2021-24

May 1, 2021

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-24.pdf

Development of prosthetic limbs that offer more degrees of freedom in gesture control can benefit from peripheral neural activity recording. A network of miniaturized wireless implants that sit locally near residual peripheral nerves in amputees and record and transmit high resolution neural activity can enhance the functionality of such prosthetics. Such a network can be realized using small ultrasonically operating motes and be interrogated with a single-element external transducer. Multiple access protocols are adopted to permit simultaneous communication with the individual motes. This overall system is constrained not only by common issues associated with simultaneous multi-transmitter communication, but also by a set of requirements imposed due to the design of the ultrasonic motes, the power/data delivery protocols, the mechanical nature of ultrasonic transducers, as well as computational simplicity on the implant side. Achieving high throughput communication with the implants faces several challenges as a result. This project aims to address those issues and offers a machine learning (ML) based approach that achieves near an order of magnitude of improvement in the bit-error rate (BER) performance compared to traditional methods. Compared to state-of-the-art, this work provides 4 times higher total channel capacity and the largest number of implants.

Advisors: Rikky Muller

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BibTeX citation:

@mastersthesis{Faraji Alamouti:EECS-2021-24,
    Author= {Faraji Alamouti, Sina},
    Title= {High-throughput Ultrasonic Implant Communication Link Using ML-assisted CDMA Decoder},
    School= {EECS Department, University of California, Berkeley},
    Year= {2021},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-24.html},
    Number= {UCB/EECS-2021-24},
    Abstract= {Development of prosthetic limbs that offer more degrees of freedom in gesture control can benefit from peripheral neural activity recording. A network of miniaturized wireless implants that sit locally near residual peripheral nerves in amputees and record and transmit high resolution neural activity can enhance the functionality of such prosthetics. Such a network can be realized using small ultrasonically operating motes and be interrogated with a single-element external transducer. Multiple access protocols are adopted to permit simultaneous communication with the individual motes.
This overall system is constrained not only by common issues associated with simultaneous multi-transmitter communication, but also by a set of requirements imposed due to the design of the ultrasonic motes, the power/data delivery protocols, the mechanical nature of ultrasonic transducers, as well as computational simplicity on the implant side. Achieving high throughput communication with the implants faces several challenges as a result.  
This project aims to address those issues and offers a machine learning (ML) based approach that achieves near an order of magnitude of improvement in the bit-error rate (BER) performance compared to traditional methods. Compared to state-of-the-art, this work provides 4 times higher total channel capacity and the largest number of implants.},
}

EndNote citation:

%0 Thesis
%A Faraji Alamouti, Sina 
%T High-throughput Ultrasonic Implant Communication Link Using ML-assisted CDMA Decoder
%I EECS Department, University of California, Berkeley
%D 2021
%8 May 1
%@ UCB/EECS-2021-24
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-24.html
%F Faraji Alamouti:EECS-2021-24