Title

Power adaptive high-resolution neural data compression algorithm (PANDCA)

Author's Department

Electronics & Communications Engineering Department

Find in your Library

http://www.worldcat.org/oclc/8531175543

All Authors

Mohammed Ashraf; Hassan Mostafa; Ahmed Eladawy; Yehea Ismail

Document Type

Research Article

Publication Title

Microelectronics Journal

Publication Date

1-1-2018

doi

https://doi.org/10.1016/j.mejo.2018.01.025

Abstract

Nowadays, brain scientific research progress depends on signal compression at the implantable site to conform with the low-rate transmission through wireless connection to the outside world despite of high spatial and temporal resolution of neural data. Without data compression, these data rates conflict the neurophysiologic restrictions in terms of energy consumption and silicon area. The main goal of any implantable compression device is to get the smallest data size to be transmitted to the outside world with lowest distortion and data loss at receiver side. In this work, the neural compression algorithm is adapted according to the available harvested power budget. Therefore, the maximum signal to noise and distortion ratio (SNDR) is achieved based on the available harvested power budget without any data loss.

First Page

154

Last Page

163

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