TY  - BOOK
AU  - Mohammed Ashraf Hassan
AU  - Ahmed Eladawy , 
AU  - Hassan Mostafa , 
TI  - Design of a reconfigurable power-adaptive high-resolution neural data compression algorithm / 
PY  - 2017///
CY  - Cairo : 
PB  - Mohammed Ashraf Hassan , 
KW  - Data compression
KW  - Low-power Design
KW  - Neural signals
N1  - Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Electronics and Communications; Issued also as CD
N2  - In this thesis, five different proposed low-power image compression algorithms based on discrete cosine transform (DCT) and discrete wavelet transform (DWT) are investigated and compared to provide the best trade-off between compression performance and hardware complexity. Finally, harvested power adaptive high-resolution neural data compression is introduced to control the compression algorithm according to available harvested power. Hence, maximum signal to noise and distortion ratio (SNDR) is achieved based on the available harvested power without any data loss
ER  -