Design of a reconfigurable power-adaptive high-resolution neural data compression algorithm /
Mohammed Ashraf Hassan
Design of a reconfigurable power-adaptive high-resolution neural data compression algorithm / تصميم نموذج قابل للتكيف مع الطاقه لضغط البيانات العصبيه عالية الدقة Mohammed Ashraf Hassan ; Supervised Ahmed Eladawy , Hassan Mostafa - Cairo : Mohammed Ashraf Hassan , 2017 - 97 P. : charts , facsimiles ; 30cm
Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Electronics and Communications
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
Data compression Low-power Design Neural signals
Design of a reconfigurable power-adaptive high-resolution neural data compression algorithm / تصميم نموذج قابل للتكيف مع الطاقه لضغط البيانات العصبيه عالية الدقة Mohammed Ashraf Hassan ; Supervised Ahmed Eladawy , Hassan Mostafa - Cairo : Mohammed Ashraf Hassan , 2017 - 97 P. : charts , facsimiles ; 30cm
Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Electronics and Communications
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
Data compression Low-power Design Neural signals