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Design of a reconfigurable power-adaptive high-resolution neural data compression algorithm / Mohammed Ashraf Hassan ; Supervised Ahmed Eladawy , Hassan Mostafa

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Mohammed Ashraf Hassan , 2017Description: 97 P. : charts , facsimiles ; 30cmOther title:
  • تصميم نموذج قابل للتكيف مع الطاقه لضغط البيانات العصبيه عالية الدقة [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Electronics and Communications Summary: 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
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.08.M.Sc.2017.Mo.D (Browse shelf(Opens below)) Not for loan 01010110075419000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.08.M.Sc.2017.Mo.D (Browse shelf(Opens below)) 75419.CD Not for loan 01020110075419000

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

Issued also as CD

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