Analog-to-Digital Conversion using ANNs with Non-Linear Feedback: A Hardware-Oriented Approach - Couverture souple

Ansari, Mohd. Samar; Anjum, Syed Gulraze

 
9783659287510: Analog-to-Digital Conversion using ANNs with Non-Linear Feedback: A Hardware-Oriented Approach
Couverture souple
ISBN 10 : 3659287512 ISBN 13 : 9783659287510
Editeur : LAP LAMBERT Academic Publishing, 2012

Synopsis

Analog-to-Digital conversion is a basic signal processing task that is needed at various places in the context of modern day mixed-signal systems like instrumentation & control systems, system-on-chip, etc. It is because of the fact that most real-world signals are analog in nature whereas most on-chip computation is digital. The technical literature is replete with electronic implementations of analog to digital converters including, but not limited to, Flash ADC, Successive Approximation ADC, and Sigma-Delta ADC. Given their promise of parallel processing and fast convergence, artificial neural networks have also been employed for analog-to-digital conversion. The first such attempt employed the Hopfield Neural Network and later several variants were introduced. However, most of the existing neural circuits for analog-to-digital conversion have an underlying similarity in the sense that they are derived from the Hopfield Network Architecture. A new scheme for analog-to-digital conversion utilizing a neural circuit for solving systems of linear equations is presented. The circuit employs (2n) opamps and (n+3) resistances for an n bit ADC.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Analog-to-Digital conversion is a basic signal processing task that is needed at various places in the context of modern day mixed-signal systems like instrumentation & control systems, system-on-chip, etc. It is because of the fact that most real-world signals are analog in nature whereas most on-chip computation is digital. The technical literature is replete with electronic implementations of analog to digital converters including, but not limited to, Flash ADC, Successive Approximation ADC, and Sigma-Delta ADC. Given their promise of parallel processing and fast convergence, artificial neural networks have also been employed for analog-to-digital conversion. The first such attempt employed the Hopfield Neural Network and later several variants were introduced. However, most of the existing neural circuits for analog-to-digital conversion have an underlying similarity in the sense that they are derived from the Hopfield Network Architecture. A new scheme for analog-to-digital conversion utilizing a neural circuit for solving systems of linear equations is presented. The circuit employs (2n) opamps and (n+3) resistances for an n bit ADC.

Biographie de l'auteur

Mohd. Samar Ansari received B. Tech., M. Tech. and Ph. D. degrees in Electronics Engineering from AMU, Aligarh, India, in 2001, 2007 and 2012 respectively. He is an Assistant Professor in Dept. of Electronics Engineering, AMU. He has research interests in Analog Signal Processing and Neural Networks, and has 54 Journal and Conference publications.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2012
Langue
anglais
ISBN 10 
3659287512
ISBN 13 
9783659287510
Reliure
Broché
Nombre de pages
84
Coordonnées du fabricant
non disponible
Personne responsable
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