LAP Lambert Academic Publishing ( 2012-10-29 )
€ 49,00
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.
Book Details: |
|
ISBN-13: |
978-3-659-28751-0 |
ISBN-10: |
3659287512 |
EAN: |
9783659287510 |
Book language: |
English |
By (author) : |
Mohd. Samar Ansari |
Number of pages: |
84 |
Published on: |
2012-10-29 |
Category: |
Electronics, electro-technology, communications technology |