EconPapers    
Economics at your fingertips  
 

Optimizing for High Resolution ADC Model With Combined Architecture

Wei Ding, Heng Liu and Tao Wu
Additional contact information
Wei Ding: Key Laboratory of Earthquake Geodesy, Institute of Seismology, China Earthquake Administration, China
Heng Liu: School of information and Management, Guangxi Medical University, China
Tao Wu: Key Laboratory of Earthquake Geodesy, Institute of Seismology, China Earthquake Administration, China

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2020, vol. 14, issue 3, 118-132

Abstract: High resolution analog-digital conversion (ADC) is a key instrument to convert analog signals to digital signals, which is deployed in data acquisition system to match high resolution analog signals from seismometers systems. To achieve high resolution, architecture of Σ-△ oversampling or pipeline ADC architecture have following disadvantages: high power consumption, low linearity of modulators, and complex structure. This work presents a novel model architecture, which design principle is validated by mathematical formulations which combined advantages of both pipeline and Σ-△oversampling ADC architecture. By discussing the adverse effects of the whole ADC architecture with an external noise theoretically, an amended theoretical model is proposed according to the assessment result of a noise simulation algorithm. The simulation results represent that the whole performance of combined architecture is determined by the noise level of integrator and subtractor. Using these two components with a noise index no more than 10-7 V/√Hz, the resolution of the prototype can achieve a reservation of 144.5 dB.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 18/IJCINI.2020070106 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jcini0:v:14:y:2020:i:3:p:118-132

Access Statistics for this article

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) is currently edited by Kangshun Li

More articles in International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) from IGI Global
Bibliographic data for series maintained by Journal Editor ().

 
Page updated 2025-03-19
Handle: RePEc:igg:jcini0:v:14:y:2020:i:3:p:118-132