<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Electrochemical micro-electro mechanical systems (MEMS) seismic sensor is limited by its nonideality of frequency dependent characteristics hence interface circuits for compensation is necessary. The conventional compensation circuits are limited by high power consumption, bulky external hardware. These digital circuits are limited by inherent analog to digital conversions which consumes significant power, acquires more size and limits processing speed. This system presents field programmable analog array (FPAA) (Anadigm AN231E04) based hardware implementation of artificial neural network (ANN) model with minimized error in frequency drift in the range of 3.68% to about 0.64% as compared to ANN simulated results in the range of 23.07% to 0.99%. Single neuron consumes power of 206.62 mW with minimum block wise resource utilization. The proposed hardware uses all analog blocks removing the requirement of analog to digital converter and digital to analog converter, reducing significant power and size of interface circuit and enhancing processing speed. It gives the reliable, SMART MEMS seismic sensor with ANN-based intelligent interface circuit implemented in FPAA hardware.</p>
</abstract>

Electrochemical micro-electro mechanical systems (MEMS) seismic sensor is limited by its nonideality of frequency dependent characteristics hence interface circuits for compensation is necessary. The conventional compensation circuits are limited by high power consumption, bulky external hardware. These digital circuits are limited by inherent analog to digital conversions which consumes significant power, acquires more size and limits processing speed. This system presents field programmable analog array (FPAA) (Anadigm AN231E04) based hardware implementation of artificial neural network (ANN) model with minimized error in frequency drift in the range of 3.68% to about 0.64% as compared to ANN simulated results in the range of 23.07% to 0.99%. Single neuron consumes power of 206.62 mW with minimum block wise resource utilization. The proposed hardware uses all analog blocks removing the requirement of analog to digital converter and digital to analog converter, reducing significant power and size of interface circuit and enhancing processing speed. It gives the reliable, SMART MEMS seismic sensor with ANN-based intelligent interface circuit implemented in FPAA hardware.

MEMS Seismic Sensor with FPAA-Based Interface Circuit for Frequency-Drift Compensation Using ANN

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Electrochemical micro-electro mechanical systems (MEMS) seismic sensor is limited by its nonideality of frequency dependent characteristics hence interface...

MEMS Seismic Sensor with FPAA-Based Interface Circuit for Frequency-Drift Compensation Using ANN

Article Category: Research-Article

Online veröffentlicht: 11. Jan. 2018

Seitenbereich: 1 - 6

DOI: https://doi.org/10.21307/ijssis-2018-001

Schlüsselwörter
MEMS, Seismic sensor, Artificial neural network, FPAA, Analog signal processing

© 2018 Ramesh Pawase et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

MEMS Seismic Sensor with FPAA-Based Interface Circuit for Frequency-Drift Compensation Using ANN

Article Category: Research-Article

Online veröffentlicht: 11. Jan. 2018

Seitenbereich: 1 - 6

DOI: https://doi.org/10.21307/ijssis-2018-001

SchlüsselwörterMEMS, Seismic sensor, Artificial neural network, FPAA, Analog signal processing

© 2018 Ramesh Pawase et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
MEMS, Seismic sensor, Artificial neural network, FPAA, Analog signal processing