dc.contributor.author |
Gupta, Anu |
|
dc.contributor.author |
Gupta, Rajiv |
|
dc.date.accessioned |
2023-02-11T04:06:13Z |
|
dc.date.available |
2023-02-11T04:06:13Z |
|
dc.date.issued |
2018-11 |
|
dc.identifier.uri |
https://www.sensorsportal.com/HTML/DIGEST/P_3037.htm |
|
dc.identifier.uri |
http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/9169 |
|
dc.description.abstract |
The paper presents a power efficient and computationally less intensive intelligent hardware using artificial neural network for water quality applications. A compact Hardware Neural Network algorithm has been developed that takes four water quality parameters as the input vector and perform classification of the parameters using a Multilayer Perceptron Network. The computational complexity in the implementation of logistic function has been reduced at a mathematical level by use of approximation methods such as Pad===?=== approximation for exponential function and non- linear approximation for sigmoid function. The network improves accuracy of the output by learning by back-propagation of the error. Results show that non-linear approximation method is 34.13 % power efficient and utilizes 15.53 % less number of hardware resources in comparison to Pad===?===. ASIC implementation is compact and has 99 % less power consumption as compared to FPGA implementation of the same algorithm. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
International Frequency Sensor Association |
en_US |
dc.subject |
EEE |
en_US |
dc.subject |
Artificial Neural Network (ANN) |
en_US |
dc.subject |
Activation function |
en_US |
dc.subject |
ASIC |
en_US |
dc.subject |
Power efficient |
en_US |
dc.subject |
Water Quality |
en_US |
dc.subject |
Computational complexity |
en_US |
dc.title |
Power and Area Efficient Intelligent Hardware Design for Water Quality Applications |
en_US |
dc.type |
Article |
en_US |