Hung, Peter C., McLoone, Sean F. and Farrell, Ronan (2009) Direct and Indirect Classification of High Frequency LNA Gain Performance - A Comparison Between SVMs and MLPs. International Journal of Computing, 8 (1). pp. 24-31. ISSN 1727-6209
PDF
PH_Direct_Indirect.pdf
Download (184kB)
PH_Direct_Indirect.pdf
Download (184kB)
Abstract
The task of determining low noise amplifier (LNA) high-frequency performance in functional testing is as
challenging as designing the circuit itself due to the difficulties associated with bringing high frequency signals offchip.
One possible strategy for circumventing these difficulties is to inferentially estimate the high frequency
performance measures from measurements taken at lower, more accessible, frequencies. This paper investigates the
effectiveness of this strategy for classifying the high frequency gain of the amplifier, a key LNA performance parameter.
An indirect Multilayer Perceptron (MLP) and direct support vector machine (SVM) classification strategy are
considered. Extensive Monte-Carlo simulations show promising results with both methods, with the indirect MLP
classifiers marginally outperforming SVMs.
Item Type: | Article |
---|---|
Additional Information: | Research presented in this paper was funded by Enterprise Ireland Commercialisation Fund (EI CFTD/2003/304) under the National Development Plan. The authors gratefully acknowledge this support. |
Keywords: | High Frequency; Gain Performance; SVMs; MLPs; LNA; Functional testing; Classification; Support Vector Machines; Multilayer Perceptrons; |
Academic Unit: | Faculty of Science and Engineering > Electronic Engineering |
Item ID: | 2721 |
Depositing User: | Ronan Farrell |
Date Deposited: | 23 Oct 2012 14:55 |
Journal or Publication Title: | International Journal of Computing |
Refereed: | No |
Funders: | Enterprise Ireland Commercialisation Fund |
Related URLs: | |
URI: | https://mural.maynoothuniversity.ie/id/eprint/2721 |
Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
Repository Staff Only (login required)
Downloads
Downloads per month over past year