Next Article in Journal / Special Issue
Power-Performance Tradeoffs in Wide Dynamic Range Image Sensors with Multiple Reset Approach
Previous Article in Journal / Special Issue
A Review and Modern Approach to LC Ladder Synthesis
Article Menu

Export Article

Open AccessArticle
J. Low Power Electron. Appl. 2011, 1(1), 45-58; doi:10.3390/jlpea1010045

A Low-Power Hardware-Friendly Binary Decision Tree Classifier for Gas Identification

Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology (HKUST), Clear Water Bay, Kowloon, Hong Kong, China
*
Author to whom correspondence should be addressed.
Received: 13 December 2010 / Revised: 1 March 2011 / Accepted: 2 March 2011 / Published: 9 March 2011
(This article belongs to the Special Issue Selected Topics in Low Power Design - From Circuits to Applications)
View Full-Text   |   Download PDF [9860 KB, uploaded 9 March 2011]   |  

Abstract

In this paper, we present a hardware friendly binary decision tree (DT) classifier for gas identification. The DT classifier is based on an axis-parallel decision tree implemented as threshold networks—one layer of threshold logic units (TLUs) followed by a programmable binary tree implemented using combinational logic circuits. The proposed DT classifier circuit removes the need for multiplication operation enabling up to 80% savings in terms of silicon area and power compared to oblique based-DT while achieving 91.36% classification accuracy without throughput degradation. The circuit was designed in 0.18 μm Charter CMOS process and tested using a data set acquired with in-house fabricated tin-oxide gas sensors. View Full-Text
Keywords: binary decision tree; classifier; gas identification; hardware implementation binary decision tree; classifier; gas identification; hardware implementation
Figures

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Li, Q.; Bermak, A. A Low-Power Hardware-Friendly Binary Decision Tree Classifier for Gas Identification. J. Low Power Electron. Appl. 2011, 1, 45-58.

Show more citation formats Show less citations formats

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
J. Low Power Electron. Appl. EISSN 2079-9268 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top