Fidelity Measurement on Signal Detection Algorithms with Application to AFM Imaging

Sayan Ghosal, Murti Salapaka

Research output: Contribution to journalArticlepeer-review

Abstract

Many signal processing and decision making algorithms reported in contemporary literature characterize the performance of the proposed methods utilizing decision error rates. However there is significant need for strategies that provide quantitative assessment of the fidelity of decisions made by the algorithms. This paper develops novel techniques utilizing which fidelity measures can be assigned quantitatively on some prevalent signal detection algorithms. The developed fidelity measurement methods with the detection algorithms are employed for topography imaging utilizing dynamic mode atomic force microscope (AFM). The AFM is a versatile metrology tool for interrogating material at the nano-scale. In spite of its remarkable achievements, a key issue that remains largely unaddressed is the assessment of fidelity of the measurement data. The developed paradigm facilitates user specific priority for either detection of sample features with high decision confidence or on not missing detection of true features. The fidelity measures presented here are suitable for real-time implementation. A detailed comparative study is presented to characterize the proposed signal detection algorithms and fidelity measurement techniques under practical AFM applications. Comprehensive simulation and experimental data corroborate the effectiveness of proposed methods.

Original languageEnglish (US)
Article number8466760
Pages (from-to)52831-52842
Number of pages12
JournalIEEE Access
Volume6
DOIs
StatePublished - Sep 15 2018

Bibliographical note

Funding Information:
This work was supported in part by the USA National Science Foundation under Grant NSF-CCF 1116971 and Grant NSF-ECCS 1202411.

Keywords

  • Atomic force microscopy
  • Kalman filters
  • detection algorithms
  • estimation
  • observers
  • scanning probe microscopy
  • signal detection
  • signal processing algorithms

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