---
_id: '10584'
abstract:
- lang: eng
  text: Electrically tunable lenses (ETLs) are those with the ability to alter their
    optical power in response to an electric signal. This feature allows such systems
    to not only image the areas of interest but also obtain spatial depth perception
    (depth of field, DOF). The aim of the present study was to develop an ETL-based
    imaging system for quantitative surface analysis. Firstly, the system was calibrated
    to achieve high depth resolution, warranting the accurate measurement of the depth
    and to account for and correct any influences from external factors on the ETL.
    This was completed using the Tenengrad operator which effectively identified the
    plane of best focus as demonstrated by the linear relationship between the control
    current applied to the ETL and the height at which the optical system focuses.
    The system was then employed to measure amplitude, spatial, hybrid, and volume
    surface texture parameters of a model material (pharmaceutical dosage form) which
    were validated against the parameters obtained using a previously validated surface
    texture analysis technique, optical profilometry. There were no statistically
    significant differences between the surface texture parameters measured by the
    techniques, highlighting the potential application of ETL-based imaging systems
    as an easily adaptable and low-cost alternative surface texture analysis technique
    to conventional microscopy techniques
acknowledgement: The authors acknowledge the financial assistance provided by the
  University of Huddersfield.
article_number: '17'
article_processing_charge: Yes
article_type: original
author:
- first_name: Jorabar Singh
  full_name: Nirwan, Jorabar Singh
  last_name: Nirwan
- first_name: Shan
  full_name: Lou, Shan
  last_name: Lou
- first_name: Saqib
  full_name: Hussain, Saqib
  last_name: Hussain
- first_name: Muhammad
  full_name: Nauman, Muhammad
  id: 32c21954-2022-11eb-9d5f-af9f93c24e71
  last_name: Nauman
  orcid: 0000-0002-2111-4846
- first_name: Tariq
  full_name: Hussain, Tariq
  last_name: Hussain
- first_name: Barbara R.
  full_name: Conway, Barbara R.
  last_name: Conway
- first_name: Muhammad Usman
  full_name: Ghori, Muhammad Usman
  last_name: Ghori
citation:
  ama: Nirwan JS, Lou S, Hussain S, et al. Electrically tunable lens (ETL) - based
    variable focus imaging system for parametric surface texture analysis of materials.
    <i>Micromachines</i>. 2022;13(1). doi:<a href="https://doi.org/10.3390/mi13010017">10.3390/mi13010017</a>
  apa: Nirwan, J. S., Lou, S., Hussain, S., Nauman, M., Hussain, T., Conway, B. R.,
    &#38; Ghori, M. U. (2022). Electrically tunable lens (ETL) - based variable focus
    imaging system for parametric surface texture analysis of materials. <i>Micromachines</i>.
    MDPI. <a href="https://doi.org/10.3390/mi13010017">https://doi.org/10.3390/mi13010017</a>
  chicago: Nirwan, Jorabar Singh, Shan Lou, Saqib Hussain, Muhammad Nauman, Tariq
    Hussain, Barbara R. Conway, and Muhammad Usman Ghori. “Electrically Tunable Lens
    (ETL) - Based Variable Focus Imaging System for Parametric Surface Texture Analysis
    of Materials.” <i>Micromachines</i>. MDPI, 2022. <a href="https://doi.org/10.3390/mi13010017">https://doi.org/10.3390/mi13010017</a>.
  ieee: J. S. Nirwan <i>et al.</i>, “Electrically tunable lens (ETL) - based variable
    focus imaging system for parametric surface texture analysis of materials,” <i>Micromachines</i>,
    vol. 13, no. 1. MDPI, 2022.
  ista: Nirwan JS, Lou S, Hussain S, Nauman M, Hussain T, Conway BR, Ghori MU. 2022.
    Electrically tunable lens (ETL) - based variable focus imaging system for parametric
    surface texture analysis of materials. Micromachines. 13(1), 17.
  mla: Nirwan, Jorabar Singh, et al. “Electrically Tunable Lens (ETL) - Based Variable
    Focus Imaging System for Parametric Surface Texture Analysis of Materials.” <i>Micromachines</i>,
    vol. 13, no. 1, 17, MDPI, 2022, doi:<a href="https://doi.org/10.3390/mi13010017">10.3390/mi13010017</a>.
  short: J.S. Nirwan, S. Lou, S. Hussain, M. Nauman, T. Hussain, B.R. Conway, M.U.
    Ghori, Micromachines 13 (2022).
date_created: 2022-01-02T23:01:33Z
date_published: 2022-01-01T00:00:00Z
date_updated: 2023-08-09T10:16:10Z
day: '01'
ddc:
- '620'
department:
- _id: KiMo
doi: 10.3390/mi13010017
external_id:
  isi:
  - '000758547200001'
file:
- access_level: open_access
  checksum: 5d062cae3f1acb251cacb21021724c4e
  content_type: application/pdf
  creator: alisjak
  date_created: 2022-01-03T13:43:01Z
  date_updated: 2022-01-03T13:43:01Z
  file_id: '10601'
  file_name: 2021_Micromachines_Singh.pdf
  file_size: 5370675
  relation: main_file
  success: 1
file_date_updated: 2022-01-03T13:43:01Z
has_accepted_license: '1'
intvolume: '        13'
isi: 1
issue: '1'
keyword:
- surface texture
- electrically tunable lens
- materials
- hypromellose
- surface topography
- surface roughness
- pharmaceutical tablet
- variable focus imaging
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '01'
oa: 1
oa_version: Published Version
publication: Micromachines
publication_identifier:
  eissn:
  - 2072-666X
publication_status: published
publisher: MDPI
quality_controlled: '1'
scopus_import: '1'
status: public
title: Electrically tunable lens (ETL) - based variable focus imaging system for parametric
  surface texture analysis of materials
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2022'
...