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Micromachines
Special Issues
Intelligent Microdevices
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Intelligent Microdevices

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E：Engineering and Technology".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 2536

Special Issue Editors

Dr. Tee Hui Teo

E-Mail Website
Guest Editor
Engineering Product Development (EPD), and Science, Math & Technology (SMT), Singapore University of Technology and Design, Singapore 487372, Singapore
Interests: low-power and low-voltage design for sensor interface; mixed-signal wireless integrated circuit
Special Issues, Collections and Topics in MDPI journals
Dr. Rama Krishna Kotlanka

E-Mail Website
Guest Editor
Analog Devices, Norwood, MA 02062, USA
Interests: MEMS
Dr. Haluk Akay

E-Mail Website
Guest Editor
Massachusetts Institute of Technology, Cambridge, MA, USA
Interests: artificial intelligence; design; MEMS; sustainability

Special Issue Information

Dear Colleagues,

Due to emerging technology in Artificial Intelligence and Electric Vehicles, integrated micro/nano-electromechanical sensors and systems with intelligent algorithm represent an area of intense research – intelligent microdevices. Various algorithms such as anomality detection, predictive maintenance, remaining useful lifetime prediction, etc., are part of the desired design features of intelligent microdevices. 

One of the most interesting applications concerns the development of intelligent microdevices aimed at self-calibrating (e.g. for processes, temperature, power supply) within sophisticated sensorial systems.

Accordingly, this Special Issue seeks to showcase research papers and review articles that focus on novel methodological developments in the field of fabrication of micro/nano-electromechanical systems and the design of the related smart electronics.

Dr. Tee Hui Teo
Dr. Rama Krishna Kotlanka
Dr. Haluk Akay
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial Intelligence
  • Anomality Detection
  • Electric Vehicle
  • NEMS
  • MEMS
  • Predictive Maintenance
  • Remaining Useful Life

Published Papers (1 paper)

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Research

17 pages, 14294 KiB  
Article
Lane-GAN: A Robust Lane Detection Network for Driver Assistance System in High Speed and Complex Road Conditions
by Yan Liu, Jingwen Wang, Yujie Li, Canlin Li and Weizheng Zhang
Micromachines 2022, 13(5), 716; https://0-doi-org.brum.beds.ac.uk/10.3390/mi13050716 - 30 Apr 2022
Cited by 4 | Viewed by 2103
Abstract
Lane detection is an important and challenging part of autonomous driver assistance systems and other advanced assistance systems. The presence of road potholes and obstacles, complex road environments (illumination, occlusion, etc.) are ubiquitous, will cause the blur of images, which is captured by [...] Read more.
Lane detection is an important and challenging part of autonomous driver assistance systems and other advanced assistance systems. The presence of road potholes and obstacles, complex road environments (illumination, occlusion, etc.) are ubiquitous, will cause the blur of images, which is captured by the vision perception system in the lane detection task. To improve the lane detection accuracy of blurred images, a network (Lane-GAN) for lane line detection is proposed in the paper, which is robust to blurred images. First, real and complex blur kernels are simulated to construct a blurred image dataset, and the improved GAN network is used to reinforce the lane features of the blurred image, and finally the feature information is further enriched with a recurrent feature transfer aggregator. Extensive experimental results demonstrate that the proposed network can get robust detection results in complex environments, especially for blurred lane lines. Compared with the SOTA detector, the proposed detector achieves a larger gain. The proposed method can enhance the lane detail features of the blurred image, improving the detection accuracy of the blurred lane effectively, in the driver assistance system in high speed and complex road conditions. Full article
(This article belongs to the Special Issue Intelligent Microdevices)
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