Celebration of the 3rd Anniversary of the School of Advanced Technology of Xi’an Jiaotong-Liverpool University: Advances in AI and Microengineering

A special issue of Micromachines (ISSN 2072-666X).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2464

Special Issue Editors


E-Mail Website
Guest Editor
School of Advanced Technology of Xi'an Jiaotong–Liverpool University, Suzhou 21500, China
Interests: artificial intelligence; AI+ health care, robotics; smart home; RF and microwave applications including: antennas; filters; diplexers; couplers; RFID; UWB; WIMAX; 3G/4G/5G mobile communication networks; wireless capsule endoscopy; EM measurement and simulation; co-operative and cognitive wireless communication networks; smart-grid communication; robotics networking technology; wireless communication networks for smart and green cities (e.g., mobile APP, public transportation information)
School of Advanced Technology of Xi'an Jiaotong, Liverpool University, Suzhou 21500, China
Interests: wearable antennae; wireless power transfer in RF; microwave imaging and holography; microwave antenna design; microwave antenna measurements; radio frequency engineering
School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou 21500, China
Interests: multi-physics analysis of structures; product design; reliability design and analysis of structures; multi-scale analysis of composite materials

E-Mail Website
Guest Editor
School of Advanced Technology, Xi'an Jiaotong–Liverpool University, Suzhou 21500, China
Interests: robotics and automation at microscale; microfluidic nano-biosensors
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Advanced Technology, Xi'an Jiaotong–Liverpool University, Suzhou 21500, China
Interests: third/fourth-generation novel semiconductors; wide bandgap metal oxide; advanced synaptic electronic devices and their artificial intelligence applications (AI-integrated circuit); wearable electronics with integration of bio-sensors and TENG
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The School of Advanced Technology (SAT) of Xi'an Jiaotong–Liverpool University (XJTLU) is a research- and practice-led school formed by the merger of the Department of Computer Science and Software Engineering and the Department of Electrical and Electronic Engineering. Founded in 2020, SAT is dedicated to research excellence. It provides a platform for interdisciplinary research and education and establishes a world-class research and educational environment for researchers, students, and industry partners to collaborate and innovate. SAT also provides students with a challenging and stimulating environment to unlock their full potential. Upon graduation, students will become highly sought-after candidates with strong academic knowledge and extensive transferable skills in communication, teamwork, and project management.

In recognition of these achievements, Micromachines is planning a dedicated Special Issue entitled “Celebration of the 3rd Anniversary of the School of Advanced Technology of Xi’an Jiaotong–Liverpool University: Advances in AI and microengineering”. This Special Issue will collect high-quality full research articles or comprehensive literature reviews within the broad scope of AI and microengineering. We invite you to contribute original research papers and comprehensive review articles applying AI and microengineering in, but not limited to, the below areas:

  • Interactive and visual technologies (IVT);
  • Cybersecurity, communications and signal processing;
  • Advanced microelectronics and energy technology;
  • Machine learning and data analytics;
  • Mechatronics and robotics.

Prof. Dr. Eng Gee Lim
Dr. Mark Leach
Dr. Min Chen
Dr. Pengfei Song
Dr. Chun Zhao
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.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

15 pages, 3931 KiB  
Article
Deep Learning for Microfluidic-Assisted Caenorhabditis elegans Multi-Parameter Identification Using YOLOv7
by Jie Zhang, Shuhe Liu, Hang Yuan, Ruiqi Yong, Sixuan Duan, Yifan Li, Joseph Spencer, Eng Gee Lim, Limin Yu and Pengfei Song
Micromachines 2023, 14(7), 1339; https://doi.org/10.3390/mi14071339 - 29 Jun 2023
Cited by 2 | Viewed by 1906
Abstract
The Caenorhabditis elegans (C. elegans) is an ideal model organism for studying human diseases and genetics due to its transparency and suitability for optical imaging. However, manually sorting a large population of C. elegans for experiments is tedious and inefficient. The microfluidic-assisted C. [...] Read more.
The Caenorhabditis elegans (C. elegans) is an ideal model organism for studying human diseases and genetics due to its transparency and suitability for optical imaging. However, manually sorting a large population of C. elegans for experiments is tedious and inefficient. The microfluidic-assisted C. elegans sorting chip is considered a promising platform to address this issue due to its automation and ease of operation. Nevertheless, automated C. elegans sorting with multiple parameters requires efficient identification technology due to the different research demands for worm phenotypes. To improve the efficiency and accuracy of multi-parameter sorting, we developed a deep learning model using You Only Look Once (YOLO)v7 to detect and recognize C. elegans automatically. We used a dataset of 3931 annotated worms in microfluidic chips from various studies. Our model showed higher precision in automated C. elegans identification than YOLOv5 and Faster R-CNN, achieving a mean average precision (mAP) at a 0.5 intersection over a union ([email protected]) threshold of 99.56%. Additionally, our model demonstrated good generalization ability, achieving an [email protected] of 94.21% on an external validation set. Our model can efficiently and accurately identify and calculate multiple phenotypes of worms, including size, movement speed, and fluorescence. The multi-parameter identification model can improve sorting efficiency and potentially promote the development of automated and integrated microfluidic platforms. Full article
Show Figures

Figure 1

Back to TopTop