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 2917

Special Issue Editors


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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

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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

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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

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Published Papers (1 paper)

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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 3 | Viewed by 2298
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
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