AI砌積木／室內導航平台

砌積木是不少人的嗜好，如果把積木交到AI手上，它砌出來的作品，會帶給我們甚麼驚喜？有科技公司利用Wi-Fi指紋技術，製作電子地圖，解決室內向來較難接收訊號的痛點。

 

Date: 
Monday, December 28, 2020
Media: 
TVB
Media LInk: 
https://is.gd/qhIzQ1

CUHK Faculty of Engineering Develops a Multilayer Roll-to-roll Printing System Achieving Submicron Overlay Accuracy for the First Time Ever Enabling Manufacturing of Low Cost Flexible Electronics

Date: 
2020-12-23
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Professor Shih-Chi Chen and his team from the Department of Mechanical and Automation Engineering, the Faculty of Engineering, The Chinese University of Hong Kong (CUHK), have developed a flexure-based multilayer roll-to-roll (R2R) printing system that simultaneously achieves nanometer-level printing resolution and submicron-level overlay accuracy, enabling mass production of high resolution flexible electronics at low cost. The research result has been recently published in the journal Precision Engineering.
 
R2R printing technologies have been widely used in many important fields, such as the fabrication of organic photovoltaics and touch screen electrodes, due to their tremendous advantages in throughput and cost as well as the capability to fabricate multilayer functional structures and devices on various flexible substrates, including ultra-thin glasses and polyethylene terephthalate (PET) films. A precision multilayer R2R printing system will help realize the fabrication of high performance flexible electronics.
 
Flexure-based multi-axis nanopositioner realizes nanometer-level contact printing on flexible substrates.
 
Optical gratings and transparent electrodes produced by optical and electron-beam lithography are essential components in a wide range of optoelectronics devices, such as touch screens, organic light emitting diodes (LED), and organic photovoltaic cells. However, these processes are complex, expensive, and need to be performed in a cleanroom, resulting in low productivity and high cost. On the other hand, although soft lithography-based techniques, such as microcontact printing (MCP), can overcome the diffraction limit to achieve nanometer resolution and operate in a non-cleanroom environment, large scale manufacturing has yet to be realized due to the challenging requirements in the ultraprecise printing force and system stability.
 
To solve this problem, Professor Shih-Chi Chen and his team have recently developed and constructed a multilayer R2R printing system for fabrication of flexible electronic devices, where various contact printing methods can be applied to the system. The new R2R system is based on two flexure-based multi-axis positioners, which demonstrate nanometer-level repeatability and multi-axis error correction capability, and achieves 100s nm precision in combination with multiple-input and multiple-output closed-loop control algorithms. Experiments indicated that the system can control the roller position within 200 nm and reach a highest print resolution of 100 nm in a non-cleanroom environment. The R2R system can readily be scaled up for cost effective and high throughput fabrication of flexible electronics.
 
New vision-based multi-axis alignment method achieves submicron overlay accuracy for the first time in R2R printing history
 
State-of-the-art R2R systems can only print multilayer patterns with an accuracy of tens of microns, largely due to the use of traditional mechanical components and bearings with low repeatability and precision as well as conventional methods for monitoring the web position with unsatisfactory sensitivity. This prevents the manufacturing of high-resolution multi-layer electronic and photonic devices in micro-nano scale, such as organic thin-film field effect transistor (FET) and photonic metamaterials, e.g., terahertz perfect absorber.
 
To address the issue, the research team introduced a vision-based alignment method and algorithm to the R2R printing system, where a pair of low cost cameras are employed to monitor the hybrid alignment marks; the acquired images are processed in real time by the pattern recognition and phase estimation algorithms to produce high resolution position feedback signals for controlling the two multi-axis roller positioners. Experimental results show that the system achieves better than 1 μm layer-to-layer registration accuracy – the first demonstration of submicron overlay accuracy on a R2R system. Based on this system, FETs were continuously fabricated on a 4-inch PET web to verify the precision, reproducibility and stability of the system.
 
Professor Chen pointed out that the new R2R system has substantially extended the performance envelope of R2R printing technologies to realise emerging applications that require nanometer resolution and submicron overlay accuracy, e.g., flexible printed circuits and various optoelectronic devices. The system can readily be scaled up for industrial processes and generate impact to the manufacturing industry.

Professor Shih-Chi Chen, Professor, Department of Mechanical and Automation Engineering, Faculty of Engineering, CUHK.

 

The multilayer roll-to-roll printing system developed by CUHK achieves submicron overlay accuracy for the first time ever in a non-cleanroom environment.

 

Professor Shih-Chi Chen and his team Dr. Li Chenglin have developed a flexure-based multilayer roll-to-roll printing system that simultaneously achieves nanometer-level printing resolution and submicron-level overlay accuracy.

 

The multilayer roll-to-roll printing system developed by CUHK.

 

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Prof. Evangeline F.Y. Young Recognized by ACM as 2020 Distinguished Member

Date: 
2020-12-21
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The Association for Computing Machinery (ACM) has named Professor Evangeline F.Y. Young, Department of Computer Science and Engineering, a Distinguished Member. 
 
She joins 64 individuals globally who have received this recognition in 2020 for their accomplishments that move the computing field forward. Evangeline Young is an expert in the area of Electronic Design Automation, a field that applies various optimization techniques in computer science to enhance the design of computer chips and systems. Her research interests include physical design, optimization, algorithms and AI. She and her dedicated students have developed open source academic physical designing tools for placement, routing and AI chip design, which have won them many times championships and prizes in renowned EDA contests and challenges organized by industry. Their works have also received best paper awards from top-tier conferences.
 
ACM, the Association for Computing Machinery, is the world’s largest educational and scientific computing society, uniting computing educators, researchers and professionals to inspire dialogue, share resources and address the field’s challenges. ACM strengthens the computing profession’s collective voice through strong leadership, promotion of the highest standards, and recognition of technical excellence. ACM supports the professional growth of its members by providing opportunities for life-long learning, career development, and professional networking.
 
The ACM Distinguished Member program recognizes up to 10 percent of ACM worldwide membership based on professional experience as well as significant achievements in the computing field. It is expected that a Distinguished Member serves as a mentor and role model, guiding technical career development and contributing to the field beyond the norm. 
 
Find out more about Prof. Young’s work.
 
 

Professor Evangeline F.Y. Young

 

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CSE

綫控機械人高空工作 髹油砌磚樣樣皆能

本港建築工地自動化，取代人力的機械人，多數是機械臂和搬運AGV機器人。但是工地面積大，負重高，負責高空牆身等飛簷走壁工作，如何以機器人代勞？一直是問題。
處理大空間工程，一般靠搭建棚架和高空吊船。中文大學研究開發綫控機械人，取代一些高空工作，負重較大，以往需技術人手，而危險性高、工作環境惡劣的工種。綫控機械人甚至應用在校內建築項目，創作較難以人手完成的建築作品。
 
Date: 
Friday, December 18, 2020
Media: 
Sing Tao Daily
Media LInk: 
https://www.it-square.hk/archives/13948

「工無不克」 – Making HK IT!

今日係創新科技署主辦、香港科技園協辦第一屆「城市創科大挑戰」啟動禮，並公布比賽詳情，歡迎傳媒透過視頻參與。
 
「城市創科大挑戰」旨在邀請各界人士，就與市民息息相關議題提出創科方案。疫情下，今屆比賽圍繞「環境的可持續發展」及「保持社交聯繫」，徵集各界有關智慧生活創新方案，一起「智創香港新常態」。
 
Date: 
Friday, December 18, 2020
Media: 
IT-Square
Media LInk: 
https://www.it-square.hk/archives/13944#more-13944
Name: 
WANG Liwei
Title ( post ): 
Assistant Professor
Department: 
Computer Science and Engineering
email: 
lwwang [at] cse.cuhk.edu.hk
phone: 
3943 8419
Avatar: 
Class: 
faculty_member
Chinese Name: 
王歷偉
glossary_index: 
W

中大研測菌機械人 又快又準

隨着新冠肺炎肆虐全球，快速檢測的需求飆升，香港中文大學（中大）研發創新微型機械人快速自動檢測系統「 QuickCAS」，第一代QuickCAS針對檢測難辨梭菌，由現存需時二至四小時，大幅縮短到15至30分鐘便可完成檢測，成本只是醫院傳統檢測的六分之一。團隊表示，至今實驗準繩度為100%，期望能加快對傳染病的診斷。系統將進行臨床試驗，期望明年在醫院投入試用。

Date: 
Wednesday, December 16, 2020
Media: 
大公報
Media LInk: 
https://is.gd/Cya9O7

中大研發15分鐘驗腸病毒 識別難辨梭菌 每次成本僅50元

在第四波新冠疫情下，醫護工作負擔沉重，如何減輕他們的工作量成為燃眉之急。香港中文大學醫學院及工程學院的團隊研發了一款微型機械人快速自動檢測系統「QuickCAS」，針對醫院內最常見的腸道感染病原體「難辨梭菌」（Clostridium Difficile），聲稱最快15分鐘完成檢測，成本僅約50元，是現時化學檢測方法成本的六分之一。

Date: 
Wednesday, December 16, 2020
Media: 
信報
Media LInk: 
https://is.gd/oVsLFs

CUHK Develops Novel Microrobotic Diagnostic System to Accurately Diagnose Infectious Pathogens with Full Automation and Low Cost

Date: 
2020-12-16
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CUHK has recently developed a fully automated, low cost and rapid microrobotic diagnostic system with comparable sensitivity and specificity to clinical detection methods. The research team is now studying the application of this microrobotic system for multiple pathogens including the COVID-19. This system has been developed by a collaborative research team led by Professor Li ZHANG, Associate Professor, Department of Mechanical and Automation Engineering, Professor Margaret IP, Professor, Department of Microbiology, Professor Joseph SUNG, Mok Hing Yiu Professor of Medicine and Director of the Institute of Digestive Disease, and Professor Sunny WONG, Associate Professor, Department of Medicine and Therapeutics.
 
Pathogen detention is indispensable for accurate diagnosis of diseases. As the pandemic of COVID-19 rages worldwide, the soaring demand for rapid testing has led to heavy workload for laboratory personnel on an unprecedented scale. Worse still, the longer the hospitalisation of patients, the higher the risk of patients being infected by pathogens that can be fatal. Hence, prompt clinical diagnosis is critical for patients showing signs of suspected infection. Under the dual pressure of pandemic and existing medical needs, shortages of medical manpower and resources including the laboratories may delay diagnosis, which can result in the suspension of other necessary medical procedures. Professor Margaret IP said, “With globalisation, the spread of infectious diseases is not restricted to geographical areas. To enhance the diagnosis of infections and control, testing using automatic rapid detection systems is the general trend.”
 
Professor Li ZHANG and his team have developed an innovative microrobotic detection system, integrating the novel fluorescent microrobots with an external magnetic actuation system to accurately detect specific pathogens in a short time. The microrobotic sensing probes are G. lucidum spores coated by a layer of iron oxide nanoparticles and functionalised with carbon dots. By analysing the changes in the fluorescence signal of the microrobots under green light excitation, the system can determine the presence of pathogen in patients’ samples. In addition, the system uses an external magnetic field to remotely actuate the microrobots, speeding up the fluorescence quenching and thus shortening the detection time.
 
The first generation of microrobotic detection system, “QuickCAS”, aims at detecting Clostridium difficile (C. diff), a common pathogen of nosocomial infection. The research team is now entering clinical trials, with the goal of testing in hospitals next year. In view of the pandemic disease, the team has worked closely with Professor Margaret IP in utilising the microrobotic detection system for the COVID-19 diagnosis. The development of multiple pathogens detection using the microrobotic detection system is underway, covering common pathogens such as Streptococcus pneumoniae, Salmonella, pathogenic Escherichia coli and Helicobacter pylori, and it is expected to benefit medical institutions worldwide.
 
Current chemical detection methods rely on the reaction between pathogen and biomolecular reagents. These bioreagents typically need to be refrigerated or frozen to preserve their structure and viability. On the other hand, the microrobots are stable for transportation and storage under room temperature. Professor Li ZHANG said, “As QuickCAS uses physical detection methods, the reagents do not require refrigeration. It successfully breaks through the pain points of current chemical detection methods. In the future, medical centres in remote and poor areas or small scale healthcare service providers will have the opportunity to provide accurate clinical diagnostic services.”
 
Additionally, the existing methods of C. diff detection take 2 to 4 hours, but QuickCAS only takes 15 to 30 minutes to complete, and the cost has been greatly reduced from approximately HK$300 to about HK$50 per test. The automated system can not only provide hospitals with timely diagnosis and treatment for patients, but also reduce the workload and the risk of infection of medical staff during laboratory tests. Moreover, infection controls can be implemented earlier to prevent infection outbreak. By simplifying the testing procedures, even junior laboratory technicians with only basic training can operate the system, alleviating the pressure from the current shortage of experienced laboratory technicians.
 
Demonstrating 20 innovative projects at the “InnoCarnival 2020”
 
CUHK will participate in the InnoCarnival 2020, organised by the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government, from 23 to 31 December on the virtual exhibition platform. Members of the public are welcome to visit CUHK’s virtual booth to learn more about the microrobotic detection system and see another 19 innovative projects. This year, to deepen the understanding of the CUHK projects, an online quiz competition will be launched during the InnoCarnival 2020, with fabulous prizes to be won.
 
Date: 23 to 31 December 2020
Time: 24 hours
Virtual exhibition website: http://innocarnival.hk
 
This article was originally published on CUHK Communications and Public Relations Office website.
 

The project team demonstrates the detection process of microrobotic detection system, “QuickCAS”.

 

The sample chip hold by Professor Li ZHANG (left) will be put into “QuickCAS” for auto analysis. The team is developing the multiple pathogens detection in the future, by inserting different types of sample chip into “QuickCAS”.

 

The first generation of microrobotic detection system, “QuickCAS”, aims at detecting Clostridium difficile (C. diff), a common pathogen of nosocomial infection.

 

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Prof. Wong Kam Fai Elected Fellow of the Association for Computational Linguistics 2020

Date: 
2020-12-14
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Professor Wong Kam Fai, Associate Dean (External Affairs) of the Faculty of Engineering and Director of the Centre for Innovation and Technology), CUHK has been named Fellow of the Association for Computational Linguistics (ACL) in the class of 2020, in recognition of his significant contributions to social media processing, particularly in Chinese information retrieval, opinion mining, microblog processing and rumour detection.
 
Established in 2011, the ACL Fellows programme recognises its members whose contributions to the field of computational linguistics have been most extraordinary in terms of scientific and technical excellence, service to the association and the community and/or educational or outreach activities with broad impact.
 
Professor Wong is among the first researchers to investigate natural language processing (NLP) for microblogs, such as Twitter and Wechat. In today’s social media, the results of this timely research work are influential for future work in social media analysis. As a pioneer in the field, Professor Wong used different discourse integration techniques to identify contextual information in microblog repost trees, and proposed a novel summarisation system by effectively differentiating leader and follower messages on repost trees based on content-level structure information. He also introduced temporal features for microblog processing, especially for rumour detection. In his research paper “Detect Rumours Using Time Series of Social Context Information on Microblogging”, he adopted the change of word and emoji patterns over time for rumour detection. It became an open corpus for rumour detection, which has been widely used by the community, with over 400 citations globally over the past three years. In recent years, his work in rumour detection has been well published in ACL.
 
Over the years, Professor Wong has always been keen to transfer impactful research to commercial application. He co-founded Wisers Information Ltd in Hong Kong in early 1998 with his student, opening up a whole new era of newspaper digitisation, ahead of giants like Google and Baidu. In addition, he received the Second-class Award in Scientific and Technological Progress from the Ministry of Education, China (2017) based on his applied research and development work on the industrialisation of trilingual-based retrieval and understanding platforms on large scale social media. The system “SAMUL”, a toolkit for sentiment analysis for multi-language application developed by his team has contributed to software development kits for multilingual sentiment analysis, which includes extraction of credibility, content and mood of the message author. SAMUL can read Cantonese, Mandarin and English, and is eligible to be extended to other alphabetic languages. With SAMUL, his team received Silver Medal and Prize of the Ministry of Scientific Research and Innovation- Romania in the 47th International Exhibition of Inventions of Geneva in 2019.

Professor Wong Kam Fai

 

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