Department of Electronic and Information Engineering

Artificial Intelligence and Robotics Focus Area

Leader  : Professor Kenneth Lam

Deputy Leader  : Professor M.W. Mak

Members : Dr Chris ChanDr Y.L. ChanDr H. HuDr Bonnie LawDr Frank LeungDr K.T. LoDr Daniel Lun


The “Artificial Intelligence and Robotics” Focus Area explores basic research on artificial intelligence and robotics, in particular extending artificial intelligence to robotics. The missions of the team are:

  • to carry out original research on artificial intelligence and robotics, and their applications;
  • to act as a strong research base to foster international collaborations; and
  • to transfer academic research results into practical uses to benefit the industrial, commercial, and service sectors.

Research is carried out on machine learning, computer vision, and speech and image processing techniques for

  • 3D and multi-view videos, coding, transcoding, ultra HDTV, mobile videos (Dr Y.L. Chan)
  • 3D scene and object reconstruction, 3D object detection and recognition (Professor Kenneth Lam, Dr Daniel Lun)
  • Bioinformatics, DNA in signal processing, forensics (Dr Bonnie Law)
  • Computational intelligence, intelligent control, robotics (Dr Frank Leung)
  • Facial image analysis, emotion recognition, face recognition (Professor Kenneth Lam)
  • Image and video super-resolution, image restoration and processing (Dr Chris Chan, Professor Kenneth Lam)
  • Privacy-preserving machine learning, adversarial machine learning, deep learning, deep neural networks (Dr H. Hu, Professor Kenneth Lam, Dr Daniel Lun, Professor M.W. Mak)
  • Speech processing, recognition, speaker verification (Dr Daniel Lun, Professor M.W. Mak)


Professor Kenneth Lam

Professor Lam has been actively involved in professional activities. In particular, he was the Secretary of the IEEE ICIP 2010, a Technical Co-Chair of IEEE PCM 2010, and a General Co-Chair of IEEE ICSPCC 2012, APSIPA ASC 2015, and IEEE ICME 2017. He was the Chairman of the IEEE HK Chapter of Signal Processing between 2006 and 2008. He received an Honorable Mention of the Annual Pattern Recognition Society Award for outstanding contribution to the Pattern Recognition Journal in 2004, and a number of best paper awards.

Professor Lam was the Director-Student Services and the Director-Membership Services of the IEEE Signal Processing Society. He was an Associate Editor of IEEE Trans. on Image Processing, and Digital Signal Processing. He was also an Editor of HKIE Transactions, and an Area Editor of the IEEE Signal Processing Magazine. Currently, he is the VP-Publications of the Asia-Pacific Signal and Information Processing Association (APSIPA). He also serves as an Associate Editor of APSIPA Trans. on Signal and Information Processing, and EURASIP Int. Journal on Image and Video Processing. His current research interests include human face recognition, image and video processing, and computer vision.

Research highlights:

Professor Lam has published more than 110 journal publications and 150 conference publications. More than half of his publications are on face-image analysis and recognition. He has published many good technical papers, with good citations. In particular, he has proposed many different algorithms for tackling the problems of illumination, facial-expression, and pose variations for face recognition, as well as algorithms for face detection, facial landmark localization, facial feature extraction, etc. In recent years, he has focused more on research on low-resolution (LR) face recognition, where he proposed super-resolving facial features, rather than super-resolving the LR query face. By super-resolving facial features directly from the low-resolution query image, the estimated higher-resolution features are more accurate than those extracted from a super-resolved, distorted face image. >He also proposed an approach, which considers face super-resolution and recognition, simultaneously. These two tasks are related and can help each other. Considering them simultaneously can achieve better performances for both face super-resolution and recognition. He also proposed new research for high-resolution face recognition. His proposed idea is totally different from conventional face-recognition methods. Rather than analysing and comparing the facial features between face images for face recognition, he devised a method to detect pores in face images, and matching two faces using pore-scale features. The advantage of the approach is that the pore patterns are unique for different people, and this is sufficient to make an accurate decision by using a small facial skin region. He has also built a data set for learning pore patterns. Recently, he has also proposed various algorithms for age-invariant face recognition. In particular, feature-encoding methods have been applied to further enhance the robustness of deep features.

A large number of the pore patterns from two face images of the same subject can be matched, but only a few pore patterns can be matched for face images of different subjects.

Dr Daniel Lun

Dr Lun is a Chartered Engineer, a fellow of IET, a corporate member of HKIE and a senior member of IEEE. He has published more than 150 international journals and conference papers in the areas of wavelets theory, signal and image enhancement, computational imaging and deep neural networks. He and his research students have also received three best paper awards in international conferences. Dr Lun is active in professional activities. He was the Chairman of the IEEE Hong Kong Chapter of Signal Processing in 1999–2000. He was the General Chair/Co-Chair and Technical Co-Chair of a number of international conferences. He received the Certificate of Merit from the IEEE Signal Processing Society for dedication and leadership in organizing the 2010 IEEE Int. Conference on Image Processing (ICIP). He was the Editor of HKIE Transactions published by the HKIE in the area of Electrical Engineering and also the leading guest editor of a special issue of EURASIP Journal of Advances in Signal Processing. He is currently an Associate Editor of IEEE Signal Processing Letters and IEEE Open Journal of Circuits and Systems.

Research highlights:

In recent years, Dr Lun’s research team focuses on the investigation of deep learning based signal and image processing methods. In their works on 3D object measurement, efficient data-driven fringe projection profilometry (FPP) techniques were developed to improve the robustness of the 3D scanning operation. It was the first time that both the 3D information and 2D texture of the target object could be obtained by a single-shot FPP process. The team also made important contribution to the study of image reflection removal. New multiple-view and single-view image reflection removal algorithms were proposed. In these algorithms, the Wasserstein Generative Adversarial Network was used to effectively regenerate the background gradients for the reconstruction of the background image while removing the reflection image. For the work in vision-based place recognition, discriminative ConvNet features were adopted for the development of a place recognition method that can effectively search the scene database to identify the scene that best matches with the captured scene. The team also applies deep learning technique to structural health monitoring. By analyzing the electromechanical impedance (EMI) signals around a mechanical structure using a Convolutional Neural Network, its health condition can be predicted with an accuracy of over 90%.

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Dr Y.L. Chan

Dr Chan is actively involved in professional activities. In particular, Dr Chan serves as an Associate Editor of IEEE Trans. on Image Processing, which is the best journal in the field of image and video processing. He was the Secretary of the 2010 IEEE Int. Conference on Image Processing (ICIP 2010), which is the IEEE sponsored flagship conference in the image processing area. He was also the Publications Chair of the IEEE Int. Conference on Multimedia and Expo (ICME 2017), the Technical Program Co-Chair of 2014 Int. Conference on Digital Signal Processing (DSP 2014), etc. Besides, he has published over 130 research papers in various international journals and conferences. Many of them appear in the world’s top-level international journals, such as IEEE Trans. on Image Processing, IEEE Trans. on Circuits and Systems for Video Technology and IEEE Trans. on Multimedia. His research interests include multimedia technologies, image and video compression, video transcoding, digital TV/HDTV, 3DTV/3DV, Multiview plus depth coding, machine learning for video coding, and future video coding standards including Versatile Video Coding (VVC), screen content coding, light-field video coding, and 360-degree omnidirectional video coding.

Research highlights:

The current research focus of Dr Chan is Content-Aware Video Coding. Dr Chan and his group has witnessed the rapid developments of various video coding technologies in the past decade. Recently, many different types of new videos attract both academic and industry researchers into new application domains. It includes screen content video, light field video, 360 degree video, etc. The group’s present and future compression achievements gear towards different directions as shown in the below figure. The state-of-the-art video coding standards treat each video equally, and are content unaware. To handle a wide variety of different video characteristics, different coding units (CUs) within the same frame may be coded with different modes and be partitioned into different sub-blocks, which results in a huge number of different modes for handling different types of videos. The group believes that various types of video will have different characteristics. The idea is to adaptively exploit the features in various types of video by machine learning such that the coding framework can make very fast decision with negligible quality loss. It means that the group’s coding framework is now content aware. In the recent development, the group successfully designed online-learning-based Bayesian Decision Rule for fast intra mode and CU partitioning algorithm in HEVC screen content coding. The BDBR and complexity performance are now the best in the literature. The group  then uses the decision tree, random forest and deep learning for SCC mode decision resulting in further BDBR improvement. These works are the first few researches using the learning approaches for coding the new screen content video. For other new video formats such as light-field and 360 degree videos, the group can further extent this concept to these application domains. Up to now, there is no work in these areas using the learning approach for coding these types of video. Dr Chan and his group believe that their current research is heading to a direction where fruitful results could be achieved in the development of the next generation video coding.

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Dr Chris Chan

Dr Chan has published over 160 research papers in various international journals and conferences. His research interests include image and video compression, image restoration, and fast computational algorithms in DSP. Dr Chan is a member of IEEE. He obtained the Sir Edward Youde Memorial Fellowship in 1991 and 1992.

Dr Chan was the Chair of IEEE Hong Kong Section in 2015 and is currently Treasurer of Asia-Pacific Signal and Information Processing Association (APSIPA) Headquarters.

Research highlights:

Halftoning: Dr Chan and his team proposed a universal noise model for defining the optimal noise characteristics of a halftone. Based on the model, various halftoning algorithms were developed to satisfy different constraints for different applications. All of them can produce image hard copies of the best quality as compared with conventional algorithms in terms of various measures in different application scenarios. One of the most useful and unique features that these algorithms can achieve is that they can control the noise spectrum of their outputs and preserve the spatial features of the input in their outputs simultaneously.

Information embedding: Through various noise manipulation techniques, the team embed different information in images to achieve different goals for different applications. Some recent successful trials including scalable image presentation, image watermarking, reversible color-to-grayscale conversion and colorblind friendly printing.

Fringe Projection Profilometry: Various contributions have been made to significantly improve the accuracy in 3D surface measurement. The team was the first group to propose octa-level fringe patterns and get rid of the harmonic distortion introduced by periodic fringe patterns. This allows to realize real-time measurement and improve the measuring accuracy.

Dr H. Hu 

Dr Hu has published over 80 research papers in refereed journals, international conferences, and book chapters. Dr Hu’s research interests include: (1) mobile service authentication and assurance, (2) adversarial machine learning and defence techniques, and (3) privacy-protection techniques for networks,  IoT and mobile payment systems.  As the principal investigator, he has received over 10 million HK dollars of external research grants from Hong Kong and the mainland China, including 6 GRF projects, 1 NSFC project, 1 ITF project, and 2 Huawei collaborative research projects. He is the guest editor of ACM Transactions on Data Science, and has chaired or served in the organizing committee of many international conferences, such as ACM GIS 2020, MDM 2019, DASFAA 2011, DaMEN 2011, 2013 and CloudDB 2011, and in the programme committee of dozens of international conferences and symposiums.

Research Highlights:

Mobile service authentication and assurance: To address the trustworthy issue in mobile services, e.g., mobile social networking services, service providers should deliver services in an authenticatable manner, so that the correctness of the service results can be verified by users. Dr Hu and his group in this field have advanced the state-of-the-art authentication and assurance schemes without compromising user privacy and service efficiency.

Adversarial machine learning and defence techniques: Adversarial machine learning is the study of machine learning theory and tools in a hostile environment with internal or external adversaries. Typical security attacks in adversarial machine learning include sample poisoning, model extraction, and model evasion. The group’s works on model extraction attack and defensive skills have pushed the state-of-the-art techniques to a new level with both theoretical guarantee and practical feasibility.

Privacy-protection techniques for networks, IoT and mobile payment systems: The emerging IoT and 5G networks arouse public concern for privacy protection. The group’s research on privacy has spanned wide application domains, including databases, location-based services, wireless networks, and mobile payment systems. The group has employed a variety of technical solutions such as homomorphic encryption, differential privacy, oblivious RAM, and hardware-based trusted execution environment (TEE), such as Intel SGX and ARM TrustZone.

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Security Bootcamp 2019, co-organized with Internet Society

Dr Bonnie Law

Dr Law’s research interests include wavelet transform, image enhancement and retrieval. Recently, she has extended her study into new areas on bioinformatics and image forensics. The former involves works on gene expression and DNA sequence analysis while the latter considers source camera identification. Novel results for gene expression data analysis, biclusters detection and source identification have been achieved and published in international journals with good attention from peers, for example a paper in 2008 has been classified as a “highly access” article in BMC Bioinformatics journal (with the number of accesses of 6310 up to 7 Oct 2016).

Research Highlights:

Dr Law’s main research focus includes signal processing, DNA sequence analysis and source camera identification. Digital data has been abundant. Its source will be important in forensics analysis which can help in copyright identification or crime investigation. Dr Law and her group have worked on using various signal processing techniques for reliably identification of the source camera from a photo. In DNA sequence analysis, Dr Law worked actively on multiple DNA sequences compression in which a group of sequences are grouped and compressed simultaneously. Greedy methods have been developed to search for similarities within a group of sequences. Details regarding the greedy method and the software can be found at https://www.eie.polyu.edu.hk/~nflaw/RCC/index.html.

Dr Frank Leung

Dr Leung is an active researcher. He has published over 200 research papers and one book on Computational Intelligence, Intelligent Control, Signal Processing, and Power Electronics. His research work has a Scopus h-index of 30 with over 3200 citations. At present, he is involved in the R&D on Intelligent Signal Processing and Robots. He has served as editor, guest editor and reviewer for many international journals. He also helped the organization of many international conferences. Currently, he is Editor of the HKIE Transactions and Guest Editor of Sensors. He is also the Leader of the FENG Robotics Club.

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Robot for shooting flying-disc with machine learning

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Semi-automatic messenger-robot

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Automatic four-leg robot

Research Highlights:

Dr Leung’s current research focuses on the following three topics:

Intelligent Optimization and its Applications: Hypoglycaemia is a medical term for a body state with a low level of blood glucose. It is a common and serious side effect of insulin therapy in patients with diabetes. The group proposed a system model to measure physiological parameters continuously to provide hypoglycaemia detection for Type 1 diabetes mellitus (TIDM) patients. The contribution include the design of: (1) A model of fuzzy inference system (FIS) for hypoglycaemia detection, and (2) An intelligent optimiser for the FIS parameters.

Structure-Based Drug Design Using Computational Intelligence Techniques: Imbalanced datasets are commonly encountered in real-world classification problems. Re-sampling has become an important step to pre-process imbalanced data. A sampling strategy that is based on both over-sampling and under-sampling is proposed. The contribution is a hybrid pre-processing method (FRB+CHC) to re-sample imbalanced datasets that: (1) Creates the new samples of the smaller class based on fuzzy logic, and (2) Improve the datasets by the evolutionary computational method that under-samples both the minority and the majority samples.

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Distribution of the samples after over-sampling. The y-axis represents the values of z2 and x-axis represents the value of z1

Identification of protein-ligand binding site is an important task in structure-based drug design and docking algorithms. Different methods have been developed to predict the binding site. When scores are calculated from these methods, the algorithm for doing classification becomes very important for the prediction. The group propose the use of support vector machine (SVM) to cluster the pockets that are most likely to bind ligands based on selected attributes. The approach offers improved results over other well-known methods on the dataset LigASite and 198 drug-target protein complexes.

A Computational Intelligence Based Classifier and its Applications: Microphone recognition aims at recognizing different microphones based on the recorded speeches. In the literature, Gaussian Supervector (GSV) has been used as the feature vector representing a speech recording, which is obtained by adapting a Universal Background Model (UBM).  The team looked into GSV in detail. To improve the quality of the raw GSV, it is proposed that a kernel-based projection method that maps the raw GSV onto another dimensional space. The contribution include: (1) Confirming that GSV can outperform the averaged frame-level features, (2) Finding how GSV is affected by the UBM and other parameters, and (3) Separating the microphone response information and the speech information into different dimensions in the projected feature space for improved microphone recognition.

Professor M.W. Mak

Professor Mak has served as an associate editor of IEEE Trans. on Audio, Speech and Language Processing, Journal of Signal Processing Systems, and IEEE Biometric Compendium. He also served as a Technical Committee member of IEEE MLSP. He is a tutorial speaker in Interspeech’2016.  Professor Mak’s research interests include speaker recognition, machine learning, and bioinformatics. Professor Mak has been working extensively on machine learning, speaker recognition, and bioinformatics. He has published over 190 papers and three books in these areas. His work has been cited 3033 times (according to Google Scholars) by researchers from various countries, illustrating the international interest in his innovative work.

Research Highlights

Speaker Recognition: Professor Mak’s recent paper in domain-invariant speaker verification is the first attempt in speaker recognition that minimizes the discrepancy among multiple domains in the embedding layer of autoencoders. Professor Mak also contributes significantly to noise-robust speaker verification. He and his students proposed the SNR-invariant mixture of PLDA and DNN-driven mixture of PLDA are among the first to explore the relationship between the noise-level variability in speech and the multi-modal distribution of i-vectors.  Dr  Mak also introduced multi-task deep learning for score calibration and i-vector denoising. His another contribution in robust speaker verification is the non-parametric discriminative subspace modelling, which is the first paper that introduces SNR and duration subspaces to PLDA modeling. The work is significant in that it leads to a general model that opens up an opportunity for researchers to model all sort of variability in utterances, not only limited to noise level variability. Dr  Mak has published two textbooks: Biometric Authentication: A Machine Learning Approach, Prentice Hall, 2005 and Machine Learning for Speaker Recognition, Cambridge University Press, 2020.

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Sound-event detection and speaker recognition

Bioinformatics and Biomedical Engineering: Professor Mak has extensive contributions in bioinformatics and biomedical engineering. His research papers have appeared in Journal of Theoretical Biology, Bioinformatics, BMC Bioinformatics, PLoS One, IEEE Journal of Biomedical and Health Informatics, IEEE/ACM Trans. on Computational Biology and Bioinformatics, among other renowned journals in the field with very rigorous criteria for publication. Professor Mak has also co-authored a book, Machine Learning for Protein Subcellular Localization Prediction. Professor Mak is the first researcher who brought the idea of i-vectors to the biomedical engineering community. In particular, he applied i-vectors to capture the characteristics of patient-dependent heartbeats and used the i-vectors to adapt a patient-independent ECG classifier to patient dependent one.

Dr K.T. Lo

Dr Lo has published over 180 papers in various international journals and conference proceedings. In 2007, one of his papers on digital video processing won the Most Cited Paper Award for the Journal of Visual Communication and Image Representation. In 2012, his other paper on an analysis of multimedia encryption scheme was awarded the First Prize of the Natural Science Paper Award by Xiang Tan City, Hunan Province, P.R. China. His current research interests are in the areas of multimedia content protection, image and video coding, privacy preserving image retrieval, intelligent content distribution and Internet applications.

Research highlights:

Multimedia Content Protection: Dr Lo has been working on multimedia content protection for years. He has made contribution to the area of cryptanalysis of many different multimedia encryption techniques. Based on the findings of his cryptanalysis work, a number of image encryption techniques have been developed. Recently, he has derived a content adaptive joint image compression and encryption technique. Extensive experiments and security analysis are conducted to show the good compression and encryption performance of the group’s proposed encryption scheme. Currently, Dr Lo is working on developing encryption schemes for dedicated applications areas such as encrypted domain image processing schemes and privacy preserving image retrieval systems.

Privacy Preserving Image Retrieval System: In order to preserve the privacy of the owners of images, people tend to convert the multimedia data into unrecognizable data before uploading it to the cloud so that the content of the plain images will not be exposed to others even when the server is hacked. However, this arrangement is not compliant with plain-text based conventional information retrieval techniques. The relevant research work is trying to apply machine learning techniques to derive an effective way in measuring image features in encrypted domain and developing similarity measures for different features so as to facilitate efficient image retrieval for two classes of images: one requires high compression and computational efficiency but light protection like those images shared in various social networks, and the other class emphasizes on high security level for those high sensitive images like medical and military images.

Future Wireless Networks and IoT Focus Area

Leader: Professor Francis Lau

Members : Dr Ivan HoDr H. HuDr W. LiDr L. LiuDr K.H. Loo, Dr Y. Mao, Dr Q. Ye, Dr S. Zhang


Mobile communication networks (also called cellular communication networks) were first deployed in the 1980’s. Over the past four decades, they have evolved from the first generation (1G) analog networks, to 2G digital networks (e.g., GSM and CDMA), and then to the subsequent 3G and 4G networks. During this period of time, other wireless technologies such as WiFi and Bluetooth have also emerged and advanced continually. While these mobile/wireless networks were originally designed for human-to-human communications (e.g., voice call) or human-to-machine communications (e.g., video streaming and online games), they have recently been heavily used for machine-to-machine communications (e.g., remote sensing, fleet management, Internet of Things (IoT) devices).

According to the reports published by Statista, the number of mobile users worldwide is estimated as 6.8 billions in 2019, and is forecast to increase to 7.33 billions in 2023. Over the same period, the number of IoT devices will increase from 26.66 billions to 51.11 billions. To fulfill the ever increasing demands (e.g., more users, higher download speeds, shorter time delay) from a wide range of applications (vehicular communications, augmented reality, smart city, smart factory), the fifth generation (5G) wireless networks have begun their commercial deployments in 2019. Moreover, the largest deployment is seen in China, where the number of 5G users is predicted to be 110 millions by 2020.

The “Future Wireless Networks and IoT” Focus Area at the Department of Electronic and Information Engineering aims at researching fundamental problems related to 5G and beyond networks as well as IoT devices. Research topics include cybersecurity, vehicular networks, channel coding, Edge AI, etc.  The team is very active in teaching, research as well as professional society activities.  The team receives research funding from the Hong Kong government, the China government, and industrial partners such as Huawei. The team also carries out consultancy works for the Hong Kong government (e.g, Highways Department and OGCIO) and the industry (e.g., HKJC). The team members are briefly introduced in the following,

Dr Ivan Ho

Dr Ho’s group has extensive research experience in vehicular networks and connected vehicles. The Internet of vehicles is a very special type of Internet of things. To accurately model the performance of vehicular networks, Dr Ho and his group need to characterize vehicular mobility and connectivity correctly. The team has developed methodologies for modeling the transmission performance of vehicular ad-hoc networks (VANET) based on heterogeneous vehicle distribution. Given basic traffic information, such as velocity and traffic control signals, the throughput and delay performance of the vehicular network can be readily computed as a dynamic function of space and time, which lays the foundation for future studies and applications of vehicle-to-everything (V2X) communications and networking.

In addition, with the increasing popularity of inter-vehicle communications and the development of new protocol standards, the roads will likely be filled with high-speed wireless users in the near future. To exploit interference in high-dense vehicular networks, Dr Ho and his group studied non-orthogonal data exchange among vehicles. Specifically, with the use of physical-layer network coding and multiuser detection techniques for data communications. For connected autonomous vehicles, the group has also addressed the problem of efficient 3D road map data dissemination through developing a number of techniques based on index coding, opportunistic scheduling, and hashing.

Other than vehicular networks, Dr Ho and his group is also active in the development of IoT applications. He primarily invented the MeshRanger series wireless mesh embedded system, which won Silver Award in Best Ubiquitous Networking at the Hong Kong ICT Awards 2012. His work on indoor positioning also received Gold Medal at the International Trade Fair Ideas and Inventions New Products (iENA) in Germany in 2019. Dr Ho served/is serving as an associate editor for IEEE Access and IEEE Transactions on Circuits and Systems II, and the TPC Co-chair for the PERSIST-IoT Workshops in conjunction with ACM MobiHoc 2019 and IEEE INFOCOM 2020.

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The vehicular fog computing architecture

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Non-orthogonal V2X and its experimental studies

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Gold Medal at the International Trade Fair Ideas and Inventions New Products (iENA) 2019

Dr H. Hu

Dr Hu’s research interests include cybersecurity, data privacy, Internet of things, and machine learning. Recently, he works in the following areas: (1) wireless service  authentication and assurance, (2) adversarial machine learning and defense techniques, (3) privacy-protection techniques for database and IoT systems, (4) hardware-assisted secure database and IoT systems, (5) privacy-preserving social networks and graph systems, and (6) advanced mobile payment and blockchain.

Dr Hu is the recipient of a number of titles and awards, including IEEE MDM 2019 Best Paper Award, WAIM Distinguished Young Lecturer, VLDB Distinguished Reviewer, ACM-HK Best PhD Paper, Microsoft Imagine Cup, and GS1 Internet of Things Award. He has been invited to give keynote speeches in several international conferences, including CSS 2019 and IEEE DSC 2019. He has held 4 US patents and 2 CN patent applications. He serves in the Technical Committee on Databases, CCF and Technical Committee on Privacy Protection, China Confidentiality Association.

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“Beware of Your Location Privacy and Integrity in Social Networks,” Hong Kong RGC Public Lectures on Cyber Security, May 7, 2017

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“Beware of Your Location Privacy and Integrity in Social Networks,” Hong Kong RGC Public Lectures on Cyber Security, May 7, 2017

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Best Paper Award, 20th IEEE International Conference on Mobile Data Management (MDM’ 2019)

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Best Theory Paper Award, FML’19, in conjunction with International Joint Conferences on Artificial Intelligence (IJCAI-19). Dr Hu’s research student, Zheng Huadi received this award from Professor Yang Qiang, President of IJCAI, Chief AI Officer of WeBank.

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Media interview by Oriental Daily on the award winning project "Eyebb: Hybrid Geofencing Systems for Elderly and Child Care"

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Media interview by Oriental Daily on the award winning project “Eyebb: Hybrid Geofencing Systems for Elderly and Child Care”

Professor Francis Lau

Professor Lau’s main research interests cover channel coding and digital communications. He has contributed to the design of channel codes and channel decoders, which can be used in all types of (wired or wireless) digital communication networks as well as data storage systems (e.g., thumbdrives). His group has proposed algorithms that analyze the thresholds and hence predict error performance of low-density parity-check (LDPC) codes. With the proposed algorithms, he optimizes the LDPC code designs. He has designed a number of codes that not only work close to the Shannon limit, but possess extremely low error floor. He has received a number of research funds in this area from the Hong Kong government and from Huawei. Recently, he has explored the use of channel codes to protect the integrity of data in the world-first peptide data-storage system. The concept has been successfully realized and tested by Professor Francis Lau’s team and the team led by Dr Zhongping Yao from the Department of Applied Biology and Chemical Technology (ABCT). The inter-disciplinary project has subsequently led to a Research Impact Fund (RIF) with a total value of $13.9M, and Dr Yao and Professor Lau are, respectively, the Principal Investigator (PI) and Co-PI of the RIF project.

Professor Lau has also contributed to the research of digital communications based on chaos – another inter-disciplinary research area. He pioneered the use of Gaussian approximation in analyzing chaos-based digital communications systems. His monograph entitled “Chaos-Based Digital Communication Systems: Operation, Analysis and Evaluation” has become the textbook for researchers/engineers working in this area and has received a good number of citations.

Professor Lau served/ is serving as an associate editor/guest editor/guest associate editor of IEEE Transactions on Circuits and Systems Part I and Part II; IEEE Circuits and Systems Magazine; IEEE Access; and International Journal of Bifurcation and Chaos. He was the General Chair of International Symposium on Turbo Codes and Iterative Information Processing 2018; Chair of Technical Committee on Nonlinear Circuits and Systems, IEEE Circuits and Systems Society (2013–14); Technical Program Chair of International Symposium on Nonlinear Theory and its Applications 2016; Track Chair of IEEE International Symposium on Circuits and Systems 2010.

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Professor Lau gave an opening speech as the General Co-chair at The 10th International Symposium on Turbo Codes & Iterative Information Processing 2018.

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Participants of the 10th International Symposium on Turbo Codes & Iterative Information Processing 2018 enjoying themselves at the banquet.

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Applying error correction codes to data stored in peptides

Dr L. Liu

Dr Liu’s research interests span a wide spectrum of wireless communications, ranging from wireless energy harvesting and power transfer techniques, the next-generation cellular network, to the emerging “Internet-of-Things (IoT)” technologies. Broadly speaking, Dr Liu’s past research works have contributed to several new breakthroughs for various notoriously hard problems in wireless powered communication networks (WPCN), cloud radio access networks (C-RAN), etc. Currently, Dr Liu’s research focus lies in machine-type communications for IoT applications. Along this line, his research has contributed to new random access schemes suitable for massive machine-type communications where the wireless networks have to accommodate a large number of IoT devices. Furthermore, he has devised novel communication protocols for ultra-reliable and low latency communications such that wireless technologies can be applied for industrial automation in the forthcoming era of Industry 4.0.

During the past 10 years, Dr Liu’s research outputs have made significantly high impact with over 3000 citations according to Google Scholar. Moreover, 8 out of his 22 IEEE journal papers have been listed as ESI highly cited paper. He was the recipient of the IEEE Signal Processing Society Young Author Best Paper Award in 2017, and the Best Student Paper Award of IEEE International Conference on Wireless Communications and Signal Processing in 2011. Furthermore, he was recognized as a Highly Cited Researcher by Clarivate Analytics in 2018.

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Dr Liu (left) was attending the ceremony to celebrate the award of Highly Cited Researcher.

Dr K.H. Loo

Dr Loo’s areas of research interest include power electronics, renewable energy systems, and lighting technologies. Currently, his research focuses on developing new topologies and modulation strategies for bidirectional dual-active-bridge (DAB) converter which is widely used in emerging power electronics applications such as grid-connected energy storage systems, vehicle-to-grid (V2G) enabled electric vehicles, solid-state transformers, smart transformers, energy routers, etc. These applications require wide-range high-efficiency operation which cannot be achieved with current DAB topologies and modulation schemes. To address this problem, Dr Loo and his team proposed several immittance-based topologies and modulation schemes for DAB converters that achieve full-range soft-switching and minimum conduction loss, thus realizing high-efficiency operation of DAB converter over a wide range of operating conditions. They also study the dynamics and control design of high-order immittance-based DAB converter.

Dr Loo has authored/co-authored over 100 peer-reviewed journal and international conference papers. He is also a co-author of two US patents and one China patent on LED driving technology. These works had won two Gold Medals in the International Exhibition of Inventions, Geneva, in 2009 and 2013. He serves as an Associate Editor for the IEEE Transactions on Energy Conversion (since 2013) and IEEE Open Journal of Circuits and Systems (since 2019), and a reviewer for various international journals and conferences. He is currently Chair of the Power Electronics and Control Sub-Committee of the IEEE Technical Committee on Transportation Electrification.

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A research student working on a 2.5-kW three-phase dual-active-bridge converter prototype

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A research student working on a 2.5-kW three-phase dual-active-bridge converter prototype

Photonic Systems and Devices Focus Area

Leader  : Professor C. Lu

Members: Dr W. Chen, Professor G. Li, Professor C. Yu


Photonics underpins various engineering applications in energy, imaging, sensing, communication, nanotech, advanced manufacturing and biomedical applications. Traditionally, photonics as a discipline of science is a large field of study on its own that probes the most fundamental aspects of nature with profound consequences to humanity. As an enabling technology, photonics is typically subsumed under the fields of communication, energy, imaging to name a few and each respective sub-field of photonics is itself a growing area of research.

PolyU has a long track record of research excellence in optical communications, sensing, energy, imaging and other biomedical applications. The group collaborated extensively with the industry with numerous world-record breaking experiments, technology commercialization and spin-off companies. Various research facilities have been established including fibre draw tower and high capacity optical communication system testbed.

The “Photonic Systems and Devices” Focus Area at the Department of Electronic and Information Engineering aims to study key devices and systems for energy, imaging, sensing and communication applications. The key members and their research areas are introduced in the following.

Dr W. Chen

Dr Chen has conducted research related to optical imaging and optical metrology. Bulk devices are usually applied to achieve high resolution in imaging and microscopy, which are not suitable in many applications, e.g., endoscopes. High-resolution imaging devices with high-numerical-aperture multimode fiber will be investigated to resolve the existing problems. Multimode fibers can be designed and applied as a promising alternative in imaging systems or devices to resolve the fundamental problems and achieve high resolution. The designed multimode fiber can have high numerical aperture by using a high contrast of refractive indices between fiber core and fiber cladding. Fiber with multimode method is designed and studied, therefore aperture synthesis approaches can also be investigated and applied to achieve super-resolution in the imaging systems or devices. Various imaging methods and microscopic technologies, such as digital holography, confocal microscopy, diffractive imaging and single-pixel imaging, will be studied and combined with a high-numerical-aperture multimode fiber. The designed multimode fiber can be used as illumination source in imaging systems to illuminate the samples, such as living tissues and cells. It is believed that the designed imaging devices or systems are highly desirable in various research and application fields, e.g., high-resolution measurement of biomolecules, living tissues, cells, and many complex environments (via endoscopes), and it can have a long-term impact on high-resolution biological imaging.

Another area of Dr Chen’s research focuses on optical lossless-data transmission, optical transmission security, and information optics. It has been well known that the data transmitted by using optical means could be affected by the propagation media, such as through scattering media. For a long time, it is always desirable that high-quality or lossless data transmission using optical means can be carried out in various complex propagation environments by using simple and effective approaches. High-quality data transmission using optical means will be investigated, and different complex wave propagation environments are tested. It is believed that the designed devices or systems can fully overcome the fundamental limits existing in optical transmission areas, and the strong industrial connection can be established.

Dr Chen has also conducted research related to information optics and optical lossless-data transmission, and has authored or co-authored more than 80 international journal papers and around 35 international conference papers (including many invited talks). The total citations of the published journal papers are more than 3000 with h-index of 30 in Google Scholar, and several published journal papers are ESI highly cited papers. The research work has been carried out to fully resolve the fundamental problems existing in practical applications related to optical imaging, optical lossless-data transmission and information optics. Dr Chen’s current research interest focuses on imaging systems or devices, computational imaging, information optics, phase retrieval, optical metrology, big data and deep learning for optical imaging, optical lossless-data transmission, and high-resolution biological imaging.

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High-quality single-pixel optical imaging through scattering media

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Optical devices or systems designed for secured lossless-data transmission

Professor G. Li

Professor Li’s current research interests are solution process semiconductors-based advanced materials (organic semiconductors and hybrid perovskite semiconductors), devices and physics for energy applications (mainly solar cells and LEDs), flexible and printable electronics.  Professor Li is a pioneer in the field of organic polymer photovoltaic (OPV) technology, and has done seminal contributions in OPV morphology, device physics, and device architecture. His Nature Materials paper on morphology of high performance polymer solar cell has been cited over 6100 times on Google Scholar.

Professor Li has a good mix of academic and industrial experience.  He was Vice President of California based solar energy start-up Solarmer Energy Inc. (till 2011), which became a world leader under his leadership. He was a Research Professor in UCLA from 2011 to 2016, focusing on interface engineering and tandem polymer solar cell research. He joins the EIE Department of PolyU in August 2016. His current research program in PolyU aims at synergistically enhancing the efficiency, stability and printability of the solar cell devices through device engineering, new materials /process innovation, advanced characterizations and coating process.

Professor Li has published ~130 papers on peer-reviewed journals including Nature Materials, Nature Photonics, Science, Nature Review Materials, Chemical Reviews, Nature Communications, etc., edited one book and contributed to 7 book chapters. His articles have been cited over 58,000 times (Google Scholar), in which 5 (17) articles were cited over 3000 (1000) times each. He has an H-index of 71.

Professor Li is a Fellow of The Royal Society of Chemistry UK (FRSC); and Fellow of The International Society for Optics and Photonics (FSPIE); Thomson Reuter/Clarivate Analytics “Highly Cited Researchers” since 2014 (Material Science 2014–2019; Physics 2017–2018; Chemistry 2018).

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Organic Polymer Solar cell Research – Morphology, Physics, Materials and Devices

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Printing Solar Energy Harvesting Devices

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Hybrid Perovskite Solar Cells and Light Emitting Diodes

Professor C. Lu

Professor Lu’s research group in EIE of PolyU mainly focuses on the investigation of high capacity optical transmission and signal process techniques and their application to distributed sensing systems.  In close collaboration with Professor Alan Lau, Professor H.Y. Tam and Professor C. Yu and through working with industry partners such as Huawei Technologies, the research group has demonstrated a number of innovative approaches for realizing high capacity long haul and short reach optical communication systems. Using signal processing techniques developed for optical communication systems, various distributed fibre sensing system with improved system performance was realized. These include coherent detected Brillouin Optical Time Domain Analyser (BOTDA) system, multicore fibre based multiparameter sensing systems and forward transmission based ultra-long distance distributed vibration sensing systems.  Innovative techniques have also been developed for key performance parameter monitoring for intelligent optical network systems. The work has been presented as a number of invited talks in key conferences such as OFC, OECC and ACP and as invited papers and book chapters in leading journals of the area. A joint lab has been established with Huawei as a result of the close collaboration of which Professor Lu is the director.  Professor Lu was leading a research group in the area of optical communication and fibre devices at Nanyang Technological University and Instititure for Infocomm Research A*STAR Singapore before joining PolyU. He has worked in optical communication and fibre device area for more than 30 years. He is Fellow of the Optical Society (OSA).

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Short Reach Optical Transmission System

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 Multicore Fibre Based Distributed Sensing System

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Forward Transmission Based Distributed Vibration Sensing System

Professor C. Yu 

Professor Yu’s research group focuses on photonic devices, subsystems, optical fiber communication and sensor systems, and biomedical instruments, including 3 main topics: Carrier recovery in optical fiber communication system; Optical performance monitoring in fiber network; Optical signal processing/sensing using fiber.  As a faculty member in Singapore and Hong Kong in the last 15 years, Professor Yu has secured over 7 million US dollars grants as the PI/Co-PI, and supervised 10+ postdocs and 20+ PhD students.  Till 09/2019, he has authored/co-authored 6 book chapters, 400+ journal and conference papers (82 keynote/invited, including OFC2012 in the USA).  His group won 6 best paper awards in conferences and the championship in biomedical area in the 3rd China Innovation and Entrepreneurship Competition in 2014.

As an interesting example, his group has investigated the application of specialty optical fibers on vital signs monitoring. Healthcare has been drawing wide concern in modern society, especially in developing countries like China. Human health condition is usually assessed by some physical indicators, such as body temperature, respiration and heartbeat. Among these vital signs, respiration and heartbeat are the most widely used indicators. Respiration and heartbeat can reflect the working condition of lung and heart in order to pre-diagnose specific cardiac and pulmonary diseases. Thus, reliable as well as accurate monitoring on respiration and heartbeat has become more and more necessary, and various schemes have been proposed. Currently, wearable sensors like ECG devices for heartbeat monitoring, are the most popular methods and has been widely used in hospitals. However, these sensors require close contact or attachment on skin, which is not convenient to specific users like the elderly and children. Thus, contactless sensing schemes are drawing attention and one of them is the optical fiber sensor. Optical fiber sensor owns the advantages of high sensitivity, lightweight and electromagnetic immunity, which can be a potential candidate in healthcare applications. To explore its application on contactless vital signs monitoring, specialty optical fibers, namely few-mode and multi-core fibers are investigated in this work. Based on these two kinds of optical fibers, in-line interferometers are designed and packaged as smart mats under the mattress. The results show that vital signs signals can be detected in a contactless way, and both respiration and heartbeat ratio can be obtained with low errors. The smart bed with fiber sensor was selected to in the exhibition of the Gerontech and Innovation Expo cum Summit (GIES) in Hong Kong Convention & Exhibition Centre in 2018, which attracted lots of visitors.  Professor Yu’s group also won the silver award (out of 1916 competitors from China and overseas) in 2019 Guangdong College Students Entrepreneurship Innovation Competition, which was reported by Chinese media such as South Net and Guangdong TV.

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Vital signs monitoring experimental setup

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The smart bed with fiber sensor was selected to the exhibition of the Gerontech and Innovation Expo cum Summit (GIES) in Hong Kong Convention & Exhibition Centre in 2018.