Tutorial #1: Ekachai Leelarasmee |
Tutorial #2: Tomasz M. Rutkowski |
Tutorial #3: Waleed H. Abdulla and Iman Ardekani |
Tutorial #4: Y.-W. Peter Hong and Tsung-Hui Chang |
Tutorial #5: Koichi Shinoda and Jen-Tzung Chien |
Tutorial #6: Weisi Lin |
Tutorial #7: Ying-Dar Lin |
Tutorial #8: Oscar C. Au |
Title: Power Line Communication: Introduction and Implementation to Solar Farm Monitoring
Speaker: Prof. Ekachai Leelarasmee
Affiliation: Chulalongkorn University, Bangkok, Thailand
The use of power cable to carry both electrical energy and communication signal is presented. This power line communication or PLC technologies transmit signal into the cable in the form of modulated carrier at high frequency around 100kHz or more. PLC finds its uses in many areas such as home automation, internet access and automatic meter reading. Several standards such as G3 and PRIME have been proposed and implemented. Its advantages lie in the fact that no additional communication wires are needed and it can cover a larger area than using radio frequency, e.g. 2.4GHz, communication.
Although PLC is well known for AC power lines where communication devices are connected in parallel, it can be used in another situation where these devices are in series connection. Such a situation can be found in a DC solar farm where voltages of series connected photovoltaic panels are to be monitored remotely. Circuits that implement this type of transmission is quite different from the normal parallel PLC and will be described.
Speaker Photo and Bio
Ekachai Leelarasmee (IEEE S’2005) received Bachelor of Engineering (first class honor) in Electrical Engineering from Chulalongkorn University, Bangkok, Thailand in 1974 and Ph.D in the area of Integrated Circuit Design and Simulation Algorithm in 1982 from University of California at Berkeley where his pioneering work on the Waveform Relaxation Method earned him 3 prestigious awards Since 1974, ha has joined the teaching staff at the Electrical Engineering Department of Chulalongkorn University working on research and development projects in circuit simulation, IC and embedded system design that have won several invention awards from National Research Council of Thailand. Some of his works include design of triangular to sine shaper with quadrature outputs and smart meters. Currently, he is actively involved in solar farm monitoring system in which data are carried through the DC power lines. Dr. Ekachai Leelarasmee publishes approximately 1 journal paper and 3 conference papers a year. He is also the chair of IEEE Thailand Solid State Circuits chapter.
Title: Beyond the visual and imagery based BCI – The new developments in spatial auditory
and somatosensory based paradigms
Speaker: Prof. Tomasz M. Rutkowski
Affiliation: University of Tsukuba, Japan
The tutorial content is based on the workshops to be delivered this year by the proposer at IEEE World Haptics Congress 2013 and BCI Meeting 2013. State-of-the-art stimuli-driven BCI paradigms rely mostly on visual modalities. Recently somatosensory (tactile or haptic) and auditory modality approaches have been proposed to offer alternative ways to deliver sensory stimulation inputs which could be crucial for patients suffering from weak or lost eye-sight or hearing. Already several techniques have been developed to connect the BCI to a traditional haptic interface or to utilize those interfaces as stimulation sources. The tutorial will present recent developments and discuss pros and cons of the tactile and auditory based BCI approaches. Vibrotactile stimulation brings also a possibility to create bone-conduction sensory effect in case of the head area exciters application. This concept, created by the tutorial author and collaborators, is still very preliminary yet already has existing applications. It brings a very interesting possibility to deliver multimodal stimuli (somatosensory and auditory combined) to TLS/ALS subjects with a very fast information transfer rate. I will also present classical haptic HCI examples to discuss possible applications for future BCI prototypes. The new BCI paradigms bring also the novel data driven signal processing and machine learning methods which will be presented and discussed during the tutorial.
Speaker Photo and Bio
Tomasz M. Rutkowski received his M.Sc. in Electronics and Ph.D. in Telecommunications and Acoustics from Wroclaw University of Technology, Poland, in 1994 and 2002, respectively. He received a postdoctoral training at the Multimedia Laboratory, Kyoto University, and in 2005-2010 he worked as a research scientist at RIKEN Brain Science Institute, Japan. Currently he serves as an assistant professor at the University of Tsukuba and as a visiting scientist at RIKEN Brain Science Institute. Professor Rutkowski’s research interests include computational neuroscience, especially braincomputer interfacing technologies, computational modeling of brain processes, neurobiological signal and information processing, multimedia interfaces and interactive technology design. He is a senior member of IEEE, a member of The Society for Neuroscience, The Asia-Paciﬁc Signal and Information Processing Association (APSIPA), and The Japan Neuroscience Society. He is a member of the Editorial Board of Frontiers in Fractal Physiology and serves as a reviewer for “IEEE TNNLS, IEEE TSMC – Part B, Cognitive Neurodynamics, The Journal of Neural Engineering, and PLOS One.
Title: Active Noise Control: Fundamentals and Recent Advances
Speaker: Prof. Waleed H. Abdulla and Dr. Iman T. Ardekani
Affiliation: The University of Auckland, New Zealand
Noise is a major cause of concern in modern life! It is the fastest growing pollutant in the urban environment and causes annoyance, illness, loss of quality of life, and reduces life expectancy. Medical studies show that noise affects the nervous and hormonal systems and consequently disrupts the stability of human biological system. In this tutorial we will see how we can minimize the noise at certain location by introducing an anti-noise signal. Theoretically, a mechanical disturbance such as acoustic noise, machinery vibration, and seismic vibration can be actively decimated by an artificially-generated disturbance (control field) of the same type which is equal in magnitude and opposite in phase. For example acoustic noise at a specific spot can be reduced by introducing another noise (anti-noise) by a loudspeaker which destructively combines with the original noise to minimize it. This basic principle is the essence of Active Noise Control.
Active Noise Control (ANC) is an elegant approach to neutralize noise in the acoustic domain. ANC is developing rapidly because it permits improvements in noise control with potential benefits in size, weight reduction, and lower system cost. In addition, noise can be reduced without physical modification of the existing noise sources or their physical arrangement. ANC systems have been excelled by the digital technology revolution over time. Many algorithms and techniques have developed to realization of efficient active noise control systems to decimate noise in response to the growing demand of such systems.
In this tutorial we will discuss the fundamentals of ANC systems from the theoretical and practical aspects. We will also talk about the recent advancements, novel analysis technique based on root locus, and the 3D ANC where we discuss the pathway of optimizing a zone of silence rather than a point of silence.
Speaker Photo and Bio
Waleed H. Abdulla holds a PhD Degree from the University of Otago, New Zealand. He has been working in the University of Auckland since 2002 and currently an Associate Professor. He is Vice President- Member Relations and Development, Asia-Pacific Association for Signal and Information Processing Association (APSIPA). He has been a Visiting Researcher/Collaborator with Tsinghua University, Siena University (Italy), Essex University (UK), IDIAP (Switzerland), TIT (Japan), ETRI (Korea), HKPU (Hong Kong). He has published more than 100 refereed publications including a patent and a book. He is a member of the Editorial Boards of six journals. He has supervised more than 25 postgraduate students. He is a recipient of many awards and funded projects exceeding $900 K and was awarded JSPS, ETRI, and Tsinghua fellowships. He received the award for Excellent Teaching for 2005 and 2012 as well as Otago University Scholarship. He is a Member of APSIPA and Senior Member of IEEE. His research interest: Developing signal processing algorithms for applications, example applications: Human Biometrics, Speech Processing, Speech and Speaker recognition, Active Noise Control, Speech Enhancement, Audio Watermarking, EEG signal analysis
Iman Ardekani holds a PhD degree in Electrical and Electronic Engineering from the University of Auckland, New Zealand. After a postdoctoral fellowship at the University of Auckland, he has joined Computing and Information Technology Department of Unitec Institute of Technology, New Zealand. Iman has published more than 40 research articles in international journals and conferences. He has been an IEEE member since 2008. Also, he is one of the APSIPA News Letter editors. His research interest includes adaptive signal processing algorithms with a focus on active noise control applications.
Title: MIMO Signal Processing Techniques to Enhance Physical Layer Security
Speaker: Prof. Y.-W. Peter Hong1 , Prof. Tsung-Hui Chang2
Affiliation: National Tsing Hua University, Taiwan1
National Taiwan University of Science and Technology, Taiwan2
This tutorial provides an overview of signal processing techniques proposed to enhance information security in the physical-layer of multi-antenna wireless communication systems. Wireless physical layer secrecy has attracted much attention in recent years due to the broadcast nature of the wireless medium and its inherent vulnerability to eavesdropping. Motivated by results in information theory, signal processing techniques in both the data transmission and the channel estimation phases have been explored in the literature to enlarge the signal quality difference between the target receiver (or called the destination) and the eavesdropper. In the data transmission phase, secrecy beamforming and precoding schemes are used to enhance signal quality at the destination while limiting the signal strength at the eavesdropper. Artificial noise (AN) is also used on top of beamformed or precoded signals to further reduce the reception performance at the eavesdropper. In the channel estimation phase, training procedures are developed to enable different channel estimation performance at the destination and the eavesdropper. As a result, the effective signal-to-noise ratios at the two terminals will be different and a more favorable secrecy channel will be made available for use in the data transmission phase.
Different from most talks on physical-layer secrecy, we focus on the signal processing aspects of the problem as opposed to coding or information-theoretic discussions. In particular, this tutorial covers 4 main topics: (i) basic review of information-theoretic secrecy, (ii) secrecy beamforming and precoding techniques for data transmission, (iii) secrecy-enhancing artificial noise and jamming signal usage and (iv) training designs for discriminatory channel estimation. Extensions to more advanced wireless applications will also be discussed.
Speaker Photo and Bio
Y.-W. Peter Hong (S’01–M’05) received his B.S. degree in Electrical Engineering from National Taiwan University, Taipei, Taiwan, in 1999, and his Ph.D. degree in Electrical Engineering from Cornell University, Ithaca, NY, in 2005. He joined the Institute of Communications Engineering and the Department of Electrical Engineering at National Tsing Hua University, Hsinchu, Taiwan, in Fall 2005, where he is now an Associate Professor. His research interests include cooperative communications, physical layer secrecy, distributed signal processing for sensor networks, and PHY-MAC cross-layer designs for wireless networks.
Dr. Hong received the best paper award for young authors from the IEEE IT/COM Society Taipei/Tainan Chapter in 2005, the best paper award among unclassified papers in MILCOM 2005, the Junior Faculty Research Award from the College of EECS and from National Tsing Hua University in 2009 and 2010, respectively. He also received the IEEE Communication Society Asia-Pacific Outstanding Young Researcher Award in 2010, the Y. Z. Hsu Scientific Paper Award and the National Science Councile (NSC) Wu Ta-You Memorial Award in 2011, and the Chinese Institute of Electrical Engineering (CIEE) Outstanding Young Electrical Engineer Award in 2012. He is currently an Associate Editor for IEEE Transactions on Signal Processing and IEEE Transactions on Information Forensics and Security.
Tsung-Hui Chang (S’07-M’08) received the B.S. degree in electrical engineering and the Ph.D. degree in communications engineering from the National Tsing Hua University (NTHU), Hsinchu, Taiwan, in 2003 and 2008, respectively. Since September 2012, he has been with the Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology (NTUST), Taipei, Taiwan, as an Assistant Professor. Before joining NTUST, he held research positions with NTHU (2008-2011), and University of California at Davis, CA (2011-2012). He was also a visiting scholar of the University of Minnesota, Twin Cities, MN, and the Chinese University of Hong Kong. His research interests are widely in signal processing problems in wireless communications and smart grid, and convex optimization methods and its applications.
Title: Machine Learning for Multimedia Sequential Pattern Recognition
Speaker: Koichi Shinoda1, Jen-Tzung Chien2
Affiliation: Tokyo Institute of Technology1, Japan
National Chiao Tung University2, Taiwan
In this tutorial, we will present state-of-the-art machine learning approaches for multimedia sequential pattern recognition. As we know, the sequential patterns, e.g. speech, music, language, image and video, etc., are ubiquitous in real-world information systems. We require extensive knowledge of statistical models and design a flexible and robust system to meet heterogeneous environments. This tutorial starts from an introduction of sequential pattern modeling based on the hidden Markov models (HMMs) and survey a series of machine learning approaches to different issues in pattern recognition including mismatch conditions, poor alignment, missing labels, ambiguous classes, over-trained parameters and non-stationary environments. We will address recent advances in adaptive learning, sparse learning, semi-supervised learning and discriminative training and present challenging solutions to speech recognition, face recognition, video indexing, speaker clustering and person authentication. In speech recognition, we will present the robust statistics for feature normalization, Bayesian sparse learning and semi-supervised learning for acoustic model and semantic topic modeling for language model. An online learning for speaker clustering will be also addressed. In addition, we will present how online learning and discriminative learning work for face recognition. In video information retrieval, we will first introduce the research activities in NIST TRECVID workshop, in which many teams from all over the world compete with each other in several predetermined tasks related to video information retrieval. Then, we will explain video semantic indexing task and its state-of-the-art statistical framework based on Gaussian mixture model (GMM) supervectors and support vector machines (SVM). We will also explain how maximum a posteriori adaptation is effectively used in this task. Next we explain another task, multimedia event detection, and introduce several machine learning approaches for this task. We also briefly introduce the other tasks in TRECVID and show how machine learning is effectively used in those tasks. At last, we will point out new trends of pattern recognition and machine learning approaches for multimedia signal and information processing.
Speaker Photo and Bio
Koichi Shinoda received the B.S. and M.S. degrees from the University of Tokyo, Tokyo, Japan in 1987 and 1989, respectively, both in physics, and the D. Eng. Degree in computer science from the Tokyo Institute of Technology, Japan, in 2001. In 1989, he joined NEC Corporation, Japan, where he was involved in research on automatic speech recognition. From 1997 to 1998, he was a Visiting Scholar with Bell Labs, Lucent Technologies, Murray Hill, NJ. From June 2001 to September 2001, he was a Principal Researcher with Multimedia Research Laboratories, NEC Corporation. From October 2001 to March 2002, he was an Associate Professor with the University of Tokyo, Japan. He is currently a Professor with the Tokyo Institute of Technology. His research interests include speech recognition, video information retrieval, statistical pattern recognition, and human interfaces. He received the Awaya Prize from the Acoustic Society of Japan in 1997 and the Excellent Paper Award from the IEICE in 1998. He was Publicity Chair in INTERSPEECH2010, Video Program Co-Chair in ACM Multimedia 2012. Dr. Shinoda is a senior member of IEEE, IEICE. He is a member of ACM, IPSJ, JSAI, and ASJ. He is currently an associate editor of Computer Speech and Language and Speech Communication, Elsevier.
Jen-Tzung Chien received his Ph.D. degree in electrical engineering fromNationalTsingHuaUniversity,Hsinchu,Taiwan, in 1997. During 1997-2012, he was with theNationalChengKungUniversity,Tainan,Taiwan. Since 2012, he has been with the Department of Electrical and Computer Engineering,NationalChiaoTungUniversity, Hsinchu, where he is currently a Distinguished Professor. He held the Visiting Researcher positions at the Panasonic Technologies Inc.,Santa Barbara,CA, the Tokyo Institute of Technology,Tokyo,Japan, the Georgia Institute of Technology,Atlanta,GA, the Microsoft Research Asia,Beijing,China, and theIBMT.J.WatsonResearchCenter,Yorktown Heights,NY. His research interests include machine learning, speech recognition, face recognition, information retrieval and blind source separation. Dr. Chien is a senior member of the IEEE Signal Processing Society. He served as the associate editor of the IEEE Signal Processing Letters, in 2008-2011, the organization committee member of the ICASSP 2009, the tutorial speaker of the ICASSP 2012, and the area coordinator of the Interspeech 2012. He is appointed as the APSIPA Distinguished Lecturer for 2012-2013. He was a co-recipient of the Best Paper Award of the IEEE Automatic Speech Recognition and Understanding Workshop in 2011. He received the Distinguished Research Award from the National Science Council, Taiwan, in 2006 and 2010.
Title: Perceptual Quality Evaluation for Image and Video: from Modules to Systems
Speaker: Prof. Weisi Lin
Affiliation: Nanyang Technological University, Singapore
Since the human visual system (HVS) is the ultimate receiver and appreciator for the majority of processed images and video, it would be better to use a perceptually plausible criterion in visual signal quality evaluation, as well as the related system design and optimization. After million-years of evolution, the HVS develops unique characteristics, so it is meaningful and important to make the machine perceive as the HVS does. Significant research effort has been made toward modelling the HVS’ picture quality evaluation mechanism during the past two decades, and to apply the resultant models to various situations (e.g., quality metrics, image/video compression, watermarking, channel coding, signal restoration/enhancement, computer graphics, visual content retrieval and medical instrumentation).
In this tutorial, we will first introduce the problems under attack, the relevant physiological/psychological knowledge, and the work so far in the related fields. Afterward, we will present three major parts of this tutorial. In the first mart part, the basic computational modules are to be discussed. These include the models for signal decomposition, Just-noticeable Difference (JND), visual attention, and common artifact detection. In the second major part, different perceptually-driven types of techniques (in either embedded or standalone forms; both the classical and the state-of-the-art ones) will be presented for picture quality evaluation. Finally, we will discuss the emerging trends and future R&D possibilities.
This tutorial aims at providing a systematic, comprehensive and up-to-date overview in perception-based evaluating for images and video. It can also provide a practical user’s guide to the various relevant techniques (and those well-cited works to be highlighted), and all approaches are to be presented with clear classification, and careful comparison/comments whenever possible, based upon our understanding and experience in the said areas (in both academic and industrial aspects).
Speaker Photo and Bio
Weisi Lin obtained his PhD from King’s College, London University, UK in 1992. He taught and researched in a number of organizations in China, UK and Singapore. He served as the Lab Head of Visual Processing and the Active Department Manager in Institute for Infocomm Research. Currently, he is an Associate Professor in School of Computer Engineering, Nanyang Technological University in Singapore. His areas of expertise include image processing, perceptual signal modelling, video compression, computer vision, and multimedia communication. He is a Chartered Engineer, a fellow of the IET, and an Honorary Fellow, Singapore Institute of Engineering Technologists. He is on the editorial boards of IEEE Trans. on Multimedia, IEEE SIGNAL PROCESSING LETTERS and Journal of Visual Communication and Image Representation. He believes that good theory is practical, and keeps a good balance between academic research and industrial development. Since 2003, Weisi Lin has devoted to perception-based modeling in different domains, and perceptual image quality evaluation and visual processing. With the topics closely related to the proposed tutorial, he holds file patents, published 40+ journal papers and 60 conference papers, and authored a book and several book chapters. He served as the Lead Guest Editor for a special issue on perceptual signal processing, IEEE Journal of Selected Topics in Signal Processing, 2012. He chairs the IEEE MMTC Special Interest Group on Quality of Experience (QoE), 2012-2014. He has also been the project leader of 8 projects (with both academic and industrial fundings) in perceptual visual processing, and maintained active, long-term working relationship with a number of companies which are keen in perception-based technology. He has organized special sessions in IEEE ICME06, IEEE IMAP07, PCM09, SPIE VCIP10, APSIPA11, MobiMedia 11 and IEEE ICME 12. He gave invited/panelist/keynote/tutorial speeches in VPQM06, SPIE VCIP10, IEEE ICCCN07, PCM07, PCM09, IEEE ISCAS08, IEEE ICME09, APSIPA10, IEEE ICIP10, and IEEE MMTC QoEIG (2011). He also maintains good partnership with a number of companies that are keen on perception-driven technology for audiovisual signal processing.
Title: Research Roadmap Driven by Network Benchmarking Lab (NBL): Deep Packet
Inspection, Traffic Forensics, WLAN/LTE, Embedded Benchmarking, and Beyond
Speaker: Prof. Ying-Dar Lin
Affiliation: National Chiao Tung University, Taiwan
Most researchers look for topics from the literature. But our research has been driven mostly by development which in turn has been driven by industrial projects or lab works. We first compare three different sources of research topics. We then derive two research tracks driven by product development and product testing, named as the blue track and the green track, respectively. Each track is further divided into development plane and research plane. The blue track on product development has fostered a startup company (L7 Networks Inc.) and a textbook (Computer Networks: An Open Source Approach, McGraw-Hill 2011) at the development plane and also a research roadmap on QoS and deep packet inspection (DPI) at the research plane. On the other hand, the green track on product testing has triggered a 3rd-party test bed, Network Benchmarking Lab (NBL, www.nbl.org.tw), at the development plane and a research roadmap on traffic forensics, WLAN/LTE, and embedded benchmarking at the research plane. Throughout this talk, we illustrate how development and research could be highly interleaved. At the end, we give lessons accumulated over the past decade. The audience could see how research could be conducted in a different way.
Speaker Photo and Bio
Ying-Dar Lin is Professor of Computer Science at National Chiao Tung University (NCTU) in Taiwan. He received his Ph.D. in Computer Science from UCLA in 1993. He served as the CEO of Telecom Technology Center during 2010-2011 and a visiting scholar at Cisco Systems in San Jose during 2007–2008. Since 2002, he has been the founder and director of Network Benchmarking Lab (NBL, www.nbl.org.tw), which reviews network products with real traffic. He also cofounded L7 Networks Inc. in 2002, which was later acquired by D-Link Corp. He founded Embedded Benchmarking Lab (www.ebl.org.tw) in 2011 to extend into the review of handheld devices. His research interests include design, analysis, implementation, and benchmarking of network protocols and algorithms, quality of services, network security, deep packet inspection, P2P networking, and embedded hardware/software co-design. His work on “multi-hop cellular” was the first along this line, and has been cited over 600 times and standardized into IEEE 802.11s, WiMAX IEEE 802.16j, and 3GPP LTE-Advanced. He was elevated to IEEE Fellow in 2013 for his contributions to multi-hop cellular communications and deep packet inspection. He is currently on the editorial boards of IEEE Transactions on Computers, IEEE Computer, IEEE Network, IEEE Communications Magazine – Network Testing Series, IEEE Wireless Communications, IEEE Communications Surveys and Tutorials, IEEE Communications Letters, Computer Communications, Computer Networks, and IEICE Transactions on Information and Systems. He published a textbook “Computer Networks: An Open Source Approach” (www.mhhe.com/lin), with Ren-Hung Hwang and Fred Baker (McGraw-Hill, 2011). It is the first text that interleaves open source implementation examples with protocol design descriptions to bridge the gap between design and implementation.
Title: Next Generation Video Coding- H.265/HEVC and its extensions
Speaker: Prof. Oscar. C. Au
Affiliation: The Hong Kong University of Science and Technology, Hong Kong
In March 2013, H.265/HEVC was completed and achieved its FDIS status. It is surely the most significant event in digital video compression field in a decade. With the collaborative effort of a lot of experts, H.265/HEVC can provide approximately twice the compression performance of prior standard, i.e. maintain the same level of video quality while using only half of the bit rate. In particular, it addresses a special emphasis on the hardware friendly design and parallel-processing architectures. Now the Joint Collaborative Team on Video Coding (JCTVC) is working hard on developing the extensions of H.265/HEVC to enhance the design and address different application scenarios (e.g. enhanced chroma formats, scalable video coding (SVC), and 3D appilcations).
In this tutorial, we review the development of H.265/HEVC and the main coding tools that are accepted. We also examine some coding tools not accepted, and hotly discussed topics, which aroused a lot of attention and study during the meetings. The development status of SVC extension and 3D extension is introduced. There are plenty of research opportunities in H.265/HEVC and beyond. Participants will gain an understanding of novel techniques in the next generation video coding standards, along with some perspectives for the future applications and research opportunities.
Speaker Photo and Bio
Oscar C. Au received his B.A.Sc. fromUniv.ofTorontoin 1986, his M.A. and Ph.D. fromPrincetonUniv.in 1988 and 1991 respectively. After being a postdoctoral researcher inPrincetonUniv. for one year, he joined the Hong Kong University of Science and Technology (HKUST) as an Assistant Professor in 1992. He is/has been a Professor of the Dept. of Electronic and Computer Engineering, Director of Multimedia Technology Research Center (MTrec), and Director of the Computer Engineering (CPEG) Program in HKUST.
His main research contributions are on video and image coding and processing, watermarking and light weight encryption, speech and audio processing. Research topics include fast motion estimation for MPEG-1/2/4, H.261/3/4 and AVS, optimal and fast sub-optimal rate control, mode decision, transcoding, denoising, deinterlacing, post-processing, multi-view coding, view interpolation, depth estimation, 3DTV, scalable video coding, distributed video coding, subpixel rendering, JPEG/JPEG2000, HDR imaging, compressive sensing, halftone image data hiding, GPU-processing, software-hardware co-design, etc. He has published 50+ technical journal papers, 320+ conference papers, and 70+ contributions to international standards. His fast motion estimation algorithms were accepted into the ISO/IEC 14496-7 MPEG-4 international video coding standard and the China AVS-M standard. His light-weight encryption and error resilience algorithms are accepted into the China AVS standard. He was Chair of Screen Content Coding AdHoc Group in the JCTVC for the ITU-T H.265 HEVC video coding standard. He has 18 granted US patents and is applying for 80+ more on his signal processing techniques. He has performed forensic investigation and stood as an expert witness in the Hong Kong courts many times.