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Keynote I:

 

Big Data - Instrumentation and Signal Processing

By

 

Tariq S Durrani
University of Strathclyde Glasgow, Scotland UK
Vice President (International) Royal Society of Edinburgh
Great Master of III Project, UESTC Chengdu, China
durrani@strath.ac.uk

ABSTRACT 

Instrumentation and the ready availability of sensors is leading to a dramatic increase in the collection of Data, and the field of Big Data is now much in favour. In effect Big Data refers to the dramatic increase in the amount and rate of data being created and collected, driven by the number and types of acquisition devices and instrumentation. 

According to the US National Science Foundation “ Big Data are large, diverse, complex, longitudinal, and/or distributed data sets generated from instruments, sensors, Internet transactions, email, video, click streams, and/or all other digital sources available today and in the future’ (Core Techniques and Technologies for Advanced Big Data Science and Engineering, Solicitation 12-499). 

Illustration of the sources of Big Data include streaming data from instrumentation sensors, satellite and medical imagery, video from security cameras, as well as data derived from financial markets and operations. Big data sets from these sources can contain gigabytes or terabytes of data.

In test, measurement and control applications, engineers and scientists can collect vast amounts of data in short periods of time. Large gas turbine manufacturers report that data from instrumented electricity generating turbines, while in manufacturing test, generate over 10 terabytes of data per day. As another example, typically, more than 5 billion data points are recorded every 6 months in a plant with about 320 recording sensor measurements every second. 

In this context, the Internet of Things (IoT) may well be the most disruptive technological revolution since the advent of the Internet. Projections indicate that up to 100 billion objects will be connected to the Internet by 2020. IoT covers all types of sensors, communication protocols, computational tools, techniques, devices, processors, embedded systems, data warehousing, big data, cloud computing, server farms, grid computing etc.

A key requirement from Big Data is the need to extract relevant information to effect decisions through the use of advanced Signal Processing tools.

In this presentation, first establishing the background to Big Data and its relevance to instrumentation, the talk will touch on Data analytics, and discuss the tools needed for analysing the Data, and in particular address three aspects of advanced signal processing – streaming algorithms for recursive computation; use of graph theory, and finally the use of tensor algebra to develop signal processing techniques for Big Data.

BIOGRAPHY

Professor Tariq S Durrani received his MSc and PhD degrees in 1967 and 1970 respectively, from the University of Southampton, UK. After postdoctoral research at Southampton, he joined the University of Strathclyde, Glasgow, as a Lecturer in 1976, and was appointed Professor of Signal Processing in 1982. He was Head of the Department of Electronic and Electrical Engineering from 1986 to 1990 and Special Advisor to the Principal, on Information Technology, from 1990 to 1994. He was Deputy Principal (2000-2006) responsible for the University’s IT and computing infrastructure, Staff Development, Hunter Centre for Entrepreneurship, Centre for Lifelong Learning. He is currently Research Professor at the University Of Strathclyde.

For the past twenty-five years, he has worked on and supervised some 60 projects sponsored by the UK Research Councils, government and industry, the US Navy, and the EU, amongst others. He has supervised over 40 PhD students, and is the author/co-author of more than 350 papers and six books. His research interests are in the areas of Communications Signal/ Image Processing, Technology Management and Higher Education Management.

Professor Durrani has been a Director of:
- UK Leadership Foundation for Higher Education.
- Scottish Funding Council (Member of Council)
- UK Equality Challenge Unit
- Glasgow Chamber of Commerce

He is currently a Director of the UK National Commission for UNESCO, with responsibility of Engineering and Applied Science.

He is active in professional circles. He was the 2006-2007 President of the IEEE Engineering Management Society, and was a Past President of the IEEE Signal Processing Society. From 2010-2011 he was IEEE Director and (global) Vice President Educational Activities for the IEEE.

He is a Fellow of the Royal Society of Edinburgh, the Royal Academy of Engineering, the IEEE, the IET and the Third Word Academy of Sciences. He was awarded the OBE in December 2002 for services to higher education and electronics research.


Keynote II:

 

Biometric Data Analysis

By

 

Director of the Center for Research on Intelligent Perception and Computing at the Institute of Automation
Deputy Secretary-General of the CAS
Director General of the CAS Bureau of International Cooperation
Academician, the Chinese Academy of Sciences
Fellow of The World Academy of Sciences for the advancement of sciences in developing countries (TWAS)
International Fellow, the UK Royal Academy of Engineering
Fellow of the IEEE, Fellow of the IAPR

 

ABSTRACT

Biometric data are data captured from the human body (such as face, fingerprint and iris images) or from human behaviors (such as handwriting and walking gait). A variety of human attributes such as identity, gender, ethnicity, age and affect can be derived from such data. Previous studies on biometric data analysis have focused on identity recognition. In this talk, we attempt to present a more complete picture of biometric data analysis by discussing the status quo and future research directions of the determination from biometric data of such attributes as identity, gender, ethnicity, age and affect.

BIOGRAPHY

Tieniu Tan received his B.Sc. degree in electronic engineering from Xi'an Jiaotong University, China, in 1984, and his MSc and PhD degrees in electronic engineering from Imperial College London, U.K., in 1986 and 1989, respectively.

In October 1989, he joined the Department of Computer Science, The University of Reading, U.K., where he worked as a Research Fellow, Senior Research Fellow and Lecturer. In January 1998, he returned to China to join the National Laboratory of Pattern Recognition (NLPR), Institute of Automation of the Chinese Academy of Sciences (CAS) as a full professor. He was the Director General of the CAS Institute of Automation from 2000-2007, and the Director of the NLPR from 1998-2013. He is currently Director of the Center for Research on Intelligent Perception and Computing at the Institute of Automation and also serves as Deputy Secretary-General of the CAS and the Director General of the CAS Bureau of International Cooperation. He has published more than 450 research papers in refereed international journals and conferences in the areas of image processing, computer vision and pattern recognition, and has authored or edited 11 books. He holds more than 70 patents. His current research interests include biometrics, image and video understanding, and information forensics and security.

Dr Tan is a Member (Academician) of the Chinese Academy of Sciences, Fellow of The World Academy of Sciences for the advancement of sciences in developing countries (TWAS), an International Fellow of the UK Royal Academy of Engineering, and a Fellow of the IEEE and the IAPR (the International Association of Pattern Recognition). He is Editor-in-Chief of the International Journal of Automation and Computing. He has given invited talks and keynotes at many universities and international conferences, and has received numerous national and international awards and recognitions.

 

 

 
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