Big Data - Instrumentation and Signal Processing


Tariq S Durrani

Centre for Excellence in Signal & Image Processing

University of Strathclyde

Glasgow, Scotland UK


Key 1


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.



TARIQ S DURRANI           

Research Professor

Department of Electronic and Electrical Engineering

University of Strathclyde, Glasgow,

United Kingdom (UK)

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Tariq Durrani joined Strathclyde as Lecturer in 1976 was appointed Professor in 1982. He was Head of the Department of Electronic and Electrical Engineering from 1986 to 1990 and Special Advisor to the Principal ( University President), on Information Technology, from 1990 to 1994.  From 2000-2006 he was Deputy Principal with major responsibilities for university –wide large-scale strategic developments. He is currently Research Professor at Strathclyde. 

He has supervised some 40 PhD students, and is the author/co-author of more than 350 papers and six books.   His research interests cover Communications, Signal Processing, and Technology Management. 

He is active in professional circles. He was the 2006-2007 President of the IEEE Engineering Management Society, and is a Past President of the IEEE Signal Processing Society. He was Region Director, for Europe Middle East and Africa, of IEEE Communications Society (2009-2011).

He has been a Member of the IEEE Medal of Honor Committee, the IEEE Jack KIlby Signal Processing Medal Committee, and of the IEEE/RSE Wolfson Maxwell Medal Committee.  He was the 2010-2011 Vice President of the IEEE Educational Activities Board,

At present he is Vice President (International) of the Royal Society of Edinburgh (RSE). The RSE is Scotland’s National Academy for Science and Letters.

He has held Visiting appointments at Princeton, University of Southern California, Stirling (Scotland) and the University of Electronic Science & Technology of China in Chengdu.

He has conducted collaborative work with industry, and partnered in major European Union research programs, since 1983, including ESPRI, BRITE-EURAM, RACE Programs. He has been Program Evaluator for several EU Projects.

He has held Directorships of eight organizations, including UK National Commission for UNESCO, and has served as consultant advisor to the Governments of UK, Netherlands, Portugal, UAE, US and European Union.

He is a Fellow of the IEEE, UK Royal Academy of Engineering, Royal Society of Edinburgh, the IET and The World academy of Sciences.  In 2003 Queen Elizabeth II honoured him with the title OBE (Officer of the Order of the British Empire) ‘for services to electronics research and higher education’.