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Information engineering
Engineering discipline

Information engineering is an engineering discipline focused on the generation, distribution, and analysis of information, data, and knowledge in electrical systems, emerging in the early 21st century. It encompasses theoretical areas like machine learning, artificial intelligence, and signal processing, as well as applied fields such as computer vision and telecommunications. Rooted in Computer Engineering and related disciplines, it relies heavily on mathematics including probability and calculus. Information engineers typically hold a specialized degree and may be members of professional bodies like the Institution of Engineering and Technology, working across diverse industries.

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History

In the 1980s/1990s term information engineering referred to an area of software engineering which has come to be known as data engineering in the 2010s/2020s.14

Elements

Machine learning and statistics

Main article: Machine learning

Machine learning is the field that involves the use of statistical and probabilistic methods to let computers "learn" from data without being explicitly programmed.15 Data science involves the application of machine learning to extract knowledge from data.

Subfields of machine learning include deep learning, supervised learning, unsupervised learning, reinforcement learning, semi-supervised learning, and active learning.

Causal inference is another related component of information engineering.

Control theory

Main article: Control theory

Control theory refers to the control of (continuous) dynamical systems, with the aim being to avoid delays, overshoots, or instability.16 Information engineers tend to focus more on control theory rather than the physical design of control systems and circuits (which tends to fall under electrical engineering).

Subfields of control theory include classical control, optimal control, and nonlinear control.

Signal processing

Main article: Signal processing

Signal processing refers to the generation, analysis and use of signals, which could take many forms such as image, sound, electrical, or biological.17

Information theory

Main article: Information theory

Information theory studies the analysis, transmission, and storage of information. Major subfields of information theory include coding and data compression.18

Computer vision

Main article: Computer vision

Computer vision is the field that deals with getting computers to understand image and video data at a high level.19

Natural language processing

Main article: Natural language processing

Natural language processing deals with getting computers to understand human (natural) languages at a high level. This usually means text, but also often includes speech processing and recognition.20

Bioinformatics

Main article: Bioinformatics

Bioinformatics is the field that deals with the analysis, processing, and use of biological data.21 This usually means topics such as genomics and proteomics, and sometimes also includes medical image computing.

Cheminformatics

Main article: Cheminformatics

Cheminformatics is the field that deals with the analysis, processing, and use of chemical data.22

Robotics

Main article: Robotics

Robotics in information engineering focuses mainly on the algorithms and computer programs used to control robots. As such, information engineering tends to focus more on autonomous, mobile, or probabilistic robots.232425 Major subfields studied by information engineers include control, perception, SLAM, and motion planning.2627

Tools

In the past some areas in information engineering such as signal processing used analog electronics, but nowadays most information engineering is done with digital computers. Many tasks in information engineering can be parallelized, and so nowadays information engineering is carried out using CPUs, GPUs, and AI accelerators.2829 There has also been interest in using quantum computers for some subfields of information engineering such as machine learning and robotics.303132

See also

References

  1. "2009 lecture | Past Lectures | BCS/IET Turing lecture | Events | BCS – The Chartered Institute for IT". www.bcs.org. Retrieved 11 October 2018. https://www.bcs.org/category/10192 ↩

  2. Brady, Michael (2009). "Information Engineering & its future". Institution of Engineering and Technology, Turing Lecture. Retrieved 4 October 2018. https://www.theiet.org/events/lecture-histories/turing/2009-brady-slides.cfm ↩

  3. Roberts, Stephen. "Introduction to Information Engineering" (PDF). Oxford Information Engineering. Retrieved 4 October 2018. http://www.robots.ox.ac.uk/~sjrob/Teaching/b4_intro_all.pdf ↩

  4. "Department of Information Engineering, CUHK". www.ie.cuhk.edu.hk. Archived from the original on 15 May 2021. Retrieved 3 October 2018. https://web.archive.org/web/20210515205329/https://www.ie.cuhk.edu.hk/department/overview.shtml ↩

  5. "Information Engineering | Department of Engineering". www.eng.cam.ac.uk. 5 August 2013. Retrieved 3 October 2018. http://www.eng.cam.ac.uk/research/academic-divisions/information-engineering ↩

  6. "2009 lecture | Past Lectures | BCS/IET Turing lecture | Events | BCS – The Chartered Institute for IT". www.bcs.org. Retrieved 11 October 2018. https://www.bcs.org/category/10192 ↩

  7. Brady, Michael (2009). "Information Engineering & its future". Institution of Engineering and Technology, Turing Lecture. Retrieved 4 October 2018. https://www.theiet.org/events/lecture-histories/turing/2009-brady-slides.cfm ↩

  8. "Information Engineering | Department of Engineering". www.eng.cam.ac.uk. 5 August 2013. Retrieved 3 October 2018. http://www.eng.cam.ac.uk/research/academic-divisions/information-engineering ↩

  9. "Information Engineering Main/Home Page". www.robots.ox.ac.uk. Retrieved 3 October 2018. http://www.robots.ox.ac.uk/ ↩

  10. "Information Engineering". warwick.ac.uk. Retrieved 3 October 2018. https://warwick.ac.uk/fac/sci/eng/research/grouplist/informationengineering/ ↩

  11. "Academic Partners and Affiliates 2017/2018 – The IET". www.theiet.org. Archived from the original on 4 October 2018. Retrieved 3 October 2018. https://web.archive.org/web/20181004021124/https://www.theiet.org/academics/partners/academic-partners-list.cfm ↩

  12. "Electronic and Information Engineering – Imperial College London". Times Higher Education (THE). Archived from the original on 3 October 2018. Retrieved 3 October 2018. https://web.archive.org/web/20181003221053/https://www.timeshighereducation.com/world-university-rankings/imperial-college-london/courses/electronic-and-information-engineering ↩

  13. "Accreditation of the MEng | CUED undergraduate teaching". teaching.eng.cam.ac.uk. Retrieved 3 October 2018. http://teaching.eng.cam.ac.uk/content/accreditation-meng ↩

  14. Black, Nathan (15 January 2020). "What is Data Engineering and Why Is It So Important?". QuantHub. Retrieved 31 July 2022. https://quanthub.com/what-is-data-engineering/ ↩

  15. Bishop, Christopher (2007). Pattern Recognition and Machine Learning. New York: Springer-Verlag New York Inc. ISBN 978-0387310732. 978-0387310732 ↩

  16. Nise, Norman (2015). Control Systems Engineering. Wiley. ISBN 978-1118170519. 978-1118170519 ↩

  17. Lyons, Richard (2010). Understanding Digital Signal Processing. Prentice Hall. ISBN 978-0137027415. 978-0137027415 ↩

  18. Cover, Thomas (2006). Elements of Information Theory. Wiley-Interscience. ISBN 978-0471241959. 978-0471241959 ↩

  19. Davies, Emlyn (2017). Computer Vision: Principles, Algorithms, Applications, Learning. Academic Press. ISBN 978-0128092842. 978-0128092842 ↩

  20. Jurafsky, Daniel (2008). Speech and Language Processing. Prentice Hall. ISBN 978-0131873216. 978-0131873216 ↩

  21. Lesk, Arthur (2014). Introduction to Bioinformatics. Oxford University Press. ISBN 978-0199651566. 978-0199651566 ↩

  22. Leach, Andrew (2007). An Introduction to Chemoinformatics. Springer. ISBN 978-1402062902. 978-1402062902 ↩

  23. Siegwart, Roland (2011). Introduction to Autonomous Mobile Robots. MIT Press. ISBN 978-0262015356. 978-0262015356 ↩

  24. Kelly, Alonzo (2013). Mobile Robotics. Cambridge University Press. ISBN 978-1107031159. 978-1107031159 ↩

  25. Thrun, Sebastian (2005). Probabilistic Robotics. MIT Press. ISBN 978-0262201629. 978-0262201629 ↩

  26. Siegwart, Roland (2011). Introduction to Autonomous Mobile Robots. MIT Press. ISBN 978-0262015356. 978-0262015356 ↩

  27. Kelly, Alonzo (2013). Mobile Robotics. Cambridge University Press. ISBN 978-1107031159. 978-1107031159 ↩

  28. Barker, Colin. "How the GPU became the heart of AI and machine learning". ZDNet. Retrieved 3 October 2018. https://www.zdnet.com/article/how-the-gpu-became-the-heart-of-ai-and-machine-learning/ ↩

  29. Kobielus, James. "Powering artificial intelligence: The explosion of new AI hardware accelerators". InfoWorld. Retrieved 3 October 2018. https://www.infoworld.com/article/3290104/artificial-intelligence/powering-ai-the-explosion-of-new-ai-hardware-accelerators.html ↩

  30. Wittek, Peter (2014). Quantum Machine Learning. Academic Press. ISBN 978-0128100400. 978-0128100400 ↩

  31. Schuld, Maria (2018). Supervised Learning with Quantum Computers. Springer. ISBN 978-3319964232. 978-3319964232 ↩

  32. Tandon, Prateek (2017). Quantum Robotics. Morgan & Claypool Publishers. ISBN 978-1627059138. 978-1627059138 ↩