Organization profile

Organisation profile

The Department has an established record of research excellence, with an international reputation for pioneering work across the broad spectrum of electronic and electrical engineering. Applied engineering innovation is core to the department's research ethos and is a critical part of its culture.

Research activities are driven by six strategically focused research groups, which reflect the scale, diversity and vision of the Department and underpin its teaching programmes.

Centre for Intelligent Dynamic Communications.
Centre for Excellence in Signal & Image Processing.
Institute for Energy & Environment
Centre for Microsystems & Photonics.
Industrial Control Centre.
Centre for Ultrasonic Engineering.

The Department is also home to several unique and world-leading research facilities, including the British Energy Advanced Diagnostics Centre and the UK’s only EPSRC Doctoral Training Centre in Wind Energy Systems, specifically created to enable the UK meet its ambitious renewable energy targets.

The Department is supported by a variety of agencies and research centres worldwide, and its success in forming strong partnerships with Industry, has resulted in a prestigious and extensive scholarship programme.

With an enviable record of enterprise, entrepreneurship and research commercialisation, we excel at knowledge exchange and technology transfer, developing and integrating novel cutting edge technologies across a range of sectors in the local and global economies.

Company successes include Diagnostic Monitoring Systems (DMS), Elimpus Ltd and Industrial Systems and Control (ISC) Ltd.

With 60 academic staff and 250 researchers from over 20 countries worldwide, the Department has a vibrant international Graduate School. Research-led with state-of-the-art equipment and resources, the School is a dynamic and supportive environment for advanced study.

Fingerprint Dive into the research topics where Electronic And Electrical Engineering is active. These topic labels come from the works of this organisation's members. Together they form a unique fingerprint.

Electric potential Engineering & Materials Science

Wind turbines Engineering & Materials Science

Partial discharges Engineering & Materials Science

Controllers Engineering & Materials Science

Sensors Engineering & Materials Science

Costs Engineering & Materials Science

Condition monitoring Engineering & Materials Science

Monitoring Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Profiles

Ahmed Abdelrahim

ahmed.abdelrahim@strath.ac.uk

Electronic And Electrical Engineering - Research Assistant

Person: Research Only, Doctor of Philosophy

Ibrahim Faiek Abdulhadi

ibrahim.f.abdulhadi@strath.ac.uk

Electronic And Electrical Engineering - Knowledge Exchange

Person: Knowledge Exchange Only

Mohamed S.Kh. Abuhelala

Electronic And Electrical Engineering

Person: Doctor of Philosophy

Projects 2003 2024

71 Not started
240 Active
815 Finished

TIC LCPE MET-ocean access sensor location study (WIND-09)

McMillan, D. & Browell, J.

Scottish and Southern Energy Plc SSE PLC

1/07/19 → 31/08/19

Project: Research

EPSRC vacation bursary - StrathPEARL

Egea Alvarez, A.

3/06/19 → 9/08/19

Project: Research

Realising the value of offshore grid access for small pools

Ahmed, K., Burt, G., Emhemed, A. & Xu, L.

Oil & Gas Technology Centre Limited OGTC

3/06/19 → 31/07/20

Project: Research

Research Output 1976 2019

3D printed microneedle patches using stereolithography (SLA) for intradermal insulin delivery

Economidou, S. N., Pere, C. P. P., Reid, A., Uddin, M. J., Windmill, J. F. C., Lamprou, D. A. & Douroumis, D., 22 Apr 2019, In : Materials Science and Engineering: C . 102, p. 743-755 13 p.

Research output: Contribution to journal › Article

Stereolithography

insulin

Insulin

delivery

Printing

Access forecasting for safety-critical crew transfers in offshore environments

Browell, J., Gilbert, C. & McMillan, D., 18 Jun 2019.

Research output: Contribution to conference › Speech

A convolutional neural network based deep learning methodology for recognition of partial discharge patterns from high voltage cables

Peng, X., Yang, F., Wang, G., Wu, Y., Lee, L., Li, Z., Ahmed Bhatti, A., Zhou, C., Hepburn, D. M., Reid, A. J., Judd, M. & Siew, W. H., 19 Mar 2019, In : IEEE Transactions on Power Delivery. 10 p.

Research output: Contribution to journal › Article

Open Access

File

Partial discharges

Cables

Neural networks

Electric potential

Pattern recognition