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Summary

Juvaria Syeda is a Doctoral Researcher (Computer Engineering) based at The Welding Institute (TWI), Cambridge as part of the ICASE/EPSRC PhD Industrial Scholarship. She also holds a high merit Masters in Advance Software Engineering MSc(Eng) from Sheffield University and a MBA degree (Finance) with Distinction. 

She is currently working under the supervision of Dr. Hongying Meng at Brunel University London and Dr John Rudlin at NDT (Non-destructive Testing), TWI. The research presents an automated inspection technique that is able to perform detection and measurements of on-surface micro-flaws caused by corrosion fatigue, including classification of pits and cracks by using Image Analysis and Artificial Intelligent (AI) technology. For this, image analysis algorithm and novel Machine learning and Deep learning methods were developed and implemented that allowed automatic identification, localisation, measurement and assessment of defected areas in the rail axle and pipeline structures. 

Publications (Research outcomes):

The Conferences I have been involved in are as follows:

  • Syeda J, Rudlin J. Image Processing and deep learning possibilities for detection of small cracks within corrosion. ESIS TC 24 DGZIP Conference 2017, Workshop on Integrity of railway structures at Wittensberge, Germany
  • Syeda J. Image Analysis for complex data in Non-destructive testing, NSIRC conference 2017
  • Panggabean D, Syeda J, Beretta S, Archer R, Rudlin J. Data collection for corrosion fatigue life estimation. ESIS TC 24 DGZIP Conference 2017, Workshop on Integrity of railway structures at Wittensberge, Germany
  • Syeda J. Image processing and machine learning for detection of pits and cracks for rail axles using NDT. 57th Annual British Conference on Non-Destructive Testing (BINDT) 2018, East Midlands Conference Centre and Orchard Hotel, Nottingham, UK
  • Syeda J. Machine learning for pits and cracks in Non-destructive testing, NSIRC conference 2019

Research Engagements:

Presented the research work along with the improved project equipment used for data collection, to the following esteem dignitaries: