Projects per year
Personal profile
Research interests
Pattern recognition, computer vision and machine vision, artificial neural networks, machine learning and deep learning. Applications in medical image analysis, malware detection, industrial inspection.
Research engagement
Len engages with industry in R&D for a variety applications of machine vision and machine learning. Past projects include:
- Recognition of targets in sonar images from autonomous underwater vehicles, with Defence Industry Network.
- Recognition of radar signals with Defence Services Technology Group.
- Locomotive pantograph inspection system (PanCam) with Aurizon Ltd. The developed system has been deployed since 2007 for timely automated detection and reporting of pantograph damage and wear.
- Biscuit bake inspection system with Arnott's Biscuits Ltd. Combines colour-calibrated image analysis with machine learning to report bake colour quality.
- Inspection of recycled glass colour purity with Visy Recycling.
Research student supervision
Len supervises PhD and MRes candidates in topics related to machine learning and image analysis. He is currently supervising three PhD candidates.
Recent completions:
1. Mahmood Yousefi-Azar, “Machine learning for automatic malware representation and analysis,” PhD, 2020.
2. Tahereh Hassanzadeh, "Convolutional neural networks for prostate magnetic resonance image segmentation," MRes, 2018.
3. Robert Newport, “Radar emitter recognition using hierarchical feature extraction within magnitude and frequency domains,” MRes, 2018.
4. Saruar Alam, “Impact of MRI technology on Alzheimer's disease detection,” MRes, 2018.
5. Mahmood Yousefi-Azar, “Query-oriented single-document summarization using unsupervised deep learning,” MRes, 2016.
Teaching
Len has over 30 years experience in University teaching, ranging across areas of computer science including networks and operating systems, computer architecture, computer graphics, and programming.
Len's innovative approach to teaching involves gamification and physical modeling of difficult concepts that arise in particular subject areas such as security protocols and 3D image formation. His innovations in assessment tasks include uniquely defining the task for each student and providing immediate evaluation and feedback throughout the task.
Education/Academic qualification
Computer Science, PhD, Carnegie Mellon University
Award Date: 26 Feb 1988
Statistics, BSc(hons), Macquarie University
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Projects
- 4 Finished
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AI-enabled Identity Verification
Beheshti, A., Hamey, L., Jolfaei, A. & Asadniaye Fard Jahromi, M.
4/06/20 → 3/06/22
Project: Research
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Multimodal Machine Learning for Clinical Decision Support
Hamey, L. & Singh, S.
1/01/19 → 1/01/21
Project: Research
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Extending the Apply image processing language with application to OpenCV.
29/01/13 → 28/07/13
Project: Research
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Automated pathogen detection using time-gated luminescence microscopy
Connally, R., Piper, J., Shen, J., Iredell, J., Hamey, L. & APAI, A.
1/01/07 → 31/12/11
Project: Research
Research Outputs
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ISAR ship classification using transfer learning
Zhao, W., Heng, A., Rosenberg, L., Nguyen, S. T., Hamey, L. & Orgun, M., 2022, 2022 IEEE Radar Conference (RadarConf22) proceedings. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 6 p.Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review
1 Citation (Scopus) -
Mutual information and feature importance gradient boosting: automatic byte n-gram feature reranking for Android malware detection
Yousefi-Azar, M., Varadharajan, V., Hamey, L. & Chen, S., Jul 2021, In: Software - Practice and Experience. 51, 7, p. 1518-1539 22 p.Research output: Contribution to journal › Article › peer-review
3 Citations (Scopus) -
Show, tell and summarise: learning to generate and summarise radiology findings from medical images
Singh, S., Karimi, S., Ho-Shon, K. & Hamey, L., Jul 2021, In: Neural Computing and Applications. 33, 13, p. 7441-7465 25 p.Research output: Contribution to journal › Article › peer-review
2 Citations (Scopus) -
A survey on anomalous behavior detection for elderly care using dense-sensing networks
Deep, S., Zheng, X., Karmakar, C., Yu, D., Hamey, L. & Jin, J., 2020, In: IEEE Communications Surveys and Tutorials. 22, 1, p. 352-370 19 p.Research output: Contribution to journal › Article › peer-review
29 Citations (Scopus) -
Automatic recognition of student engagement using deep learning and facial expression
Mohamad Nezami, O., Dras, M., Hamey, L., Richards, D., Wan, S. & Paris, C., 2020, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part III. Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M. & Robardet, C. (eds.). Cham, Switzerland: Springer, Springer Nature, p. 273-289 17 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11908 LNAI).Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review
20 Citations (Scopus)