All 2019 seminars will be listed here.
For time and location, please see at the individual seminar.
- 07 February 2019 (12.00pm, Thursday, WT102) Prof. Bodo Rosenhahn (Univ. Hannover)Topic on “TBD.”
- 31 January 2019 (12.00pm, Thursday, WT102) Dr. Xiuhui Wang (China Jiliang University China)Topic on “Gait Recognition Based on Machine Learning.”
- Gait is one of the promising biometrics which can be used in human identification. Although other biometrics, such as human face and fingerprint, have been widely used in commercial applications, gait is still at its nascent stage. In this talk, we will review the state-of-the-art approaches for gait recognition. Then, two open-access gait databases will be introduced, i.e., CASIA gait database and OU-ISIR gait database. Finally, our experimental results will be evaluated.
- 31 January 2019 (12.30pm, Thursday, WT102) Dr. Xia Li (China Jiliang University China)Topic on “Gradient Coils Design with Regularization Method for Superconducting Magnetic Resonance Imaging (MRI).”
- In this talk, we propose an approach to the design of gradient coils for superconducting magnetic resonance imaging (MRI). The designed method takes use of Fourier series expansions to describe the continuous current density of the coil surface and then employs stream function technique to extract the coil wires. During the numerical simulation, a linear equation is constructed and solved with the use of a Tikhonov regularization scheme. Using this method, the gradient coils with high level of linearity are designed. Our contributions are to expend the current densities of coils into Fourier series analytically as well as optimize the parameters of regularization from the plotted curve.
24 January 2019 (12.00pm, Thursday, WT102) Mr. Mohammad Norouzifard (EEE, AUT)Topic on “Diagnosis of Glaucomatous Optic Neuropathy by an Optimised Deep Learning Model.”
Early glaucoma diagnosis and treatment are essential to reduce vision loss rates. Hence, the development of an image-based computer model diagnosis system for medical imaging is required as an auxiliary tool to detect glaucoma in the early stage which would be very beneficial in primary care. Developing such a system for this kind of applications is very challenging. A special purpose model, such as defined for a robust application, is promising for an accurate classification to detect glaucoma with more flexibility at the systems. In this research project, I tried to use GPU-based cloud-computing services to achieve a deep learning model with time efficiency and high performance in training, validation and test steps.
In summary, this talk aims at a proposal of an optimized and robust classifier towards an efficient system with high performance, targeting early detection of glaucoma where it is high demand in New-Zealand and worldwide to decrease the loss of vision. Deep transfer learning architectures would be an appropriate solution to combat the lack of data to detect glaucoma. Data collection has been costly, so I will try to develop an accurate model to classify healthy vs glaucoma patients with private and public datasets in an appropriate time duration. Therefore, deep multi-layer perceptron, deep convolutional neural network and pre-trained models are compared with each other to achieve an optimized model for glaucoma detection.