no code implementations • 25 Oct 2021 • M. Hamed Mozaffari, Li-Lin Tay
Application of independent component analysis (ICA) as an unmixing and image clustering technique for high spatial resolution Raman maps is reported.
no code implementations • 1 Jun 2021 • M. Hamed Mozaffari, Li-Lin Tay
Some studies have attempted to extend this technique to the classification of pure compounds in an unknown mixture.
no code implementations • 26 Apr 2021 • M. Hamed Mozaffari, Li-Lin Tay
In this paper, a modified version of active contour models in one-dimensional space has been proposed for the baseline correction of Raman spectra.
no code implementations • 22 Sep 2020 • Md. Aminur Rab Ratul, Maryam Tavakol Elahi, M. Hamed Mozaffari, Won-Sook Lee
In this study, we have presented a new deep convolutional neural network (DCNN), namely PS8-Net, to enhance the accuracy of eight-class PSS prediction.
no code implementations • 18 Jun 2020 • M. Hamed Mozaffari, Li-Lin Tay
Specifically, we highlight the use of this powerful deep learning technique for handheld Raman spectrometers taking into consideration the potential limit in power consumption and computation ability of handheld systems.
no code implementations • 16 Mar 2020 • M. Hamed Mozaffari, Won-Sook Lee
This paper presents a new novel approach of automatic and real-time tongue contour tracking using deep neural networks.
no code implementations • 22 Nov 2019 • M. Hamed Mozaffari, Won-Sook Lee
The result was asserted that visualizing the articulator's system as biofeedback to language learners will significantly improve articulation learning efficiency.
no code implementations • 10 Nov 2019 • M. Hamed Mozaffari, Md. Aminur Rab Ratul, Won-Sook Lee
The progress of deep convolutional neural networks has been successfully exploited in various real-time computer vision tasks such as image classification and segmentation.
no code implementations • 10 Jun 2019 • M. Hamed Mozaffari, Won-Sook Lee
Domain adaptation is an alternative solution for this difficulty by transferring the weights from the model trained on a large annotated legacy dataset to a new model for adapting on another different dataset using fine-tuning.
no code implementations • 10 Jun 2019 • M. Hamed Mozaffari, Won-Sook Lee
Employing the power of state-of-the-art deep neural network models and training techniques, it is feasible to implement new fully-automatic, accurate, and robust segmentation methods with the capability of real-time performance, applicable for tracking of the tongue contours during the speech.