Search Results for author: M. Hamed Mozaffari

Found 10 papers, 0 papers with code

Spectral unmixing of Raman microscopic images of single human cells using Independent Component Analysis

no code implementations25 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.

Image Clustering

Raman spectral analysis of mixtures with one-dimensional convolutional neural network

no code implementations1 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.

Data Augmentation

One-dimensional Active Contour Models for Raman Spectrum Baseline Correction

no code implementations26 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.

PS8-Net: A Deep Convolutional Neural Network to Predict the Eight-State Protein Secondary Structure

no code implementations22 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.

Protein Secondary Structure Prediction

A Review of 1D Convolutional Neural Networks toward Unknown Substance Identification in Portable Raman Spectrometer

no code implementations18 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.

Real-time Ultrasound-enhanced Multimodal Imaging of Tongue using 3D Printable Stabilizer System: A Deep Learning Approach

no code implementations22 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.

IrisNet: Deep Learning for Automatic and Real-time Tongue Contour Tracking in Ultrasound Video Data using Peripheral Vision

no code implementations10 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.

Image Classification

Transfer Learning for Ultrasound Tongue Contour Extraction with Different Domains

no code implementations10 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.

Domain Adaptation Transfer Learning

BowNet: Dilated Convolution Neural Network for Ultrasound Tongue Contour Extraction

no code implementations10 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.

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