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Full-Text Articles in Engineering
Research Outreach Interdisciplinary Activity To Classify Olive Oil Blends Integrating Multicolor Imaging, Image Processing, And Machine Learning, Allan Abraham, Kameshwaran Balachandran
Research Outreach Interdisciplinary Activity To Classify Olive Oil Blends Integrating Multicolor Imaging, Image Processing, And Machine Learning, Allan Abraham, Kameshwaran Balachandran
Undergraduate Research
This outreach undergraduate research project presents a low-cost method to distinguish the quality of different olive oils. The proposed method is based on an indirect measurement of the chlorophyll molecules present when a green laser diode illuminates the oil sample. Oil blends can be classified into five classes (no olive oil, light olive oil, medium olive oil, olive oil, and extra virgin olive oil) by quantifying the ratio of the red channel versus the green channel along the laser illumination path from a color image. After labeling each oil blend, a convolutional neural network has been implemented and trained to …
Lightweight Deep Neural Network Models For Electromyography Signal Recognition For Prosthetic Control, Ahmet Mert
Lightweight Deep Neural Network Models For Electromyography Signal Recognition For Prosthetic Control, Ahmet Mert
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, lightweight deep learning methods are proposed to recognize multichannel electromyography (EMG) signals against varying contraction levels. The classical machine learning, and signal processing methods namely, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), root mean square (RMS), and waveform length (WL) are adopted to convolutional neural network (CNN), and long short-term memory neural network (LSTM). Eight-channel recordings of nine amputees from a publicly available dataset are used for training and testing the proposed models considering prosthetic control strategies. Six class hand movements with three contraction levels are applied to WL and RMS-based feature extraction. After that, they …
Deep Learning For Detection Of Upper And Lower Body Movements, Kyle B. Lacroix
Deep Learning For Detection Of Upper And Lower Body Movements, Kyle B. Lacroix
Electronic Thesis and Dissertation Repository
When humans repeat the same motion, the tendons, muscles, and nerves can be damaged, causing repetitive stress injuries (RSI). Symptoms usually begin slowly and become more intense and constant over time. If the motions that lead to RSI are recognized early, these injuries can be prevented. A preventative approach could be implemented in factories to warn workers about possible injuries. By detecting the movements that can cause RSI, the worker can be alerted to stop carrying out those movements. For this purpose, machine learning models can detect human motion with the human activity recognition (HAR) model. HAR models typically require …
Enhanced Two-Step Deep-Learning Approach For Electromagnetic-Inverse-Scattering Problems: Frequency Extrapolation And Scatterer Reconstruction, Huan Huan Zhang, He Ming Yao, Lijun Jiang, Michael Ng
Enhanced Two-Step Deep-Learning Approach For Electromagnetic-Inverse-Scattering Problems: Frequency Extrapolation And Scatterer Reconstruction, Huan Huan Zhang, He Ming Yao, Lijun Jiang, Michael Ng
Electrical and Computer Engineering Faculty Research & Creative Works
The electromagnetic-inverse-scattering (EMIS) problem is solved by a novel two-step deep-learning (DL) approach in this article. The newly proposed two-step DL approach not only predicts the multifrequency EM scattered field, but also overcomes the limitation of the conventional methods for solving EMIS problems, such as expensive computational cost, strong ill-conditions, and invalidity on high contrast. In the first step, the complex-valued deep residual convolutional neural network (DRCNN) is utilized to predict multifrequency EM scattered fields only using single-frequency EM scattered field information. Based on a new complex-valued deep convolutional encoder-decoder (DCED) structure, the second step utilizes the obtained multifrequency EM …
Implementing The Fast Full-Wave Electromagnetic Forward Solver Using The Deep Convolutional Encoder-Decoder Architecture, He Ming Yao, Lijun Jiang, Michael Ng
Implementing The Fast Full-Wave Electromagnetic Forward Solver Using The Deep Convolutional Encoder-Decoder Architecture, He Ming Yao, Lijun Jiang, Michael Ng
Electrical and Computer Engineering Faculty Research & Creative Works
In this communication, a novel deep learning (DL)-based solver is proposed for the electromagnetic forward (EMF) process. It is based on the complex-valued deep convolutional neural networks (DConvNets) comprising an encoder network and a corresponding decoder network with pixel-wise regression layer. The encoder network takes the incident EM wave and the contrast (permittivity) distribution of the object as the input. It channels the processed data into the corresponding decoder network to predict the total EM field due to the scatter of the input incident EM wave. The training of the proposed DConvNets is done using the simple synthetic dataset. Due …