no code implementations • 27 Oct 2022 • Soheil Zabihi, Elahe Rahimian, Amir Asif, Arash Mohammadi
Advancements in Biological Signal Processing (BSP) and Machine-Learning (ML) models have paved the path for development of novel immersive Human-Machine Interfaces (HMI).
no code implementations • 28 Mar 2022 • Soheil Zabihi, Elahe Rahimian, Amir Asif, Arash Mohammadi
In other words, we propose a hybrid framework based on the transformer architecture, which is a relatively new and revolutionizing deep learning model.
no code implementations • 3 Jan 2022 • Parnian Afshar, Arash Mohammadi, Konstantinos N. Plataniotis, Keyvan Farahani, Justin Kirby, Anastasia Oikonomou, Amir Asif, Leonard Wee, Andre Dekker, Xin Wu, Mohammad Ariful Haque, Shahruk Hossain, Md. Kamrul Hasan, Uday Kamal, Winston Hsu, Jhih-Yuan Lin, M. Sohel Rahman, Nabil Ibtehaz, Sh. M. Amir Foisol, Kin-Man Lam, Zhong Guang, Runze Zhang, Sumohana S. Channappayya, Shashank Gupta, Chander Dev
Lung cancer is one of the deadliest cancers, and in part its effective diagnosis and treatment depend on the accurate delineation of the tumor.
no code implementations • 2 Jan 2022 • Raika Karimi, Arash Mohammadi, Amir Asif, Habib Benali
To elicit SSmVEP, we designed a novel and innovative dual frequency aggregated modulation paradigm, referred to as the Dual Frequency Aggregated steady-state motion Visual Evoked Potential (DF-SSmVEP), by concurrently integrating "Radial Zoom" and "Rotation" motions in a single target without increasing the trial length.
no code implementations • 31 Dec 2021 • Soheil Zabihi, Elahe Rahimian, Fatemeh Marefat, Amir Asif, Pedram Mohseni, Arash Mohammadi
Objective: The paper focuses on development of robust and accurate processing solutions for continuous and cuff-less blood pressure (BP) monitoring.
no code implementations • 17 Oct 2021 • Elahe Rahimian, Soheil Zabihi, Amir Asif, Dario Farina, S. Farokh Atashzar, Arash Mohammadi
Advances in biosignal signal processing and machine learning, in particular Deep Neural Networks (DNNs), have paved the way for the development of innovative Human-Machine Interfaces for decoding the human intent and controlling artificial limbs.
no code implementations • 1 Oct 2021 • Soheil Zabihi, Elahe Rahimian, Soumya Sharma, Sean K. Sethi, Sara Gharabaghi, Amir Asif, E. Mark Haacke, Mandar S. Jog, Arash Mohammadi
Brain iron deposition, in particular deep gray matter nuclei, increases with advancing age.
no code implementations • 25 Sep 2021 • Elahe Rahimian, Soheil Zabihi, Amir Asif, Dario Farina, S. Farokh Atashzar, Arash Mohammadi
We propose a novel Vision Transformer (ViT)-based neural network architecture (referred to as the TEMGNet) to classify and recognize upperlimb hand gestures from sEMG to be used for myocontrol of prostheses.
no code implementations • 19 Dec 2020 • Morteza Mirzaei, Amir Asif, Hassan Rivaz
Despite capabilities of elastography techniques in estimating displacement in both axial and lateral directions, estimation of axial displacement is more accurate than lateral direction due to higher sampling frequency, higher resolution and having a carrier signal propagating in the axial direction.
no code implementations • 11 Nov 2020 • Elahe Rahimian, Soheil Zabihi, Amir Asif, Dario Farina, Seyed Farokh Atashzar, Arash Mohammadi
This work is motivated by the recent advances in Deep Neural Networks (DNNs) and their widespread applications in human-machine interfaces.
no code implementations • 3 Feb 2020 • Soroosh Shahtalebi, Amir Asif, Arash Mohammadi
In this work, a Siamese architecture, which is developed based on Convolutional Neural Networks (CNN) and provides a binary output on the similarity of two inputs, is combined with OVR and OVO techniques to scale up for multi-class problems.
1 code implementation • 9 Nov 2019 • Elahe Rahimian, Soheil Zabihi, Seyed Farokh Atashzar, Amir Asif, Arash Mohammadi
The proposed innovative XceptionTime is designed by integration of depthwise separable convolutions, adaptive average pooling, and a novel non-linear normalization technique.