Search Results for author:
Found 14 papers, 3 papers with code
DynaShare: Task and Instance Conditioned Parameter Sharing for Multi-Task Learning
Multi-task networks rely on effective parameter sharing to achieve robust generalization across tasks.
Transformer-based Hand Gesture Recognition via High-Density EMG Signals: From Instantaneous Recognition to Fusion of Motor Unit Spike Trains
Additionally, the CT-HGR framework can perform instantaneous recognition using sEMG image spatially composed from HD-sEMG signals.
Light-weighted CNN-Attention based architecture for Hand Gesture Recognition via ElectroMyography
Advancements in Biological Signal Processing (BSP) and Machine-Learning (ML) models have paved the path for development of novel immersive Human-Machine Interfaces (HMI).
HYDRA-HGR: A Hybrid Transformer-based Architecture for Fusion of Macroscopic and Microscopic Neural Drive Information
At the same time, advancements in acquisition of High-Density sEMG signals (HD-sEMG) have resulted in a surge of significant interest on sEMG decomposition techniques to extract microscopic neural drive information.
Multi-Content Time-Series Popularity Prediction with Multiple-Model Transformers in MEC Networks
Most existing datadriven popularity prediction models, however, are not suitable for the coded/uncoded content placement frameworks.
TraHGR: Transformer for Hand Gesture Recognition via ElectroMyography
In other words, we propose a hybrid framework based on the transformer architecture, which is a relatively new and revolutionizing deep learning model.
ViT-HGR: Vision Transformer-based Hand Gesture Recognition from High Density Surface EMG Signals
The proposed Vision Transformer-based Hand Gesture Recognition (ViT-HGR) framework can overcome the aforementioned training time problems and can accurately classify a large number of hand gestures from scratch without any need for data augmentation and/or transfer learning.
BP-Net: Cuff-less, Calibration-free, and Non-invasive Blood Pressure Estimation via a Generic Deep Convolutional Architecture
Objective: The paper focuses on development of robust and accurate processing solutions for continuous and cuff-less blood pressure (BP) monitoring.
TEDGE-Caching: Transformer-based Edge Caching Towards 6G Networks
As a consequence of the COVID-19 pandemic, the demand for telecommunication for remote learning/working and telemedicine has significantly increased.
Hand Gesture Recognition Using Temporal Convolutions and Attention Mechanism
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.
Q-Net: A Quantitative Susceptibility Mapping-based Deep Neural Network for Differential Diagnosis of Brain Iron Deposition in Hemochromatosis
Brain iron deposition, in particular deep gray matter nuclei, increases with advancing age.
TEMGNet: Deep Transformer-based Decoding of Upperlimb sEMG for Hand Gestures Recognition
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.
FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography
This work is motivated by the recent advances in Deep Neural Networks (DNNs) and their widespread applications in human-machine interfaces.
XceptionTime: A Novel Deep Architecture based on Depthwise Separable Convolutions for Hand Gesture Classification
The proposed innovative XceptionTime is designed by integration of depthwise separable convolutions, adaptive average pooling, and a novel non-linear normalization technique.
