Qin Hu — accepted AI-conference papers

2023

On the Potentials of Surface Tactile Imaging and Dilated Residual Networks for Early Detection of Colorectal Cancer Polyps

Nethra Venkatayogi, Qin Hu, Ozdemir Can Kara, Tarunraj G. Mohanraj, S. Farokh Atashzar, Farshid Alambeigi

IROS 2023poster

This study proposes a novel diagnosis framework to decrease the early detection miss rate of colorectal cancer (CRC) polyps by using a hypersensitive vision-based tactile sensor (HySenSe) and a deep residual neural network. The HySenSe generates high-resolution 3D textural images of 160 realistic po…

Cited by 6 Source ↗Scholar ↗

2022

Deep Heterogeneous Dilation of LSTM for Transient-Phase Gesture Prediction Through High-Density Electromyography: Towards Application in Neurorobotics

Tianyun Sun, Qin Hu, Jacqueline Libby, Seyed Farokh Atashzar

RA-L 2022

Deep networks have been recently proposed to estimate motor intention using conventional bipolar surface electromyography (sEMG) signals for myoelectric control of neurorobots. In this regard, Deepnets are generally challenged by long training times (affecting practicality and calibration), complex

Cited by 24 Source ↗Scholar ↗

2022

Hand Gesture Recognition via Transient sEMG Using Transfer Learning of Dilated Efficient CapsNet: Towards Generalization for Neurorobotics

Eion Tyacke, Shreyas P. J. Reddy, Natalie Feng, Rama Edlabadkar, Shucong Zhou, Jay Patel +2

RA-L 2022

There has been an accelerated surge in utilizing the deep neural network to decode central and peripheral activations of the human nervous system to boost the spatiotemporal resolution of neural interfaces used in human-centered robotic systems, such as prosthetics, and exoskeletons. Deep learning m

Cited by 18 Source ↗Scholar ↗

2022

Social Welfare Maximization in Cross-Silo Federated Learning

Jianan Chen, Qin Hu, Honglu Jiang

ICASSP 2022accepted

As one of the typical settings of Federated Learning (FL), cross-silo FL allows organizations to jointly train an optimal Machine Learning (ML) model. In this case, some organizations may try to obtain the global model without contributing their local training, lowering the social welfare. In this p…

Cited by 0 Source ↗Scholar ↗

2021

Temporal Dilation of Deep LSTM for Agile Decoding of sEMG: Application in Prediction of Upper-Limb Motor Intention in NeuroRobotics

Tianyun Sun, Qin Hu, Paras Gulati, Seyed Farokh Atashzar

RA-L 2021

The spectrotemporal information content of surface electromyography has shown strong potential in predicting the intended motor command. During the last decade, with accelerated exploitation of powerful deep-learning techniques aligned with advancements in active prostheses and neurorobots, a great

Cited by 36 Source ↗Scholar ↗

2021

Toward Deep Generalization of Peripheral EMG-Based Human-Robot Interfacing: A Hybrid Explainable Solution for NeuroRobotic Systems

Paras Gulati, Qin Hu, Seyed Farokh Atashzar

RA-L 2021

This letter investigates the feasibility of a generalizable solution for human-robot interfaces through peripheral multichannel Electromyography (EMG) recording. We propose a tangential approach in comparison to the literature to minimize the need for (re)calibration of the system for new users. The

Cited by 48 Source ↗Scholar ↗