Name: Intelligent Multimodal Computing and Sensing Laboratory (IMICS Lab) - Texas State University
Type: Organization
Bio: This is the public GitHub page of the Intelligent Multimodal Computing and Sensing Laboratory (IMICS Lab)
Location: United States of America
Blog: https://imics-lab.github.io/
Intelligent Multimodal Computing and Sensing Laboratory (IMICS Lab) - Texas State University's Projects
BioDiffusion paper source code and docs using a template repo
This repository hosts various examples of human biosignal analysis.
Implementation of self-supervised image-level contrastive pretraining methods using Keras.
Repository for paper "Predicting ICU and Hospital Length of Stay for COVID-19 Patients"
Source code for self-supervised EEG data transfer learning
Code repository for paper: Fusion of Learned Representations for Multimodal Sensor Data Classification
Intelligent Multimodal Computing and Sensing (IMICS) Laboratory @ Computer Science – Texas State University
Source code for Individual Convolution of Ankle, Hip, and Wrist Data for Activities-of-Daily Living Classification
Generate numpy arrays for classification tasks from public datasets
Source code for PETRA 2024 paper: "Many-to-Many Prediction for Effective Modeling of Frequent Label Transitions in Time Series"
Source files for Model Evaluation Approaches to Human Activity Recognition from Time-Series Data to be presented at AIME2021
Predicting Cardiac and Respiratory Events in the PSG-Audio Dataset
The repository contains source code and data from the paper titled "Recurrence and Self-Attention vs the Transformer for Time-Series Classification: A Comparative Study".
A template repository for research projects
Source code for WISHWell 2022 Paper
Enhancing Time-Series Prediction with Temporal Context Modeling: A Bayesian and Deep Learning Synergy
This repository maintains the code and experiments of the paper titled "Understanding Social Anxiety: Detecting Stress Factors through Text Analysis", as well as other text analysis tools.
Time series embeddings
A Python-based labeling tool that uses self-supervised learning and visualizations to assist humans in labeling time series data
This is a fork of the CL-HAR repo for implementing Self-supervised Contrastive Learning on time series sensor data