research
∙
09/13/2023
A Multi-label Classification Approach to Increase Expressivity of EMG-based Gesture Recognition
Objective: The objective of the study is to efficiently increase the exp...
share
research
∙
09/13/2023
User Training with Error Augmentation for Electromyogram-based Gesture Classification
We designed and tested a system for real-time control of a user interfac...
research
∙
07/22/2023
A Vision for Cleaner Rivers: Harnessing Snapshot Hyperspectral Imaging to Detect Macro-Plastic Litter
Plastic waste entering the riverine harms local ecosystems leading to ne...
research
∙
03/19/2023
Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks
Multitemporal hyperspectral unmixing (MTHU) is a fundamental tool in the...
research
∙
03/01/2023
On Parametric Misspecified Bayesian Cramér-Rao bound: An application to linear Gaussian systems
A lower bound is an important tool for predicting the performance that a...
research
∙
05/12/2022
Neural Network-based OFDM Receiver for Resource Constrained IoT Devices
Orthogonal Frequency Division Multiplexing (OFDM)-based waveforms are us...
research
∙
04/13/2022
Hybrid Neural Network Augmented Physics-based Models for Nonlinear Filtering
In this paper we present a hybrid neural network augmented physics-based...
research
∙
10/12/2021
Cubature Kalman Filter Based Training of Hybrid Differential Equation Recurrent Neural Network Physiological Dynamic Models
Modeling biological dynamical systems is challenging due to the interdep...
research
∙
04/17/2021
Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing
Autoencoder (AEC) networks have recently emerged as a promising approach...
research
∙
01/02/2020
Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixing
The recent evolution of hyperspectral imaging technology and the prolife...
research
∙
09/20/2019
Deep Generative Models for Library Augmentation in Multiple Endmember Spectral Mixture Analysis
Multiple Endmember Spectral Mixture Analysis (MESMA) is one of the leadi...
research
∙
08/19/2019
A Blind Multiscale Spatial Regularization Framework for Kernel-based Spectral Unmixing
Introducing spatial prior information in hyperspectral imaging (HSI) ana...
research
∙
02/14/2019
Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixing
Endmember (EM) spectral variability can greatly impact the performance o...
research
∙
01/02/2019
Improved Hyperspectral Unmixing With Endmember Variability Parametrized Using an Interpolated Scaling Tensor
Endmember (EM) variability has an important impact on the performance of...
research
∙
11/02/2018
Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral Variability
Traditional hyperspectral unmixing methods neglect the underlying variab...
research
∙
08/30/2018
Super-Resolution for Hyperspectral and Multispectral Image Fusion Accounting for Seasonal Spectral Variability
Image fusion combines data from different heterogeneous sources to obtai...
research
∙
08/02/2018
A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability
Spectral variability in hyperspectral images can result from factors inc...
research
∙
03/16/2018
A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixing
Tensor-based methods have recently emerged as a more natural and effecti...
research
∙
12/05/2017
Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing
Sparse hyperspectral unmixing from large spectral libraries has been con...
research
∙
10/20/2017
Generalized linear mixing model accounting for endmember variability
Endmember variability is an important factor for accurately unveiling vi...
research
∙
03/01/2016
Technical Report: Band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem
Kernel-based nonlinear mixing models have been applied to unmix spectral...
research
∙
03/18/2015
