Journal Papers

NON-INVASIVE HEART RATE ESTIMATION FROM BALLISTOCARDIOGRAMS USING BIDIRECTIONAL LSTM REGRESSION

Abstract: Non-invasive heart rate estimation is of great importance in daily monitoring of cardiovascular diseases. In this paper, a bidirectional long short term memory (bi-LSTM) regression network is developed for non-invasive heart rate estimation from the ballistocardiograms (BCG) signals. The… Read More

A BENCHMARK DATASET FOR INDIVIDUAL TREE CROWN DELINEATION IN CO-REGISTERED AIRBORNE RGB, LIDAR AND HYPERSPECTRAL IMAGERY FROM THE NATIONAL ECOLOGICAL OBSERVATION NETWORK

Abstract: Broad scale remote sensing promises to build forest inventories at unprecedented scales. A crucial step in this process is designing individual tree segmentation algorithms to associate pixels into delineated tree crowns. While dozens of tree delineation algorithms have been… Read More

ENSEMBLE CORRDET WITH ADAPTIVE STATISTICS FOR BAD DATA DETECTION

Abstract: Smart grid (SG) systems are designed to leverage digital automation technologies for monitoring, control and analysis. As SG technology is implemented in increasing numbers of power systems, SG data becomes increasingly vulnerable to cyber-attacks. Classic analytic physics-model based bad… Read More

OVERCOMING SMALL DATASETS PUBLISHED IN COMPUTERS AND ELECTRONICS IN AGRICULTURE!

Congratulations to our labmates, Weihuang Xu, Guohao Yu and Alina Zare, as well as collaborators Brenden Zurweller, Diane Rowland, Joel Reyes-Cabrera, Felix Fritschi, Roser Matamala and Thomas Juenger!  Their paper, “Overcoming Small Minirhizotron Datasets Using Transfer Learning”, was published in… Read More

ROOT IDENTIFICATION WITH MULTIPLE INSTANCE LEARNING ACCEPTED TO MACHINE VISION AND APPLICATIONS!

Congratulations to our labmates Guohao Yu, Alina Zare and Hudanyun Sheng, as well as collaborators, Roser Matamala, Joel Reyes-Cabrera, Felix Fritschi and Thomas Juenger! Their paper, “Root Identification in Minirhizotron Imagery with Multiple Instance Learning”, was recently accepted to Machine… Read More