machine learning – TechConnect Briefs

Athlete Engineering BaseLine Ecosystem: innovative technologies to enhance human performance

Luczak T., Burch R., Nelsen P., Freeman C., Chander H., Ball J., Jones J.A., Barlow J., Saucier D., Duclos M., Grice S., Taquino M., Mississippi State University, US

The Athlete engineering BaseLine Ecosystem (AeBLE) is a collection of integrated innovations impacting human performance, health, and wellness. AeBLE integrates artificial intelligence-driven mobile markerless motion capture, embedded wearable sensors, and augmented and virtual immersive technologies [...]

Automated Image Processing and Analysis Tools for DNA Origami Nanostructure Characterization by Atomic Force Microscopy

Chiriboga M., Díaz S.A., Medintz I.L., United States Naval Research Laboratory, US

The DNA origami technique is highly utilized in the DNA nanotechnolgy field for its ability to self-assemble geometric architectures with near atomic precision. The structural fidelity will vary depending on the assembly reaction conditions. Therefore, [...]

Using Robotics to Assemble Graphene Supercapacitor

Wu C., Wu S., Kim J., Magluyan D., Kawamoto-Kindred D., Zhou Y.H., Cao N., Zhao H., Kuang Z., Kidd T., Wu C., Wu S., Dobbs S., California State Polytechnic University, Pomona, US

Robotics is a field that seeks to use more precise and accurate mechanical manipulation to improve the speed, efficiency, and safety of certain tasks where human actions would be more of a liability than a [...]

Machine Learning for Automated Hepatic Fat Quantification

Sagreiya H., Akhbardeh A., Durot I., Rubin D.L., University of Pennsylvania, US

Nonalcoholic fatty liver disease is a global pandemic. This study investigated whether ultrasound point shear wave elastography measurements could predict hepatic fat quantification using a machine larning (ML) algorithm trained with fat quantification on MRI. [...]

Robot Axis Control Using a Differential Learning Algorithm

Abegaz B., Loyola University of Chicago, US

The motion control of robotic arms using brushless DC motors has been implemented using various mechanisms, including a model predictive controller (MPC), a fuzzy controller and supervised machine learning methods. In order to provide a [...]

Learning the Physics and Chemistry of Surfaces via Machine Vision and Deep Data Analysis

Ziatdinov M., Maksov A., Kalinin S.V., Oak Ridge National Laboratory, US

The last decade has witnessed an enormous increase in the size and quality of datasets produced by microscopic and spectroscopic experimental techniques. Particularly, the recent advances in scanning transmission electron and scanning probe microscopies have [...]