Author: anry

Arie Nakhmani is an associate professor of Electrical and Computer Engineering at the University of Alabama at Birmingham (UAB). He joined UAB in 2013. Prior to that, he completed his postdoctoral training at Georgia Tech and Boston University. He is an Associate Scientist in the Comprehensive Cancer Center and director of the ANRY lab at UAB. He is the author of over 120 peer-reviewed research publications and book chapters on robust control, machine learning, signal and image analysis. He is also an author of "Modern Control: State-Space Analysis and Design Methods" textbook published by McGraw Hill in 2020.

The BRAIN Initiative Grant is Funded

We have got one of the most prestigious grants for brain research. Congratulations to Dr. Harrison Walker from the Neurology Department, who is the PI on this grant, and our team.
For more information:
UAB receives prestigious BRAIN Initiative grant for deep brain stimulation in Parkinson’s disease

Our Lab Work with Drones was Featured in UAB Magazine and other Media

Our work was featured in the following media:

Visual Tracking of Nanoplatforms Under Magnetic Field

We use the tools of nanotechnology and develop a visual tracking algorithm for motion analysis of nanoplatforms under the influence of an external magnetic field. We have studied quantitative relationships between nanoplatform sizes, fluid flow rate and viscosity, external magnetic field strength and their effects on the nanoplatform trajectories.

Machine learning project 1

Visual tracking project 1

Image analysis project 1

Spirometry function based COPD detection and staging

Wikipedia states that Spirometry (meaning the measurement of breath) is the most common of the pulmonary function tests, measuring lung function, specifically the amount (volume) and/or speed (flow) of air that can be inhaled and exhaled. Spirometry is an important tool used for generating pneumotachographs, which are helpful in assessing conditions such as asthma, pulmonary fibrosis, cystic fibrosis, and Chronic obstructive pulmonary disease (COPD).

Presently accepted methods for COPD are based on the selection of a few specific points from the output graph and they are not reliable enough. In this project, we develop additional more robust measures for staging COPD.