Current Research Activities

August 2017 - Our current project is the development of baseline data on the performance of the AIM DotCode Standard symbology. This bar code is designed for use in high speed printing applications and is currently being used in the European tobacco industry. Here is a video of the test rig we are using. Additionally, we would like to thank Cognex and Microscan for their support in providing the scanners you see in the video.


DotCode Test system from Kevin Berisso on Vimeo.


Past Research Activities

May 2015 - We are currently looking into the robustness of 2D matrix and stacked symbologies that have built in error correction. Previous research done in 1993 indicates that symbologies such as Data Matrix might experience a character substitution error at between 1-in-10.5 million and 1-in-613 million characters. In an attempt to confirm (or possibly improve upon) these numbers, we have begun a large scale test that will bring us into the billions of characters scanned. Below is the real-time count of scans and characters processed to date. 

   2D matrix testing

We would like to thank our numerous supporters, including Datalogic (scanners), GS1 (sample images) and LVS (verifiers) for their help in making this study possible.

July 2014 - Our latest endeavor is into graph databases. We are working with Neo4j's engine and are basing our research on the use of graphs on real-world RFID based data sets. Be sure to check back here... once we are done, we will be posting some of our findings. We would like to thank Graph Story for their immense help (if you need a graph solution, be sure to check them out - they are some great guys)!

March 2014 - At present, the lab is currently involved with the AIM Internet of Things committee, and is working on helping to develop a matrix of examples of where and how the Internet of Things can be used. The goal is to help companies who wish to become involved in the Internet of Things to have a better understanding of what is considered Internet of Things.