Applications

Latest Innovations

Sherlock: A digital forensic analysis toolkit

Professor Campbell and graduate student Imani Palmer have developed a forensic analysis toolkit that takes an intelligent analytical approach to investigate hacking...

Attack Tagger

Inventors from University of Illinois at Urbana-Champaign and National Center for Supercomputing Applications have developed an end-to-...

Native DNA Based Storage and Computing via Single Strand Nicking

Prof. Milenkovic from the University of Illinois at Urbana-Champaign has developed a new method for DNA based storage of information. In...

ASPIRE: the Purely Data-Driven Crop Predictor

Dr. Guan from the University of IL has developed the crop predicting framework ASPIRE. This purely data-driven process mitigates assumptions in predictions. It can work...

Deep Learning for Fashion Compatibility and Style

Drs Ranjitha Kumar, David Forsyth, and their graduate students have developed a interactive chatbot that provides fashion recommendations based on user input and clothing...

Solid-State Drive

The University of Illinois’ TimeSSD and TimeKits tools are a firmware-level augmentation to solid state drive (SSD) architecture. The technology uses intrinsic...

QuickStop –The Quickest Misinformation Detection Algorithm

Dr. Xiaohan Kang from the University of IL, in collaboration with inventors from Arizona State University and Carnege Mellon University, has developed QuickStop, an...

SemiSynBio: An On-Chip Nanoscale Storage System Using Chimeric DNA

Dr. Milenkovic from the University of Illinois has developed an on-chip integrated nanosytem for writing, storage, access, and reading of large data volumes that utilizes...

ωLog: Transparent Interpretation and Integration of Layered Software Architecture Event Streams

Prof. Bates at the University of Illinois has developed ωLog, a software application which collects application context through analysis of event logs, and integrates that...

Image Processing and Error-Correction in DNA via Inpainting and Filtering

Dr. Olgica Milenkovic and Dr. Charles Schroeder at the University of Illinois has developed an error-correction method for DNA-based data storage of massive image datasets.  Previous methods for...

Dr. Olgica Milenkovic and Dr. Charles Schroeder at the University of Illinois has developed an error-correction method for DNA-based data storage of massive image datasets.  Previous methods for addressing errors use Reed-Solomon codes at both the individual oligo and pool of oligo level to reconstruct missing strings from redundantly encoded oligos. 

This method uses deep learning techniques to utilize natural redundancy present in the images to perform inpainting on the missing pixels, reconstructing the missing information, and avoiding the requirement for synthesizing and sequencing redundancy in the oligos.

Pages