Research | WaveOPT LAB | University of Illinois Chicago

Search

WaveOPT LAB

Research

Research Spotlights

check2 Heading link

Efficient Graph Clustering for Big Data

Graph-based clustering or community detection is an important focus area of data mining, social network inference, and machine learning. This is the process of partitioning data points (known as nodes) into different clusters in such a way that...
Signal Design for Interference Reduction

A focus area of our research in WaveOPT LAB is the design of sequences with excellent interference reduction properties— particularly those with small auto/cross-correlation. Such sequences form an essential component of a large set of information...
Signal Design for Eye Tracking

The main goal of research in this area is to lay the theoretical ground and further establish novel frameworks for the design and optimization of visual signals that are instrumental in eye tracking technology as a non-invasive approach for Parkin...
Nonconvex Shredded Signal Reconstruction

Restoration of shredded signals remains a relevant and significant challenge in archaeological and forensic efforts. In this project, we present a novel approach for reconstruction of shredded signals (including text documents and images) within a...
Guaranteed Nonconvex Optimization

Due to the difficulties encountered in finding the global optima of non-convex problems, approximation or local optimization algorithms have been a key component of the non-convex literature. Recently, there has been a spurt of interest in non-con...
Waveform Design for Next-Generation Sensing System

Waveform processing and design for radar have been of interest to engineers, system theorists and mathematicians in the last couple of decades. In the last decade, however, the radar world has been revolutionized by a significant increase in the c...

What is Signal Processing? Heading link