In the last ten years, sparsity-driven sensing and recovery techniques called compressed sensing (CS) has received much attention in signal processing community. So far, foundation of CS theory has been successfully established and the key principle of CS is being actively applied to various applications including machine learning, medical imaging, 5G wireless communications and network. It is expected that the CS will play an important role in implementing future digital devices as the expense for achieving super-resolution and high dimension for data processing is growing rapidly. In this seminar, we will review the basic theory behind the CS and discuss the key principles useful in applying the CS technique to real applications. We will go through various recovery algorithms and discuss their shortcomings that should be overcome by the researchers. Finally, we will introduce our recent research results along with its potential applications.
Jun Won Choi is currently an assistant professor in Electrical and Biomedical Engineering Department, Hanyang University. His research area is signal processing and machine learning. He finished BS and MS from Seoul National University and earned Ph. D. degree at University of Illinois at Urbana-Champaign (UIUC). After graduation, he worked at Qualcomm, San Diego and participated in research on design of advanced wireless communication systems. He is currently working on compressed sensing, localization, mmWave communications, sensor and array signal processing, deep learning-based perception, etc.