H
Heung-No Lee
Researcher at Gwangju Institute of Science and Technology
Publications - 198
Citations - 3344
Heung-No Lee is an academic researcher from Gwangju Institute of Science and Technology. The author has contributed to research in topics: Decoding methods & Compressed sensing. The author has an hindex of 22, co-authored 195 publications receiving 2511 citations. Previous affiliations of Heung-No Lee include University of California, Los Angeles & University of Pittsburgh.
Papers
More filters
Journal ArticleDOI
Formation of oxygen vacancies and Ti 3+ state in TiO 2 thin film and enhanced optical properties by air plasma treatment
TL;DR: This is the first time it is reported that simply air plasma treatment can also enhances the optical absorbance and absorption region of titanium oxide (TiO2) films, while keeping them transparent.
Journal ArticleDOI
Visible and UV photo-detection in ZnO nanostructured thin films via simple tuning of solution method
TL;DR: The adopted low cost and simplest approach makes the pristine ZnO-NSs applicable for wide-wavelength applications in optoelectronic devices.
Journal ArticleDOI
Coordinating transmit power and carrier phase for wireless networks with multi-packet reception capability
TL;DR: This article proposes a feedback-based transmit power and carrier phase adjustment scheme that estimates the symbol energy and the carrier phase offset for each transmitter’s received signal, computes the optimal received power levels, and feeds the optimal transmit power level and phase shift information back to the transmitters.
Proceedings ArticleDOI
A simple and effective cross layer networking system for mobile ad hoc networks
TL;DR: A novel cross layer design concept that could improve the network throughput significantly for mobile ad hoc networks by utilizing channel reservation control packets employed at the MAC layer for exchanging timely channel estimation information to enable an adaptive selection of a spectrally efficient transmission rate.
Journal ArticleDOI
Sparse representation-based classification scheme for motor imagery-based brain-computer interface systems.
TL;DR: The results showed that the SRC scheme provides highly accurate classification results, which were better than those obtained using the well-known linear discriminant analysis classification method.