Ms. Shuimei Zhang has successfully defended her Ph.D. dissertation, “Strategies for Sparsity-based Time-Frequency Analyses,” on December 14, 2020. Because of the COVID pandemic, the dissertation presentation was delivered virtually through Zoom.

Shuimei Zhang became the first Ph.D. recipient of the Advanced Signal Processing (ASP) Lab after her advisor, Dr. Yimin D. Zhang, has moved to Temple University.

Shuimei’s dissertation focuses on the analysis of nonstationary signals that are widely observed in many real-world applications. Joint time-frequency (TF) domain representations provide a time-varying spectrum for their analyses, discrimination, and classifications. Nonstationary signals commonly exhibit sparse occupancy in the TF domain. In this dissertation, we incorporate such sparsity to enable robust TF analysis in impaired observing environments with missing samples. The objective of Shuimei’s work is to develop novel signal processing techniques that offer effective TF analysis capability in the presence of missing samples. Two mutually related methods that recover missing entries in the instantaneous autocorrelation function and reconstruct high-fidelity TF representations were developed. These techniques approach full-data results with negligible performance loss. She also developed a novel machine learning-based approach that effectively offers crossterm-free TF representations with effective autoterm preservation.

Shuimei’s dissertation is available in her personal website In addition to her work on TF analyses, Shuimei also carried out research activities on sparse array processing and participated in the work on spectrum sharing. She received the Best Student Paper Award at the 2020 IEEE Sensor Array and Multichannel Signal Processing Workshop.

Shuimei’s Ph.D. studies were supported by the Temple University Presidential Fellowship and the National Science Foundation.

Visit “Shuimei Zhang: Reaching Maturity and Confidence” to know more about Shuimei’s journey at the ASP Lab.