Ph.D Theses

You are here

Inside Signal Processing Newsletter Home Page

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

10 years of news and resources for members of the IEEE Signal Processing Society

Ph.D Theses

Chen, Jeane, (Northwestern University) “Poly(lactide-co-glycolide) Microspheres for Localized, Magnetic Resonance Imaging Monitored, Transcatheter Delivery of Sorafenib to Hepatocellular Carcinoma”, Advisor: Larson, Andrew C., Shea, Lonnie D

Ruan, Yiye,(The Ohio State University) “Joint Dynamic Online Social Network Analytics Using Network, Content and User Characteristics”, Advisor: Srinivasan Parthasarathy

Daali, Amy Wafa. (The University of Texas at San Antonio), “PCA based algorithm for longitudinal brain tumor stage classification and dynamical modeling of tumor decay in response to VB-111 virotherapy”, Advisor: Jamshidi, Mo

Kumar, Shamanth. (Arizona State University) “Social Media Analytics for Crisis Response”, Advisor: Liu, Huan.

Yousof Mortazavi (The University of Texas at Austin) "Analog-to-Digital Converter Circuit and System Design to Improve with CMOS Scaling", Advisor: Brian L. Evans

Youssef, Khalid. (University of California, Los Angeles), “Progress toward the Next Generation of Bioreactors for 3D Tissue Engineering”, Advisor: Louis Bouchard

Shi, Feng. (Lehigh University) “Signal processing techniques and applications” (2015), Advisor: Yan, Zhiyuan

Zhou, Yin. (University of Delaware) “Sparse signal processing for machine learning and computer vision”, Advisor: Barner, Kenneth E.

Signal sparse representation solves inverse problems to find succinct expressions of data samples as a linear combination of a few atoms in the dictionary or codebook. This model has proven effective in image restoration, denoising, inpainting, compression, pattern classification and automatic unsupervised feature learning.

Pages

SPS on Twitter

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar