Doctor of Philosophy (Ph.D.)
Electrical Engineering
University of Virginia
2002
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Staff
Staff
John M. M. Anderson
Interim Dean
Department/Office
- Office of the Dean, CEA
School/College
- College of Engineering and Architecture (CEA)
Biography
John M. M. Anderson holds a Ph.D. in electrical engineering from the University of Virginia, an M.S. in electrical engineering from the Georgia Institute of Technology, and an Sc.B. in electrical engineering from Brown University. After completing his doctoral studies, Anderson joined the Department of Electrical and Computer Engineering at the University of Florida. While at the University of Florida, Anderson was a visiting faculty member in the Department of Electrical and Computer Engineering at the University of Maryland in College Park. Since 2002, Dr. Anderson has been a faculty member in the Department of Electrical Engineering and Computer Science at Howard University in Washington, DC. Currently, Anderson is Dean of the Howard University College of Engineering and Architecture, as well as a professor of electrical engineering. In addition to his experiences in academia, Anderson has served as a health science administrator for the National Institute for Biomedical Imaging and Bioengineering at the National Institutes of Health and as an associate editor for the Institute of Electrical and Electronics Engineers Signal Processing Letters. Dr. Anderson is an NSF CAREER Award recipient and a holder of several patents.
Education & Expertise
Education
Master of Science (M.S.)
Electrical Engineering
Georgia Institute of Technology
1987
Bachelor of Science (Sc.B.)
Electrical Engineering
Brown University
1985
Expertise
Image Processing, Signal Processing, Medical Imaging
Dr. Anderson's general research interests lie in the areas of signal and image processing. Currently, the problem of reconstructing images for impulse and step frequency ground penetrating radar systems is receiving his greatest attention. In the past, he has developed image reconstruction algorithms for medical imaging modalities such as positron emission tomography and X-ray computed tomography.
Academics
Academics
Fundamentals of Signals and Systems
Communications Theory
Probability and Random Variables
Signal Processing I, II
Math Laboratory I, II
Featured News
Featured News
Read: US Black Engineer and Information Technology | Meet the engineering deans at ABET-accredited Historically Black Colleges and Universities
Publications and Presentations
Publications and Presentations
MAP image reconstruction for landmine and IED detection using ground penetrating radar
MAP image reconstruction for landmine and IED detection using ground penetrating radar
In this article, we develop an iterative maximum a posteriori (MAP) estimation algorithm for reconstructing sub-surface images from ground penetrating radar (GPR) data. Note, the larger goal of our research is to use the resulting GPR images, along with appropriate detection algorithms, to identify landmines and improvised explosive devices.
High-resolution range profiling via weighted SPICE in stepped-frequency radar
High-resolution range profiling via weighted SPICE in stepped-frequency radar
Weighted SPICE is a unifying approach for four user parameter-free algorithms, namely SPICE, LIKES, SLIM and IAA. The latter three algorithms can be interpreted as weighted versions of SPICE with different data-dependent weights. Weighted SPICE is originally proposed for spectral estimation and array processing. We show that it can be used to obtain HRRPs in stepped-frequency radar as well, and comparisons among these four methods for HRR processing are also discussed in this paper.
High-Resolution Multiple-Input Multiple-Ouput FLGPR Imaging
High-Resolution Multiple-Input Multiple-Ouput FLGPR Imaging
Multiple-input multiple-output forward-looking ground-penetrating radar (GPR) systems can be used to detect landmines. To enhance its performance, two data-dependent imaging algorithms, based on the sparse learning via iterative minimization (SLIM) and sparse covariance-based estimation (SPICE) techniques for high-resolution imaging, applied to the time-domain GPR data, were previously developed. Time-domain SLIM (TD-SLIM) and time-domain SPICE (TD-SPICE) yield higher resolution and lower sidelobe compared with data-independent approaches such as delay-and-sum and recursive sidelobe minimization.
