Don H. Johnson Don H. Johnson

J.S. Abercrombie Professor Emeritus of Electrical & Computer Engineering
Abercrombie A221               713.348.4956 (office)
ECE Department, MS #366        713.348.5686 (FAX)
Rice University 
6100 Main Street
Houston, Texas 77005


Office Hours: Thursdays, 1-4 PM. Appointments anytime.


Course and Activity Web Pages

ELEC 241: Fundamentals of Electrical Engineering I
ELEC 531: Statistical Signal Processing (TTh 1:00-2:15, KH107)
Fundamentals of Electrical Engineering (Coursera)
Short course: An Introduction to LaTeX, October 13, 2009
Errata to Johnson & Dudgeon, Array Signal Processing: Concepts and Techniques
Auditory Neuroscience: How is Sound Processed by the Brain?
Biography of Edward L. Norton, co-originator of the Mayer-Norton equivalent circuit
Historical outline of electrical and computer engineering
Brief thoughts on teaching

Don Johnson received the S.B. and S.M. degrees in 1970, the E.E. degree in 1971, and the Ph.D. degree in 1974, all in electrical engineering from the Massachusetts Institute of Technology. He joined M.I.T. Lincoln Laboratory as a staff member in 1974 to work on digital speech systems. In 1977, he joined the faculty of the Electrical and Computer Engineering Department at Rice University, where he is currently the J.S. Abercrombie Professor in that department and Professor in the Statistics Department. At MIT and at Rice, he received several institution-wide teaching awards, including Rice's George R. Brown Award for Excellence in Teaching and the George R. Brown award for Superior Teaching four times. He was a cofounder of Modulus Technologies, Inc. He was President of the IEEE's Signal Processing Society, and he received the Signal Processing Society's Meritorious Service Award for 2000 and was one of the IEEE Signal Processing Society's Distinguished Lecturers. Professor Johnson is a Fellow of the IEEE.

Professor Johnson's present research activities focus on issues in statistical signal processing. Particular areas of interest are determining the weave characteristics of the canvases of master paintings. A curriculum vita (PDF) is available as well as a list of recent publications, some of which have not appeared in print.

Non-Gaussian Statistical Signal Processing

All signal processing techniques exploit signal structure; when the signals are random, we want to understand the probabilistic structure of irregular, ill-formed signals. Such signals can be either be bothersome (noise) or information-bearing (discharges of single neurons). Our research is predicated on the notion that a deep understanding of a signal's structure will result in signal processing algorithms that can either suppress bothersome signals or enhance information-bearing ones. Current research ranges from fundamental studies of non-Gaussian signals and how systems extract and represent information to applying these theories to the analysis of neural data and modeling of how neural structures process information.

Stationary non-Gaussian signals occur frequently in practical situations. For example, the amplitude distributions of ambient underwater sounds and of background electromagnetic signals have been found to deviate strongly from a Gaussian characterization.

Neural discharges are modeled as stochastic point processes, which have no waveform, thereby disallowing Gaussian models.

Combined discharges of neural populations and DNA sequences represent examples of symbolic data, which have amplitudes selected from a finite set: the signal takes on values drawn from the alphabet representing base pairs {A, C, G, T}. These signals are particularly interesting since amplitudes have no mathematical operations defined for them: No field or group can be meaningfully defined for them. We have found ways of computing the Fourier and wavelet transforms of symbolic signals.

Neural Information Processing

In the nervous system, sensory information is represented in single neurons by sequences of action potentials—brief, isolated pulses having identical waveforms—occurring randomly in time. These signals are usually modeled as point processes; however, these point processes have a dependence structure and, because of the presence of a stimulus, are non-stationary. Thus, sophisticated non-Gaussian signal processing techniques are needed to analyze data recorded from sensory neurons to determine what aspects of the stimulus are being emphasized and how emphatic that representation might be. A paper analyzes well-established data analysis techniques for single-neuron discharge patterns. Another recent paper describes how we applied our theory of information processing to neural coding. Another paper describes information theoretic (capacity) results for neural populations and hints at how they can be applied to neural prosthetics.

Art Forensics

Signal processing aids the area of art forensics by developing algorithms for assessing paintings in various ways. As part of the Thread Counting Project centered at Cornell, Professor Johnson developed the best performing algorithm for determining canvas weave densities in x-ray images made of van Gogh paintings (provided to the Project by the Van Gogh Museum, Amsterdam). An example x-ray from a portion of a van Gogh painting and the 2D spectrum shows how the weave of the canvas corresponds distinctive spectral points. By estimating the frequency at which the vertical-axis peak occurs for swatches taken throughout the painting's x-ray, a map of how the horizontal-thread count varies across the canvas can be obtained. A paper describes the theoretical underpinnings of the algorithm and a second paper describes the approach without equations. A short presentation describes our basic approach and a description of interpreting thread count and thread angle results. A technically oriented paper and another without equations describe applying thread count results to determining if paintings may have come from the same canvas roll. Some complications in interpreting thread-density pattern matches are described in a recent paper.

Fundamentals of Spectral Approach to Thread Counting

Painting x-ray

Original x-ray swatch

Weave Map