Don H. Johnson - US grants

This project represents part of a long-term research effort intending to describe and analyze the functions of cell groups within the superior olivary complex of the cat. The proposed work will be a collaborative effort involving a sensory neurophysiologist, C. Tsuchitani of the UTHSC-H, and a communication-signal processing theorist, D. H. Johnson of Rice University. The objective of this project is to determine the manner in which auditory information is encoded in the responses of lower auditory neurons by utilizing statistical communication theory, i.e., the theory of point processes, as the mathematical framework of the representation of discharge patterns. The proposed research will concentrate on the neural network which gives rise to the binaural responses of lateral superior olivary (LSO) neurons. Activity will be recorded extracellularly from single units in the LSO and from units in the anteroventral cochlear nucleus and the medial nucleus of the trapezoid body, which are presumed to innervate the LSO. Histological methods will be used to confirm unit location within these nuclei. Monaural stimuli will be used to elicit responses that will be used to establish the form of the statistical model of unit discharges. LSO unit discharges to binaural stimuli will serve to provide measures of the statistics of the inhibitory response and to develop the model of LSO unit binaural responses. Once the models of the LSO and its inputs have been established, the LSO network model will be developed and its consistency checked by comparing "input" and "output" quantities. Data collection and initial data analysis will be carried out at the UTHSC-H facility. The facilities at Rice University will provide the computing capabilities required for the statistical modeling of the single unit discharges.

This project is a continuation of a long-term research effort directed toward describing and modeling the functions of cell groups within the superior olivary complex of the cat. The proposed work is a collaborative effort involving a sensory neurophysiologist, C. Tsuchitani of the University of Texas Health Science Center at Houston (UTHSC-H), and a communication- signal processing theorist, D.H. Johnson of Rice University. The objective of this project is to determine the manner in which auditory information is encoded in the responses of lower auditory neurons by utilizing statistical communications theory, i.e., the theory of point processes, as the mathematical framework of the representation of single unit discharge patterns. Binaural hearing provides significant advantages over monaural hearing in perceptual tasks such as sound detection, localization of sound sources and speech intelligibility in a noisy background. The lateral superior olive (LSO) is believed to be of primary importance in binaural processing of high (greater than 1 kHz) frequency information. The proposed research is concentrating on the neural network which gives rise to the responses of the LSO neurons to stimulation of the two ears. The single unit discharges of LSO units and units presumed to provide inputs to the LSO (i.e., the primary-like units of the anteroventral cochlear nucleus and of the medial nucleus of the trapezoid body) are recorded extracellularly. Histological methods will be used to confirm unit location with the aid of deposits made with the recording electrode. Monaural stimuli are used to elicit responses that are used to establish the form of the statistical model of unit discharges. LSO unit discharges to binaural stimuli are providing measures of the statistics of the inhibitory response and will be used to develop the model of LSO unit binaural responses. Once the models of the LSO and its inputs have been established, the LSO network model will be developed and its consistency checked by comparing input and output quantities. Models of binaural localization will be studied to develop estimates of the limits of localization acuity imposed by physical constraints. These limits will provide a baseline measure of performance which can be compared with the limits placed on acuity by the model of LSO unit processing of binaural information. Data collection and initial data analysis are carried out at the UTHSC-H facility under the direction of Dr. C. Tsuchitani. Dr. D.H. Johnson directs the statistical modeling of the single unit discharges at Rice University.

9309263 Johnson DH Hearing with two ears (binaural) is much better than with just one (monaural) for detecting and locating sound sources, and understanding speech in a noisy background. Within the mammalian brain, in the brainstem, there is a structure called the superior olivary complex that is the first place in the auditory pathway directly involved in the neural processing that underlies binaural hearing. This complex contains nerve cells formed into groups called nuclei. Two of these are the lateral superior olive nucleus (LSO), and the medial nucleus of the trapezoid body. Both are part of a neural network, the LSO network, that is specifically involved in binaural processing of mid- to high-frequency sounds, those above 1.5kHz. This work is a collaboration between a sensory neurophysiologist and a signal-processing theorist using an interdisciplinary approach. Responses of neurons in the LSO network are recorded for controlled acoustic stimuli which approximate normal "free-field" binaural conditions, and computational models describe how these responses devleop the neural representation of binaural signals. Signal array processing techniques also will be used to determine the consequent contributions of the LSO network to binaural perception. This work will have impact beyond auditory neuroscience to sensory processing in general, to computational modelling of networks, and to devices for aiding or enhancing hearing capabilities. ***

Johnson Rice University is establishing a two-part database composed of sampled signals and signal processing software with the Signal Processing Society serving as the gatekeeper. This database will be accessible through the InterNet at no charge to users. Data and programs are being solicited from university, industrial, and military sources. The data will provide a needed testbed for evaluation of signal processing algorithms; the software will provide the signal processing community with state-of-the-art algorithms and simulation systems.

Companies frequently team their employees over widespread regions when undertaking large and detailed projects. At present, these enterprises use leased analog and digital communication lines and will no doubt switch to computer networks as the "Information Superhighway" becomes a reality. From a pedagogical point of view, this modern approach to teaming requires that educators develop in their graduates the skills required for this new reality. These skills include, among others, identifying expertise and interest within a larger distributed group, segmenting tasks in a meaningful fashion, integrating designs across a high-speed network, verifying performance against specifications, and compiling and writing a comprehensive final report. Without these skills, our students face a competitive disadvantage when confronted with cheaper, more classically trained engineering talent now prevalent in the global marketplace. To confront this challenge to engineering education, this project has designed and implemented team learning and team teaching using the Internet. The principal investigators are well-acquainted teaching and research faculty who specialize in digital signal processing. The project features signal processing courses with a heavy emphasis on distributed experimentation. The equipment used to support these new courses is located at each university involved in the project and will form a large connected multimedia laboratory. These individual laboratories support audio and video transmission necessary for facilitating real-time student interaction over the Internet. Relevant experiments and learning aides are being developed and will be disseminated electronically through standard Internet services and through the development of a multimedia textbook authored by the investigators.

In this research the goal is to develop a nonparametric adaptive communications receiver strategy by exploiting results from the theory of universal classification and data compression. Analysis based upon the "typicality" of the data has become one of the primary theoretical and practical concepts in modern information theory. The so-called "Type" (appropriate histogram estimators of the amplitude distribution) have not only been shown to be sufficient statistics for both classification and compression, but have also been shown to converge exponentially fast to the true underlying distribution. Because type-based detectors make no a priori assumptions about the channel characteristics and measure the quantities needed to provide asymptotically optimal reception, these detectors can theoretically achieve the same exponential error rate as the clairvoyant receiver that knows the channel characteristics perfectly and uses them optimally. Measurements made during succeeding transmissions, possibly using the data transmissions themselves in a decision feedback paradigm, can be used to track channel variations. The approach will be extended to deal with intersample dependencies and colored noise by using channel measurements in an optimally effective way. Two techniques drawn from data compression work, context trees and Lempel-Ziv (universal) coding, will be examined to determine which represents channel-induced dependencies most efficiently and which is most adaptable. By merging systems based on these two theories, a receiver that adapts to unknown, time-varying channels will be developed. It will be demonstrated that this receiver can be used in multiuser channels, both wireless and optical fiber, with little modification. The research will develop the theoretical underpinnings of this adaptive receiver strategy, and will develop a complete software implementation of the receiver, using as test data actual communication channel recordings. This work will be performed by a close collaboration of researchers from Rice University and George Mason University.

PROPOSAL #0105558
WILLIAM MARSH RICE UNIVERSITY
PI: JOHNSON, DON H.

The closely allied fields of signal processing and information theory have never found common ground. Signal processing focuses on how signals can represent information and how systems manipulate and change signal structure. Information theory revolves around the structure of information that signals represent, but ignores what information is meaningful to the receiver by concentrating on efficient compression and communication. The research develops a new theory of information processing that weds these two disciplines and has the dual goals of understanding how effectively signals, no matter what their nature, can represent information and of quantifying how well systems process information. Because of the theory's generality, we analyze both communication systems, to probe how effectively they convey information and meaning, and neural processing systems, to understand how neural groups process and represent information.

We quantify how well signals represent information by computing an information-theoretic distance (it obeys the Data Processing Theorem) between signals associated with two instances of the encoded
information. We assume that the signals are stochastic, and the distance measures how different are the probability distributions associated with the signals. We use the Kullback-Leibler distance because it is related both to optimal classifier performance via Stein's Lemma and to optimal least-squares estimator performance through the Cramer-Rao bound. A larger distance thus corresponds to a more effective representation of the information. The information processing ability of a system is measured by the information transfer ratio, defined to be the ratio of distances computed at the system's input and output. With this ratio, we quantify how well an information processing system behaves as an information filter.

0216467
Moshe Y. Vardi
Don H. Johnson; Ken W. Kennedy; John M. Mellor-Crummey; Willy Zwaenepoel
MRI: Acquisition of CITI Terascale Cluster \(CTC\)

This proposal, requiring access to the kinds of experimental computational resources needed for scalability experiments, aims to support scalability to thousands of processors. Achieving this goal requires experimentation of computational facilities of sufficient size to establish that solutions will scale to large systems. A high-performance computational cluster with a peak performance of approximately one teraflop, supporting both compute- and data-intensive science and engineering, will enable researchers to make fundamental advances in diverse areas such as biochemistry, biology, chemistry, computational mathematics, computer science and engineering, earth science, economics, physics, political science, and psychology. Experiments planned include:
a. Scalability of compiler techniques for systems with hundreds of processors and deep memory and communication hierarchies;
b. Development, simulation, and testing of scalable Web services on hundreds of processors;
c. Simulations of ad hoc multihop wireless networks scaling to thousands of nodes;
d. Scalable algorithms for Monte-Carlo studies of the physics of heavy ion collisions;
e. Design and evaluation of scalable optimization algorithms based on component frameworks;
f. Extraction and analysis of data on hundreds of millions of international events, to better predict and understand international conflicts (extend the Kansas Data System); and
g. Scalability tests and practical application of new algorithms for modeling and simulation of biomolecular interactions using several thousand flexibility parameters.

By integrating the equipment in the existing curriculum the educational impact is expected to be large, going beyond a course in parallel programming. Several programs are already in place addressing diversity.

0538934
Burris

This award is to William Marsh Rice University to support the activity described below for 36 months. The proposal was submitted in response to the Partnerships for Innovation Program Solicitation (NSF-05566).

Partners

William Marsh Rice University (lead institution) and National Instruments

The primary objective of the proposal is as follows: to revolutionize the way science and engineering are taught by breaking away from traditional textbook and lecture-based education in order to build a new framework, where communities of educators, students, and field practitioners continually interact, collaborate, connect, and explore active content. The specific objectives of the project are to 1) build a world-wide community of educators, students, and practitioners in DSP led by Rice faculty and NI technology evangelists which will develop and refine a critical mass of free Connexions DSP course materials; 2) enrich these materials with interactive LabVIEW visualizations to make the concepts come alive and encourage experimentation, exploration, and design; 3) develop semantic mathematics representations for displaying and exploring science and engineering concepts based on MathML--in particular a suite of tools for authoring, sharing, and exploring mathematics on the web; 4) translate the materials into a number of languages, including Spanish, to reach both local and worldwide audiences; 5). Study the marketing and business issues associated with growing and sustaining the project into a win-win for both the university and industrial partners; and 6) assess the projects impact and widely disseminate the lessons learned.

Potential Educational and Economic Impact

There is a crisis in engineering education today, with decreasing enrollments, less engaged and less prepared students, and pressure to cover increasing amounts of material. Curricula are increasingly stove-piped and disconnected, in spite of research indicating that for women and underrepresented minority students, the study of science and engineering is made meaningful by connections to other fields. Moreover, a leading complaint from industry regarding engineering graduates is their lack of collaboration and team skills and lack of hands-on design experience.

The intellectual merit of the project follows. A new approach has been identified for building and sustaining virtual educational communities around active content and applying the results to the spectrum of engineering education venues: university undergraduate and graduate courses, industrial training and continuing education, just-in-time learning on the job, and high-school laboratories. The research involves and balances education, community development, technology development, marketing and business planning, and impact assessment. The foundation for the project is provided by Rices Connexions Project (cnx.rice.edu) and by NIs LabVIEW (ni.com) DSP platform. Connexions is an open-access repository of free scholarly materials and an open-source software toolkit to help authors publish and collaborate, instructors rapidly build and share custom courses, and learners explore the links among concepts, courses, and disciplines. LabVIEW is a personal computer-based DSP system for interactively visualizing, processing, and interacting with multimedia such as audio, images, and video from a wide range of applications.

The broader impacts of the activity follow. This research include the development of people-resources and technologies that will substantially increase the performance and capabilities of engineering educators and that will open up education into underdeveloped parts of the State of Texas, the Nation, and the world. In particular, education in DSP and related technologies is critical to sustain the high-tech complex in Dallas, Austin, and Houston, Texas. Additional educational impacts include the training of undergraduate and graduate students involved in this project and two workshops that will bring together DSP educators and practitioners to share their knowledge and build communities.

This project is addressing a crisis throughout engineering education, and cyberinfrastructure education regarding decreasing enrollments, under preparation and disengagement of students, and the pressure of faculty to cover increasing volumes of material. Curricula have become stove-piped and disconnected, in spite of research indicating that science and engineering education best resonates with women and underrepresented minority students and when clear connections are drawn to transformative applications and other fields of study. Industry routinely lobbies for better engineering graduates that are at ease when collaborating on teams, and eager to attack hands-on design challenges.

The academic/industrial/professional society partnership between Rice University, Georgia Institute of Technology, Rose-Hulman Institute of Technology, the University of Texas at El Paso, National Instruments, Texas Instruments, Hewlett-Packard, and the Institute for Electrical and Electronics Engineers Signal Processing Society directly attacks these issues by aiming to revolutionize the way they teach and learn about cyberinfrastructure. They are guided by a common vision: to prepare the cyberinfrastructure leaders of tomorrow, to break away from the traditional textbook, lecture, homework-based approach to education, and to build a new framework where a vibrant network of educators, students, and field practitioners continually interact, collaborate, connect, and explore interactive content.

The innovative aspects and scientific merits of this collaborative project lie in their new approach to building and sustaining virtual educational communities around interactive content and applying the results to the full spectrum of engineering education venues: university undergraduate and graduate courses, industrial training and continuing education, just-in-time on the job learning, and high-school laboratories. Their research focuses on one strategic discipline in engineering, signal processing, and involves and balances education, community development, technology development, marketing and business planning, and impact assessment.
The partnership is:
1. Implementing a light-weight Technology Framework that enables faculty and student users to exploit and expand upon the existing signal processing education content;
2. Building a signal processing Education Network of champions from faculty, students, and industry leaders nationwide that continually expands, improves, and diversifies the materials and that promotes the use of the framework both at network member institutions and at institutions in the wider engineering education community;
3. Assessing the effectiveness of the framework and network for accelerating adoption and use as well as the value of the mentoring and support provided by the network of champions;
4. Widely Disseminating the results and lessons learned.

Broader impacts of this research include the development of people-resources and technologies that will substantially increase the performance and capabilities of engineering educators, effectively opening up engineering education for motivated self-learners in all parts of the nation as well as the world. In particular, education in digital signal processing and related technologies is critical in sustaining many high-tech industries. Finally, digital signal processing educators, practitioners and students will be brought together to form dynamic knowledge sharing communities that greatly impact education not only on their home campuses but around the world.

PROJECT ABSTRACT
?Counting Van Gogh and Vermeer?

Automated thread counting algorithms, in development since 2007 by Professors Rick Johnson
(Cornell University) and Don Johnson (Rice University), are poised for a profound impact on the
practice of technical art history. This work is a pioneering effort in the emerging application of
signal and image processing to the analysis of paintings. The approach is based on the utilization
of spectral analysis of x-rays of paintings. X-rays can display the periodic intensity pattern due
to the greater thickness of the lead white ground between the canvas threads. With the addition
of innovative application-specific signal processing, local peaks in the spectrum of the x-ray data
can reveal the numbers of threads per centimeter, i.e. a thread count, separately for (nearly) ver-
tically and (nearly) horizontally oriented threads. Such thread counts have been used previously,
but their manual acquisition proved too costly to be done thoroughly. The introduction of the
capability to assemble previously unthinkable thread density and angle pattern maps of high de-
tail across the entire surface of a painting leads to correlation-based identification of canvases
sharing the same pattern in thread density variations. Such weave maps and matches can now be
assembled across all of the paintings on canvas of a single artist or school, thereby significantly
extending the art historian?s capabilities in, for example, dating and attribution.
The scale, breadth, and depth of such thread-counting projects represent a bold leap in the
capabilities of technical art history. This grant supports the analysis phase of the first projects
of such grand scope: the thread counting and subsequent weave matching among (i) all of the
paintings by Vincent van Gogh (for which data can be obtained by the end of 2010) and (ii)
all of the paintings by the Delft School during the career of Johannes Vermeer (for which data
can be obtained by the end of 2010). This grant places Professors Rick and Don Johnson in
the center of the data fusion, data analysis and database creation activities that are scheduled
to occur in Amsterdam during the spring of 2011. The archives being established of thread
count reports, including weave density maps providing fingerprints for weave matching and angle
maps for characterizing cusping, form a groundbreaking resource at a time when museums are
just beginning to address the technological and cultural barriers to technical data sharing among
museums and collaborating researchers outside the museum. This project is part of a long-term
effort that aims to expand the utility of thread counting from x-rays to all suitable oil paintings on
canvas, and to photos of unlined backs of old master paintings and of the raw canvas, for example,
of the modern colorfield painters and of densely-woven, multi-pattern fabrics prominent in the
design and decorative arts.

Investigators from Rice University and Duke University will build a Personalized Cyberlearning System, designed around three principles from cognitive science (retrieval practice, spacing, and enhanced feedback), that leverages advances in machine learning and makes use of an existing instructional content material and problem set database aimed at undergraduate engineering students. The system will use artificial intelligence methods to optimize practice and feedback for students. Research will seek to advance knowledge, in a real-world setting, about a range of issues concerning how feedback facilitates learning, how individual differences come in to play, as well as those more specifically aimed at the development of the learning technology system itself.

The project is important as part of the effort to harness the vast quantities of information on the web to personalize instruction for a wide range of learners. Moreover, the development of such cyberlearning technologies holds promise for opening up STEM education for motivated self-learners while also allowing access to a large volume of material for a range of students who might not otherwise have it.