Chung-Chieh J. Kuo - US grants

The numerical solution of ordinary and partial differential equations (ODEs and PDEs) provides an important class of large scale scientific computing problems. Fourier analysis has been used by applied mathematicians as a simple and effective tool to analyze the numerical properties of approximation schemes and solution methods for ODEs and PDEs. This includes three main objectives: (1) exploiting Fourier tools used in the areas of digital filtering and digital control for solving ODEs and elliptic PDEs, (2) analyzing numerical ODE and elliptic PDE algorithms with Fourier techniques so that they can be understood by electrical engineers in a more familiar setting, and (3) development of new parallel algorithms and study of their performnce on parallel algorithms and study of their performance on parallel computers such as the Connection Machine CM/2 and Alliant FX/80.

Develop numerical models and high-performance algorithms for image processing, computer vision and graphics with applications in HDTV, remote sensing, radargrammetry, target recognition and tracking, scientific visualization, computer animation, and computer-aided education. Produce high-quality scientific software for algorithms on parallel or distributed computing systems, and evaluate their performance.

9350309 Kuo Develop numerical models and algorithms for signal and image processing, computer vision and graphics with multiresolution techniques such as wavelets, hierarchical splines and fractals. Produce high-quality scientific software in parallel or distributed computing systems for applications including HDTV, target recognition and tracking, scientific visualization, computer animation, multimedia and computer-aided education.

9422106 Leahy This award is to purchase equipment dedicated to research in computer and information science and engineering. Specifically, the equipment will be used for research in multi-dimensional signal and image processing, including in particular: 1) fusion of multimodal neuroimaging data; 2) adaptive quantization of image and video; 3) automatic target recognition via deformable template matching; 4) design of high resolution diffractive optics for photonic interconnections and computing; and 5) advanced adaptive multidimensional and array signal processing. Common to all of these projects is a need for access to fast numerical computation and high resolution visualization and display capabilities. The goal of this project is to set up a state of the art facility for processing, visualization and display of multidimensional data. Towards this end, a computer for high performance numerical computation, and a RAM-based workstation for display of high resolution video image sequences with a high performance graphics capability will be purchased. ***

9724567 Sawchuk, Alexander McLeod, Dennis University of Southern California High Performance of Processors and Networks for Video Compression, Distributed Visualization, Database Systems and Collaborative Telepresence USC has received a Major Research Instrumentation award for the acquisition of processing hardware; a special purpose high-speed-resolution digital video storage and display hardware; and miscellaneous data communications hardware; for an Integrated Media Research Network (IMRN) to support research and training programs in high-performance multimedia, graphics, visualization, and database systems. Research projects to be supported include the generation, compression and transmission of real-time video over shared networks; processing of remote high resolution 3-D visualization and computation-intensive graphics; robust distribution and networking of interactive multimedia data within a heterogeneous distributed computing environment; distributed database management techniques for video and audio servers; and utilization of multiprocessor computers for collaborative telepresence over long physical distances. Besides making the enhanced facilities available to students pursuing research in high-performance graphics, visualization and database systems, USC plans to connect the IMRN to the Institution's School of Engineering's Instructional Television network which supports two-way live interactive broadcasts of regular credit courses from engineering, computer science and mathematics. It is envisaged that this connection will allow students to work on research projects anywhere on the main campus, medical school campus and USC's Information Sciences Institute and will allow classroom demonstrations over these locations as well as the local Los Angeles area. Research results on database management for video data and distance learning will contribute directly to these distance learning activities.

This research project will develop methods for content-based indexing of music databases, using a combination of signal processing and knowledge-based methods, design of statistical algorithms for enabling queries using sung or hummed melodies, and design of robust search techniques for retrieving the queried information, especially in the presence of uncertainty. The research approach, which is based on statistical modeling, is user-centric and comprises three major components: (1) Representation and Indexing - musical information utilizing theories and knowledge about human musical intuition, and music perception and cognition. (2) Query Formulation and Interaction Modality - algorithm design for enabling interaction with music data through humming, a natural activity. (3) Search and Retrieval - algorithm design to match user query against the database that will be robust to uncertainties and errors in the query generation.

The architecture will include a front-end recognizer, that converts the humming signal to notes using a statistical pattern recognition approach, which interfaces with a back-end music database that is indexed using perceptually viable features. The search process matching a query against indices is formulated as a statistical information retrieval problem. The project will employ progressively rich indexing representation including repeating patterns for recurring themes, chord, beat, and key information. Derivation of such music-theoretic knowledge will benefit from a principled approach of mathematically modeling tonality in music. The statistical framework allows for handling variability and uncertainty in query formulation and retrieval. It also enables providing for quality of solution in the query results.

This work will contribute not only to the specific economically significant application area of music, but also to general information science knowledge on how to index and search humanly-meaningful patterns in complex sensory data. The project will also serve as a vehicle to foster cross-disciplinary graduate and undergraduate research and education.