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Slideshow

Robotics

The main focus of the robotics research group is the development of autonomous mobile robots (AMRs). With AMRs there are two primary issues to deal with: (1) cognitive behavior, and (2) motion. Cognitive behavior addresses problem solving using sensory inputs and desired goals. Motion deals with aspects of movement from simple robotic arm movement to autonomous rovers in unknown environments. Cognitive behavior is the current focus of the research group. Two projects currently underway involve on-board image processing of video camera inputs for decision making, and the development of an evolutionary computing approach to controller configuration (possibly using field programmable gate arrays). In addition, the controller evolution project is attempting to provide for automatic (rule directed) behavior specification.

Real-Time Systems

In real-time systems, many events have specific timing constraints.  If these constraints are violated, a system failure occurs.  These types of systems are used in many applications incuding airplane autopilot systems and powerplant controllers.  Because these systems are often used in safety critical applications, it is essential that we can guarantee the timing requirements will be met before the system is used.  To this end, we analytically develop tests to guarantee all jobs will meet their deadlines.

Parallel Processing

The parallel processing group is pursuing both the advanced use and the development of parallel processing systems. Since parallel processing systems are being used in the most compute-intensive applications, we have been investigating the implications of parallel processing in the areas of interest to us: image processing, robot vision, satellite data processing, matrix reduction, nonlinear wave equations, banded, circulant, and Toeplitz systems of equations, multivariable partial differential equations, and VLSI physical design.

Since parallel systems are often awkward to quite difficult to implement applications on, we have an interest in improved programming, networking, and development environments for parallel systems. We have implemented parallel algorithms on pipeline systems, hypercube systems, and SIMD systems (the MasPar). We have proposed a new parallel systems architecture (the Reconfigurable MultiRing) that is more efficient, easier to program, and lower cost for certain applications.

Databases and Distributed Information Systems

Today's information systems utilize a variety of sophisticated software tools and systems. Database systems form the core technology supporting modern information systems. Previous work in this area has focused on semantic data models, knowledge-based systems, transaction management, GUI query tools, and state-of-the-art database systems (object-oriented, distributed and federated). Ongoing efforts include work in interoperable information systems (with emphasis on transactional workflow management), global information systems (with emphasis on infrastructure for managing heterogeneous data, meta-data for digital media, and information brokering), and intelligent information systems (with emphasis on integrating knowledge, data and models).

Cortical Architecture Imaging and Discovery

The CAID (cortical architecture imaging and discovery) lab's research mainly focuses on the discovery of structural and functional architectures of the cerebral cortex via brain imaging and computational modeling. Our long-term goals are to discover the fundamental principles that sculpt the cerebral cortex from organizational, developmental and evolutionary perspectives, and to understand the relationship between cortical structure and function. We are interested in the cortical folding mechanisms, cortical structural connectivity and connectomes, higher-order cortical functional interactions, temporal and frequency dynamics of brain functions, and functional interaction of perception, cognition and environments. We mainly use multi-scale, multi-modal imaging data as the information source, and use a wide range of computational approaches to build models and develop theories. We have strong interests in applying the discovered principles and theories to better understand the dysfunctions of neurological, psychiatric, neurodevelopmental and neurodegenerative disorders including Alzheimer's disease, Schizophrenia, Prenatal Cocaine Exposure, Post-traumatic Stress Disorder, Autism, and Depression, among other brain conditions.

Computer Networks

Networks are becoming increasingly complex as the needs for speed, bandwidth, robustness, and security increase. The network research group focuses on the problem of building efficient, scalable and secure networks and applications. The research topics include building fast packet forwarding and inspections, designing methods to reduce deployment efforts for network protocols and applications, building scalable network services, and improving the accuracy and performance of network security systems. Examples of recent studies include asymmetric protocol modifications for streaming media and network storages, scalable online game servers, and network-based anti-SPAM systems.

Computer Vision and Image Processing

A variety of problems in low- and high-level vision are studied.

The low-level vision (i.e. image processing) problems being addressed are edge detection, stereo correlation, contour grouping, image segmentation, and figure-ground discrimination. Various computational approaches such as genetic algorithms, simulated annealing, neural networks, and parallel and distributed processing are being investigated in the context of these low-level vision problems.

In high-level vision, the current research is focused on the identification and localization of objects in range and intensity images from prestored CAD models. Efficient recognition and localization algorithms based on graph theory such as subgraph isomorphism and hypergraph monomorphism are being investigated.

Issues related to efficient retrieval from large object model databases are also being addressed. In particular, hierarchical index and hash structures well suited for object models represented as attributed relational hypergraphs are being investigated.

The research in low- and high-level vision is being applied to several application areas such as automated industrial inspection, geographic information systems and multi-media systems.

Computational Intelligence

In conjunction with the Artificial Intelligence Center, several studies in computational intelligence have been conducted. Genetic algorithms and simulation are used to find good (in many cases near-optimal) solutions to hard problems that are intractable using traditional techniques. Examples include: multiple fault diagnosis, battlefield communication network configuration, chromosome reconstruction, edge detection, equation development for describing relationships in complex data, and the snake-in-the-box problem.

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