/ James M. Goodwin / Professor
/ Noriaki Asada / Professor
/ Jaques J. Vidal / Visiting Professor
/ Goutam Chakraborty / Assistant Professor
/ Yasushi Kikuchi / Assistant Professor
The present members of the Multimedia Software Systems Laboratory have diverse backgrounds and research interests. The primary areas of expertise of the current members include numerical analysis, nonlinear dynamics, complex systems, real and artificial neural networks, the application of physical principles to computing, genetic algorithms, fuzzy logic, fuzzy systems, nonlinear optimization algorithms, infrared astronomy, physical properties of cosmic dust, interplanetary physics, laser applied measurement technology, applied optics, motion analyses by video camera systems, recognition of facial expression and the like.
Research being done in this laboratory includes studying the use of visualization techniques to enhance the understanding of mathematical problems, for example, investigation of 3D graphs of complex functions using time, color, sound, as the necessary ``fourth dimension''. The display of zeros or critical points of functions appears to aid in understanding underlying mathematical structures.
Infrared camera can see not only thermal state of materials and animals but also chemical bonding state of molecules and atom species through spectroscopic techniques. These techniques can help to recognize human emotion through human surface color, including infrared color. A total system that consists of multi-CCD-cameras, high speed video cameras, infrared cameras and force vector mats system is very useful system to understand human motion and human facial expressions.
With the increasing speed of computation and communication, new generation computers and networks are aimed to support multimedia(MM) applications in addition to their existing responsibilities. Due to large size and time sensitiveness of data, it is usually a hard task to deliver the services to the expectations of the users. But the basic intention of the user, for the MM services, usually have a wide scope for tolerance and flexibility. There is scope of making variations in user's requirement, but still satisfying the user. Degradation in quality to accommodate new user, or improving of the quality due to lowering of traffic, within the range of user's flexible intention would improve the throughput of the resources as well as extend MM services more widely.
For creating the infrastructure, we need to develop two areas, to define a fuzzy language to express the user's intention and to take care of dynamically scheduling the resources through proper interpretation of user's request as well as the present system status. A network architecture to fulfill these requirements is already proposed.
Other research underway in the laboratory lies in nonlinear and complex systems and dynamics, real and artificial neural networks, the application of physical principles to computing, genetic algorithms, fuzzy logic and the like. Multimedia applications related to these ideas, are also being developed, as is the development of educational courseware and joint projects using Multimedia, hypertext, hypermedia, and virtual reality techniques.
Nonlinear dynamics methods for the control of unstable or chaotic systems (e.g. human heart, multiple trailer trucks backing up, high performance aircraft, economic systems) are becoming prominent, and are under study in this laboratory. Because these methods draw heavily on topology, they can be clarified by interactive graphical presentations. Multimedia display and interaction with controller and controlled system can enhance understanding and support the research.
Laboratory members have participated actively in scientific meetings, both in Japan and abroad. They are involved in joint research projects involving faculty from such institutions as the University of California, Los Angeles (UCLA) and the University of Texas, in the U.S. They have presented and participated in seminars, and in presentation of scientific results in fully refereed publications.
The laboratory is the coordinator for coursework in computer music and in the interaction of brain waves with physical devices. It has a wide variety of equipment available for use by students and faculty, typically interconnected via the campus network. A coursework in astronomical observation by cooled CCD cameras is coordinated, too. Students learn how to use equipment to get some data into computer system, how to process image data, how to analyze these data and how to study the nature through this courseware project.
Refereed Journal Papers
As most of the real-time scheduling problems are known as hard problems, approximate or heuristic scheduling approaches are extremely important for solving them. The paper presents a new heuristic scheduling approach based on a modified Hopfield-Tank neural network to schedule tasks with deadlines and resource requirements in a multiprocessor system. In this approach, fast heuristic scheduling is achieved by performing a heuristic scheduling policy in conjunction with backtracking on the neural network. The results from our previous work, using the same neural network with-out backtracking, are included as a case with zero back tracking. Extensive simulation, which includes comparison with the conventional heuristic approach, is used to validate the effectiveness of our approach.
High speed of computers connected by fast communication links, virtually represent an enormously big distributed computing system. At the same time communication between man with the machine is also becoming all the more important with increasing computer users from different facets of life. New networking problems as regards to service to the user, seamless communication between hosts, failure correction and integration of new technologies crop up every day. The root of the problem is the adaptation of the computer network to different changes. In this paper we define Flexible Computer Communication Network(FN), where we propose an uniform solution to most of the networking problems. An agent oriented implementation of the flexible network is outlined. The conversion of existing network to flexible network is incremental, and therefore practicable.
No abtract.
In this paper a new concept of a flexible network is formalized as a foundation of the next generation distributed systems. Considering the requisite properties of the flexible networks, we proposed an architecture of the flexible network called agent-oriented flexible information network (AFIN) model. The model is based on the multi-agent paradigm. Furthermore, we focussed on communication network which is a core component of the flexible information network. We proposed an agent-oriented flexible communication network (AFCN) model and demonstrate with an example the advantages of the proposed architecture.
With the rapid advent of Computer and Communication technologies, and successful merging of the two fields resulting in proliferation of novel applications, losely coupled distributed computing systems have recently become the most attractive field of research of Information processing. The trend is that, all the computers connected by high speed links will virtually represent a colossally big distributed computing system. At the same time the facilities should be extendible to people from different facets of life. The present infrastructure is not ready to assimilate such enormously big and divergent elements under a single framework. Flexible computing environment is a step towards defining and implementing such an environment. The main problem is the adaptation of the different elements to changes. Till now many researchers aimed to solve the problem of adaptation focusing on particular areas. We introduce some general definitions and properties, which are needed to be satisfied by the flexible system. We propose an implementation methodology, and introduce some simple examples for illustration.
In real World, when a user communicates with his peer, the user share only proper information with his peer, using his own knowledge about the content of the information and the relation with the peer. The user behaves differently with different users as the topic changes. The user also changes his status of communication through such interactions, i.e. adapts and refines his knowledge. Defining such an adaptive behaviour to update the knowledge of an agent is still to be explored. The agent introduced in this paper distinguishes the peer through it's attribute and adapts the behavior with the help of initially supplied partial knowledge. The adaptive behavior mainly pertains to the relationship between the users and the topic of interest. We propose the concept of the users' attribute as a yardstick to measure the relationship of the peer. The communication uses WWW interface and multimedial mail system to provide the hypertext-style multimedia information.
Real-time communication networks are designed mainly to support multimedia applications, especially the interactive ones, which require a guarantee of Quality of Service (QoS). Moreover, Multicasting is needed as there are usually more than two peers who communicate together using multimedia applications. As for the routing, network has to find an optimum (least cost) multicast route, that has enough resources to provide or guarantee the required QoS. This problem is called QoS constrained multicast routing and was proved to be NP-complete problem. In contrast to the existing heuristic approaches, in this paper we propose a modified version of Hopfield neural network model to solve QoS (delay) constrained multicast routing. By the massive parallel computation of neural network, it can find near optimal multicast route very fast, when implemented by hardware. Simulation results show that the proposed model has the performance near to optimal solution and comparable to existing heuristics.
Feed Forward network classifier trained with a finite set of available samples tries to estimate properly the different class boundaries in the input feature space. The aim is that the network would then be able to classify unknown new samples with some confidence. For problems of practical interest, due to the complexity of the class boundaries, noise in the available sample set and finite (usually inadequate) number of available samples, it is a hard task to ensure (1) optimum network size with respect to the available sample set of unknown distribution and noise characteristics, (2) the optimum value of the network parameters. Several researchers tried to estimate a bound on network size depending on sample size, or estimate the prediction error in terms of the number of parameters in the model and size of the available sample set. Other practical approaches either start with a small network and grow it to proper size, or with a big network and prune it optimally. These methods are based on particular network structures. We propose an idea for ascertaining proper network size for maximizing generalization as well as correct classification, and propose an algorithm to grow the network to that size. The algorithm is generic and applicable to any FF network. In this paper we worked with a special variety of $\Phi$-network, proposed earlier.
Hopfield neural network model for finding an optimal i.e. shortest path between two nodes in a graph was proposed recently in some literatures. In this paper, we present a modified version of Hopfield model to find an optimal tree (least total cost tree) from a source node to a number of destination nodes, where each path from source to a destination must satisfy a constraint condition (delay bound condition). This problem is called Constrained Steiner Tree (CST) problem, and is proved to be NP-complete. The adaptive coefficient control method of the proposed Hopfield energy function is also developed. Through computer simulation, it is shown that proposed model could always find a near-optimal valid solution.
Feed Forward neural network trained with a small set of noisy samples are prone to overtraining and poor generalization. On the other side, a very small network could not be trained at all because it would be biased by its own architecture. Thus, it is an old problem to ascertain that a well trained network would also deliver good generalization. Theoretical results give bounds on generalization error, but with worst case estimations which is of less practical use. In practice cross-validation is used to estimate generalization. We propose a method to construct network so as to ascertain good generalization, even after sufficient training. Simulations show very good results in support of our algorithm. Some theoretical aspects are discussed.
Much research efforts are presently involved in developing network protocols suitable for multimedia services through LAN and even Internet. Due to large size and time sensitiveness of data, it is usually a hard task to deliver the services up to the expectations of the users. But the basic intention of the user, for the multimedia services, usually have a wide scope for tolerance and flexibility. If that flexibility is explored and exploited, it is possible to basically satisfy the user even with marginal resources. It is important to intelligently interpret user's request as well as distribute the system and network resources optimally. In this paper we are proposing an Intelligent Soft Resource Reservation Protocol(ISORRP) for realizing acceptable running quality of application by intelligent interpretation of users' requests and optimum distribution of available resources among them. In this architecture QoS to a session is only flexibly guaranteed, but always to a level acceptable to the user.