Large-scale effects of locally interacting agents are called emergent properties of the system. Emergent properties are often surprising because they can be hard to anticipate the full consequences of even simple forms of interaction. In this paper we address the following questions: how do heterogeneous agents generate emergent coordination, and how do they manage and self-organize macroscopic orders from bottom up without any central authority? These questions will depend crucially on how they interact and adapt their behavior. Agents myopically evolve their behavior based on the threshold rules, which are obtained as the functions of the collective behavior and their idiosyncratic utilities. We obtain the micro-macro dynamics that relate the aggregate behavior with the underlying individual behavior. We show agents' rational behavior combined with the behavior of others produce stable macro behavior, and sometimes unanticipated cyclic behavior. We also consider the roles of conformists and nonconformists to manage emergent macro behavior. As a specific example, we address an emergent and evolutionary approach for designing the efficient network routings.

In this paper agents' interactions are defined in terms of cooperation, coordination and competition. As for cooperation and coordination problems, we focus on knowledge sharing of agents, define agencies as organizations of agents, propose a method to extract organizational knowledge for interacting agents. In case of competition, knowledge sharing is impossible. Therefore, modeling and formalization of strategic decision making and uncertainty management is required. We present an incomplete game theoretical based decision making method for competitive agents.

As Information Technology progresses, our daily lives are getting "connected" more and more. At the same time, however, problems are appearing. The center of these problems can be captured as the "Communication Overflow. " To cope with such problems, we propose an approach that tries to provide a communication environment that assists users in managing their communication activities. The key notion of this approach is to enhance the "Awareness of Connectedness. " Here, agents which are suggestive of awareness of connectedness play an important role. In this paper, we describe the key notion and introduce a brief road-map towards the environment for the awareness of connectedness. Two candidate tools for the environment are described. The first one is a visualization tool for communication media that provides feedback of users' communication activities. Its purpose is to enhance the awareness for communication. The second tool is a simple, intuitive interactive media that exchanges the statuses of users. It is an alternative network communication media that might be suitable for very light-weight, almost-acknowledge-only communication mode. Some results on an experiment of these two tools are also reported.

Systems comprised of multiple interacting mobile agents provide an alternate network computing paradigm that integrates remote data access, message exchange and migration; which up until now have largely been considered independently. On the surface distributed systems design could be helped by a complete specification of the different interaction patterns, however the number of possible designs in any large scale system undergoes a combinatorial explosion. As a consequence this paper focuses on basic one-to-one agent interactions, or paradigms, which can be used as building blocks; allowing larger system characteristics and performance to be understood in terms of their combination. This paper defines three basic agent paradigms and presents associated performance models. The paradigms are evaluated quantitatively in terms of network traffic, overall processing time and size of memory used, in the context of a distributed DB system developed using the Bee-gent Agent Framework. Comparison of the results and models illustrates the performance trade-off for each paradigm, which are not represented in the models, and some implementation issues of agent frameworks. The paper ends with a case study of how to select an appropriate paradigm.

This paper presents an accurate method for finding the 3D control points of the B-Spline curves. This method can automatically fit a set of data points with piecewise geometrically continuous cubic B-Spline curves. Iterating algorithm has been used for finding the 2D control points. And a new approach for shape reconstruction based on the control points of the curves on the object's surface is proposed. B-Spline patch, the extension of the B-Spline curves to surface, provides recovering the shape of the object in 2D approach. The 3D control points of the cubic B-Spline curves are computed from the factor decomposition of the measurement matrix of 2D control points. The multiple object approach is also proposed to reconstruct the 3D shape of each curves of an object. Some experiments are demonstrated to confirm the effectiveness of our proposed method.

We propose a method for compensating distortion of image by calibrating intrinsic camera parameters by image registration which does not need point-to-point correspondence. The proposed method divides the registration between a calibration pattern and a distorted image observed by a camera into two steps. The first step is the straightforward registration from the pattern in order to correct the displacement due to projection. The second step is the backward registration from the observed image for compensating the distortion of the image. Both of the steps use Gauss-Newton method, a nonlinear optimization technique, to minimize residuals of intensities so that the pattern and the observed image become the same. Experimental results show the usefulness of the proposed method. Finally we discuss the convergence of the proposed method which consists of the two registration steps.

A new method for object search is proposed. Conventional template matching schemes tend to fail in presence of irregularities and ill-conditions like background variations, illumination fluctuations resulting from shadowing or highlighting etc. The proposed scheme is robust against such irregularities in the real world scenes since it is based on matching gradient information around each pixel, computed in the form of orientation codes, rather than the gray levels directly. A probabilistic model for robust matching is given and verified by real image data. Experimental results for real world scenes demonstrate the effectiveness of the proposed method for object search in the presence of different potential causes of mismatches.

This paper discusses recognition up to intensities of mix of primary facial expressions in real time. The proposed recognition method is compatible with the MPEG-4 high level expression Facial Animation Parameter (FAP). In our method, the whole facial image is considered as a single pattern without any block segmentation. As model features, an expression vector, viz. low global frequency coefficient (DCT) changes relative to neutral facial image of a person is used. These features are robust and good enough to deal with real time processing. To construct a person specific model, apex images of primary facial expression categories are utilized as references. Personal facial expression space (PFES) is constructed by using multidimensional scaling. PFES with its generalization capability maps an unknown input image relative to known reference images. As PFES possesses linear mapping characteristics, MPEG-4 high level expression FAP can be easily calculated by the location of the input face on PFES. Also, temporal variations of facial expressions can be seen on PFES as trajectories. Experimental results are shown to demonstrate the effectiveness of the proposed method.

Large amounts of color-printed documents are published now everyday. Some OCR approaches of color-printed document images are provided, but they cannot normally work if the input images skew. In the past years, many algorithms are provided to detect the skew of monochrome document images but none of them process color-printed document images. All of these methods assume that text is printed in black on a white background and cannot be applied to detect skew in color-printed document images. In this paper, we propose an algorithm to detect the skew angle of a color-printed document image and reconstruct it. Our approach first determines variation of color-transition count at each angle (from -45 to +45) and the angle of maximal variation is regarded as the skew angle. Then, a scanning-line model reconstructs the image. We test 100 color-printed document images of various kinds and get good results (93 succeed and 7 fail). The average processing time of A4 size image is 2.76 seconds and the reconstruction time is 3.97 seconds on a Pentium III 733 PC.

This paper explores how to extend the dependency pair technique for proving termination of higher-order rewrite systems. We show that the termination property of higher-order rewrite systems can be checked by the non-existence of an infinite R-chain, which is an extension of Arts' and Giesl's result for the first-order case. It is clarified that the subterm property of the quasi-ordering, used for proving termination automatically, is indispensable.

The Active HYpermedia Delivery System (AHYDS) facilitates the access to multimedia information over a large-scale network and wide spectrum of media. We developed intelligent access facilities that build on the access paradigms supported by current web applications. This facility generalizes not only different kinds of logical data models (relational, object, hyperlink), but also access mechanisms of multimedia applications to make them customizable and scalable. This paper proposed the distributed management mechanism of the AHYDS platform. The major contribution of this paper is the mechanism for distributed multimedia delivery management over large-scale network and heterogeneous environment. We also propose the mechanism to manage huge multimedia data.

ATM networks are connection-oriented. Making a call requires first sending a message to do an admission control to guarantee the connections' QoS (quality of service) in the network. In this paper, we focus on the problem of translating a global QoS requirement into a set of local QoS requirements in ATM networks. Usually, an end-user is only concerned with the QoS requirements on end-to-end basis and does not care about the local switching node QoS. Most of recent research efforts only focus on worst-case end-to-end delay bound but pay no attention to the problem of distributing the end-to-end delay bound to local switching node. After admission control, when the new connection is admitted to enter the network, they equally allocate the excess delay and reserve the same bandwidth at each switch along the path. But, this can not improve network utilization efficiently. It motivates us to design a novel local QoS requirement allocation scheme to get better performance. Using the number of maximum supportable connections as the performance index, we derive an optimal delay allocation (OPT) policy. In addition, we also proposed an analysis model to evaluate the proposed allocation scheme and equal allocation (EQ) scheme in a series of switching nodes with the Rate-controlled scheduling architecture, including a traffic shaper and a non-preemptive earliest-deadline-first scheduler. From the numerical results, we have shown the importance of allocation policy and explored the factors that affect the performance index.

Routing security is related to the confidentiality of the route taken by the data transmitted over the network. If the route is detected by the adversary, the probability is higher that the data are lost or the data can be intercepted by the adversary. Therefore, the route must be protected. To accomplish this, we select an intermediate node secretly and transmit the data using this intermediate node, instead of sending the data to the destination node using the shortest path. Furthermore, if we use a number of secret routes from the starting node to the destination node, data security is much stronger since we can transmit partial data rather than the entire data along a secret route. In this paper, the routing algorithm for multiple secret paths on MRNS (Mixed Radix Number System) Network, which requires O(l) for the time complexity where l is the number of links on a node, is presented employing the HCLS (Hamiltonian Circuit Latin Square) and is analyzed in terms of entropy.

The design and implementation of a novel English writing environment is described. The system integrates modern computer and networking technologies with analytical tools from linguistics and language pedagogy to construct an advanced English writing environment. The system is not only suitable for students in learning English, but also of benefit to teachers in making comments and detecting learners' common difficulties. Furthermore, the collected essays from students and comments from teachers constitute a useful learner corpus. This is also of benefit to researchers in analyzing learners' persistent errors. In order to allow global access from the Internet, the system is web-based. Users, for example, students, teachers, and researchers, may access the system through web browsers. The system was developed in a cooperative effort of Computers And Networking (CAN) laboratory and the Research in English Acquisition and Pedagogy (REAP) Group at Tamkang University. The system has been piloted by six English faculty members at Tamkang University and is currently being used in five high schools in Taiwan. The learner corpus currently consists of over 800,000 word tokens of learners' writing.

In this paper, we explore the possibility of applying associative memories for locating frontal views of human faces in complex scenes. An appealing property of the associative-memory-based face detection system is that learning of the associative memory may be achieved by using a simple Hebbian learning rule. In addition, a simple heuristic rule is used to quickly filter a certain amount of nonface images at the very beginning of the whole detection procedure. By using the rule, we won't waste unnecessary computational resources on those nonface images. A database consisting of 74 images was used to test the performance of our associative-memory-based human face detection system.

Although previous studies using artificial neural networks have been actively applied to object shape recognition, little attention has been paid to the recognition of spatial elements (e.g. position, rotation and size). In the present study, a rotation and size spreading associative neural network (RS-SAN net) is proposed and the efficacy of the RS-SAN net in object orientation (rotation), size and shape recognition is shown. The RS-SAN net pays attention to the fact that the spatial recognition system in the brain (parietal cortex) is involved in both the spatial (e.g. position, rotation and size) and shape recognition of an object. The RS-SAN net uses spatial spreading by spreading layers, generalized inverse learning and population vector methods for the recognition of the object. The information of the object orientation and size is spread by double spreading layers which have similar tuning characteristics to spatial discrimination neurons (e.g. axis orientation neurons and size discrimination neurons) in the parietal cortex. The RS-SAN net simultaneously recognizes the size of the object irrespective of its orientation and shape, the orientation irrespective of its size and shape, and the shape irrespective of its size and orientation.

Recognition of specified wave patterns in one-dimensional signals is an important task in many application areas such as computer science, medical science, and geophysics. Many researchers have tried to automate this task with various techniques, recently the soft computing algorithms. This paper proposes a new neuro-fuzzy recognition system for detecting one-dimensional wave patterns using wavelet coefficients as features of the signals and evolution strategy as the training algorithm of the system. The neuro-fuzzy recognition system first trains the wavelet coefficients of the training wave patterns and then evaluates the degree of matching between test wave patterns and the training wave patterns. This system was applied to picking first arrival events in seismic data. Experimental results with three seismic data showed that the system was very successful in terms of learning speed and performances.

This paper describes a method of age-group classification of young males based on their facial images. The facial shapes of males and females are mostly formed by age 20 and 15, respectively. Our study only considered young males as they have a longer period during which facial shape is a determining factor in age estimation. Age classification was carried out using artificial neural networks. We employed 440 facial images in our experiment, composed of 4 different photographic images taken at ages 12, 15, 18 and 22 of 110 young males. Two methods of age classification were used, each employing different features extracted from the facial images, namely, "mosaic features" and "KL features. " As a result, we obtained about an 80% successful classification rate using mosaic features, and a slightly lower rate using KL features. We also analyzed the connection weights between the hidden and input layers of the trained networks, and examined facial features characteristic to each age group.

A remote auscultation support system was developed that compresses and records in real time the patient's breath sound and heart sound, obtained using a stethoscope, and sends this data to an attending doctor at a hospital via network. For real-time recording of the breath sound and heart sound, special-purpose, high-quality sound coding technology was developed and incorporated in the system. This sound coding technology enables the amount of data to be reduced to about 1/18 with virtually no deterioration of the properties of the auscultation sound, high-speed transmission of this data using network, and remote diagnosis of the auscultation sound by a medical specialist. The auscultation locations of each patient, together with the doctor, stethoscoper, and patient database are input into the system in advance at the hospital. At the patient's home or sanatorium, the auscultation sound is recorded according to a human body display that shows auscultation locations, and then sent to the hospital. To ensure patient confidentiality when the auscultation data is transmitted via network, the system scrambles the auscultation data and allows only the attending doctor to play and diagnose the auscultation sound. These features not only support an understanding of the condition of patients being treated at home, but they also enable the construction of an auscultation database for electronic charts that allows auscultation results to be shared within the hospital. When this remote auscultation support system was manufactured and its performance was assessed, virtually the same waveform was obtained for the recorded and played breath sound as for the original breath sound. Results showed that even at a sampling frequency of 11 kHz, remote diagnosis by a medical specialist was in fact possible. Furthermore, if auscultation data of 10 seconds per location for 10 locations is sent, the amount of data sent is only about 120 Kbytes. Since this amount of data converts to only about 25 pages of electronic mail text, even via the existing mobile network the auscultation sounds of many patients can be sent efficiently.

In this paper, an attempt was made to evaluate mental workload using chaotic analysis of EEG. EEG signals registered from Fz and Cz during a mental task (mental addition) were recorded and analyzed using attractor plots, fractal dimensions, and Lyapunov exponents in order to clarify chaotic dynamics and to investigate whether mental workload can be assessed using these chaotic measures. The largest Lyapunov exponent for all experimental conditions took positive values, which indicated chaotic dynamics in the EEG signals. However, we could not evaluate mental workload using the largest Lyapunov exponent or attractor plot. The fractal dimension, on the other hand, tended to increase with the work level. We concluded that the fractal dimension might be used to evaluate a mental state, especially a mental workload induced by mental task loading.