This paper presents a middleware system for multi-agents on a distributed system as a general test-bed for bio-inspired approaches. The middleware is unique to other approaches, including distributed object systems, because it can maintain and migrate a dynamic federation of multiple agents on different computers. It enables each agent to explicitly define its own deployment policy as a relocation between the agent and another agent. This paper describes a prototype implementation of the middleware built on a Java-based mobile agent system and its practical applications that illustrates the utility and effectiveness of the approach in real distributed systems.

Intermittent periodic transmit (IPT forwarding) has been proposed as a highly efficient packet forwarding method for wireless multihop networks. With IPT forwarding, packets are intermittently transmitted by a source node and each relaying node forwards a relaying packet immediately after receiving it. The frequency reuse space attained by this scheme is proportional to the given transmit period and if the transmit period is adequately chosen, interference between adjoining nodes in transmission can be removed. The IPT forwarding evaluations conducted to date have assumed that the transmission conditions were ideal. However, actual conditions are clearly more complex. In this paper, we develop testbeds of wireless multihop node and study the feasibility of IPT forwarding in a large-scale building with 4 stories. Each node is equipped with 802.11b wireless interface in the Ad-Hoc mode and proprietary protocols for IPT forwarding that we developed for these experiments.

We demonstrate a novel display structure for color electronic paper for the first time. Fully transparent amorphous oxide TFT array is directly deposited onto color filter array and combined with E Ink Imaging Film. Taking advantage of the transparent property of the oxide TFT, the color filter and TFT array are positioned at the viewing side of the display. This novel "Front Drive" display structure facilitates the alignment of the color filter and TFT dramatically.

In this paper, we present a new method to enhance classification performance of a multiple classifier system by combining a boosting technique called AdaBoost.M2 and Kernel Discriminant Analysis (KDA). To reduce the dependency between classifier outputs and to speed up the learning, each classifier is trained in a different feature space, which is obtained by applying KDA to a small set of hard-to-classify training samples. The training of the system is conducted based on AdaBoost.M2, and the classifiers are implemented by Radial Basis Function networks. To perform KDA at every boosting round in a realistic time scale, a new kernel selection method based on the class separability measure is proposed. Furthermore, a new criterion of the training convergence is also proposed to acquire good classification performance with fewer boosting rounds. To evaluate the proposed method, several experiments are carried out using standard evaluation datasets. The experimental results demonstrate that the proposed method can select an optimal kernel parameter more efficiently than the conventional cross-validation method, and that the training of boosting classifiers is terminated with a fairly small number of rounds to attain good classification accuracy. For multi-class classification problems, the proposed method outperforms both Boosting Linear Discriminant Analysis (BLDA) and Radial-Basis Function Network (RBFN) with regard to the classification accuracy. On the other hand, the performance evaluation for 2-class problems shows that the advantage of the proposed BKDA against BLDA and RBFN depends on the datasets.

In the current broadcasting system or Internet content distribution system, content providers distribute decoders (STB) that contain secret keys for content decryption, prior to content distribution. A content provider sends encrypted content to each user, who then decodes it with his or her STB. While users can get the services at their houses if they have an STB, it is hard for them to get the services outside their houses. A system that allowed users to carry around their secret keys would improve usability, but it would require countermeasures against secret key exposure. In this paper, we propose such an extended broadcasting system using tokens and group signature. The content providers can control the number of keys that users can use outside their houses. The system enables the broadcasters to minimize the damage caused by group signature key exposures and the user to get services outside his or her home.

This paper introduces a texture analysis mechanism utilizing multiresolution technique to reduce false motion detection and hence thoroughly improve the interpolation results for high-quality deinterlacing. Conventional motion-adaptive deinterlacing algorithm selects from inter-field and intra-field interpolations according to motion. Accurate determination of motion information is essential for this purpose. Fine textures, having high local pixel variation, tend to cause false detection of motion. Based on hierarchical wavelet analysis, this algorithm provides much better perceptual visual quality and considerably higher PSNR than other motion adaptive deinterlacers as shown. In addition, a recursive 3-field motion detection algorithm is also proposed to achieve better performance than the traditional 2-field motion detection algorithm with little memory overhead.

In this paper, we describe a new ultrasonic oscillosensor and its application in a biological information measurement system. This ultrasonic sensor has a cylindrical tank of 26 mm (diameter)20 mm (height) filled with water and an ultrasonic probe. It detects the vibration of the target object by obtaining echo signals reflected from the water surface. This sensor can noninvasively detect the vibration of a patient by placing it under a bed frame. We propose a recognition system for humans in bed. Using this sensor, we could determine whether or not a patient is in the bed. Moreover, we propose a heart rate monitoring system using this sensor. When our system was tested on four volunteers, we successfully detected a heart rate comparable to that in the case of using an electrocardiograph. Fuzzy logic plays a primary role in the recognition. Consequently, this system can noninvasively determine whether a patient is in the bed as well as their heart rate using a constraint-free and compact device.

In recent years, algorithms based on Computer Algebra ([1]-[3]) have been introduced into a range of control design problems because of the capacity to handle unknown parameters as indeterminates. This feature of algorithms in Computer Algebra reduces the costs of computer simulation and the trial and error process involved, enabling us to design and analyze systems more theoretically with the behavior of given parameters. In this paper, we apply Computer Algebra algorithms to H∞ control theory, representing one of the most successful achievements in post-modern control theory. More specifically, we consider the H∞ norm minimization problem using a state feedback controller. This problem can be formulated as follows: Suppose that we are given a plant described by the linear differential equation = Ax + B1w + B2u, z = Cx + Du, where A,B1,B2,C,D are matrices whose entries are polynomial in an unknown parameter k. We apply a state feedback controller u = -F x to the plant, where F is a design parameter, and obtain the system = (A - B2F)x + B1w, z =(C - DF)x. Our task is to compute the minimum H∞ norm of the transfer function G(s)(=(C - DF)(sI - A + B2F)-1B1) from w to z achieved using a static feedback controller u = -Fx, where F is a constant matrix. In the H∞ control theory, it is only possible to check if there is a controller such that ||G(s)||∞ < γ is satisfied for a given number γ, where ||G(s)||∞ denotes the H∞ norm of the transfer function G(s). Thus, a typical procedure to solve the H∞ optimal problem would involve a bisection method, which cannot be applied to plants with parameters. In this paper, we present a new method of solving the H∞ norm minimization problem that can be applied to plants with parameters. This method utilizes QE (Quantifier Elimination) and a variable elimination technique in Computer Algebra, and expresses the minimum of the H∞ norm as a root of a bivariate polynomial. We also present a numerical example to illustrate each step of the algorithm.

Monotonic condition, a well-known sufficient condition for Schur stability of real polynomials, is relaxed. The condition reads that a series of strictly and monotonically decreasing positive coefficients of the polynomials yields Schur stability. It is shown by inspecting the original proof that equalities are allowed in all the inequalities but two which are located at appropriate positions.

It has been shown that the output information produced by the soft output Viterbi algorithm (SOVA) is too optimistic. To compensate for this, the output information should be normalized. This letter proposes a simple normalization technique that extends the existing sign difference ratio (SDR) criterion. The new normalization technique counts the sign differences between the a-priori information and the extrinsic information, and then adaptively determines the corresponding normalization factor for each data block. Simulations comparing the new technique with other well-known normalization techniques show that the proposed normalization technique can achieve about 0.2 dB coding gain improvement on average while reducing up to about 1/2 iteration for decoding.

An energy management circuit is proposed for self-powered ubiquitous sensor modules using vibration-based energy. With the proposed circuit, the sensor modules work with low duty cycle operation. Moreover, a two-tank circuit as a part of the energy management circuit is utilized to solve the problem that the average power density of ambient energy always varies with time while the power consumption of the sensor modules is constant and larger than it. In addition, the long start-up time problem is also avoided with the timing control of the proposed energy management circuit. The CMOS implementation and silicon verification results of the proposed circuit are also presented. Its validity is further confirmed with a vibration-based energy generation. The sensor module is used to supervise the vibration of machines and transfer the vibration signal discontinuously. A piezoelectric element acts as the vibration-to-electricity converter to realize battery-free operation.

This research aims to develop automatic knowledge mining of causality from texts for supporting an automatic question answering system (QA) in answering 'why' question, which is among the most crucial forms of questions. The out come of this research will assist people in diagnosing problems, such as in plant diseases, health, industrial and etc. While the previous works have extracted causality knowledge within only one or two adjacent EDUs (Elementary Discourse Units), this research focuses to mine causality knowledge existing within multiple EDUs which takes multiple causes and multiple effects in to consideration, where the adjacency between cause and effect is unnecessary. There are two main problems: how to identify the interesting causality events from documents, and how to identify the boundaries of the causative unit and the effective unit in term of the multiple EDUs. In addition, there are at least three main problems involved in boundaries identification: the implicit boundary delimiter, the nonadjacent cause-consequence, and the effect surrounded by causes. This research proposes using verb-pair rules learnt by comparing the Naïve Bayes classifier (NB) and Support Vector Machine (SVM) to identify causality EDUs in Thai agricultural and health news domains. The boundary identification problems are solved by utilizing verb-pair rules, Centering Theory and cue phrase set. The reason for emphasizing on using verbs to extract causality is that they explicitly make, in a certain way, the consequent events of cause-effect, e.g. 'Aphids suck the sap from rice leaves. Then leaves will shrink. Later, they will become yellow and dry.'. The outcome of the proposed methodology shown that the verb-pair rules extracted from NB outperform those extracted from SVM when the corpus contains high occurence of each verb, while the results from SVM is better than NB when the corpus contains less occurence of each verb. The verb-pair rules extracted from NB for causality extraction has the highest precision (0.88) with the recall of 0.75 from the plant disease corpus whereas from SVM has the highest precision (0.89) with the recall of 0.76 from bird flu news. For boundary determination, our methodology can handle very well with approximate 96% accuracy. In addition, the extracted causality results from this research can be generalized as laws in the Inductive-Statistical theory of Hempel's explanation theory, which will be useful for QA and reasoning.

Content delivery network improves end-user performance by replicating Web contents on a group of geographically distributed sites interconnected over the Internet. However, with the development whereby content distribution systems can manage dynamically changing files, an important issue to be resolved is consistency management, which means the cached replicas on different sites must be updated if the originals change. In this paper, based on the analytical formulation of object freshness, web access distribution and network topology, we derive a novel algorithm as follows: (1) For a given content which has been changed on its original server, only a limited number of its replicas instead of all replicas are updated. (2) After a replica has been selected for update, the latest version will be sent from an algorithm-decided site instead of from its original server. Simulation results verify that the proposed algorithm provides better consistency management than conventional methods with the reduced the old hit ratio and network traffic.

To meet users' multi-service requests under dynamic and heterogenous environment with high-assurance, the Autonomous Network-Based Integration System based on Faded Information Field (FIF) has been proposed, which permits to actively integrate the correlated information services according to the current situation of the system. However, the increase in the total number of users' requests and changes in users' preferences cause the unbalancing load in the system and the overload in the locality. In this paper, based on the autonomous access distribution in the locality, a new approach of autonomous correlated services access is proposed to reduce the load of the system and achieve the adaptability and timeliness of correlated services utilization. We proved the effectiveness of the proposed technology through the simulation and the results show that the system can improve the average response time not only for joint requests of correlated services, but also for separate requests of each service under changing environments.

In the acquisition of projection data of X-ray CT, logarithm operation is indispensable. But noise distribution is nonlinearly projected by the logarithm operation, and this deteriorates the precision of CT number. This influence becomes particularly remarkable when only a few photons are caught with a detector. It generates a strong streak artifact (SA) in a reconstructed image. Previously we have clarified the influence of the nonlinearity by statistical analysis and proposed a correction method for such nonlinearity. However, there is a problem that the compensation for clamp processing cannot be performed and that the suppression of SA is not enough in photon shortage state. In this paper, we propose a new technique for correcting the nonlinearity due to logarithm operation for noisy data by combining the previously presented method and an adaptive filtering method. The technique performs an adaptive filtering only when the number of captured photons is very few. Moreover we quantitatively evaluate the influence of noise on the reconstructed image in the proposed method by the experiment using numerical phantoms. The experimental results show that there is less influence on spatial resolution despite suppressing SA effectively and that CT number are hardly dependent on the number of the incident photons.

Educational websites contain rich knowledge components on a web page. Detecting student attention on web pages fulfills the recommendation of adequate knowledge components to students based on students' current interests. Previous studies have shown the application of learner attention in intelligent learning systems. This study proposes a methodology to analyze student on-line mouse movement patterns that indicate student attentions. The methodology can be combined with learning systems that implement pedagogical models such as inquiry-based learning and problem-solving learning activities. The feasibility and effectiveness of the proposed methodology have been evaluated by student mouse movements in problem-solving scenarios.

The monitoring of performance in VoIP traffic has become vital because users generally expect VoIP service quality that is as high as that of PSTN services. A lightweight method of processing by extracting VoIP flows from Internet traffics is proposed in this paper. Estimating delay variations and the packet loss ratio using knowledge about specific features and the characteristics of VoIP flows, i.e., the inter-packet gap (IPG) which is constant in VoIP flows, is also proposed. Simulation with actual traffic trace is used to evaluate the method, and this revealed that delay variations (IPG variance) can be accurately estimated by monitoring only a few percentage of all flows. The proposed method can be used as a first-alert tool to monitor large amounts of flows to detect signs of degradation in VoIP flows. The method can be used by ISPs to estimate whether VoIP flow performance is adequate within their networks and at ingress from other ISPs.

Employee performance appraisal is an effective way to determine the performance of the employees in an organization. A study conducted on companies in Thailand revealed that majority of the companies do not use computer-based employee appraisal system. In the traditional appraisal system, the paper-based appraisal system causes a lot of manual work, is time-consuming, not secure, not flexible, difficult to analyze the performance and see the trend of performance improvement of the employee. We have developed a web-based performance appraisal system, which provides a secure and easy way to perform the appraisal. In our system, the competencies are flexible and can be customized according to the specific job responsibility. Our system is goal-orientated as it calculates the objective scores. The system is connected to the database which is easily accessible. The first stage of our system is the 'Selection Stage' in which the managers and employees can select the competencies and objectives that they want to evaluate for performance appraisal according to the job positions. The second stage is the 'Appraisal/Evaluation Stage' where managers can rate the employees according to different priority levels of competencies and objectives. Moreover, at this stage, employees can perform self-evaluation and 360-degree evaluation for their colleagues, subordinates and managers. The final stage is the 'Development Planning Stage' where the managers and employees can compare their appraisal results, discuss and plan for future training or further steps for reaching the objectives and improving employee's competencies. From user testing, the system was found to be more efficient compared to the traditional appraisal system in the issues like: help evaluate the true abilities of employees, help employees understand organizational goals, and provide fast and effective feedback. The users found the system easy to understand and use and were more satisfied with the overall effectiveness of the system.

A practical optical character reader is required to deal with not only common fonts but also complex designed fonts. However, recognizing various kinds of decorative character images is still a challenging problem in the field of document image analysis. Since appearances of such decorative characters are complicated, most general character recognition systems cannot give good performances on decorative characters. In this paper, an algorithm that recognizes decorative characters by structural analysis using a graph-matching technique is proposed. Character structure is extracted by using topographical features of multi-scale images, and the extracted structure is represented by a graph. A character image is recognized by matching graphs of the input and standard patterns. Experimental results show the effectiveness of the proposed algorithm.

In this letter, we propose an enhanced method for inter-layer motion prediction in scalable video coding (SVC). For inter-layer motion prediction, the use of refined motion data in the Fine Granular Scalability (FGS) layer is proposed instead of the conventional use of motion data in the base quality layer to reduce the inter-layer redundancy efficiently. Experimental results show that the proposed method enhances coding efficiency without increasing the computational complexity of the decoder.