The Goal-Orientation is one of the key features in agent systems. This paper proposes a new methodology for multi-agent system development based on Goal Net model. The methodology covers the whole life cycle of the agent system development, from requirement analysis, architecture design, detailed design to implementation. A Multi-Agent Development Environment (MADE) that facilitates the design and implementation of agent systems is presented. A case study on an agent-based e-learning system developed using the proposed methodology is illustrated in this paper.

A hybrid evolutionary neuro-fuzzy system (HENFS) is proposed in this paper, where the weighted Gaussian function (WGF) is used as the membership function for improved premise construction. With the WGF, different types of the membership functions (MFs) can be accommodated in the rule base of HENFS. A new hybrid algorithm of random optimization (RO) algorithm incorporated with the least square estimation (LSE) is presented. Based on the hybridization of RO-LSE, the proposed soft-computing approach overcomes the disadvantages of other widely used algorithms. The proposed HENFS is applied to chaos time series identification and industrial process modeling to verify its feasibility. Through the illustrations and comparisons the impressive performances for unknown system identification can be observed.

Activity patterns of metabolic subnetworks, each of which can be regarded as a biological function module, were focused on in order to clarify biological meanings of observed deviation patterns of gene expressions induced by various chemical stimuli. We tried to infer association structures of genes by applying the multivariate statistical method called graphical Gaussian modeling to the gene expression data in a subnetwork-wise manner. It can be expected that the obtained graphical models will provide reasonable relationships between gene expressions and macroscopic biological functions. In this study, the gene expression patterns in nematodes under various conditions (stresses by chemicals such as heavy metals and endocrine disrupters) were observed using DNA microarrays. The graphical models for metabolic subnetworks were obtained from these expression data. The obtained models (independence graph) represent gene association structures of cooperativities of genes. We compared each independence graph with a corresponding metabolic subnetwork. Then we obtained a pattern that is a set of characteristic values for these graphs, and found that the pattern of heavy metals differs considerably from that of endocrine disrupters. This implies that a set of characteristic values of the graphs can representative a macroscopic biological meaning.

To obtain a robust acoustic model for a certain speech recognition task, a large amount of speech data is necessary. However, the preparation of speech data including recording and transcription is very costly and time-consuming. Although there are attempts to build generic acoustic models which are portable among different applications, speech recognition performance is typically task-dependent. This paper introduces a method for automatically building task-dependent acoustic models based on selective training. Instead of setting up a new database, only a small amount of task-specific development data needs to be collected. Based on the likelihood of the target model parameters given this development data, utterances which are acoustically close to the development data are selected from existing speech data resources. Since there are too many possibilities for selecting a data subset from a larger database in general, a heuristic has to be employed. The proposed algorithm deletes single utterances temporarily or alternates between successive deletion and addition of multiple utterances. In order to make selective training computationally practical, model retraining and likelihood calculation need to be fast. It is shown, that the model likelihood can be calculated fast and easily based on sufficient statistics without the need for explicit reconstruction of model parameters. The algorithm is applied to obtain an infant- and elderly-dependent acoustic model with only very few development data available. There is an improvement in word accuracy of up to 9% in comparison to conventional EM training without selection. Furthermore, the approach was also better than MLLR and MAP adaptation with the development data.

Acoustic modeling in speech recognition uses very little knowledge of the speech production process. At many levels our models continue to model speech as a surface phenomenon. Typically, hidden Markov model (HMM) parameters operate primarily in the acoustic space or in a linear transformation thereof; state-to-state evolution is modeled only crudely, with no explicit relationship between states, such as would be afforded by the use of phonetic features commonly used by linguists to describe speech phenomena, or by the continuity and smoothness of the production parameters governing speech. This survey article attempts to provide an overview of proposals by several researchers for improving acoustic modeling in these regards. Such topics as the controversial Motor Theory of Speech Perception, work by Hogden explicitly using a continuity constraint in a pseudo-articulatory domain, the Kalman filter based Hidden Dynamic Model, and work by many groups showing the benefits of using articulatory features instead of phones as the underlying units of speech, will be covered.

Minimum Bayes risk estimation and decoding strategies based on lattice segmentation techniques can be used to refine large vocabulary continuous speech recognition systems through the estimation of the parameters of the underlying hidden Markov models and through the identification of smaller recognition tasks which provides the opportunity to incorporate novel modeling and decoding procedures in LVCSR. These techniques are discussed in the context of going 'beyond HMMs', showing in particular that this process of subproblem identification makes it possible to train and apply small-domain binary pattern classifiers, such as Support Vector Machines, to large vocabulary continuous speech recognition.

This study shows the effectiveness of using gamma distribution in the speech power domain as a more general prior distribution for the model-based speech enhancement approaches. This model is a super-set of the conventional Gaussian model of the complex spectrum and provides more accurate prior modeling when the optimal parameters are estimated. We develop a method to adapt the modeled distribution parameters from each actual noisy speech in a frame-by-frame manner. Next, we derive and investigate the minimum mean square error (MMSE) and maximum a posterior probability (MAP) estimations in different domains of speech spectral magnitude, generalized power and its logarithm, using the proposed gamma modeling. Finally, a comparative evaluation of the MAP and MMSE filters is conducted. As the MMSE estimations tend to more complicated using more general prior distributions, the MAP estimations are given in closed-form extractions and therefore are suitable in the implementation. The adaptive estimation of the modeled distribution parameters provides more accurate prior modeling and this is the principal merit of the proposed method and the reason for the better performance. From the experiments, the MAP estimation is recommended due to its high efficiency and low complexity. Among the MAP based systems, the estimation in log-magnitude domain is shown to be the best for the speech recognition as the estimation in power domain is superior for the noise reduction.

This paper describes a method of generating F0 contours from natural F0 segmental shapes for speech synthesis. The extracted shapes of the F0 units are basically held invariant by eliminating any averaging operations in the analysis phase and by minimizing modification operations in the synthesis phase. The use of natural F0 shapes has great potential to cover a wide variety of speaking styles with the same framework, including not only read-aloud speech, but also dialogues and emotional speech. A linear-regression statistical model is used to "manipulate" the stored raw F0 shapes to build them up into a sentential F0 contour. Through experimental evaluations, the proposed model is shown to provide stable and robust F0 contour prediction for various speakers. By using this model, linguistically derived information about a sentence can be directly mapped, in a purely data-driven manner, to acoustic F0 values of the sentential intonation contour for a given target speaker.

Given the availability of high-speed Ethernet and HW based protocol offload, clustered systems using a commodity network fabric (e.g., TCP/IP over Ethernet) are expected to become more attractive for a range of e-business and data center applications. In this paper, we describe a comprehensive simulation to study the performance of clustered database systems using such a fabric. The simulation model currently supports both TCP and SCTP as the transport protocol and models an Oracle 9i like clustered DBMS running a TPC-C like workload. The model can be used to study a wide variety of issues regarding the performance of clustered DBMS systems including the impact of enhancements to network layers (transport, IP, MAC), QoS mechanisms or latency improvements, and cluster-wide power control issues.

Face motion is composed of rigid motion and non-rigid motion. The rigid motion occurs from movements of the human head and the non-rigid motion derives from human's facial expression. In this paper, we present a technique for estimating these rigid/non-rigid motions of the human face simultaneously. First, we test whether the face motion is rigid. If it is rigid motion, we estimate the translation and rotation parameters over image sequences. Otherwise, the non-rigid motion parameters based on the spring-mass-damper (SMD) model are estimated using optical flow. We separate the rigid motion parameters explicitly from the non-rigid parameters for parameters de-coupling, so that we can achieve the face motion estimation more accurately and more efficiently. We will describe the details of our methods and show their efficacy with experiments.

It has been a very important issue to evaluate the performance of transmission control protocol (TCP), and the importance is still growing up because TCP will be deployed more widely in future wireless as well as wireline networks. It is also the reason why there have been a lot of efforts to analyze TCP performance more accurately. Most of these works are focusing on overall TCP end-to-end throughput that is defined as the number of bytes transmitted for a given time period. Even though each TCP's fast recovery strategy should be considered in computation of the exact time period, it has not been considered sufficiently in the existing models. That is, for more detailed performance analysis of a TCP implementation, the fast recovery latency during which lost packets are retransmitted should be considered with its relevant strategy. In this paper, we extend the existing models in order to capture TCP's loss recovery behaviors in detail. On the basis of the model, the loss recovery latency of three TCP implementations can be derived with considering the number of retransmitted packets. In particular, the proposed model differentiates the loss recovery performance of TCP using selective acknowledgement (SACK) option from TCP NewReno. We also verify that the proposed model reflects the precise latency of each TCP's loss recovery by simulations.

A method for fast but yet accurate performance evaluation of processor architecture is mostly desirable in modern processors design. This paper proposes one such method which can measure cycle counts and power consumption of pipelined processors. The method first develops a trace-driven performance simulation model and then employs simulation reuse in simulation of the model. The trace-driven performance modeling is for accuracy in which performance simulation uses the same execution traces as constructed in simulation for functional verification. Fast performance simulation can be achieved in a way that performance for each instruction in the traces is evaluated without evaluation of the instruction itself. Simulation reuse supports simulation speedup by elimination of an evaluation at the current state, which is identical to that at a previous state. The reuse approach is based on the property that application programs, especially multimedia applications, have many iterative loops in general. A performance simulator for pipeline architecture based on the proposed method has been developed through which greater speedup has been made compared with other approaches in performance evaluation.

Based on hybrid system theory, we propose a modeling and control approach for a multi-contact planar manipulation system, whereby a dexterous manipulation task is formulated as a mixed logic dynamical (MLD) model. The MLD model provides the possibility of carrying out the selection of modes, the timing for mode switching, and the determination of the continuous control input simultaneously in a systematical way. Model predictive control (MPC) is adopted for the synthesis of the dexterous hand manipulation system. The solution of the MPC can be found by using mixed integer quadric programming (MIQP) algorithm, and corresponds to the optimal motion of the hand manipulation. The validation of the proposed approach is shown by some simulation results.

The problem of aggregating different stochastic process into a unique one that must be characterized based on the statistical knowledge of its components is a key point in the modeling of many complex phenomena such as the merging of traffic flows at network nodes. Depending on the physical intuition on the interaction between the processes, many different aggregation policies can be devised, from averaging to taking the maximum in each time slot. We here address flows averaging and maximum since they are very common modeling options. Then we give a set of axioms defining a general aggregation operator and, based on some advanced results of functional analysis, we investigate how the decay of correlation of the original processes affect the decay of correlation (and thus the self-similar features) of the aggregated process.

Seasonal ARIMA model is a good traffic model capable of capturing the behavior of a network traffic stream. In this paper, we give a general expression of seasonal ARIMA models with two periodicities and provide procedures to model and to predict traffic using seasonal ARIMA models. The experiments conducted in our feasibility study showed that seasonal ARIMA models can be used to model and predict actual wireless traffic such as GSM traffic in China.

This paper presents a systematic methodology for the system-level assessment of signal integrity and electromagnetic compatibility effects in high-speed communication and information systems. The proposed modeling strategy is illustrated via a case study consisting of a critical coupled net of a complex system. Three main methodologies are employed for the construction of accurate and efficient macromodels for each of the sub-structures typically found along the signal propagation paths, i.e. drivers/receivers, transmission-line interconnects, and interconnects with a complex 3D geometry such as vias and connectors. The resulting macromodels are cast in a common form, enabling the use of either SPICE-like circuit solvers or VHDL-AMS equation-based solvers for system-level EMC predictions.

This paper incorporates statistical language models into an on-line handwriting recognition system for devices with limited memory and computational resources. The objective is to minimize the error recognition rate by taking into account the sentence context to disambiguate poorly written texts. Probabilistic word n-grams have been first investigated, then to fight the curse of dimensionality problem induced by such an approach and to decrease significantly the size of the language model an extension to class-based n-grams has been achieved. In the latter case, the classes result either from a syntactic criterion or a contextual criteria. Finally, a composite model is proposed; it combines both previous kinds of classes and exhibits superior performances compared with the word n-grams model. We report on many experiments involving different European languages (English, French, and Italian), they are related either to language model evaluation based on the classical perplexity measurement on test text corpora but also on the evolution of the word error rate on test handwritten databases. These experiments show that the proposed approach significantly improves on state-of-the-art n-gram models, and that its integration into an on-line handwriting recognition system demonstrates a substantial performance improvement.

Power bus noise problem has become a major concern for both EMC engineers and board designers. A fast algorithm, based on the cavity-mode model, was employed for analyzing resonance characteristics of multilayer power bus stacks interconnected by vias. The via is modeled as an inductance and its value is given by a simple expression. Good agreement between the simulated results and measurements demonstrates the effectiveness of the cavity-mode model, together with the via model.

Wireless Internet technologies have been developing and users are now able to access more information anywhere through small screen mobile devices. However, due to the limits of cost, bandwidth and screen size in a wireless environment, it is important to minimize interactions between a mobile user and his handheld device, as well as the amount of data transmitted. In this paper we present an interactive evolutionary approach for user-oriented Web search by using mobile devices. To verify this approach, a series of experiments has been conducted. The results show that our approach can allocate the information a user needs within only a few user-system interactions. It substantially reduces the number of retrieved pages a user has to visit. This is especially an important benefit to mobile users.

An overview on the physics and circuit design oriented background of the advanced compact model HICUM is presented. Related topics such as the approach employed for geometry scaling and parameter extraction are briefly discussed. A model hierarchy is introduced, that addresses a variety of requirements encountered during the increasingly complicated task of designing analog and high-frequency circuits.